Evaluating the impact of test-trace-isolate for COVID-19 management and alternative strategies

Kun Zhang; Zhichu Xia; Shudong Huang; Gui-Quan Sun; Jiancheng Lv; Marco Ajelli; Keisuke Ejima; Quan-Hui Liu

doi:10.1371/journal.pcbi.1011423

. 2023 Sep 1;19(9):e1011423. doi: 10.1371/journal.pcbi.1011423

Evaluating the impact of test-trace-isolate for COVID-19 management and alternative strategies

Kun Zhang ¹, Zhichu Xia ², Shudong Huang ¹, Gui-Quan Sun ^3,⁴, Jiancheng Lv ¹, Marco Ajelli ^5,^#, Keisuke Ejima ^6,^*,^#, Quan-Hui Liu ^1,^*,^#

Editor: David S Khoury⁷

PMCID: PMC10501547 PMID: 37656743

Abstract

There are many contrasting results concerning the effectiveness of Test-Trace-Isolate (TTI) strategies in mitigating SARS-CoV-2 spread. To shed light on this debate, we developed a novel static-temporal multiplex network characterizing both the regular (static) and random (temporal) contact patterns of individuals and a SARS-CoV-2 transmission model calibrated with historical COVID-19 epidemiological data. We estimated that the TTI strategy alone could not control the disease spread: assuming R₀ = 2.5, the infection attack rate would be reduced by 24.5%. Increased test capacity and improved contact trace efficiency only slightly improved the effectiveness of the TTI. We thus investigated the effectiveness of the TTI strategy when coupled with reactive social distancing policies. Limiting contacts on the temporal contact layer would be insufficient to control an epidemic and contacts on both layers would need to be limited simultaneously. For example, the infection attack rate would be reduced by 68.1% when the reactive distancing policy disconnects 30% and 50% of contacts on static and temporal layers, respectively. Our findings highlight that, to reduce the overall transmission, it is important to limit contacts regardless of their types in addition to identifying infected individuals through contact tracing, given the substantial proportion of asymptomatic and pre-symptomatic SARS-CoV-2 transmission.

Author summary

Among the various targeted NPIs, the Test-Trace-Isolate (TTI) strategies have been commonly adopted to mitigate and control the spread of infectious diseases. Previous studies evaluating the effectiveness of TTI strategy on SARS-CoV-2 spreading have presented contrasting results, which could be due to the nature of observation studies and imperfect reflection of characteristics of the disease and interventions including TTI in the analyses. To properly account for the contact patterns, here we propose a novel static-temporal multiplex network model describing the static contacts and temporal contacts encountered in different settings (like households, schools, workplaces or general community), which captures the essential nature of social behavior relevant to transmission risk and enables us to assess the effectiveness of the TTI strategy. Our proposed model suggests that TTI strategy solely could not control the spread of SARS-CoV-2 and it should be combined with other social distancing policy to reduce the overall transmission of SARS-CoV-2.

Introduction

Since the first diagnosed COVID-19 case, the spread of SARS-CoV-2 has caused more than 600 million confirmed cases and 6 million deaths [1] as of 21 December 2022, which has posed overwhelming stress to the public health capacity and presenting huge challenges for the human societies [2]. To control the transmission of SARS-CoV-2 and minimize morbidity and mortality, many countries initially implemented aggressive non-pharmaceutical interventions (NPIs) such as nationwide lockdowns, at a time when effective pharmaceutical treatments were not available [3,4]. However, due to the substantial impact of the lockdowns on the functioning of the society and the availability of new options to limit COVID-19 burden such as vaccines [5], these aggressive NPIs were gradually lifted [6]. Along with vaccination campaigns, milder NPIs (e.g., masks, hand hygiene, and physical distancing) and more targeted NPIs such as reactive school-closures [7–10], screening of individuals in specific occupations [11,12], and Test-Trace-Isolate [13,14] were implemented and became crucial parts of mitigation strategies [15,16].

Among the various targeted NPIs, the Test-Trace-Isolate (TTI) strategy has been widely adopted for many infectious diseases beyond COVID-19, such as SARS [17], H7N2 influenza [18], and Ebola [19]. For COVID-19, TTI yielded contrasting results. Early studies for the UK [20], China [21], South Korea [22] and New Zealand [23] showed that a timely implementation of contact tracing has the potential to contain a SARS-CoV-2, while studies for Canada [24] and France [25] showed the opposite. These discrepancies could be explained by several reasons. First, other interventions were implemented along with TTI, which hinders the possibility to pinpoint the effectiveness of TTI. Second, TTI was implemented differently. For example, regional differences in testing capacity, tracing rate, and population compliance impact the effectiveness of TTI [26]. For example, Contreras et al. found that once the number of new cases exceeds the tracing capacity, a steep rise of new cases is expected to occur in the following weeks [26]. Hellewell et al. showed that, together with the fraction of pre-symptomatic transmission, the delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable [13]. Chiu et al.’s study indicates that it is necessary to double testing or tracing rate to contain COVID-19 in the US [27]. Davis et al. found that reporting and adherence have remarkable influence on the impact of contact tracing [28]. Third, the mitigation level of TTI is influenced by the characteristic of the disease. Indeed, effectiveness of TTI could be naturally limited by the high proportion of pre-symptomatic and asymptomatic transmission of SARS-CoV-2 [29], because the TTI is primarily triggered by identifying symptomatic patients. Oran et al. [30] estimated that nearly 40% to 45% of SARS-CoV-2 infections occur during the pre-symptomatic and asymptomatic stages, which is crucial to the effectiveness of symptom-driven interventions [31]. Furthermore, a remarkable fraction of transmission from symptomatic individuals occurs before symptom onset [32–34]. Bi et al. suggested that the effectiveness of isolation and contact tracing highly depends on the number of asymptomatic infections [21]. Thus, studies comprehensively assessing the effectiveness of TTI accounting for the characteristics of the disease and in the presence/absence of other interventions are warranted to define best practices for the use of TTI to control SARS-CoV-2 transmission.

Given that interpersonal contacts are the main transmission route for SARS-CoV-2 and other respiratory pathogens [35][36] and that TTI limits such contacts, contact patterns should be properly accounted in the assessment of the effectiveness of TTI. In fact, fomite transmission of SARS-CoV-2 is considered rare [37,38]. A key factor that limits the effectiveness of TTI is the number of contacts that an index case can remember and ultimately be traced [39]. In general, individual’s contacts can be classified into two groups [40–42]. The first group includes individuals that are met regularly, such as household members, schoolmates, and colleagues; here referred to as “static contacts” [43,44]. The second group includes individuals that are met occasionally and/or randomly [45,46], such as individuals met at the supermarket, on the bus, etc. These contacts, here referred to as “temporal contacts”, are hard to trace but were proven to be a relevant source of exposure for SARS-CoV-2 [47,48]. Despite their importance, most modeling studies that focused on accessing the impact of TTI on the spread of SARS-CoV-2 have not or only partially reflected such static-temporal structure of the contact network. For example, some studies have not considered individuals’ contact networks, and assumed homogeneous mixing either implicitly or explicitly [13,14,26,28,49]. Other studies considered complex contact patterns but connections were static [7,50]. Several studies have attempted to incorporate temporal information in the network structure [51]. One strategy that has been adopted is the use of weighted links to indicate the frequencies or duration of contacts. For example, Firth et al. [52] constructed a static social network based on social contact data and movement data and weighted each contact by the frequency of days that a contact was observed; however, it is worth noting that this type of simplification may limit the capacity of the network model to represent various distributions of epidemic sizes [53]. A second strategy was the use of line graphs to represent the movement from source to target and the corresponding time points. This method requires specific location information and chronological order, which is highly demanding for data acquisition. Because the significance of temporal contact network on pathogen transmission [47,48] and on the different effectiveness of TTI for static (relatively easy to identify) vs. temporal (difficult to identify) contacts, considering both static and temporal contact patterns into a single modeling framework will help us better evaluate effectiveness of TTI.

In this work, we developed a novel infection transmission model considering a static-temporal multiplex network. The transmission model was used to access the effectiveness of TTI on SARS-CoV-2 spread considering an epidemiological context consistent with the initial phase of the COVID-19 pandemic (i.e., ancestral SARS-CoV-2 lineages, fully susceptible population, no vaccines and antiviral therapies). We run epidemic simulations over one year for different values of the reproduction number and under different TTI strategies coupled with interventions restricting contacts. Simulated scenarios are compared in terms of COVID-19 burden (e.g., number of averted infections and deaths) and social and implementation costs of TTI (e.g., peak number of simultaneously isolated individuals, peak number of individuals that needs to be traced).

Methods

Overview of transmission simulation with TTI

To simulate SARS-CoV-2 transmission dynamics in a population, we developed a stochastic agent-based model [54] on a static-temporal multiplex network. The disease progression was modelled through a susceptible-exposed-infectious-removed (SEIR) scheme. The static-temporal multiplex network was composed of two layers: the static contact layer and the temporal contact layer. The links on the static contact layer describe regular contacts, such as contacts among household members, schoolmates, and colleagues, which is assumed to be static over the epidemic period. The links on the temporal contact layer describe occasional contacts, which are assumed to change over time and are updated every day in our simulation. Furthermore, we implemented TTI in the simulation and examined its effectiveness in reducing disease burdens over one year.

Contact survey data

To reconstruct the network of contacts, we relied on data collected in a contact survey conducted between December 2017 and May 2018 in Shanghai, China [46]. Similar to the POLYMOD study [55], the survey collected basic socio-demographic information about each study participant (e.g., age, sex) and detailed information about each individual contacted by the participant during the 24 hours period before filling in the questionnaire, where a contact was defined as either a two-way conversation with three or more words in physical presence or a physical skin-to-skin contact. Information collected for each contact included age/age group, sex, contact location and duration, whether the contact was physical or not, and the social setting where the contact took place (e.g., school, workplace). The original contact survey data shows that households, schools, and workplaces were the social settings accounting for the majority of contacts but participants also reported large number of contacts (20+) that were difficult to record individually, like those related to social events (i.e., contacts in the general community).

Static-temporal multiplex network model

We developed a novel static-temporal multiplex network (Fig 1A). The network is composed of two layers: a static contact layer and a temporal contact layer, which represent age-specific “regular” contact patterns (i.e., contacts which occur in households, schools, and workplaces) and “occasional” contact patterns (e.g., contacts taking place during social events), respectively. In each layer, nodes and links represent individuals and contacts, respectively. A population of 100,000 individuals is synthesized following the age distribution from the census data of Shanghai, China [56] and connected on the static contact layer and the temporal contact layer. A configuration model was used to construct both the static and temporal contact layers [57], preserving the age-specific regular and occasional contact patterns observed in the contact survey conducted in Shanghai, China [46]. On the static contact layer, links are generated following age-dependent contact frequencies (Fig 1B). Specifically, for each node, the number of links was sampled from the corresponding age-specific distribution. The static contact layer is assumed constant (thus the links are not re-generated or disappear during the simulation of an epidemic). On the temporal contact layer, social events are generated, in which all participants are connected. Social events are generated following the proportion of individuals participating to social events as derived from the contact survey data (Fig 1C), the distribution of the event size (Fig 1D), and age distributions of the participants of the social event (S3 and S4 Figs). Notably, school-age or working-age individuals are more likely to have contacts on the temporal contact layer (Fig 1C). The links on the temporal contact layer are updated at each time step of the simulation (i.e., every day). The average numbers of contacts for the static and temporal contact layers are 7.15 and 13.7, respectively, which add up to 20.85 contacts per day and is consistent with the original data (an average of 19.3 contacts per day) and other estimates obtained before the COVID-19 pandemic in European countries (e.g., 19.77 and 17.46 contacts per day on average were reported in Italy and Luxembourg, respectively) [55]. More details are available in S1 Supplementary Methods (1. Contact survey data for the static-temporal multiplex network and 2. Static-Temporal Multiplex Network).

SARS-CoV-2 transmission model

We developed a stochastic agent-based model of SARS-CoV-2 transmission to evaluate the impact of the test-trace-isolate strategy in mitigating COVID-19 burden. Briefly, SARS-CoV-2 transmission is simulated according to an SLIR (susceptible, latent, infectious, removed) scheme, where infectious individuals were further divided into pre-symptomatic (P), symptomatic (S), and asymptomatic (A) individuals (S9 Fig). If a susceptible individual i is connected with an infectious (either pre-symptomatic, symptomatic, or asymptomatic) individual j, susceptible individual i could be infected by infectious individual j with a layer-specific probability and become latent. After a latent period, latent individuals either become infectiousness pre-symptomatic or asymptomatic. Once they develop symptoms, pre-symptomatic individuals are categorized as symptomatic. Finally, when symptomatic and asymptomatic individuals are no longer infectious, they are categorized as removed. Given that we are interested in simulating a short time period (365 days), removed individuals are assumed to be fully protected from future re-infections.

The probability of infection for each pair of infected and susceptible individuals is determined by three factors: age-specific susceptibility to infection [33], type of contact (static or temporal) [32], and the status of the infectious individual (P, I or A) [33]. The incubation period was assumed to follow a Gamma distribution with a mean of 6.3 days and a standard deviation of 4.3 (shape = 2.08, rate = 0.33) [33]. We considered transmission to start 2 days before symptom onset [33]. The duration of the infectious period was chosen such that distribution of the generation time in the simulation match an estimated mean of 7.0 days (IQR: 3.6–11.3) [32].

As a measure of disease burden, we estimated the cumulative number of infections, symptomatic infections, hospitalizations, ICUs, and deaths from the simulated epidemics. As a measure of both the social and implementation cost of TTI, we estimated the peak daily number of individuals that need to be traced (i.e., those with positive test result) and the peak daily number of individuals that are simultaneously isolated.

In our baseline analysis, we considered R₀ = 2.5 (thus, if not specified, R₀ = 2.5). Simulations were initialized with a fully susceptible population (thus reflecting the situation at the start of the pandemic), except for 5 randomly selected latent individuals. For each analysis, results are based on 100 stochastic model realizations. A complete list of model parameters is reported in S1 Table. A more detailed description of the transmission model is available in S1 Supplementary Methods (3. SARS-CoV-2 Transmission model).

Test-Trace-Isolate strategy

Symptom-based testing, contact tracing, and case isolation (namely, the Test-Isolate-Trace strategy) was implemented as follows:

Symptom-based testing. A fraction (80% in the baseline) of individuals who develop symptoms are tested by reverse transcription polymerase chain reaction (RT-PCR) tests. The time intervals between symptom onset and sample collection, and between sample collection and laboratory diagnosis were accounted for as well as the sensitivity of the RT-PCR test, which depends on the timing of sample collection [58].
Contact tracing. Contact tracing is triggered by a positive test result (see an example in Fig 2). All contacts of each positive individual on the static contact layer are traced. On the temporal contact layer, those who are connected within 4 days before sample collection are traced with a probability of 50%. The tracing process is assumed to be completed when a positive individual is identified, and the sample is immediately collected from the traced individuals.
Isolation. Before sample collection, links on both layers are active as usual. Between sample collection and test result, links on temporal contact layer are disconnected, as we assume that individuals would avoid unnecessary activities (Fig 2). If the test result is negative (false negative), the disconnected links recover, as individuals go back to normal activity. If the test result is positive (true positive), the links of infected individuals on both layers are disconnected (i.e., isolated) for 14 days.

Fig 2 — Once the primary case A develops symptoms, the sample is collected with delay. After the sample collection, A’s contacts on the temporal contact layer are disconnected until the test result is returned. If the test result is positive (which is the case in this figure), A is isolated, thus the contacts on both layers are disconnected over 14 days. If the test result is negative (i.e., false negative), the disconnected links recover. A infects individual B through a contact on the temporal contact layer (before A’s sample collection) and infects individual C through a contact on the static contact layer before the test result is returned. Individuals B and C are traced and tested immediately after the infection of A is confirmed. The links of B and C on the temporal layer are disconnected until the test results are returned. If the infection of the traced individuals (B in this figure) is confirmed, further contact trace is triggered. If the infection of the traced individuals (C in this figure) is not confirmed (i.e., false negative), the disconnected links recover.

Full description of the TTI strategy is available in S1 Supplementary Methods (5. The Test-Isolate-Trace strategy), and values and description of all TTI parameters are listed in S2 Table.

Results

Effectiveness and social/implementation cost of TTI

Without interventions, the cumulative number of infections (i.e., infection attack rate; IAR) and deaths reached 81.2% (95% CI: 80.8–81.7%) (Fig 3A) and 4.5 (95% CI: 4.2–4.9) per 1,000 people (Fig 3B), respectively. The proportion of pre-symptomatic transmission was 57.0% (95% CI: 56.3–57.7%), which is similar to the value reported in the analysis of the transmission events in Hunan, China [33]. The Test-Trace-Isolate strategy reduced infections and deaths by 24.5% (95% CI: 23.2–26.4%) and 19.3% (95% CI: 8.7–28.8%), respectively, which is consistent with previous studies [26]. The results for other metrics of disease burden are reported in S1 Supplementary Methods (S10 Fig). The peak daily number of traced individuals and the peak daily number of simultaneously isolated individuals were 13.7 (95% CI: 13.0–14.5) per 1,000 people (Fig 3C), and 177.3 (95% CI: 171.4–184.0) per 1,000 people (Fig 3D), respectively.

Fig 4 — A Reductions in the cumulative infections with different probabilities that contacts on the temporal layer to be successfully traced. The vertical error bars indicate the 95% CI. B as in A, but the days to trace contacts on the temporal contact layer before sample collection of the primary cases were varied. C as in A, but the time from symptom onset to sample collection was varied. D as in A, but the time from sample collection to laboratory diagnosis was varied.

We run a variety of sensitivity analyses varying the parameters regulating TTI. Two parameters, the probability that contacts on the temporal layer to be successfully traced and the number of days contacts are traced on the temporal contact layer before sample collection of the primary case, are positively associated with a reduction of IAR (Fig 4A and 4B), as more infected individuals are isolated. A shorter time interval from symptom onset to sample collection is associated with a higher reduction of IAR (Fig 4C), because contacts of tested individuals on the temporal contact layer are promptly disconnected from the network after the sample collection, in agreement in previous studies [7].

The impact of the time interval from sample collection to laboratory diagnosis (T_cr) on the reduction of IAR depends on the pathogen transmissibility (R₀) (Fig 4D). Longer T_cr decreases the IAR reduction under a low transmissibility scenario (R₀ = 1.3); however, it increases the IAR reduction under a high transmissibility scenario (R₀ = 2.5). This complex association is due to the balance between the probability of secondary transmission from isolated individuals and the probability that secondary infections can be effectively isolated. The former probability decreases with shorter T_cr, because their links on the static layer remain active while they wait for the test result. Furthermore, it increases with high transmissibility. Meanwhile, the probability that secondary infections can be effectively isolated decreases with shorter T_cr, under which the traced (and infected) individuals may not be properly identified by the test because of low test sensitivity during the early phase of infection (many secondary transmissions occur around symptom onset of primary cases, given that the incubation period [6.3 days] and the generation time [7.0 days] are close). Furthermore, it decreases with high transmissibility because more asymptomatic or pre-symptomatic individuals (lower sensitivity) are tested. Indeed, the observed true positive rate, especially among those identified through contact tracing, was reduced by the high value of R₀ (S11B and S11D Figs). To quantify the impact of this higher test positive rate among traced individuals, we designed a null model with the same parameter settings except for the test sensitivity for the pre-symptomatic and asymptomatic individuals, which was set so that the observed true positive rate among traced individuals is the same as for the model with T_cr = 4 (Fig 5B). Thus, the difference in IAR reduction between the null and baseline models (27.6% with R₀ = 1.3; Fig 5A) is due to the increased true positive rate for traced individuals. This difference between the null model and the model with T_cr = 4 (23.9% with R₀ = 1.3; Fig 5A) is associated with a larger number infections occurring while test results are not available yet. Thus, when R₀ is low (i.e., 1.3), IAR is reduced by 3.7%, while it increases by 5.2% when R₀ is high (2.5).

Fig 5 — A Reduction in the cumulative infections. B True positive rate among the traced individuals. “T_cr = 1 (baseline)” and “T_cr = 4” are the models setting the time from sample collection to laboratory diagnosis as 1 day or 4 days, respectively. “T_cr = 1 (null model)” is the model setting the time from sample collection to laboratory diagnosis as 1 day, but the true positive rate among the traced individuals is the same as the model with “T_cr = 4”. Other parameter settings are the same.

Overall, from our sensitivity analyses, we found that TTI can be implemented more efficiently by increasing the likelihood of tracing contacts or reducing testing delays. However, TTI alone is not sufficient to control an epidemic even in a best-case scenario. To mitigate COVID-19 burden, combining TTI with other interventions would need to be considered. Indeed, during the COVID-19 pandemic, multiple non-pharmaceutical interventions (NPIs), such as mask wearing and improved hygiene practices [4] were implemented along with TTI, the combination of which resulted in a reduction of the transmission risk (and of the effective reproduction number). Here we run simulations for different values of the reproduction number to resemble the effect of different NPIs that are implemented to lower the transmission risk. In addition, we also run simulations with a higher value R₀ (i.e., = 2.8) to account for the variability in R₀ estimates, for instance due to different variants, socio-demographic characteristics, and contact patterns [13]. Our simulations show that TTI is more effective for lower values of R₀. For example, when R₀ is 1.3, TTI can reduce the total number of infections and deaths by 48.1% (95% CI: 37.6–58.5%) and 40.4% (95% CI: 23.5–54.4%), respectively. The social and implementation costs of TTI are reduced for lower R₀ values. For example, when R₀ is 1.3, the peak daily number of individuals to be traced and the peak daily number of simultaneously isolated individuals drop to 0.8 (95% CI: 0.6–1.1) per 1,000 people and 8.8 (95% CI: 5.7–12.5) per 1,000 people, respectively. Therefore, combining TTI with NPIs not only increases the effectiveness of TTI strategy, but also reduces the social and implementation costs associated with TTI.

We further run sensitivity analyses varying the parameters unrelated to TTI, namely: (1) infectiousness of asymptomatic individuals, χ(A); (2) the number of initially infected individuals; (3) the probability that a symptomatic individual is tested, P_test. The number of initially infected individuals had no impact on the effectiveness of TTI (S13 Fig); however, lower χ(A) and higher P_test were associated with higher effectiveness of TTI (S12A and S14A Figs) and deaths (S12B and S14B Figs).

A point of discussion during the pandemic has been whether individuals should be isolated while waiting for the result of a test [59–61]. In the main analysis, we assumed that only the links on the temporal contact layer are disconnected while waiting for the test result. Here, we run two sensitivity analyses: (1) links on both layers are disconnected (TTI-strict) or (2) all links remain active (TTI-relaxed) while waiting for the test result. The effectiveness of TTI was lower when a less restrictive TTI is considered (Fig 6A). Specifically, for R₀ = 2.5, the reduction of the IAR was reduced by 45.3% and increased by 17.6% when considering TTI-relaxed and TTI-strict, respectively (Fig 6A), as compared with TTI (baseline). Meanwhile, the mean isolation period was 1.5 days and 10.5 days longer for TTI-relaxed and TTI-strict, respectively (Fig 6B). The longer isolation period of TTI-relaxed was due to the larger number of infections, whereas that of TTI-strict is due to its mechanism, as individuals are isolated while waiting for the test result. These findings suggest that unnecessary gatherings and social activities should be avoided while waiting for test results; however, avoiding regular activities (such as going to schools or offices) may not be necessary, considering the trade-offs between the effectiveness of the TTI strategy and its social and implementation costs.

Fig 6 — A Reductions in the cumulative infections with different TTI strategies. (TTI-relaxed) Contacts on both layers remains while the test results are waited. (TTI-strict) Contacts on both layers are disconnected while the test results are waited. B as in A, but for the mean isolation period.

Reactive distancing policy

We considered to combine TTI with the other interventions by varying R₀ in the sensitivity analysis. However, reduced values of R₀ does not have a practical interpretation as there is no clear connection between the magnitude of reduction in R₀ and specific interventions. Here, we explicitly simulated a reactive social distancing policy as an example. Limiting social events and gathering has been adopted in many countries and regions as a social distancing policy to limit SARS-CoV-2 transmission, especially at early phase of the pandemic.

We analyzed two social distancing policies: 1. reactive social distancing, i.e., limiting unnecessary gathering and events only (a fraction of contacts on temporal contact layer is disconnected) [62], 2. reactive “all-level” distancing, limiting both unnecessary gathering and events and necessary activities (a fraction of contacts on both contact layers is disconnected) [63]. Those policies are triggered when the cumulative number of detected cases exceeds a predefined threshold value. Details are reported in S1 Supplementary Methods (6. Reactive distancing policy) and S3 Table.

We estimate reactive social distancing to have a substantial impact in reducing COVID-19 burden (Fig 7A and 7B). For example, when R₀ = 2.5 and 50% or 75% of the contacts on the temporal layers are disconnected (P_social = 0.5 or 0.75), the IAR reduction reached 37.8% (95% CI: 36.4–39.7%) and 45.6% (95% CI: 44.4–46.7%), respectively. Reactive “all-level” distancing further reduced COVID-19 burden (Fig 7C and 7D). When 30% of contacts on the static layer and 50% of contacts on the temporal layer are disconnected (P_social = 0.5, P_static = 0.3), 68.1% (95% CI: 64.3–71.3%) of infections could be averted (Fig 7C).

Discussion

Test-Trace-Isolate strategies have been commonly adopted to mitigate and control the spread of infectious diseases [64]. Previous modelling studies have identified several key factors that are crucial to determine the effectiveness of TTI, such as the reproduction number and the proportion of transmissions that occur before symptom onset [13].

On top of those previous studies, we incorporated an essential feature of social behavior shaping the transmission risk. TTI primarily serves to control (in-person) contacts between individuals. However, contacts are highly heterogeneous, and incorporating proper contact patterns in transmission models is essential to assess TTI strategies. Here, we proposed a novel static-temporal multiplex network that incorporates two complementary types of contacts: regular contacts on a static contact layer and random contacts on temporal contact layer. On this comprehensive contact network, we developed a stochastic agent-based SARS-CoV-2 transmission model, which enabled us to implement individual-level interventions accounting for the natural heterogeneity of human contact patterns.

Using our model, we provided a quantitative assessment of the effectiveness of TTI strategies, and found that TTI alone can only partially mitigate COVID-19 burden. This is due to the high proportion of pre-asymptomatic and asymptomatic transmission, which hinders the successful and prompt identification of infected individuals when a symptom-based trigger is considered. When R₀ is 2.2, given that infectiousness onset almost coincides with symptom onset (infectiousness appears 0.51–0.77 days before symptom onset on average), Peak et al [65]. found that tracing contacts and quarantine or active monitoring individuals triggered by symptom presence have a certain potential to control the SARS-CoV-2 spreading. However, in situations where less resources are available for tracing, or the adherence to such interventions is low, the effectiveness of TTI could be low. For example, if less than 20% of the population follows the rule of self-reporting and isolation due to symptoms or tracing, large outbreaks are unlikely to be avoided even with scaled-up tracing and quick turnaround testing [28]. We further examined reactive social distancing strategies to be combined with TTI. Specifically, we considered two types of reactive social distancing: 1) a moderate one, which limits only non-essential contacts (e.g., gatherings, social events), thus reducing contacts on the temporal contact layer only; 2) and a strict one, which limits not only non-essential contacts but also a part of regular contacts on the static contact layer (similar to what happens when reactive school and workplace closures are adopted). We found that limiting temporal contacts alone could avert less than 50% of the infections, especially when the transmissibility potential is high (R₀≥2.2). Furthermore, limiting both temporal and regular contacts could remarkably enhance the effectiveness of the TTI strategies, as the main transmission route of SARS-CoV-2 transmission is through regular contacts. Thus, especially when effective therapeutics or vaccines were not available, stay-at-home orders to suppress epidemic spread were considered.

Our study has several strengths. First, our model is calibrated using epidemiological and contact survey data. In particular, the structure of the contact network is based on individual-level contact patterns data stratified by age and social setting. However, it is worth noting that, to reconstruct the static-temporal network, we used contact survey data collected before the COVID-19 pandemic. The pandemic has altered the contact patterns of the population in ways that go beyond the implementation of social distancing policies, including individual considerations about the perceived risk of infection [66]. Future studies are needed to estimate contact patterns in the post-pandemic era. Second, we estimated the social and implementation costs of TTI as well as its effectiveness. The social and implementation costs of TTI can be defined in different ways, but further cost-effectiveness analyses are needed to estimate economic costs and broader social implications of TTI strategies as well as other interventions. Finally, our stochastic agent-based model with its static-temporal network structure allows the analysis of individual-level interventions with a high degree of fidelity and flexibility. In this study, we analyzed TTI; however, the model can be used to examine other individual-level NPIs as well as pharmacological interventions, such as vaccination programs, and antiviral treatments and prophylaxis.

Our study has several limitations. First, we grouped contacts that take place in the household, school, and workplace settings into a single layer (i.e., static contact). Therefore, our network does not explicitly replicate the structure of each household, school, and workplace. As such, our network cannot capture the saturation effect of household transmission observed in previous studies [67,68], which may lead our model to slightly overestimate the infection attack rate. Moreover, we did not consider heterogeneity in the transmission risk for contacts on the same layer. For example, the transmission risk of contacts between household members may differ from that of contacts between classmates, and transmission may also differ between individuals due to different behaviors. Further studies are needed to investigate the granularity of contact patterns that is needed to capture epidemiological dynamics with/without interventions. Second, in our simulations, we assumed complete connection among people attending the same social event, which might not reflect the reality. Contacts may also depend on types of individual (not just age, as considered in this study). The assumption of full connectivity may increase the risk of pathogen transmission among individuals in the same group, as each susceptible individual in that group can be infected by any infectious member of the group. Third, our simulations assumed an instantaneous turnaround time for contact tracing, but contact tracing is often implemented through interviews conducted by an operator; thus, it may take several days to gather all information and identify contacts. Kretzschmar et al. ‘s study [69] have shown that the tracing delay has certain impact on the contact tracing effectiveness, although lower than the testing delay. Thus, the assumption of an instantaneous turnaround time for contact tracing may lead to a slightly overestimation of the effectiveness of TTI. Fourth, we did not consider the possibility of false positive test results. Although rare, false positive results can increase the social and implementation costs of TTI. This aspect can be particularly relevant for cost-effectiveness analyses.

Conclusion

In conclusion, we proposed a novel static-temporal multiplex network that captures contact patterns. Using this model, we examined the effectiveness and the social/implementation costs of TTI and identify key parameters regulating its effectiveness. We found that TTI alone could not control a COVID-19 outbreak. The infection attack rate can however be reduced by 24.5% when R₀ = 2.5; when NPIs are implemented to lower the transmission risk (e.g., R₀ = 1.3), about 48.3% of the infections can be averted. Our findings highlight the importance of combining TTI with an overall reduction of transmission obtained by limiting contacts regardless of their types, given the substantial proportion of asymptomatic and pre-symptomatic transmission and high reproduction number of the ancestral SARS-CoV-2 lineages as well as subsequent variants.

Supporting information

S1 Supplementary Methods. 1. Contact survey data for the static-temporal multiplex network.

2. Static-Temporal Multiplex Network. 3. SARS-CoV-2 Transmission model. 4. Calibration. 5. The Test-Trace-Isolate strategy. 6. Reactive distancing policy.

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S1 Fig. The age-group-dependent contact frequency of individual contacts from 0 to 39.

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S2 Fig. The age-group-dependent contact frequency of individual contacts over 40.

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S3 Fig. The Gaussian kernel fitted age-group-dependent age distribution of contactees of individual contacts from 0 to 39.

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S4 Fig. The Gaussian kernel fitted age-group-dependent age distribution of contactees of individual contacts over 40.

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S5 Fig. The Gaussian kernel fitted age-group-dependent age distribution of participants in gathering and events from 0 to 39.

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S6 Fig. The Gaussian kernel fitted age-group-dependent age distribution of participants in gathering and events over 40.

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S7 Fig. Contact matrix representing the static contact layer.

Colors represent the mean number of contacts between an individual in given age group with individuals with any age group.

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S8 Fig. Contact matrix representing the temporal contact layer.

The panels show two realizations of the temporal contact layers for two randomly selected day of a simulation. Colors represent the mean number of contacts between an individual in given age group with individuals with any age group.

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S9 Fig. SARS-CoV-2 transmission model.

The detailed description of symbols in the figure referred to S1 Table.

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S10 Fig. Disease burdens under the baseline TTI strategy.

A. The number of symptomatic infections per 1,000 people without the TTI strategy (orange dots) and the reduction by the baseline TTI strategy (blue bars). Reproduction numbers (R₀) are varied. The vertical error bars indicate the 95% CI. B. The same as A, but using the number of hospitalization as the disease burden. C. The same as A, but using the number of ICU patients as the disease burden.

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S11 Fig. Impact of the time from sample collection to laboratory diagnosis.

A. Average number of secondary infection (per a primary case) while waiting for the test results. B. observed true positive rate. C. as in B, but only for the symptomatic individuals. D. as in B, but only for those recruited through contact tracing.

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S12 Fig. Impact of the relative infectiousness of asymptomatic individuals.

A. Reduction in the cumulative infections under different reproduction number and the relative infectiousness of asymptomatic cases. The vertical error bars indicate the 95% CI. B. as in A, but reduction in deaths.

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S13 Fig. Impact of the number of initial infected individuals.

A. Reduction in the cumulative infections under different reproduction number and the initial number of cases. The vertical error bars indicate the 95% CI. B. as in A, but reduction in deaths.

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S14 Fig. Impact of the probability to test a symptomatic individual.

A. Reduction in the cumulative infections under different reproduction number and the probability to test a symptomatic individual. The vertical error bars indicate the 95% CI. B. as in A, but reduction in deaths.

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S15 Fig. Impact of the reactive threshold values.

A. Reduction in the cumulative infections under different reproduction number and the reactive threshold values for the reactive social distancing. The vertical error bars indicate the 95% CI. B. as in A, but reduction in deaths. C. as in A, but for the reactive all-level distancing. D. as in C, but reduction in deaths.

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S16 Fig. Daily new infections and new deaths.

A. and B. for the daily new infections and daily new deaths under the unmitigated scenario (without TTI) with reproduction number of R₀ = 1.3, 2.5 and 2.8, the shadows indicate the 95% CI; C. and D. for baseline where TTI is implemented under the scenario that the reactive social distancing is triggered when the cumulative number of detected cases exceeds 50, specifically, 50% of contacts on the temporal layer are disconnected, and under the scenario that the reactive all-level distancing is triggered when the cumulative number of detected cases exceeds 50, specifically, 50% of contacts on the temporal layer are disconnected and 30% of contacts on the static contact layer are disconnected. R₀ = 2.5. E. and F. as in C. and D., but R₀ = 2.8.

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S1 Table. Parameters for SARS-Cov-2 transmission model and disease burden.

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S2 Table. Parameters for TTI.

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S3 Table. Parameters for reactive distancing policies.

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Data Availability

Data and code needed to replicate the results of our analyses are available from GitHub at: https://github.com/QH-Liu/Test-Trace-Isolate.

Funding Statement

Q.-H. L. has received funding from the National Natural Science Foundation of China (No. 62003230), the National Social Science Foundation of China (No. 20&ZD112). J. L. has received funding from the 111 Project under grant agreement B21044. K. E. has received funding from Lee Kong Chian School of Medicine Start-up Grant (No. NU38OT000297). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.WHO COVID-19 Dashboard. Geneva: World Health Organization, 2020. Available online: https://covid19.who.int/ (2022) [Google Scholar]
2.Kaye AD, Okeagu CN, Pham AD, Silva RA, Hurley JJ, Arron BL, et al. Economic impact of COVID-19 pandemic on healthcare facilities and systems: International perspectives. Best Practice & Research. 2021;35(3):293–306. doi: 10.1016/j.bpa.2020.11.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Lau H, Khosrawipour V, Kocbach P, Mikolajczyk A, Schubert J, Bania J, et al. The positive impact of lockdown in Wuhan on containing the COVID-19 outbreak in China. Journal of Travel Medicine. 2020;27(3). doi: 10.1093/jtm/taaa037 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Flaxman S, Mishra S, Gandy A, Unwin HJT, Mellan TA, Coupland H, et al. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature. 2020;584(7820):257–61. doi: 10.1038/s41586-020-2405-7 [DOI] [PubMed] [Google Scholar]
5.Olivier C, Yuriy G, Michael W. The Cost of the Covid-19 Crisis: Lockdowns, Macroeconomic Expectations, and Consumer Spending. National Bureau of Economic Research. 2020. 10.3386/w27141 [DOI] [Google Scholar]
6.Di Domenico L, Pullano G, Sabbatini CE, Boelle PY, Colizza V. Impact of lockdown on COVID-19 epidemic in Ile-de-France and possible exit strategies. BMC Medicine. 2020;18(1):240. doi: 10.1186/s12916-020-01698-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Liu QH, Zhang J, Peng C, Litvinova M, Huang S, Poletti P, et al. Model-based evaluation of alternative reactive class closure strategies against COVID-19. Nature Communications. 2022;13(1):322. doi: 10.1038/s41467-021-27939-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.United Nations Educational. School closure information because of COVID-19. https://en.unesco.org/covid19/educationresponse (2020). [Google Scholar]
9.Di Domenico L, Pullano G, Sabbatini CE, Boelle PY, Colizza V. Modelling safe protocols for reopening schools during the COVID-19 pandemic in France. Nature Communications. 2021;12(1):1073. doi: 10.1038/s41467-021-21249-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Munday JD, Sherratt K, Meakin S, Endo A, Pearson CAB, Hellewell J, et al. Implications of the school-household network structure on SARS-CoV-2 transmission under school reopening strategies in England. Nature Communications. 2021;12(1):1942. doi: 10.1038/s41467-021-22213-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Black JRM, Bailey C, Przewrocka J, Dijkstra KK, Swanton C. COVID-19: the case for health-care worker screening to prevent hospital transmission. Lancet. 2020;395(10234):1418–20. doi: 10.1016/S0140-6736(20)30917-X [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Smith DRM, Duval A, Zahar JR, Opatowski L, Temime L, Nosocomial E-MWG. Rapid antigen testing as a reactive response to surges in nosocomial SARS-CoV-2 outbreak risk. Nature Communications. 2022;13(1). doi: 10.1038/s41467-021-27845-w [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Hellewell J, Abbott S, Gimma A, Bosse NI, Jarvis CI, Russell TW, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Global Health. 2020;8(4):e488–e96. doi: 10.1016/S2214-109X(20)30074-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Kucharski AJ, Klepac P, Conlan AJK, Kissler SM, Tang ML, Fry H, et al. Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: a mathematical modelling study. Lancet Infectious Diseases. 2020;20(10):1151–60. doi: 10.1016/S1473-3099(20)30457-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Gosce L, Phillips PA, Spinola P, Gupta DRK, Abubakar PI. Modelling SARS-COV2 Spread in London: Approaches to Lift the Lockdown. Journal of Infection. 2020;81(2):260–5. doi: 10.1016/j.jinf.2020.05.037 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Aleta A, Martin-Corral D, Pastore YPA, Ajelli M, Litvinova M, Chinazzi M, et al. Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nature Human Behaviour. 2020;4(9):964–71. doi: 10.1038/s41562-020-0931-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Glasser JW, Hupert N, McCauley MM, Hatchett R. Modeling and public health emergency responses: lessons from SARS. Epidemics. 2011;3(1):32–7. doi: 10.1016/j.epidem.2011.01.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Eames KT, Webb C, Thomas K, Smith J, Salmon R, Temple JM. Assessing the role of contact tracing in a suspected H7N2 influenza A outbreak in humans in Wales. BMC Infectious Diseases. 2010;10:141. doi: 10.1186/1471-2334-10-141 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Swanson KC, Altare C, Wesseh CS, Nyenswah T, Ahmed T, Eyal N, et al. Contact tracing performance during the Ebola epidemic in Liberia, 2014–2015. PLoS Neglected Tropical Diseases. 2018;12(9):e0006762. doi: 10.1371/journal.pntd.0006762 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Keeling MJ, Hollingsworth TD, Read JM. Efficacy of contact tracing for the containment of the 2019 novel coronavirus (COVID-19). Journal of Epidemiology and Community Health. 2020;74(10):861–6. doi: 10.1136/jech-2020-214051 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Bi QF, Wu YS, Mei SJ, Ye CF, Zou X, Zhang Z, et al. Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study. Lancet Infectious Diseases. 2020;20(8):911–9. doi: 10.1016/S1473-3099(20)30287-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Chen YH, Fang CT, Huang YL. Effect of Non-lockdown Social Distancing and Testing-Contact Tracing During a COVID-19 Outbreak in Daegu, South Korea, February to April 2020: A Modeling Study. International Journal of Infectious Diseases. 2021;110:213–21. doi: 10.1016/j.ijid.2021.07.058 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Baker MG, Kvalsvig A, Verrall AJ, Telfar-Barnard L, Wilson N. New Zealand’s elimination strategy for the COVID-19 pandemic and what is required to make it work. New Zealand Medical Journal. 2020;133(1512):10–4. [PubMed] [Google Scholar]
24.Wu JH, Tang B, Bragazzi NL, Nah K, McCarthy Z. Quantifying the role of social distancing, personal protection and case detection in mitigating COVID-19 outbreak in Ontario, Canada. Journal of Mathematics in Industry. 2020;10(1). doi: 10.1186/s13362-020-00083-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Pullano G, Di Domenico L, Sabbatini CE, Valdano E, Turbelin C, Debin M, et al. Underdetection of cases of COVID-19 in France threatens epidemic control. Nature. 2020; doi: 10.1038/s41586-020-03095-6 [DOI] [PubMed] [Google Scholar]
26.Contreras S, Dehning J, Loidolt M, Zierenberg J, Spitzner FP, Urrea-Quintero JH, et al. The challenges of containing SARS-CoV-2 via test-trace-and-isolate. Nature Communications. 2021;12(1):378. doi: 10.1038/s41467-020-20699-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Chiu WA, Fischer R, Ndeffo-Mbah ML. State-level needs for social distancing and contact tracing to contain COVID-19 in the United States. Nature Human Behaviour. 2020;4(10):1080–+. doi: 10.1038/s41562-020-00969-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Davis EL, Lucas TCD, Borlase A, Pollington TM, Abbott S, Ayabina D, et al. Contact tracing is an imperfect tool for controlling COVID-19 transmission and relies on population adherence. Nature Communications. 2021;12(1). doi: 10.1038/s41467-021-25531-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Fraser C, Riley S, Anderson RM, Ferguson NM. Factors that make an infectious disease outbreak controllable. Proceedings of the National Academy of Sciences of the United States of America. 2004;101(16):6146–51. doi: 10.1073/pnas.0307506101 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Oran DP, Topol EJ. Prevalence of Asymptomatic SARS-CoV-2 Infection A Narrative Review. Annals of Internal Medicine. 2020;173(5):362–+. doi: 10.7326/M20-3012 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Moghadas SM, Fitzpatrick MC, Sah P, Pandey A, Shoukat A, Singer BH, et al. The implications of silent transmission for the control of COVID-19 outbreaks. Proceedings of the National Academy of Sciences of the United States of America. 2020;117(30):17513–17515. doi: 10.1073/pnas.2008373117 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Sun K, Wang W, Gao L, Wang Y, Luo K, Ren L, et al. Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2. Science. 2021;371(6526). doi: 10.1126/science.abe2424 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Hu S, Wang W, Wang Y, Litvinova M, Luo K, Ren L, et al. Infectivity, susceptibility, and risk factors associated with SARS-CoV-2 transmission under intensive contact tracing in Hunan, China. Nature Communications. 2021;12(1):1533. doi: 10.1038/s41467-021-21710-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.He X, Lau EHY, Wu P, Deng X, Wang J, Hao X, et al. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nature Medicine. 2020;26(5):672–5. doi: 10.1038/s41591-020-0869-5 [DOI] [PubMed] [Google Scholar]
35.Bauch CT, Galvani AP. Epidemiology. Social factors in epidemiology. Science. 2013;342(6154):47–9. doi: 10.1126/science.1244492 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Leung NHL. Transmissibility and transmission of respiratory viruses. Nature Reviews Microbiology. 2021;19(8):528–45. doi: 10.1038/s41579-021-00535-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Pitol AK, Julian TR. Community Transmission of SARS-CoV-2 by Surfaces: Risks and Risk Reduction Strategies. Environmental Science & Technology Letters. 2021;8(3):263–9. doi: 10.1021/acs.estlett.0c00966 [DOI] [PubMed] [Google Scholar]
38.Harvey AP, Fuhrmeister ER, Cantrell ME, Pitol AK, Swarthout JM, Powers JE, et al. Longitudinal Monitoring of SARS-CoV-2 RNA on High-Touch Surfaces in a Community Setting. Environmental Science & Technology Letters. 2021;8(2):168–75. doi: 10.1021/acs.estlett.0c00875 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Garry M, Hope L, Zajac R, Verrall AJ, Robertson JM. Contact Tracing: A Memory Task With Consequences for Public Health. Perspectives on Psychological Science. 2021;16(1):175–87. doi: 10.1177/1745691620978205 [DOI] [PubMed] [Google Scholar]
40.Louail T, Lenormand M, Picornell M, Garcia Cantu O, Herranz R, Frias-Martinez E, et al. Uncovering the spatial structure of mobility networks. Nature Communications. 2015;6:6007. doi: 10.1038/ncomms7007 [DOI] [PubMed] [Google Scholar]
41.Sun L, Axhausen KW, Lee DH, Huang X. Understanding metropolitan patterns of daily encounters. Proceedings of the National Academy of Sciences of The United States Of America. 2013;110(34):13774–9. doi: 10.1073/pnas.1306440110 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Zhang MX, Verbraeck A, Meng RQ, Chen B, Qiu XG. Modeling Spatial Contacts for Epidemic Prediction in a Large-scale Artificial City. Journal of Artificial Societies and Social Simulation. 2016;19(4). 10.18564/jasss.3148 [DOI] [Google Scholar]
43.Mistry D, Litvinova M, Pastore YPA, Chinazzi M, Fumanelli L, Gomes MFC, et al. Inferring high-resolution human mixing patterns for disease modeling. Nature Communications. 2021;12(1):323. doi: 10.1038/s41467-020-20544-y [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Litvinova M, Liu QH, Kulikov ES, Ajelli M. Reactive school closure weakens the network of social interactions and reduces the spread of influenza. Proceedings of the National Academy of Sciences of The United States Of America. 2019;116(27):13174–81. doi: 10.1073/pnas.1821298116 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Mousa A, Winskill P, Watson OJ, Ratmann O, Monod M, Ajelli M, et al. Social contact patterns and implications for infectious disease transmission—a systematic review and meta-analysis of contact surveys. Elife. 2021;10. doi: 10.7554/eLife.70294 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Zhang J, Klepac P, Read JM, Rosello A, Wang X, Lai S, et al. Patterns of human social contact and contact with animals in Shanghai, China. Scientific Reports. 2019;9(1):15141. doi: 10.1038/s41598-019-51609-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Liu J, Liao X, Qian S, Yuan J, Wang F, Liu Y, et al. Community Transmission of Severe Acute Respiratory Syndrome Coronavirus 2, Shenzhen, China, 2020. Emerging Infectious Diseases. 2020;26(6):1320–3. doi: 10.3201/eid2606.200239 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Aleta A, Martin-Corral D, Bakker MA, Pastore YPA, Ajelli M, Litvinova M, et al. Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas. Proceedings of the National Academy of Sciences of The United States Of America. 2022;119(26):e2112182119. doi: 10.1073/pnas.2112182119 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Grantz KH, Lee EC, D’Agostino McGowan L, Lee KH, Metcalf CJE, Gurley ES, et al. Maximizing and evaluating the impact of test-trace-isolate programs: A modeling study. PLoS Medicine. 2021;18(4):e1003585. doi: 10.1371/journal.pmed.1003585 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Moon SA, Scoglio CM. Contact tracing evaluation for COVID-19 transmission in the different movement levels of a rural college town in the USA. Scientific Reports. 2021;11(1):4891. doi: 10.1038/s41598-021-83722-y [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Bansal S, Read J, Pourbohloul B & Meyers LA. The dynamic nature of contact networks in infectious disease epidemiology. Journal of Biological Dynamics. 2010; 4:5, 478–489, doi: 10.1080/17513758.2010.503376 [DOI] [PubMed] [Google Scholar]
52.Firth JA, Hellewell J, Klepac P, Kissler S, CMMID COVID-19 Working Group; Kucharski AJ, Spurgin LG. Using a real-world network to model localized COVID-19 control strategies. Nature Medicine. 2020;26(10):1616–1622. doi: 10.1038/s41591-020-1036-8 [DOI] [PubMed] [Google Scholar]
53.Vernon MC, Keeling MJ. Representing the UK’s cattle herd as static and dynamic networks. Proceedings. Biological Sciences. 2009;276(1656):469–76. doi: 10.1098/rspb.2008.1009 [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Perez L, Dragicevic S. An agent-based approach for modeling dynamics of contagious disease spread. International Journal of Health Geographics. 2009;8:50. doi: 10.1186/1476-072X-8-50 [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R, Massari M, Salmaso S, Tomba GS, Wallinga J, Heijne J, Sadkowska-Todys M, Rosinska M, Edmunds WJ. Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Medicine. 2008; 5(3):e74. doi: 10.1371/journal.pmed.0050074 [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Yang J, Marziano V, Deng X, Guzzetta G, Zhang J, Trentini F, et al. Despite vaccination, China needs non-pharmaceutical interventions to prevent widespread outbreaks of COVID-19 in 2021. Nature Human Behavior. 2021;5(8):1009–20. doi: 10.1038/s41562-021-01155-z [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Aleta A, Ferraz de Arruda G, Moreno Y. Data-driven contact structures: From homogeneous mixing to multilayer networks. PLoS Computational Biology. 2020;16(7):e1008035. doi: 10.1371/journal.pcbi.1008035 [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Xiao AT, Tong YX, Gao C, Zhu L, Zhang YJ, Zhang S. Dynamic profile of RT-PCR findings from 301 COVID-19 patients in Wuhan, China: A descriptive study. Journal of Clinical Virology. 2020;127:104346. doi: 10.1016/j.jcv.2020.104346 [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Wilder-Smith A, Freedman DO. Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. Journal of Travel Medicine. 2020;27(2). doi: 10.1093/jtm/taaa020 [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Chen PJ, Mao LJ, Nassis GP, Harmer P, Ainsworth BE, Li FZ. Coronavirus disease (COVID-19): The need to maintain regular physical activity while taking precautions. Journal of Sport and Health Science. 2020;9(2):103–4. doi: 10.1016/j.jshs.2020.02.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Samieefar N, Yari Boroujeni R, Jamee M, Lotfi M, Golabchi MR, Afshar A, et al. Country Quarantine During COVID-19: Critical or Not? Disaster Medicine and Public Health Preparedness. 2021;15(4):e24–e5. doi: 10.1017/dmp.2020.384 [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Brauner JM, Mindermann S, Sharma M, Johnston D, Salvatier J, Gavenciak T, et al. Inferring the effectiveness of government interventions against COVID-19. Science. 2021;371(6531). doi: 10.1126/science.abd9338 [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Bo Y, Guo C, Lin C, Zeng Y, Li HB, Zhang Y, et al. Effectiveness of non-pharmaceutical interventions on COVID-19 transmission in 190 countries from 23 January to 13 April 2020. International Journal of Infectious Diseases. 2021;102:247–53. doi: 10.1016/j.ijid.2020.10.066 [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Eames KT, Keeling MJ. Contact tracing and disease control. Proceedings of the Royal Society B: Biological Sciences. 2003;270(1533):2565–71. doi: 10.1098/rspb.2003.2554 [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Peak CM, Kahn R, Grad YH, Childs LM, Li R, Lipsitch M, et al. Individual quarantine versus active monitoring of contacts for the mitigation of COVID-19: a modelling study. Lancet Infectious Diseases. 2020;20(9):1025–1033. doi: 10.1016/S1473-3099(20)30361-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Zhang J, Litvinova M, Liang Y, Wang Y, Wang W, Zhao S, et al. Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China. Science. 2020;26;368(6498):1481–1486. doi: 10.1126/science.abb8001 [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Liu QH, Ajelli M, Aleta A, Merler S, Moreno Y, Vespignani A. Measurability of the epidemic reproduction number in data-driven contact networks. Proceedings of the National Academy of Sciences of the United States of America. 2018;115(50):12680–12685. doi: 10.1073/pnas.1811115115 [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Scalia Tomba G, Svensson A, Asikainen T, Giesecke J. Some model based considerations on observing generation times for communicable diseases. Mathematical Biosciences. 2010;223(1):24–31. doi: 10.1016/j.mbs.2009.10.004 [DOI] [PubMed] [Google Scholar]
69.Kretzschmar ME, Rozhnova G, Bootsma MCJ, van Boven M, van de Wijgert JHHM, Bonten MJM. Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study. Lancet Public Health. 2020;5(8):e452–e459. doi: 10.1016/S2468-2667(20)30157-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1011423.r001

Decision Letter 0

Thomas Leitner, David S Khoury

16 May 2023

Dear Liu,

Thank you very much for submitting your manuscript "Evaluating the impact of Test-Trace-Isolate for COVID-19 management and alternative strategies" for consideration at PLOS Computational Biology.

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Reviewer's Responses to Questions

Comments to the Authors:

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Reviewer #1: The authors implement an agent-based model of SARS-CoV-2 disease spread where the contact patterns of agents are governed by two separate contact layers. The first represents close, regular contacts and the second represents casual, sporadic contacts. They incorporate the effects of test-trace-isolate strategies on contact rates within this modelling framework and investigate the consequences under different scenarios in terms of outcomes averted (total infections and deaths) and the cost of implementation (peak numbers of daily tests and simultaneously isolating individuals). The authors performed sensitivity analysis for several key parameters.

Overall, the study seems robust. However, I have a number of suggestions for improvement.

Major

Please include more details in the methods section. Unless the authors are limited by word counts, I suggest shifting some of the details from the methods supplement into the main text.

It is not clear in the methods section that contact survey data are used to inform the contact rates. This is the first and only mention (line 144): “More detailed description of the survey data and network generation process is available in Supplementary Methods.” There is no description of the survey data in the methods so how can it be more detailed in the SM. Further, please provide a more detailed description of the contact survey design and resulting data structure. It is not sufficient to point to the reference. It must be at least be summarised here.

The first time the model is described as a stochastic agent-based model is in the Discussion. This type of detail must be included in the methods section.

Your network includes household contacts. Does your model incorporate household structure? If not, worth reflecting on the implications of this in the Discussion.

Please include some plots of new infections (and deaths potentially too) over time. This is partly to confirm that the model behaviour is as expected, but also the authors state that the simulations are run for 1 year. Does the epidemic in all scenarios, including in the sensitivity analyses, “complete” within 1 year? It would also be useful to visualise for the reactive distancing scenarios how the epidemic trajectory changes with the addition of social restrictions.

In the discussion, please discuss your work in the context of other studies. You started to do this (very briefly) in the Results (line 197) but this should be done more comprehensively in the discussion. E.g. how do your findings, including the relationship between different parameters and outcomes, compare to other modelling studies e.g., https://doi.org/10.1016/S1473-3099(20)30361-3

You mention various limitations in the discussion. Please elaborate by speculating on how these limitations may have impacted your results.

Minor

I suggest stating how your study fits into the epidemiological and policy context of the COVID-19 pandemic. The baseline R0 of 2.5 and other features (fully susceptible population??) of your analysis suggest that the study is focused on the era of ancestral SARS-CoV-2 virus (I.e. before the emergence of the Alpha variant and availability of vaccines).

I suggest deleting the first line of the introduction as this statement is not true.

Line 52. I suggest deleting “and the etiology was not clear” as this statement is not true. The etiology was clear in March 2020 when many countries first implemented lockdowns.

Line 52. “…due to the huge impacts of the lockdowns on the functioning of the society” AND the availability of other control measures such as vaccines.

Line 76. What do you mean by “silent transmission”?

Line 79. Remove “etc” or explain what it is referring to.

Line 177. The first time SEIR is mentioned, it should be spelled out in full.

For visualising the contact rates between age groups, it would be useful to see plots of the contact matrices for both the static and temporal contact layers.

Line 145. The average numbers of contacts. Is this per day? 20+ seems quite high. Is this consistent with the survey data?

How did you initialise the simulations? Is the population fully susceptible?

Line 218. Typographical error. Captured by “contact tracing”.

Lines 218-219. Consider swapping “chance” with “probability”.

Line 237 - what do you mean by “better implemented”?

Line 356. I would strengthen this sentence. Delays are known to impact contact tracing effectiveness and have previously been investigated in modelling studies e.g. https://doi.org/10.1016/S2468-2667(20)30157-2

In the introduction, the authors mention that few studies of TTI have considered network structure. Please review this study by Firth et al “Using real-world network to model localised COVID-19 control strategies” . It seems relevant and should warrant a mention in the introduction and discussion. 10.1038/s41591-020-1036-8.

Worth noting in the discussion that you have used pre-pandemic contact data and it’s likely that contact rates and structures changed during the pandemic (even in the absence of government advice or mandated social restrictions). Indeed, this has been reported in many settings, and is evident through mobility data such as from Google. Further note, contact rates are most likely to spontaneously reduce when the perceived risk of infection is higher (i.e. as incidence increases). Therefore, the reactive distancing scenarios explored in your analysis need not only represent government policy scenarios, they could represent spontaneous changes in population behaviour.

Line 365. What do you mean by “highly infectious characteristics”? High transmissibility?

Reviewer #2: I want to start by thanking the authors for the submission. Like many of us, I have read a huge amount of COVID modelling papers but this one brings novelty and methodological advancement which has been an enjoyable read.

The paper is well written and I believe methodologically sound.

Introduction

I would like to see a little bit more background on using networks in infectious diseases modelling (for example Bansal 2010: https://www.tandfonline.com/doi/full/10.1080/17513758.2010.503376) just to highlight (what I think is) the novelty of the two layers and how the TTI system is implemented only in one layer.

It's quite unclear what the "cost" that is mentioned and calculated several times is. I particularly note the title for Figure 3 talks about a cost but the panels of Figure 3 are all epidemiological indicators (cases, deaths, number of contacts traced). My guess is these all could have a dollar value assigned to them, but if this is the case that needs to be included in the main manuscript.

Also on cost, are false positives included in the TTI scheme? From a dynamical systems perspective it doesn't matter (other than contacts being isolated when they don't need to be), but a false positive also means more contacts to be traced, and presumably more tests to be run, each of which costs money and will affect cost-benefit analysis.

Conclusion

I'd like to see a comment saying how effective TTI can be (or that it can't fully control highly infectious conditions) in here, at the moment it reads quite generically.

Minor comments:

Line 94: However, most of modelling studies -> most modelling studies

**********

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Reviewer #1: No: Data and code were not available for review.

Reviewer #2: No: Authors state data and code will be made available upon acceptance, but it is not currently available.

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PLoS Comput Biol. 2023 Sep 1;19(9):e1011423. doi: 10.1371/journal.pcbi.1011423.r002

Author response to Decision Letter 0

19 Jul 2023

Attachment

Submitted filename: response-letter.docx

Click here for additional data file.^{(2.8MB, docx)}

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1011423.r003

Decision Letter 1

Thomas Leitner, David S Khoury

9 Aug 2023

Dear Liu,

We are pleased to inform you that your manuscript 'Evaluating the impact of Test-Trace-Isolate for COVID-19 management and alternative strategies' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology.

Best regards,

David S. Khoury

Academic Editor

PLOS Computational Biology

Thomas Leitner

Section Editor

PLOS Computational Biology

***********************************************************

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #2: All requested changes have been made, I have nothing to add.

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

Reviewer #2: None

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Reviewer #2: Yes: Michael J Lydeamore

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1011423.r004

Acceptance letter

Thomas Leitner, David S Khoury

25 Aug 2023

PCOMPBIOL-D-23-00428R1

Evaluating the impact of Test-Trace-Isolate for COVID-19 management and alternative strategies

Dear Dr Liu,

I am pleased to inform you that your manuscript has been formally accepted for publication in PLOS Computational Biology. Your manuscript is now with our production department and you will be notified of the publication date in due course.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript.

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Thank you again for supporting PLOS Computational Biology and open-access publishing. We are looking forward to publishing your work!

With kind regards,

Zsuzsanna Gémesi

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Supplementary Methods. 1. Contact survey data for the static-temporal multiplex network.

2. Static-Temporal Multiplex Network. 3. SARS-CoV-2 Transmission model. 4. Calibration. 5. The Test-Trace-Isolate strategy. 6. Reactive distancing policy.

(DOCX)

Click here for additional data file.^{(45.2KB, docx)}

S1 Fig. The age-group-dependent contact frequency of individual contacts from 0 to 39.

(TIF)

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S2 Fig. The age-group-dependent contact frequency of individual contacts over 40.

(TIF)

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S3 Fig. The Gaussian kernel fitted age-group-dependent age distribution of contactees of individual contacts from 0 to 39.

(TIF)

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S4 Fig. The Gaussian kernel fitted age-group-dependent age distribution of contactees of individual contacts over 40.

(TIF)

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S5 Fig. The Gaussian kernel fitted age-group-dependent age distribution of participants in gathering and events from 0 to 39.

(TIF)

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S6 Fig. The Gaussian kernel fitted age-group-dependent age distribution of participants in gathering and events over 40.

(TIF)

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S7 Fig. Contact matrix representing the static contact layer.

Colors represent the mean number of contacts between an individual in given age group with individuals with any age group.

(TIF)

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S8 Fig. Contact matrix representing the temporal contact layer.

(TIF)

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S9 Fig. SARS-CoV-2 transmission model.

The detailed description of symbols in the figure referred to S1 Table.

(TIF)

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S10 Fig. Disease burdens under the baseline TTI strategy.

(TIF)

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S11 Fig. Impact of the time from sample collection to laboratory diagnosis.

(TIF)

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S12 Fig. Impact of the relative infectiousness of asymptomatic individuals.

(TIF)

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S13 Fig. Impact of the number of initial infected individuals.

A. Reduction in the cumulative infections under different reproduction number and the initial number of cases. The vertical error bars indicate the 95% CI. B. as in A, but reduction in deaths.

(TIF)

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S14 Fig. Impact of the probability to test a symptomatic individual.

(TIF)

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S15 Fig. Impact of the reactive threshold values.

(TIF)

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S16 Fig. Daily new infections and new deaths.

(TIF)

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S1 Table. Parameters for SARS-Cov-2 transmission model and disease burden.

(DOCX)

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S2 Table. Parameters for TTI.

(DOCX)

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S3 Table. Parameters for reactive distancing policies.

(DOCX)

Click here for additional data file.^{(12KB, docx)}

Attachment

Submitted filename: response-letter.docx

Click here for additional data file.^{(2.8MB, docx)}

Data Availability Statement

Data and code needed to replicate the results of our analyses are available from GitHub at: https://github.com/QH-Liu/Test-Trace-Isolate.

[pcbi.1011423.ref001] 1.WHO COVID-19 Dashboard. Geneva: World Health Organization, 2020. Available online: https://covid19.who.int/ (2022) [Google Scholar]

[pcbi.1011423.ref002] 2.Kaye AD, Okeagu CN, Pham AD, Silva RA, Hurley JJ, Arron BL, et al. Economic impact of COVID-19 pandemic on healthcare facilities and systems: International perspectives. Best Practice & Research. 2021;35(3):293–306. doi: 10.1016/j.bpa.2020.11.009 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref003] 3.Lau H, Khosrawipour V, Kocbach P, Mikolajczyk A, Schubert J, Bania J, et al. The positive impact of lockdown in Wuhan on containing the COVID-19 outbreak in China. Journal of Travel Medicine. 2020;27(3). doi: 10.1093/jtm/taaa037 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref004] 4.Flaxman S, Mishra S, Gandy A, Unwin HJT, Mellan TA, Coupland H, et al. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature. 2020;584(7820):257–61. doi: 10.1038/s41586-020-2405-7 [DOI] [PubMed] [Google Scholar]

[pcbi.1011423.ref005] 5.Olivier C, Yuriy G, Michael W. The Cost of the Covid-19 Crisis: Lockdowns, Macroeconomic Expectations, and Consumer Spending. National Bureau of Economic Research. 2020. 10.3386/w27141 [DOI] [Google Scholar]

[pcbi.1011423.ref006] 6.Di Domenico L, Pullano G, Sabbatini CE, Boelle PY, Colizza V. Impact of lockdown on COVID-19 epidemic in Ile-de-France and possible exit strategies. BMC Medicine. 2020;18(1):240. doi: 10.1186/s12916-020-01698-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref007] 7.Liu QH, Zhang J, Peng C, Litvinova M, Huang S, Poletti P, et al. Model-based evaluation of alternative reactive class closure strategies against COVID-19. Nature Communications. 2022;13(1):322. doi: 10.1038/s41467-021-27939-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref008] 8.United Nations Educational. School closure information because of COVID-19. https://en.unesco.org/covid19/educationresponse (2020). [Google Scholar]

[pcbi.1011423.ref009] 9.Di Domenico L, Pullano G, Sabbatini CE, Boelle PY, Colizza V. Modelling safe protocols for reopening schools during the COVID-19 pandemic in France. Nature Communications. 2021;12(1):1073. doi: 10.1038/s41467-021-21249-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref010] 10.Munday JD, Sherratt K, Meakin S, Endo A, Pearson CAB, Hellewell J, et al. Implications of the school-household network structure on SARS-CoV-2 transmission under school reopening strategies in England. Nature Communications. 2021;12(1):1942. doi: 10.1038/s41467-021-22213-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref011] 11.Black JRM, Bailey C, Przewrocka J, Dijkstra KK, Swanton C. COVID-19: the case for health-care worker screening to prevent hospital transmission. Lancet. 2020;395(10234):1418–20. doi: 10.1016/S0140-6736(20)30917-X [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref012] 12.Smith DRM, Duval A, Zahar JR, Opatowski L, Temime L, Nosocomial E-MWG. Rapid antigen testing as a reactive response to surges in nosocomial SARS-CoV-2 outbreak risk. Nature Communications. 2022;13(1). doi: 10.1038/s41467-021-27845-w [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref013] 13.Hellewell J, Abbott S, Gimma A, Bosse NI, Jarvis CI, Russell TW, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Global Health. 2020;8(4):e488–e96. doi: 10.1016/S2214-109X(20)30074-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref014] 14.Kucharski AJ, Klepac P, Conlan AJK, Kissler SM, Tang ML, Fry H, et al. Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: a mathematical modelling study. Lancet Infectious Diseases. 2020;20(10):1151–60. doi: 10.1016/S1473-3099(20)30457-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref015] 15.Gosce L, Phillips PA, Spinola P, Gupta DRK, Abubakar PI. Modelling SARS-COV2 Spread in London: Approaches to Lift the Lockdown. Journal of Infection. 2020;81(2):260–5. doi: 10.1016/j.jinf.2020.05.037 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref016] 16.Aleta A, Martin-Corral D, Pastore YPA, Ajelli M, Litvinova M, Chinazzi M, et al. Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nature Human Behaviour. 2020;4(9):964–71. doi: 10.1038/s41562-020-0931-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref017] 17.Glasser JW, Hupert N, McCauley MM, Hatchett R. Modeling and public health emergency responses: lessons from SARS. Epidemics. 2011;3(1):32–7. doi: 10.1016/j.epidem.2011.01.001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref018] 18.Eames KT, Webb C, Thomas K, Smith J, Salmon R, Temple JM. Assessing the role of contact tracing in a suspected H7N2 influenza A outbreak in humans in Wales. BMC Infectious Diseases. 2010;10:141. doi: 10.1186/1471-2334-10-141 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref019] 19.Swanson KC, Altare C, Wesseh CS, Nyenswah T, Ahmed T, Eyal N, et al. Contact tracing performance during the Ebola epidemic in Liberia, 2014–2015. PLoS Neglected Tropical Diseases. 2018;12(9):e0006762. doi: 10.1371/journal.pntd.0006762 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref020] 20.Keeling MJ, Hollingsworth TD, Read JM. Efficacy of contact tracing for the containment of the 2019 novel coronavirus (COVID-19). Journal of Epidemiology and Community Health. 2020;74(10):861–6. doi: 10.1136/jech-2020-214051 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref021] 21.Bi QF, Wu YS, Mei SJ, Ye CF, Zou X, Zhang Z, et al. Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study. Lancet Infectious Diseases. 2020;20(8):911–9. doi: 10.1016/S1473-3099(20)30287-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref022] 22.Chen YH, Fang CT, Huang YL. Effect of Non-lockdown Social Distancing and Testing-Contact Tracing During a COVID-19 Outbreak in Daegu, South Korea, February to April 2020: A Modeling Study. International Journal of Infectious Diseases. 2021;110:213–21. doi: 10.1016/j.ijid.2021.07.058 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref023] 23.Baker MG, Kvalsvig A, Verrall AJ, Telfar-Barnard L, Wilson N. New Zealand’s elimination strategy for the COVID-19 pandemic and what is required to make it work. New Zealand Medical Journal. 2020;133(1512):10–4. [PubMed] [Google Scholar]

[pcbi.1011423.ref024] 24.Wu JH, Tang B, Bragazzi NL, Nah K, McCarthy Z. Quantifying the role of social distancing, personal protection and case detection in mitigating COVID-19 outbreak in Ontario, Canada. Journal of Mathematics in Industry. 2020;10(1). doi: 10.1186/s13362-020-00083-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref025] 25.Pullano G, Di Domenico L, Sabbatini CE, Valdano E, Turbelin C, Debin M, et al. Underdetection of cases of COVID-19 in France threatens epidemic control. Nature. 2020; doi: 10.1038/s41586-020-03095-6 [DOI] [PubMed] [Google Scholar]

[pcbi.1011423.ref026] 26.Contreras S, Dehning J, Loidolt M, Zierenberg J, Spitzner FP, Urrea-Quintero JH, et al. The challenges of containing SARS-CoV-2 via test-trace-and-isolate. Nature Communications. 2021;12(1):378. doi: 10.1038/s41467-020-20699-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref027] 27.Chiu WA, Fischer R, Ndeffo-Mbah ML. State-level needs for social distancing and contact tracing to contain COVID-19 in the United States. Nature Human Behaviour. 2020;4(10):1080–+. doi: 10.1038/s41562-020-00969-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref028] 28.Davis EL, Lucas TCD, Borlase A, Pollington TM, Abbott S, Ayabina D, et al. Contact tracing is an imperfect tool for controlling COVID-19 transmission and relies on population adherence. Nature Communications. 2021;12(1). doi: 10.1038/s41467-021-25531-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref029] 29.Fraser C, Riley S, Anderson RM, Ferguson NM. Factors that make an infectious disease outbreak controllable. Proceedings of the National Academy of Sciences of the United States of America. 2004;101(16):6146–51. doi: 10.1073/pnas.0307506101 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref030] 30.Oran DP, Topol EJ. Prevalence of Asymptomatic SARS-CoV-2 Infection A Narrative Review. Annals of Internal Medicine. 2020;173(5):362–+. doi: 10.7326/M20-3012 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref031] 31.Moghadas SM, Fitzpatrick MC, Sah P, Pandey A, Shoukat A, Singer BH, et al. The implications of silent transmission for the control of COVID-19 outbreaks. Proceedings of the National Academy of Sciences of the United States of America. 2020;117(30):17513–17515. doi: 10.1073/pnas.2008373117 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref032] 32.Sun K, Wang W, Gao L, Wang Y, Luo K, Ren L, et al. Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2. Science. 2021;371(6526). doi: 10.1126/science.abe2424 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref033] 33.Hu S, Wang W, Wang Y, Litvinova M, Luo K, Ren L, et al. Infectivity, susceptibility, and risk factors associated with SARS-CoV-2 transmission under intensive contact tracing in Hunan, China. Nature Communications. 2021;12(1):1533. doi: 10.1038/s41467-021-21710-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref034] 34.He X, Lau EHY, Wu P, Deng X, Wang J, Hao X, et al. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nature Medicine. 2020;26(5):672–5. doi: 10.1038/s41591-020-0869-5 [DOI] [PubMed] [Google Scholar]

[pcbi.1011423.ref035] 35.Bauch CT, Galvani AP. Epidemiology. Social factors in epidemiology. Science. 2013;342(6154):47–9. doi: 10.1126/science.1244492 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref036] 36.Leung NHL. Transmissibility and transmission of respiratory viruses. Nature Reviews Microbiology. 2021;19(8):528–45. doi: 10.1038/s41579-021-00535-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref037] 37.Pitol AK, Julian TR. Community Transmission of SARS-CoV-2 by Surfaces: Risks and Risk Reduction Strategies. Environmental Science & Technology Letters. 2021;8(3):263–9. doi: 10.1021/acs.estlett.0c00966 [DOI] [PubMed] [Google Scholar]

[pcbi.1011423.ref038] 38.Harvey AP, Fuhrmeister ER, Cantrell ME, Pitol AK, Swarthout JM, Powers JE, et al. Longitudinal Monitoring of SARS-CoV-2 RNA on High-Touch Surfaces in a Community Setting. Environmental Science & Technology Letters. 2021;8(2):168–75. doi: 10.1021/acs.estlett.0c00875 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref039] 39.Garry M, Hope L, Zajac R, Verrall AJ, Robertson JM. Contact Tracing: A Memory Task With Consequences for Public Health. Perspectives on Psychological Science. 2021;16(1):175–87. doi: 10.1177/1745691620978205 [DOI] [PubMed] [Google Scholar]

[pcbi.1011423.ref040] 40.Louail T, Lenormand M, Picornell M, Garcia Cantu O, Herranz R, Frias-Martinez E, et al. Uncovering the spatial structure of mobility networks. Nature Communications. 2015;6:6007. doi: 10.1038/ncomms7007 [DOI] [PubMed] [Google Scholar]

[pcbi.1011423.ref041] 41.Sun L, Axhausen KW, Lee DH, Huang X. Understanding metropolitan patterns of daily encounters. Proceedings of the National Academy of Sciences of The United States Of America. 2013;110(34):13774–9. doi: 10.1073/pnas.1306440110 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref042] 42.Zhang MX, Verbraeck A, Meng RQ, Chen B, Qiu XG. Modeling Spatial Contacts for Epidemic Prediction in a Large-scale Artificial City. Journal of Artificial Societies and Social Simulation. 2016;19(4). 10.18564/jasss.3148 [DOI] [Google Scholar]

[pcbi.1011423.ref043] 43.Mistry D, Litvinova M, Pastore YPA, Chinazzi M, Fumanelli L, Gomes MFC, et al. Inferring high-resolution human mixing patterns for disease modeling. Nature Communications. 2021;12(1):323. doi: 10.1038/s41467-020-20544-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref044] 44.Litvinova M, Liu QH, Kulikov ES, Ajelli M. Reactive school closure weakens the network of social interactions and reduces the spread of influenza. Proceedings of the National Academy of Sciences of The United States Of America. 2019;116(27):13174–81. doi: 10.1073/pnas.1821298116 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref045] 45.Mousa A, Winskill P, Watson OJ, Ratmann O, Monod M, Ajelli M, et al. Social contact patterns and implications for infectious disease transmission—a systematic review and meta-analysis of contact surveys. Elife. 2021;10. doi: 10.7554/eLife.70294 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref046] 46.Zhang J, Klepac P, Read JM, Rosello A, Wang X, Lai S, et al. Patterns of human social contact and contact with animals in Shanghai, China. Scientific Reports. 2019;9(1):15141. doi: 10.1038/s41598-019-51609-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref047] 47.Liu J, Liao X, Qian S, Yuan J, Wang F, Liu Y, et al. Community Transmission of Severe Acute Respiratory Syndrome Coronavirus 2, Shenzhen, China, 2020. Emerging Infectious Diseases. 2020;26(6):1320–3. doi: 10.3201/eid2606.200239 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref048] 48.Aleta A, Martin-Corral D, Bakker MA, Pastore YPA, Ajelli M, Litvinova M, et al. Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas. Proceedings of the National Academy of Sciences of The United States Of America. 2022;119(26):e2112182119. doi: 10.1073/pnas.2112182119 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref049] 49.Grantz KH, Lee EC, D’Agostino McGowan L, Lee KH, Metcalf CJE, Gurley ES, et al. Maximizing and evaluating the impact of test-trace-isolate programs: A modeling study. PLoS Medicine. 2021;18(4):e1003585. doi: 10.1371/journal.pmed.1003585 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref050] 50.Moon SA, Scoglio CM. Contact tracing evaluation for COVID-19 transmission in the different movement levels of a rural college town in the USA. Scientific Reports. 2021;11(1):4891. doi: 10.1038/s41598-021-83722-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref051] 51.Bansal S, Read J, Pourbohloul B & Meyers LA. The dynamic nature of contact networks in infectious disease epidemiology. Journal of Biological Dynamics. 2010; 4:5, 478–489, doi: 10.1080/17513758.2010.503376 [DOI] [PubMed] [Google Scholar]

[pcbi.1011423.ref052] 52.Firth JA, Hellewell J, Klepac P, Kissler S, CMMID COVID-19 Working Group; Kucharski AJ, Spurgin LG. Using a real-world network to model localized COVID-19 control strategies. Nature Medicine. 2020;26(10):1616–1622. doi: 10.1038/s41591-020-1036-8 [DOI] [PubMed] [Google Scholar]

[pcbi.1011423.ref053] 53.Vernon MC, Keeling MJ. Representing the UK’s cattle herd as static and dynamic networks. Proceedings. Biological Sciences. 2009;276(1656):469–76. doi: 10.1098/rspb.2008.1009 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref054] 54.Perez L, Dragicevic S. An agent-based approach for modeling dynamics of contagious disease spread. International Journal of Health Geographics. 2009;8:50. doi: 10.1186/1476-072X-8-50 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref055] 55.Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R, Massari M, Salmaso S, Tomba GS, Wallinga J, Heijne J, Sadkowska-Todys M, Rosinska M, Edmunds WJ. Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Medicine. 2008; 5(3):e74. doi: 10.1371/journal.pmed.0050074 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref056] 56.Yang J, Marziano V, Deng X, Guzzetta G, Zhang J, Trentini F, et al. Despite vaccination, China needs non-pharmaceutical interventions to prevent widespread outbreaks of COVID-19 in 2021. Nature Human Behavior. 2021;5(8):1009–20. doi: 10.1038/s41562-021-01155-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref057] 57.Aleta A, Ferraz de Arruda G, Moreno Y. Data-driven contact structures: From homogeneous mixing to multilayer networks. PLoS Computational Biology. 2020;16(7):e1008035. doi: 10.1371/journal.pcbi.1008035 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref058] 58.Xiao AT, Tong YX, Gao C, Zhu L, Zhang YJ, Zhang S. Dynamic profile of RT-PCR findings from 301 COVID-19 patients in Wuhan, China: A descriptive study. Journal of Clinical Virology. 2020;127:104346. doi: 10.1016/j.jcv.2020.104346 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref059] 59.Wilder-Smith A, Freedman DO. Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. Journal of Travel Medicine. 2020;27(2). doi: 10.1093/jtm/taaa020 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref060] 60.Chen PJ, Mao LJ, Nassis GP, Harmer P, Ainsworth BE, Li FZ. Coronavirus disease (COVID-19): The need to maintain regular physical activity while taking precautions. Journal of Sport and Health Science. 2020;9(2):103–4. doi: 10.1016/j.jshs.2020.02.001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref061] 61.Samieefar N, Yari Boroujeni R, Jamee M, Lotfi M, Golabchi MR, Afshar A, et al. Country Quarantine During COVID-19: Critical or Not? Disaster Medicine and Public Health Preparedness. 2021;15(4):e24–e5. doi: 10.1017/dmp.2020.384 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref062] 62.Brauner JM, Mindermann S, Sharma M, Johnston D, Salvatier J, Gavenciak T, et al. Inferring the effectiveness of government interventions against COVID-19. Science. 2021;371(6531). doi: 10.1126/science.abd9338 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref063] 63.Bo Y, Guo C, Lin C, Zeng Y, Li HB, Zhang Y, et al. Effectiveness of non-pharmaceutical interventions on COVID-19 transmission in 190 countries from 23 January to 13 April 2020. International Journal of Infectious Diseases. 2021;102:247–53. doi: 10.1016/j.ijid.2020.10.066 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref064] 64.Eames KT, Keeling MJ. Contact tracing and disease control. Proceedings of the Royal Society B: Biological Sciences. 2003;270(1533):2565–71. doi: 10.1098/rspb.2003.2554 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref065] 65.Peak CM, Kahn R, Grad YH, Childs LM, Li R, Lipsitch M, et al. Individual quarantine versus active monitoring of contacts for the mitigation of COVID-19: a modelling study. Lancet Infectious Diseases. 2020;20(9):1025–1033. doi: 10.1016/S1473-3099(20)30361-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref066] 66.Zhang J, Litvinova M, Liang Y, Wang Y, Wang W, Zhao S, et al. Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China. Science. 2020;26;368(6498):1481–1486. doi: 10.1126/science.abb8001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref067] 67.Liu QH, Ajelli M, Aleta A, Merler S, Moreno Y, Vespignani A. Measurability of the epidemic reproduction number in data-driven contact networks. Proceedings of the National Academy of Sciences of the United States of America. 2018;115(50):12680–12685. doi: 10.1073/pnas.1811115115 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1011423.ref068] 68.Scalia Tomba G, Svensson A, Asikainen T, Giesecke J. Some model based considerations on observing generation times for communicable diseases. Mathematical Biosciences. 2010;223(1):24–31. doi: 10.1016/j.mbs.2009.10.004 [DOI] [PubMed] [Google Scholar]

[pcbi.1011423.ref069] 69.Kretzschmar ME, Rozhnova G, Bootsma MCJ, van Boven M, van de Wijgert JHHM, Bonten MJM. Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study. Lancet Public Health. 2020;5(8):e452–e459. doi: 10.1016/S2468-2667(20)30157-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Evaluating the impact of test-trace-isolate for COVID-19 management and alternative strategies

Kun Zhang

Zhichu Xia

Shudong Huang

Gui-Quan Sun

Jiancheng Lv

Marco Ajelli

Keisuke Ejima

Quan-Hui Liu

Roles

Abstract

Author summary

Introduction

Methods

Overview of transmission simulation with TTI

Contact survey data

Static-temporal multiplex network model

Fig 1. Static-temporal multiplex network model and social contact pattern.

SARS-CoV-2 transmission model

Test-Trace-Isolate strategy

Fig 2. Example of the Test-Trace-Isolate strategy.

Results

Effectiveness and social/implementation cost of TTI

Fig 3. Effectiveness and social/implementation cost of TTI.

Fig 4. Sensitivity analyses varying the parameters regulating the test and trace processes.

Fig 5. Analysis of baseline with longer delay varying the time from sample collection to laboratory diagnosis.

Fig 6. Effectiveness and burden of TTI-relaxed and TTI-strict.

Reactive distancing policy

Fig 7. Effectiveness of reactive distancing policy coupled with the TTI strategy.

Discussion

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Thomas Leitner

David S Khoury

Roles

Author response to Decision Letter 0

Decision Letter 1

Thomas Leitner

David S Khoury

Roles

Acceptance letter

Thomas Leitner

David S Khoury

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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