Tracing and analysis of 288 early SARS-CoV-2 infections outside China: A modeling study

Francesco Pinotti; Laura Di Domenico; Ernesto Ortega; Marco Mancastroppa; Giulia Pullano; Eugenio Valdano; Pierre-Yves Boëlle; Chiara Poletto; Vittoria Colizza

doi:10.1371/journal.pmed.1003193

. 2020 Jul 17;17(7):e1003193. doi: 10.1371/journal.pmed.1003193

Tracing and analysis of 288 early SARS-CoV-2 infections outside China: A modeling study

Francesco Pinotti ^1,^#, Laura Di Domenico ^1,^#, Ernesto Ortega ^2,^#, Marco Mancastroppa ^3,⁴, Giulia Pullano ^1,⁵, Eugenio Valdano ^1,⁶, Pierre-Yves Boëlle ¹, Chiara Poletto ¹, Vittoria Colizza ^1,^*

Editor: Richard Zachariah Aandahl⁷

PMCID: PMC7367442 PMID: 32678827

Abstract

Background

In the early months of 2020, a novel coronavirus disease (COVID-19) spread rapidly from China across multiple countries worldwide. As of March 17, 2020, COVID-19 was officially declared a pandemic by the World Health Organization. We collected data on COVID-19 cases outside China during the early phase of the pandemic and used them to predict trends in importations and quantify the proportion of undetected imported cases.

Methods and findings

Two hundred and eighty-eight cases have been confirmed out of China from January 3 to February 13, 2020. We collected and synthesized all available information on these cases from official sources and media. We analyzed importations that were successfully isolated and those leading to onward transmission. We modeled their number over time, in relation to the origin of travel (Hubei province, other Chinese provinces, other countries) and interventions. We characterized the importation timeline to assess the rapidity of isolation and epidemiologically linked clusters to estimate the rate of detection. We found a rapid exponential growth of importations from Hubei, corresponding to a doubling time of 2.8 days, combined with a slower growth from the other areas. We predicted a rebound of importations from South East Asia in the successive weeks. Time from travel to detection has considerably decreased since first importation, from 14.5 ± 5.5 days on January 5, 2020, to 6 ± 3.5 days on February 1, 2020. However, we estimated 36% of detection of imported cases. This study is restricted to the early phase of the pandemic, when China was the only large epicenter and foreign countries had not discovered extensive local transmission yet. Missing information in case history was accounted for through modeling and imputation.

Conclusions

Our findings indicate that travel bans and containment strategies adopted in China were effective in reducing the exportation growth rate. However, the risk of importation was estimated to increase again from other sources in South East Asia. Surveillance and management of traveling cases represented a priority in the early phase of the epidemic. With the majority of imported cases going undetected (6 out of 10), countries experienced several undetected clusters of chains of local transmissions, fueling silent epidemics in the community. These findings become again critical to prevent second waves, now that countries have reduced their epidemic activity and progressively phase out lockdown.

Vittoria Colizza and colleagues shed light on the early spread of SARS-CoV-2 from China to other countries.

Author summary

Why was this study done?

Originating from China, COVID-19 outbreak has now become a global pandemic, with more than 4 million cases reported across all continents.
Underdetection of imported cases from China in the early phase of the epidemic played a crucial role in the spreading of the virus across and within countries.
We quantified importations over time in light of the implemented travel ban in China and assessed delay and rate of detection of the first imported cases responsible for seeding the epidemic across multiple countries.

What did the researchers do and find?

We collected information on all international cases outside China officially confirmed in the period from January 3 to February 13, 2020.
We developed a statistical model to predict trends in importations and predicted a rebound effect in importations from South East Asia.
By analyzing clusters of local transmission, we estimated the detection rate at 36%.

What do these findings mean?

Travel bans adopted in China contributed to reducing the growth rate of exportations; however, they did not prevent international seeding.
The majority of imported cases went undetected, generating extensive chains of local transmission in countries outside China. This led to silently spreading epidemics in seeded countries.

Introduction

After being first identified in China in January 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reached all countries worldwide within a few months [1]. Massive intervention measures [2] were implemented by Chinese authorities in late January to control the epidemic. Countries outside China promptly reinforced border controls and intensified active surveillance to rapidly detect and isolate importations, trace contacts, and isolate suspect cases [3,4]. Such a massive response did not prevent countries to face importations of cases and experience extended independent chains of local transmission [5–7], ultimately resulting in sustained local spread.

The effectiveness of measures aiming at preventing importations and local transmissions critically depends on disease epidemiology and natural history of the infection [8,9], as well as the volume of importations [3]. For the case of coronavirus disease 2019 (COVID-19), the presence of an incubation period, during which infected individuals carry on their usual activities (including travel), was a major challenge for screening controls at airports [8]. Moreover, mild non-specific symptoms and transmission before the onset of clinical symptoms [10–12] compromised infection control measures for importations and onward transmissions [9]. Imported cases went undetected and contributed to the global spread of the disease [13–18].

Here we systematically collected and analyzed data on the first 288 COVID-19 confirmed cases outside China, to characterize the international spread of COVID-19 pandemic during its early phase. We show that the detailed information carried by case histories provided early evidence of the COVID-19 specific features that enabled the virus to escape containment efforts and reach pandemic proportion. We analyzed importations that were successfully isolated and those leading to onward transmission, characterizing their case timeline. We developed a statistical model to describe trends in importations up to mid-February and quantified the proportion of undetected imported cases.

Methods

Data collection and synthesis

We collected all international cases confirmed by official public health sources in the period from January 3 to February 13, 2020. Case history was reconstructed by searching the scientific literature, official public health sources, and news [19–39]. Case history included dates of travel and symptom onset, date of COVID-19 confirmation, date of hospital admission, date of case isolation, travel history, epidemiological link with other cases, and hospitalization history. International cases included imported cases, secondary cases out of China, and repatriations. Cases from cruises were not considered here. Information was extracted by LDD and EO and checked by MM. Additional cases in the period from February 14 to February 27 were collected and used to validate the results.

The full database, along with the database describing clusters, was made publicly available [40].

This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline. STROBE checklist can be found in the Supporting Information (S1 STROBE Checklist). The study did not have a protocol.

Descriptive analysis

For imported cases with full information on the timeline of events, we computed the average duration from travel to onset, from travel to hospitalization, and from hospitalization to reporting. We used analysis of variance to compare groups of imported cases that generated or did not generate local transmissions. We extended the analysis to all imported cases combining cases with full and partial information on the timeline. We used the analysis of variance and multiple imputation for missing data. Results were combined using Rubin’s approach [41].

Modeling and predicting importations

We modeled the total number of imported cases out of China over time accounting for date of travel, delay in reporting, and source areas.

We distinguished between 3 different sources: Hubei province (H), the rest of China (C), other countries (O). We modeled imported cases over time as a piecewise exponential function depending on the source and on travel restrictions in place. We assumed a different situation in Hubei province and the rest of the world because of the level of awareness in the different phases of the outbreak. The exponential functions are defined as follows:

I_{S, t} = {\begin{matrix} I_{S}^{p r e} {* e}^{r_{S}^{p r e} t}, t \leq T_{S} \\ I_{S}^{p o s t} {* e}^{r_{S}^{p o s t} t}, t > T_{S} \end{matrix}, S = H, C

I_{S, t} = I_{S} {* e}^{r_{S}^{} t}, S = O

where $r_{H}^{p r e}$ is the growth rate of cases coming from Hubei, and $r_{C}^{p r e}, r_{O}^{}$ , with $r_{C}^{p r e} = r_{O}^{}$ , the growth rates of cases coming from the rest of China and other countries, respectively. Travel restrictions were modeled by assuming a discontinuity in the growth rate. For Hubei, we assumed the growth rate to change from $r_{H}^{p r e}$ to $r_{H}^{p o s t}$ after the travel ban of January 23, 2020 (indicated with T_H); for the rest of China, we assumed an analogous change from $r_{C}^{p r e}$ to $r_{C}^{p o s t}$ after January 29, 2020 (T_C), date of first flight cancellations [42]. No change was considered for the other countries (r_O constant over time), as no restrictions of travel were established toward these countries. The scale parameters of the exponential functions ( $I_{H}^{p r e}, {I_{H}^{p o s t}, I}_{C}^{p r e}, I_{C}^{p o s t}, I_{O}$ ) were assumed to be different among the 3 sources, to account for different traveling volumes and dates of beginning of importations.

We modeled the time τ from importation to detection of a case with a gamma distribution, g_t(τ), conditioned to the date of case importation, t. The distribution g_t(τ) was assumed to have constant coefficient of variation (SD/mean) achieved by a constant shape parameter and a rate parameter varying smoothly in time to capture change in surveillance efficiency.

We used a Bayesian framework to fit the model to imported cases by origin, travel date, and confirmation date. Cases with partial information (e.g., missing date and/or origin of travel) were included by defining latent variables marginalized out during inference. The model was then used to predict imported cases 2 weeks in the future. All details of the analysis are reported in the Supporting Information (S1 Text).

Estimation of underdetection of imported cases

We analyzed clusters of transmission generated by imported cases (index case(s) in each cluster) to estimate undetected importations. A cluster can be seeded by more than 1 index case, e.g., by infected family members who traveled together. The number of clusters of local transmission was modeled with a multinomial distribution according to the number of index cases and whether these were imported cases. As detailed in the Supporting Information, the likelihood function was a function of 4 observable quantities: the number of observed clusters with 1 imported index case (x₁); the number of observed clusters with more than 1 imported index case (x₂); the number of known imported cases causing no onward transmission ( $\tilde{y}$ ); the number of clusters for which an index case was not identified (z); and a fifth and unobserved quantity, the number of undetected imported cases who did not start onward transmission (w). Maximization of the likelihood allowed to estimate w from the records of x₁, x₂, $\tilde{y}$ , z and to compute the expected number of unobserved imported cases. Additional details are described in the Supporting Information (S1 Text).

Results

Timeline of travel-related cases

We collected 288 cases, including 163 imported cases, 109 cases involved in local transmissions, 30 repatriations, and 1 case of unknown origin. Fifteen cases were classified as both imported and local transmissions, since they contracted the infection outside China and traveled to a different country once infected (ES01, ES02, GB03, GB04, GB05, GB06, GB07, GB08, KR12, KR16, KR17, KR19, MY09, TH20, and TH21 in our database [40]).

Fig 1 summarizes the timeline of imported cases. Symptom onset occurred after the travel to the destination country for almost all cases for which date of travel and of onset are available (68 out of 73, 93%). Complete information was available for 51 (31%) imported cases, with quality of information decreasing over time (S2 Text, Fig A).

Among imported cases with full information, the delay from travel to hospitalization was longer in cases that generated secondary transmissions (mean of 10 ± 0.97 days compared with 5.5 ± 0.67 days, p = 0.003). Overall, the duration from travel to first event (whether symptom onset, or hospitalization for asymptomatic) was also longer, although the difference was not statistically significant (5.0 ± 0.9 days versus 3.7 ± 0.5 days, p = 0.08). Durations of hospitalization were instead comparable among the 2 groups of cases (1.5 ± 0.7 days versus 2.6 ± 0.4 days for cases that generated or did not generate secondary transmissions, respectively). Including imported cases with missing information through imputation, we found the same trend though smaller in magnitude and not statistically significant (delay from travel to hospitalization 9.8 ± 1.2 versus 8.3 ± 0.5 days p = 0.3; delay from travel to onset 5.8 ± 1.1 versus 4.2 ± 0.5 p = 0.16, for cases that generated or did not generate secondary transmissions, respectively). This suggests that importations with missing information may be closer in characteristics to index cases leading to onward transmission.

The statistical model predicted a decrease in the average time from travel to detection from 14.5 ± 5.5 days on January 5, 2020, to 6 ± 3.5 days on February 1, 2020 (Fig 2).

Predicting travel-related cases

The model predicted a rapid exponential growth of importations from Hubei, with a growth rate $r_{H}^{p r e} = 0.26$ (95% CI 0.21–0.31), corresponding to a doubling time of 2.8 days. In comparison, the exponential growth from other territories (rest of China and countries other than China) was slow, $r_{C}^{p r e} = r_{O}^{} = 0.04$ (95% CI 0.00–0.08). After the implementation of travel restrictions, a negative growth rate was estimated, signaling a decline in imported cases. The decline was sharp for Hubei ( $r_{H}^{p o s t} = - 0.64$ [95% CI −0.85 to −0.48]) and more gradual for the rest of China ( $r_{C}^{p o s t} = - 0.19$ [95% CI −0.54, 0.00]). All estimated parameters and their confidence intervals are reported in the Supporting Information (S2 Text, Table B).

The predicted trend of all imported cases over time is shown in Fig 3, compared with the observed data. Reported importations were predicted to remain stationary in the second and third week of February and to rise again because of the effect of transmission clusters outside China. Imported cases after February 13, 2020, were in agreement with model predictions (Fig 3) up to February 24. Data in the successive 3 days diverged from our predictions as they were linked with early detection of multiple imported cases in the Middle East and Europe from sustained local transmission in Iran and Italy.

Transmission clusters outside China

Forty-two transmission clusters were identified out of China in the timeframe under study. Table 1 summarizes the size and country of each cluster. Clusters were grouped according to whether the index case (1) was a traveling case identified prior to cluster detection, (2) a traveling case not identified or identified retrospectively once the cluster was observed, or (3) completely unknown. Assuming that clusters of unknown origin were linked to one of the already observed imported cases—or, in other words, not linked to an undetected imported case—led to an estimate of 76 (95% CI 49–118) undetected imported cases. In this scenario, detected cases would amount to 65% of all imported cases. Assuming instead that all clusters of unknown origin were due to undetected imported cases increased the number of undetected cases to 225 (95% CI 186–369), i.e., detected cases would correspond to only 36% of the total.

Table 1. Summary of transmission clusters according to the type of index case.

Index Case	Number of Clusters	Clusters (size)
Traveler(s) identified prior to cluster detection^a	15	cDE01 (16), cFR02 (12), cVN02 (7), cKR01 (5), cSG04^b (5), cKR04 (3), cMY01 (3), cSG11^b (3), cVN01 (3), cGB01 (2), cKR02 (2), cKR03 (2), cKR05 (2), cUS01 (2), cUS02 (2)
Traveler(s) not identified or retrospectively identified^c	8	cSG01 (10), cSG02 (8), cJP01 (4), cCA01 (3), cKR06 (3), cTH04 (3), cFR01 (2), cJP02 (2)
Unknown^d	19	cSG13 (8), cSG09 (5), cJP03 (3), cJP06 (3), cSG14 (3), cJP04 (2), cJP05 (2), cJP07 (2), cSG03 (2), cSG05 (2), cSG06 (2), cSG07 (2), cSG08 (2), cSG10 (2), cSG12 (2), cTH01 (2), cTH02 (2), cTH03 (2), cAE01^e

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^aThe index case was identified independently from secondary transmissions.

^bCluster associated with 2 traveling cases.

^cThe index case was either identified retrospectively following case investigation prompted by the detection of secondary cases or the identity was not identified; however, the cluster was linked to a specific location/circumstance visited by Chinese travelers (shop, conference, bus tour).

^dNo connection with other case or source of infection has been identified yet.

^eInsufficient information on the size of the cluster.

Discussion

We reviewed here all confirmed cases out of China from January 3 to February 13, 2020, and gathered detailed information on case history and epidemiological links to (1) identify salient epidemiological features, (2) assess the impact of travel restrictions in China on the importations of cases worldwide, and (3) evaluate the effectiveness of control measures against importations. We found a rapid exponential growth of importations from Hubei, up to the closure of Wuhan airport preventing further travel of cases, combined with a slower growth from other countries in South East Asia. The estimated growth of importation before interventions is compatible with a doubling time of 2.8 days. Time from travel to detection considerably decreased across time, from 14.5 ± 5.5 days on January 5, 2020, to 6 ± 3.5 days on February 1, 2020. However, our estimates indicate that only 36% of imported cases were detected. This study is restricted to the early phase of the pandemic, when China was the only large epicenter and extensive silent transmission in other countries, such as Iran and Italy, was not discovered yet.

The substantially larger growth of importations from China compared with other territories is related to a stronger epidemic activity in the Hubei province, origin of the outbreak, with respect to other affected areas [5]. This difference is likely an outcome of the containment measures in China [2,43,44] and of the increased awareness following their implementation [45–49], leading to more efficient control. Identification, rapid management of cases, and contact tracing were indeed proposed by the World Health Organization as key to contain the epidemic globally.

We found that the travel ban in Wuhan produced a sharp decline in importations from the region. Combined with local containment measures in the rest of China and other countries in Southeast Asia, this resulted in a substantial overall reduction of exported cases worldwide. Indeed, after peaking at the end of January, registered traveling cases declined to plateau at very low levels. On one side, this shows that strict travel bans may be beneficial by reducing importations to manageable levels and by giving countries the time to prepare and strengthen their surveillance systems in the short term, as signaled by a reduction of the interval from travel date to detection over time. On the other hand, however, the decline likely occurred too late when local spreading was already established in many countries outside the epicenter of the epidemic [50,51]. At the end of February, sustained community transmission in Iran and Italy led to exportations to several other countries in the world in a timeframe of few days [52,53]. This was facilitated by the fact that no local transmission was reported in these countries, preventing the alert to other countries for them to deploy targeted surveillance and control. At that time, indeed, case definition for the importation of a COVID-19 suspect case was based exclusively on China as the origin of exposure or travel [54,55], with few exceptions including East Asian countries [56,57].

Monitoring imported cases is critical in territories with sporadic epidemic activity where the attention is concentrated on preventing the introduction of cases. At the time of writing, countries such as China and Australia show limited local transmission and are concentrating strong efforts in border controls to avoid a second epidemic wave, that could be fueled by the large proportion of the population still susceptible to SARS-CoV-2. Strict measures, such as massive testing and quarantine of incoming travelers, are being implemented to block silent introductions of cases as it occurred at the beginning of the pandemic.

We found that during the first half of February, countries outside China witnessed an increasing reporting of clusters with no known epidemiological link [5,16]. Our estimates indicate a detection ability of 36% to ascertain imported cases in countries outside China. This means that approximately 6 imported cases out of 10 have gone undetected. Previous detection rates estimates range from 27% [15] to 38% [15,17], with variations across countries [13,15]. Ascertainment was estimated to be even lower (approximately 10%) when assessed on repatriations [58]. Here, we excluded from this analysis all repatriation events and cruises with outbreaks, as conditions for detection and identification may be different.

Underdetection may have been due to several different factors including asymptomatic infections, infections with mild clinical symptoms, health-seeking behavior and declaration of travel history, case definition, and underdiagnosis. A relative long prodrome phase preceding symptom onset (approximately 2 days [59]) may have limited the tracing and isolation of contacts. We found, indeed, that traveling cases generating transmissions following importation were, in general, spending more time in the community prior to hospitalization—although this result was diluted upon imputation of missing values—signaling that the period of mild or no symptoms during which the individual carries on a normal life is important for the generation of secondary cases. The epidemic emergency starting with one single critical case on February 21, 2020, in Italy and accumulating hundreds of cases in few days [16] showed that clusters had gone undetected and epidemiological links with the index case were not found. Subsequent developments of the pandemic showed that underdetection of importations was a clear pattern in fueling large-scale epidemics that spread unseen in several countries before the first critical cases were finally detected [52,60].

Our study is affected by limitations. Underdetection of imported cases during the early phase of the pandemic might have been higher than what estimated here, as our analysis is conditional to the identification of clusters of cases. Underdetection may also proceed from the imperfect characteristics of RT-PCR (reverse transcription-polymerase chain reaction) tests used to identify infected cases. Some cases tested for SARS-CoV-2 could have been falsely negative, and this would affect both analyses presented in the manuscript. This is in line with our conclusion that a large part of imported cases may have been undetected. As the number of detected imported cases grew considerably, the quality of the information on case history degraded over time. We used modeling and imputation to account for the missing information regarding the most recent imported cases. Extension of this study beyond the early phase of the pandemic is limited by the strong spatiotemporal heterogeneities currently characterizing the pandemic. Our assumptions are based on a context of localized epicenter and border controls targeted against importations from China and countries in South East Asia who experienced local transmission during the early phase. As the pandemic regime shifted toward a multiple delocalized epicenter in late February, additional travel bans were implemented worldwide to prevent importations from Europe and North America. This led to heavy disruptions of the flight network and a change of focus toward within-country transmission.

Our findings provide critical epidemiological understanding on the rate of importation and detection of cases during the early phase of the pandemic. Though effective in reducing international spread, the travel ban in Wuhan did not prevent the seeding of the pandemic in other countries, later becoming new epicenters of the pandemic. The epidemiological features of COVID-19 facilitated the silent spread of the epidemic in seeded countries. The lessons learnt during the early phase of the pandemic become critical again to prevent second waves now that countries have reduced their epidemic activity and progressively phase out lockdown.

Supporting information

S1 Text. Statistical methods.

Modeling traveling cases, delay from arrival to detection and index case detection probability.

(PDF)

Click here for additional data file.^{(166KB, pdf)}

S2 Text. Additional results.

Dataset of international cases, results of likelihood estimation, sensitivity analyses, and analysis of imported clusters.

(PDF)

Click here for additional data file.^{(460.8KB, pdf)}

S1 Table. Official sources for international cases.

(PDF)

Click here for additional data file.^{(75.9KB, pdf)}

S1 STROBE Checklist

(PDF)

Click here for additional data file.^{(69.6KB, pdf)}

Abbreviations

COVID-19: coronavirus disease 2019
RT-PCR: reverse transcription-polymerase chain reaction
SARS-CoV-2: severe acute respiratory syndrome coronavirus 2
STROBE: Strengthening the Reporting of Observational Studies in Epidemiology

Data Availability

The database was made publicly available by the authors: “COVID-19 international cases as of Feb 13.” https://docs.google.com/spreadsheets/d/1X_8KaA7l5B_JPpwwV3js1L6lgCRa3FoH-gMrTy2k4Gw/edit?usp=sharing.

Funding Statement

This study is partially funded by the Agence National de la Recherce (ANR, https://anr.fr/) through the project DATAREDUX (ANR-19-CE46-0008-03) to VC; the European Union with grants RECOVER (H2020-101003589) and MOOD (H2020-874850, https://ec.europa.eu/programmes/horizon2020/en) to PYB, CP, and VC; REACTing (https://reacting.inserm.fr/) through the COVID-19 funding to VC; the Municipality of Paris (https://www.paris.fr/) through the programme Emergence(s) to FP and CP; INSERM-INRIA partnership for research on public health and data science to LDD. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Med. doi: 10.1371/journal.pmed.1003193.r001

Decision Letter 0

Adya Misra

28 Feb 2020

Dear Dr Colizza,

Thank you for submitting your manuscript entitled "Lessons learnt from 288 COVID-19 international cases: importations over time, effect of interventions, underdetection of imported cases" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff [as well as by an academic editor with relevant expertise] and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by .

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Adya Misra, PhD,

Senior Editor

PLOS Medicine

PLoS Med. doi: 10.1371/journal.pmed.1003193.r002

Decision Letter 1

Adya Misra

14 Apr 2020

Dear Dr. Colizza,

Thank you very much for submitting your manuscript "Lessons learnt from 288 COVID-19 international cases: importations over time, effect of interventions, underdetection of imported cases" (PMEDICINE-D-20-00657R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. You will note that both reviewers and editors request additional data are used to validate your model. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We expect to receive your revised manuscript by May 05 2020 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosmedicine/s/submission-guidelines#loc-methods.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

Title-Please revise your title according to PLOS Medicine's style. Your title must be nondeclarative and not a question. It should begin with main concept if possible. "Effect of" should be used only if causality can be inferred, i.e., for an RCT. Please place the study design ("A randomized controlled trial," "A retrospective study," "A modelling study," etc.) in the subtitle (ie, after a colon).

Abstract- last sentence of the methods and findings section should be a limitation of your methodology

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

Introduction- please update the background information as new evidence has come to light since your submission

Methods- since more data are now available- please update the international cases (as possible) in order to validate your model. Please email me at amisra@plos.org to discuss this, as needed

Please specify the sources of your data in the methods section providing citations instead of placing this in an SI file

Please present and organize the Discussion as follows: a short, clear summary of the article's findings; what the study adds to existing research and where and why the results may differ from previous research; strengths and limitations of the study; implications and next steps for research, clinical practice, and/or public policy; one-paragraph conclusion.

Discussion-please revise the language in this section esp lines 184-190, line 206-209

You may wish to expand on local transmission, underdetection and undiagnosis of cases worldwide here

Please replace epidemic with pandemic

Please comment on second waves of infections as imported cases are rising in China

Please ensure that the study is reported according to the STROBE guideline, and include the completed STROBE checklist as Supporting Information. 1 Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (SChecklist)."

The STROBE guideline can be found here: http://www.equator-network.org/reporting-guidelines/strobe/

When completing the checklist, please use section and paragraph numbers, rather than page numbers.

Comments from the reviewers:

Reviewer #1: This is a review for "Lessons learnt from 288 COVID-19 international cases: importations over time, effect of interventions, underdetection of imported cases" by Pinotti et al. This paper presents results from statistical inference on a model of case importation along with responsive intervention in regards to the early stages of the COVID-19 outbreak. The research is novel and of interest to a potentially large audience but I believe that the paper needs revisions before it is ready for publication.

1) The language in this paper needs to be reframed as analysis of the early stages of an outbreak rather than a description of an ongoing and changing situation. Current sections read somewhat like a news article rather than journal article. Many of these problems can be fixed by changing tense.

----

23-24 "With the majority of imported cases going undetected (6 out of 10), countries should be prepared for the possible emergence of several undetected clusters of chains of local transmissions."

184 "The reduced volume of exported cases worldwide following the travel ban may have given countries the time to prepare and strengthen their surveillance systems, as signaled by a reduction of the interval from travel date to detection over time."

192 "...from East Asian countries [29]. ECDC and WHO currently base their case definition on travel from China only [30,31], but this may rapidly change in the next days."

2) There are not enough references from primary sources - this paper should be framed around existing scientific knowledge from previous outbreaks. About a third of the references are from primary sources (published articles or books), another third are from organisational reports or medRxiv and the last third are from online sources (e.g. Twitter, online news). Additionally, a couple citations appear to be incomplete.

----

S. Bhatia et al., "Report 6: Relative sensitivity of international surveillance," p. 6.

3) Several sections were difficult to read or had grammatical errors.

----

13 "We characterized importations timeline..."

76 The definition in this section is confusing. Here S = H, C while in the supplementary information (which is clearer) S = H, C, O. Perhaps include S on the second line of the piecewise equation where S = O?

86 It is hard to read subsequent sentences where one ends and the other starts with a variable or function name.

98-101 In "Estimation of under-detection of imported cases" it is not clear what you are saying here or how the multinomial is defined. The next part of the section (where you define the likelihood) clarifies but the intro in lines 98-101 is confusing:

"We modelled the number of such 'cluster seeds', i.e. groups of index cases, with a multinomial distribution depending on the portion of cluster seeds of size 1 or greater than 1 (for simplicity, this was taken as 2), on the probability of detection of a seed, and on occurrence of secondary transmission."

197-198 What has an ability of 36%?

"Our estimates indicate an ability of 36% to detect imported cases in countries outside China."

199 This reads poorly (perhaps "Previous detection rate estimates..." ?)

"Previous estimates range from 27% [13] to 38% [13, 15] detection rates, with variations across

200 countries [13, 15]."

4) Supplementary Information and Methods

----

There is a section with a sensitivity analysis where the ban date in Wuhan is a day later. It would be nice to have model comparison using LOO or WAIC between a few select models (piecewise vs single exponential, etc...) Your model should explain the data better than a naive model and it would be easy to show here.

Fig S2:

a) The MCMC chains appear to be thinned; this should be noted in the caption.

b) You should state if the mixing in STAN reported any divergence if you are showing convergence of the chains.

c) You state that both the black line and histogram show the posterior distribution, but the black lines look a bit more like priors to me.

5) Possible additional data for analysis

----

I realise the current COVID-19 pandemic is ongoing and any additional analysis will not be comprehensive. But given that there is onging data, the paper would benefit from comparing model predictions to a more current set of data.

Reviewer #2: Pinotti and colleagues have developed a mathematical model, using a Bayesian inference framework, to forecast trends in imported cases in China and estimate the percentage of imported cases and changes rate of detection of these cases. They used available data on 288 COVID-19 cases from a variety of data sources. Two growth functions are used: one for China - split by hubei and rest of China and separate growth function for other countries, with a break in growth rate denoted by Ts when restrictions were put in place. A separate growth function is used for other countries but assumes no discontinuity in growth rate. The mathematics are fully documented in supplemental materials and will seem complex but fundamentally - this is largely modelling growth functions with a structured break due to travel bans over a period of time. Latent variables were used to incorporate missing data where cases had incomplete information, authors used an inference technique to marginalise ("summing out" the probability of a random variable given the joint probability distribution). The Bayesian framework means that there are quite a few distributions to fit around model parameters. A slight nit-pick of this would be that the supplemental could potentially better rationalise the authors assumption no the choice of distribution. It may seem familiar to those with mathematical modelling experience, but it would not likely to be obvious to most. For instance, gamma distribution skewed nature to reflect change in surveillance; also rationale needed to truncated the gamma distribution on the detection delay to 25 days. Apart from some minor text issues (Line 25 in Supplemental - I think the authors meant "tuples" here), I think the model is quite reflective of what is occurring currently. Empirically, the model fits the data very well. The rebound in imported cases is occurring in China (and other areas) is now occurring so it would be interesting to conduct some follow-up work given how fast paced new data is coming in. One final issue is potentially, which the authors do not address in the discussion is the impact of inaccuracies of testing. Some studies have characterised multiple shedding routes (https://www.ncbi.nlm.nih.gov/pubmed/32065057?dopt=Abstract). Accurate testing methods are work in progress but it's worth noting that when new studies do come out on these parameters, it would be worth incorporating potential false negative results which may have on the impact of rebounding infection rates from imported cases. Apart from these comments - I think this study is important and merits publications.

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2020 Jul 17;17(7):e1003193. doi: 10.1371/journal.pmed.1003193.r003

Author response to Decision Letter 1

12 May 2020

Attachment

Submitted filename: point_by_point_reply.docx

Click here for additional data file.^{(29.5KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1003193.r004

Decision Letter 2

Adya Misra

27 May 2020

Dear Dr. Colizza,

Thank you very much for re-submitting your manuscript "Lessons learnt from the first 288 COVID-19 international cases: a modelling study" (PMEDICINE-D-20-00657R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by xxx reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Jun 03 2020 11:59PM.

Sincerely,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

Please adapt the title to better match journal style. We suggest: "Tracing and analysis of 288 early SARS-CoV-2 infections outside China: a modeling study".

Please add some additional quantitative details of interest to the "methods and findings" subsection of your abstract. For example, the observation noted at lines 177-178 might be worth quoting; likewise the doubling time mentioned at line 185.

At line 37, please begin the sentence "Our findings indicate that ..." or similar.

At line 183 and in other places, we are not sure that "Nowcasting" is a widely understood scientific term: please consider rephrasing.

Early in the methods section of your main text, please state whether the study had a protocol or prespecified analysis plan (and if so attach the document as an attachment, referred to in the text). Please highlight analyses that were not prespecified.

Please restructure the early part of the "Discussion" section of your main text: the first paragraph should summarize the study's main findings, with these being discussed in subsequent paragraphs.

Please remove the section on "Funding" from the end of the text (this information will appear in the metadata).

In your reference list, please ensure that all citations meet journal style (italics should be rendered in plain text). For reference 9, for example, formatting should be as follows: Fraser C, Riley S, Anderson RM, Ferguson NM. Factors that make an infectious disease outbreak controllable. Proc Natl Acad Sci U S A. 2004;101:6146-6151.

Please rename the STROBE attachment "S1_STROBE_Checklist". In the "Section" column, please add paragraph numbers to accompany the section where individual items appear (avoiding line or page numbers, which generally change in the event of publication).

Comments from Reviewers:

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. doi: 10.1371/journal.pmed.1003193.r005

Decision Letter 3

Adya Misra

16 Jun 2020

Dear Dr. Colizza,

On behalf of my colleagues and the academic editor, Dr. Richard Zachariah Aandahl, I am delighted to inform you that your manuscript entitled "Tracing and analysis of 288 early SARS-CoV-2 infections outside China: a modeling study" (PMEDICINE-D-20-00657R3) has been accepted for publication in PLOS Medicine.

PRODUCTION PROCESS

Before publication you will see the copyedited word document (in around 1-2 weeks from now) and a PDF galley proof shortly after that. The copyeditor will be in touch shortly before sending you the copyedited Word document. We will make some revisions at the copyediting stage to conform to our general style, and for clarification. When you receive this version you should check and revise it very carefully, including figures, tables, references, and supporting information, because corrections at the next stage (proofs) will be strictly limited to (1) errors in author names or affiliations, (2) errors of scientific fact that would cause misunderstandings to readers, and (3) printer's (introduced) errors.

If you are likely to be away when either this document or the proof is sent, please ensure we have contact information of a second person, as we will need you to respond quickly at each point.

PRESS

A selection of our articles each week are press released by the journal. You will be contacted nearer the time if we are press releasing your article in order to approve the content and check the contact information for journalists is correct. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact.

PROFILE INFORMATION

Now that your manuscript has been accepted, please log into EM and update your profile. Go to https://www.editorialmanager.com/pmedicine, log in, and click on the "Update My Information" link at the top of the page. Please update your user information to ensure an efficient production and billing process.

Thank you again for submitting the manuscript to PLOS Medicine. We look forward to publishing it.

Best wishes,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

Associated Data

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

Supplementary Materials

S1 Text. Statistical methods.

Modeling traveling cases, delay from arrival to detection and index case detection probability.

(PDF)

Click here for additional data file.^{(166KB, pdf)}

S2 Text. Additional results.

Dataset of international cases, results of likelihood estimation, sensitivity analyses, and analysis of imported clusters.

(PDF)

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S1 Table. Official sources for international cases.

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S1 STROBE Checklist

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Data Availability Statement

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PERMALINK

Tracing and analysis of 288 early SARS-CoV-2 infections outside China: A modeling study

Francesco Pinotti

Laura Di Domenico

Ernesto Ortega

Marco Mancastroppa

Giulia Pullano

Eugenio Valdano

Pierre-Yves Boëlle

Chiara Poletto

Vittoria Colizza

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Data collection and synthesis

Descriptive analysis

Modeling and predicting importations

Estimation of underdetection of imported cases

Results

Timeline of travel-related cases

Fig 1. Timeline of importation for all imported cases.

Fig 2. Delay from travel to detection as a function of the date of travel: data points, mean, and model estimate.

Predicting travel-related cases

Fig 3. Number of imported cases by date of travel and of reporting: data points and model predictions.

Transmission clusters outside China

Table 1. Summary of transmission clusters according to the type of index case.

Discussion

Supporting information

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Adya Misra

Roles

Decision Letter 1

Adya Misra

Roles

Author response to Decision Letter 1

Decision Letter 2

Adya Misra

Roles

Decision Letter 3

Adya Misra

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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