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. 2025 Jul 10;11(4):e70483. doi: 10.1002/vms3.70483

More Timely Response to Outbreaks of Zoonotic Compared to Non‐Zoonotic Diseases in Animals in Tanzania

Esron Karimuribo 1,2,✉, Veronica Masawe 1, Lars Eik Olav 3, Doreen Ndossi 1, Ann‐Katrin Llarena 4, Calvin Sindato 2,5
PMCID: PMC12244262  PMID: 40638538

ABSTRACT

Timeliness metrics analysis is a useful approach for tracking speed in actions and identifying gaps during disease outbreaks. There is limited information on the time taken for preventing, controlling and containing disease outbreaks in human and animal populations in Tanzania. We conducted timeliness metrics analysis on zoonotic and non‐zoonotic disease outbreaks which occurred between May 2019 and April 2023 in 10 selected districts within four regions located in the Northern (Arusha and Kilimanjaro regions) and Southern Highlands zones (Mbeya and Songwe regions) of Tanzania. Field‐based outbreak milestones for selected zoonotic diseases, namely rabies and anthrax and non‐zoonotic diseases, African swine fever (ASF), contagious caprine pleuropneumonia and Peste des petits ruminants, were recorded. Modified metrics for One Health Surveillance were employed to estimate the time used for disease outbreak detection, notification, verification, risk assessment, laboratory confirmation, inter‐sectoral sharing of information (for zoonotic diseases only), response and public communication. A total of 98 disease outbreaks, of which 63 were zoonotic (64.3%) and 35 were non‐zoonotic (35.7%), were recorded. These outbreaks were distributed across the Northern (68/98) and Southern Highlands (30/98) zones. The time taken to detect zoonotic disease outbreaks was significantly shorter (median = 2 days, range = 1–48 days) than the time taken to detect non‐zoonotic disease outbreaks (median = 3 days, range = 1–40 days) (p = 0.0485). Furthermore, the time taken to detect disease outbreaks in the Northern zone was significantly shorter (median = 2 days, range = 1–48 days) than the time taken in the Southern Highlands zone (median = 7 days, range = 1–40 days) (p = 0.0010). Variation between geographical locations was observed where in the Northern zone, a shorter time was taken to verify (median = 1 day, range = 1–14 days) and to respond (median = 1 day, range = 1–30 days) to disease outbreaks than in the Southern Highlands (verification time: median = 3 days, range = 1–30 days and response time: median = 30 days, range = 1–60 days). Such differences could be due to resource disparities in the two zones and shorter distance to access diagnostic facilities in the Northern zone. This is the first field‐based timeliness metrics analysis study carried out in Tanzania. Findings of this study may be utilised to guide animal and public health interventions for effective and efficient surveillance and control of emerging and re‐emerging infectious diseases. It is recommended that more investment is carried out in emergency preparedness for the timely management of zoonotic and non‐zoonotic diseases in Tanzania.

Keywords: disease outbreaks, one Health, Tanzania, timeliness metrics

A timeliness metrics analysis study was carried out in Tanzania. A total of 98 disease outbreaks were recorded. The time taken to detect, verify and respond to zoonotic diseases was shorter than the time for non‐zoonotic diseases. More investment should be done for the timely management of disease in Tanzania.

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1. Introduction

Disease outbreaks in humans and animals occur suddenly, often in remote and hard‐to‐reach locations, and if not managed timely, they may have devastating consequences at sub‐national, national and global levels. Most of the commonly reported negative impacts of disease outbreaks in humans include loss of human lives as well as negative impacts in terms of disability and productivity loss (Murray and Lopez 1997; Nurchis et al. 2020). It has been reported that infectious diseases contribute about one quarter of deaths worldwide (Omosigho et al. 2023). In livestock, disease outbreaks cause preterm deaths, increased costs for controlling, eradication or elimination of the diseases, reduced productivity, compromised animal welfare, and restrict animal and/or animal product trade locally and internationally (Saatkamp et al. 2016).

Rapid actions to support early detection, verification, confirmation and response to disease outbreaks are critical to reduce losses associated with such outbreaks (Wilson and Brownstein 2009). In order to realise rapid control, timely detection, verification, confirmation, risk assessment and cross‐sectorial communication, especially for events that need One Health approach, are needed (Smolinski et al. 2017; Swaan et al. 2018). Timeliness metrics, that is, the time that passes between two outbreak milestones (Ending Pandemics 2020), can be used to assess the efficiency of a response to a disease outbreak and consequently the ability to prevent disease outbreaks evolving into epidemics or pandemics (Jajosky and Groseclose 2004; Crawley et al. 2021). Emphasis on timely action for each of the milestones above is based on the importance of activating faster investigation and confirmation of disease outbreaks, which consequently informs timely response by the relevant animal or public health authority to manage health threats as fast as possible.

In Tanzania, African swine fever (ASF), Peste des petits ruminants (PPR), contagious caprine pleuropneumonia (CCPP), lumpy skin disease and contagious bovine pleuropneumonia are the most common livestock disease outbreaks reported in different areas (Karimuribo et al. 2011; Chang'a et al. 2019; Chacha 2024; Makoga et al. 2024). Frequent outbreaks of zoonotic diseases are also reported in both humans and animals (domestic and wildlife), and they include rabies, anthrax, leptospirosis, and Rift Valley fever (Karimuribo et al. 2011; Masunga et al. 2022; Stephen et al. 2022; Issae et al. 2023). In 2016, Tanzania prioritised six zoonotic diseases, namely rabies, Rift Valley fever and other viral haemorrhagic fevers (Marburg and Ebola), zoonotic influenza, anthrax, Human African Trypanosomiasis (sleeping sickness) and brucellosis (URT 2022). The goal of zoonotic disease prioritisation was to identify key diseases of greatest national concern that could be targeted for joint multisectoral prevention and control using limited resources available in the country.

Globally, there is scanty information from previous studies which compared variation in response to zoonotic and non‐zoonotic diseases. Because of potential zoonotic disease spillover, when zoonotic pathogens are transmitted from animals to humans, more resources and efforts are more likely to be allocated to contain and control zoonotic disease events than what is allocated for controlling non‐zoonotic disease outbreaks (Zinsstag et al. 2016). Magnusson et al. (2022) reported that zoonotic diseases, particularly those which can cause epidemics and pandemics, usually receive more attention and financial resources compared to non‐zoonotic diseases. In Tanzania, there have not been such comparative studies on responses to zoonotic and non‐zoonotic disease outbreaks. Furthermore, there are no comparative studies that focused on establishing variation in response to disease outbreaks in different zones within Tanzania.

Studies evaluating the time taken for different actions geared towards prevention, control and containment of disease outbreaks in both human and animal populations in Tanzania are limited (Jost et al. 2010). This study focused on the evaluation of timeliness in taking different actions required for outbreak containment in human and animal populations. Comparison of zoonotic and non‐zoonotic disease outbreaks was based on the hypothesis that zoonotic diseases receive more attention than non‐zoonotic livestock diseases. Furthermore, comparison of geographical zones was based on the hypothesis that the presence of health and diagnostic facilities closer to disease outbreak sites and past experience of dealing with more epidemic diseases in the Northern zone contributes to faster actions in containing disease outbreaks than in the Southern Highlands zone. To test these hypotheses, we compared timeliness metrics for zoonotic versus non‐zoonotic disease outbreaks across different geographical regions in Tanzania.

2. Materials and Methods

2.1. Approach

This study was guided by a modified Metrics for One Health Surveillance approach (Ending Pandemics 2020). The modification removed activities that were difficult to identify and document before or after the outbreak based on field conditions in Tanzania. Activities that were excluded in this study include the date of alert of potential disease outbreak, the date when the outbreak ended and the date of conducting an after‐action review. Table 1 summarises outbreak milestones that were adopted in the current study. The metrics were adopted as: (i) time to detection measured in days from the date first clinical signs or death of an index were observed to when the outbreak threshold was reached; (ii) time to notification measured in days from when the outbreak was detected to the date when the animal or public health district authority was informed/notified; (iii) time to verification measured in days from the time the outbreak was detected to the date when the district team was deployed in the field to conduct outbreak investigation; (iv) time to confirmation measured in days from the date the outbreak was detected to the official laboratory confirmation by the sub‐national or national laboratory facility; (v) time to risk assessment measured in days from the date the outbreak was detected to the date when a risk assessment team was deployed in the field to conduct risk assessment; (vi) time to information sharing measured in days from the date the outbreak was confirmed as zoonotic disease to the date when the counterpart sector (public or animal) was officially notified; (vii) time to response measured in days from the date the outbreak was detected to the date animal or public health officials took action to stop or control the outbreak and; (viii) time to public communication measured in days from date when the outbreak was confirmed in the laboratory to the date when information about the outbreak was published in different media for public awareness.

TABLE 1.

Outbreak milestones based on modified One Health Timeliness metrics that were adopted in this study.

1. Detection 2. Notification
Date of onset of symptoms, clinical signs or death. Date an outbreak in humans or animals is officially reported to relevant authority.
3. Verification 4. Confirmation
Date outbreak is confirmed by field investigation. Date outbreak is confirmed by diagnostic or laboratory test.
5. Risk assessment 6. Inter‐sectoral information sharing
Date of evaluating the reported outbreak so as to assign a level of risk, which will guide actions to be taken. Date when one sector notified her counterpart (zoonotic diseases only).
7. Response 8. Public communication
Date an intervention to control or manage the outbreak is initiated. Date of official release of information to the public by a responsible authority.
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Note: Response for zoonotic diseases refers to multistakeholder joint response involving key sectors affected by the disease as well as other key stakeholders.

2.2. Study Area and Diseases

Guided by consultation made with the One Health Section under the Prime Minister's Office, which is a section mandated with the coordination of One Health matters in Tanzania, four regions where outbreaks of zoonotic diseases (rabies and anthrax) were reported for the past 5 years since 2019 were selected. The selected regions were in the Northern zone of Tanzania (Arusha and Kilimanjaro regions) and the Southern Highlands zone (Mbeya and Songwe regions) (Figure 1). For comparative purposes, non‐zoonotic disease outbreaks in the selected regions were included to test the hypothesis that zoonotic diseases receive more attention than non‐zoonotic livestock diseases, and ASF, CCPP and PPR were chosen as they were the most prevalent disease outbreaks reported in the study area during the past 5 years. During data collection, records from the Regional Medical Office (RMO) and Regional Veterinary Office (RVO) were used purposively to identify districts with disease outbreaks in both human and animal populations. Data on disease outbreak milestones were then collected from field‐based human and animal health personnel who were involved in managing disease outbreaks during the 5‐year period between May 2019 and April 2023.

FIGURE 1.

FIGURE 1

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Location of sites where data collection was conducted in the Northern and Southern Highlands zones.

2.3. Data Collection

A structured questionnaire was prepared and used to collect data for the timeliness metrics analysis. The questionnaire included relevant questions intended to collect data on dates of disease outbreak milestones based on metrics presented in Table 1. The questionnaire was digitised and uploaded on the AfyaData app (https://afyadata.sacids.org) to collect data during face‐to‐face interviews with respondents. Respondents were field‐based human and animal health staff, including the District Veterinary Officer, District Medical Officer, Livestock Field Officer and In‐charge of health facilities who were involved in managing disease outbreaks that occurred between May 2019 and April 2023 in the study area. Before adopting the questionnaire for data collection, it was piloted in Morogoro municipality using previous experience of detecting and managing rabies and ASF outbreaks. Data collection was carried out from 14th to 22nd April 2023.

2.4. Data Analysis

Data captured by the AfyaData app was downloaded as comma‐separated values (.csv) files and then converted to Excel files (Microsoft Office 2024). Using the add‐in Excel data analysis tool pack (XLMiner Analysis Toolpak, 2021 Frontline Systems, Inc.), descriptive statistics for all timeliness metrics, that is, time of detection, notification, verification, laboratory confirmation, risk assessment, information sharing (for zoonotic diseases only), response and public communication were computed. Because timeliness metrics data are known to be non‐parametric (Crawley et al. 2021) and confirmed during data analysis, median and range values were computed as measures of central tendency and dispersion, respectively. Computation of the statistical significance for median values from two populations adopted the Mann–Whitney U test using an online calculator (https://www.socscistatistics.com/tests/mannwhitney/default2.aspx) at the p < 0.05 significance level. A chi‐squared test was used to assess statistical significance between proportions at the 0.05 significance level.

3. Results

3.1. Source and Type of Disease Events Reported

A total number of 98 disease outbreak events were reported in the four study regions, as summarised in Table 2. The events were categorised by geographical location, and they were from the Northern zone, comprising the Arusha and Kilimanjaro regions (69.4%) and the Southern Highlands zone, comprising the Mbeya and Songwe regions (30.6%). The majority of the disease events were zoonotic (64.3%), involving either anthrax (38.8%) or rabies (25.5%). Most of them (>83.0%) had been observed within one and a half years before the study.

TABLE 2.

Distribution of 98 disease outbreak events reported by location, disease type and year of occurrence between May 2019 and April 2023.

Parameter Category No. of events reported %
Zone Northern 68 69.4
Southern Highlands 30 30.6
Region Arusha 27 27.6
Kilimanjaro 41 41.8
Mbeya 8 8.2
Songwe 22 22.4
District Karatu (Arusha) 14 14.3
Monduli (Arusha) 13 13.3
Moshi (Kilimanjaro) 2 2.0
Hai (Kilimajaro) 23 23.5
Rombo (Kilimanjaro) 5 5.1
Siha (Kilimanjaro) 11 11.2
Mbeya City (Mbeya) 8 8.2
Ileje (Songwe) 6 6.1
Mbozi (Songwe) 5 5.1
Momba (Songwe) 11 11.1
Disease category Zoonotic 63 64.3
Non‐zoonotic 35 35.7
Disease Anthrax (zoonotic) 38 38.8
Rabies (zoonotic) 25 25.5
African swine fever 21 21.4
CCPP 11 11.2
PPR 3 3.1
Year of occurrence 2019 3 3.1
2020 7 7.1
2021 6 6.1
2022 36 36.7
2023 46 46.9
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3.2. Proportions of Events With Actions Taken

A trend of proportions of disease outbreaks reported in the Northern and Southern Highlands zones is summarised in Figure 2. A significantly higher proportion of disease outbreaks were notified to the official authority in the Northern zone (63/68, 92.7%) than the proportion notified to official authority in the Southern Highlands zone (23/30, 76.7%) (p = 0.0261). Similarly, a significantly higher proportion of the disease outbreak events in the Northern zone (48/68, 70.6%) was verified than the proportion of verified disease outbreak events in the Southern Highlands zone (10/30, 33.3%) (p = 0.0005). Furthermore, significantly higher proportion of disease outbreaks were communicated to the general public in the Northern zone (58/68, 85.3%) than the proportion communicated to the general public in the Southern Highlands zone (13/30, 43.3%) (p = 0.0000). There was no significant difference in the proportions of disease outbreak events subjected to confirmation, risk assessment, information sharing between sectors, as well as response between the Northern and Southern Highlands zones.

FIGURE 2.

FIGURE 2

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Proportions of actions taken during outbreak of disease events in Northern and Southern Highlands zones of Tanzania.

Disaggregated analysis of the proportion of actions taken for zoonotic and non‐zoonotic disease events is presented in Figure 3. A significantly higher proportion of zoonotic disease outbreaks (59/63, 93.7%) were notified to the official authority than the proportion of non‐zoonotic disease outbreaks (27/35, 77.1%) (p = 0.0170). Similarly, a significant (p = 0.0410) higher proportion of the zoonotic disease outbreaks (50/63, 79.4%) was communicated to the public than the proportion of non‐zoonotic disease outbreaks (21/35, 60.0%). Only a few outbreaks (18/98, 18.0%) of both zoonotic and non‐zoonotic diseases were subjected to laboratory confirmation, and the difference between zoonotic and non‐zoonotic outbreaks was not statistically significant. Out of 63 zoonotic disease outbreak events occurring in the study area, information between the public and animal health sectors was shared in only 25 of them (39.7%). Low proportions of zoonotic disease information sharing between sectors could be explained by the siloed mentality that still exists in animal and public health sectors (Sanga et al. 2024).

FIGURE 3.

FIGURE 3

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Proportions of actions taken during outbreak of zoonotic and non‐zoonotic disease events.

3.3. Timeliness Metrics of Zoonotic and Non‐Zoonotic Disease Events

Timeliness metrics analysis for zoonotic and non‐zoonotic disease events recorded is summarised in Table 3. The time taken to detect zoonotic disease outbreaks (median = 2 days, range = 1–48 days) was significantly shorter than the time taken to detect non‐zoonotic disease outbreaks (median = 3 days, range = 1–40 days) (p = 0.0485). Furthermore, the time taken for verification of zoonotic disease outbreaks (median = 1 day, range = 1–14 days) was significantly shorter than the time taken to verify non‐zoonotic disease outbreaks (median = 2 days, range = 1–30 days) (p = 0.0307). Confirmation of zoonotic disease outbreaks was also faster (median = 4.5 days, range = 1–70 days) than the time it took to confirm non‐zoonotic disease outbreaks (median = 14 days, range = 2–30 days) (p = 0.0050). There was no significant difference in the time taken to notify, respond, and communicate to the general public between the zoonotic and non‐zoonotic disease outbreaks.

TABLE 3.

Comparison of timeliness metrics for zoonotic and non‐zoonotic disease events reported during the study.

Timeliness metrics Zoonotic Non‐zoonotic p‐value
N Median (range), days N Median (range), days
Time to detection 63 2 (1–48) 35 3 (1–40) 0.0485 *
Time to notification 59 1 (1–30) 27 2 (1–14) 0.1814
Time to verification 37 1 (1–14) 21 2 (1–30) 0.0307 *
Time to confirmation 12 4.5 (1–70) 6 14 (2–30) 0.0050 ***
Time to risk assessment 13 2 (1–14) 3 2 (1–4) n.a a
Time to information sharing 25 1 (1–30) N/A N/A n.a
Time to response 63 1 (1–60) 35 1 (1–30) 0.0885
Time to public communication 50 1 (1–15) 21 1 (1–30) 0.1335
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a

We could not compute p‐value for risk assessment timeliness as counts in the non‐zoonotic disease were less than 5.

*

Significant at p < 0.05.

***

Significant at p < 0.001.

3.4. Timeliness Metrics for Disease Events Reported in Northern and Southern Highland Zones

Analysis was further carried out to compare whether timeliness differed with geographical zones (Northern vs. Southern Highlands) as presented in Table 4. In the Northern zone, it took significantly shorter time (median = 2 days, range = 1–48 days) to detect disease outbreaks than in the Southern Highlands zone (median = 7 days, range = 1–40 days) (p = 0.0010). Verification of disease outbreaks took a relatively shorter time (median = 1 day, range = 1–14 days) in the Northern zone than in the Southern Highlands zone (median = 3 days, range = 1–30 days) (p = 0.0026). Furthermore, response to disease outbreaks took a shorter time (median = 1 day, range = 1–30 days) in the Northern zone than in the Southern Highlands zone (median = 30 days, range = 1–60 days) (p = 0.0000). Communication with the general public also took a shorter time in the Northern zone (median = 1 day, range = 1–30 days) than in the Southern Highlands zone (median = 5 days, range = 1–30 days) (p = 0.0000). There was no significant difference between the Northern and the Southern Highland zones in the time taken to notify, confirm, conduct risk assessment and share information on disease outbreaks between the public and animal health sectors.

TABLE 4.

Comparison of timeliness metrics for reporting disease events in the Northern and Southern Highland regions.

Timeliness metrics Northern Southern Highlands p‐value
N Median (range), days N Median (range), days
Time to detection 68 2 (1–48) 30 7 (1–40) 0.0010*
Time to notification 63 2 (1–30) 23 1 (1–14) 0.2090
Time to verification 48 1 (1–14) 10 3 (1–30) 0.0026*
Time to confirmation 12 6 (1–30) 6 6 (2–8) 0.4801
Time to risk assessment 8 1.5 (1–2) 8 3 (1–14) 0.0516
Time to information sharing 18 1 (1–2) 7 1 (1–30) 0.1075
Time to response 68 1 (1–30) 30 30 (1–60) 0.0000*
Time to public communication 58 1 (1–8) 13 5 (1–30) 0.0000*
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*

Significant at p < 0.001.

4. Discussion

To the best of our knowledge, this is the first study which has analysed timeliness metrics for non‐zoonotic (animal health sector) and zoonotic disease outbreaks in different geographical zones in Tanzania. The concept of timeliness metrics gained popularity in the recent past, and this could explain why similar studies are lacking in many African countries. Although timeliness metrics analysis has been done in other countries before, it was mainly based on scoping review works (Crawley et al. 2021; Fieldhouse et al. 2022). The current study, however, is based on data collected through interviews of field‐based human and animal health staff who managed disease outbreak events that occurred before the study.

4.1. Laboratory Confirmation Gaps

Findings of this study indicated a declining trend of proportions on actions required during the management of disease outbreak events from detection to laboratory confirmation and risk assessment. For instance, out of 98 disease events detected, only 18 (18.4%) were subjected to laboratory confirmation. The pattern was similar for both zoonotic and non‐zoonotic diseases. This challenge might be attributed to the lack of laboratory preparedness and other logistical support needed during outbreaks of emerging diseases, as it has been reported by others (Burnham et al. 2017). In most cases, many countries do not conduct laboratory confirmation in managing disease outbreaks in both human and animal populations because of either a lack of expertise and facilities or due to prohibitive costs involved in conducting the tests (Lazarus et al. 2011; Kelly‐Cirino et al. 2019). A similar trend was observed for the risk assessment milestone, which is usually important to guide action and planning on how to manage disease outbreaks as well as preventing future outbreaks. Failure to conduct laboratory confirmation and risk assessment is likely to compromise effective disease outbreak management by adopting non‐specific control measures and follow‐up actions.

4.2. Zoonotic and Non‐Zoonotic Disease Disparities

Observations of this study for lower proportions of acting on notification and informing the general public during outbreaks of non‐zoonotic events might be explained by the less importance attached to non‐zoonotic diseases in terms of negative impact on the public health. However, in order to take appropriate actions in containment and controlling disease outbreaks, it is important to notify the responsible authority so that timely actions, including verification, confirmation, risk assessment and response are made (Raslan 2011; Kemper et al. 2021). The general public is equally important to ensure cooperation during the institution of disease control and elimination measures.

This study also found that it took a longer time to detect and verify non‐zoonotic disease events than the zoonotic ones. This may be attributed to variation in resource allocation and capability of the public and animal health sectors. Observations from other studies as well as field experience in Tanzania indicate that the animal health sector is seriously underfunded and has fewer human resources and materials compared to the public health sector resulting in lower capacity to act timely during disease outbreaks (Ellis and Mdoe 2003; Zinsstag et al. 2016; Magnusson et al. 2022).

A similar experience of inadequate financing of the animal health sector causing inefficient veterinary services has been reported in other countries (Turkson and Brownie 1999). Häsler et al. (2017) found that scarce financial resources, competencies and capacity reduced the effectiveness of outbreak response measures and interventions.

4.3. Northern and Southern Highland Zone Disparities

The study also observed variation in timely action between the Northern and Southern Highlands zones. Detection, verification, response and public communication took a longer time in the Southern Highlands zone. The main contributing factors of these variations could be inequality in resource allocation, particularly financing of outbreak management activities, availability of supporting diagnostic facilities, as well as variation in economic levels between the two zones. Binyaruka et al. (2024) found that the Songwe region (which contributed >70% of disease outbreak events in the Southern Highlands zone) had a lower government health expenditure than Arusha and Kilimanjaro regions in the Northern zone. For the non‐zoonotic conditions, verification and laboratory confirmation are usually carried out by the District Veterinary Officer with support from the Zonal Veterinary Centres (ZVCs) and the veterinary laboratories under the Tanzania Veterinary Laboratory Agency (TVLA), respectively. In the Southern Highlands zone with six districts (Mbozi, Momba, Ileje and Mbeya City), the nearest laboratory facility, which could support confirmation of disease outbreaks is located in Iringa town situated more than 400 km from Ileje district in the Songwe region, and usually takes more than 8 h to deliver samples to the facility. For the Northern Zone, ZVC and TVLA zonal offices are located in Arusha city which is geographically closer to the six districts (Karatu, Monduli, Moshi, Hai, Rombo and Siha). An additional challenge for the zonal veterinary laboratories is their limited capacity for diagnosis, as it has also been reported by the World Organisation for Animal Health, Stemshorn et al. (2016). In case of limited financing, the work by Mkenda et al. (2004) and Aikaeli et al. (2021) clearly indicated the Southern Highlands zone to be a poorer area than the Northern zone. This could also contribute to the observed delayed timely actions in this zone.

4.4. Comparison With Other Countries in Africa

Our study findings were compared with reports from other African countries. The study by Crawley et al. (2021) documented a shorter time taken to detect (mean = 1 day, range = 0–26 days) disease outbreaks in Kenya compared to our study, where the mean time to detect a zoonotic disease was 2 days (range = 1–48 days) and 3 days (range = 1–40 days) for a non‐zoonotic disease outbreak. A scoping review conducted in Uganda by Fieldhouse et al. (2024) documented the feasibility of capturing different dates along the One Health timeliness continuum but has no comparable data on timeliness milestones. Other studies conducted in Ghana and Kenya focused on documenting timeliness of weekly surveillance data reporting on epidemic‐prone diseases, making it difficult to compare with the findings of this study (Adokiya et al. 2016; Nansikombi et al. 2023).

This study has indicated clearly variation in timeliness of managing and responding to disease outbreaks where zoonotic diseases are relatively responded to faster than the non‐zoonotic disease outbreaks. Furthermore, the Northern zone responded faster to disease outbreaks than the Southern Highlands zone.

4.5. Limitations of This Study

We acknowledge limitations of this study, especially potential recall bias based on data retrieved from field‐based staff for the period of four years. This could affect the accuracy of findings presented in this paper. The study districts were purposively selected based on guidance of the One Health Section under the Prime Minister's Office; hence findings could not represent the situation in the entire county.

5. Conclusion

Findings from this study indicate variation in time used to take action, which is affected by the type of disease outbreak (zoonotic vs. non‐zoonotic) and geographical areas (Northern vs. Southern Highlands zones). It has been found that it takes a shorter time to detect, verify and confirm zoonotic than non‐zoonotic disease outbreaks. It has further shown that in the Northern zone, it takes a shorter time to detect, verify, respond and engage the general public on disease outbreaks than in the Southern Highlands zone. Different factors including resource disparities and availability of diagnostic facilities, could explain such differences. This is the first study on timeliness metrics analysis conducted in Tanzania, which provides insights into understanding timely detection, confirmation and response to disease outbreaks.

6. Recommendations

It is recommended to consider proportionate allocation of resources to both animal and human health sectors based on the fact that zoonotic diseases are transmitted between species from animals to humans and vice versa. Increased funding of veterinary diagnostics, as well as adoption of innovative approaches, such as deployment of point of care diagnostics and mobile laboratories in the Southern Highlands zone, would improve timely disease confirmation of zoonotic and non‐zoonotic disease outbreaks in the animal populations. The development of tailor‐made courses and training of the frontline workforce to enhance timely detection and response to disease outbreaks would be an important intervention to support field‐based officers and laboratory technical staff in fighting disease outbreaks. As this work was limited to the Northern and Southern Highlands zones, similar studies should be carried out in other zones in Tanzania so as to provide a countrywide situation in the entire country.

Author Contributions

Esron Karimuribo conceived the idea, designed the study, coordinated data collection, conducted data analysis and prepared the initial draft of the manuscript. Veronica Masawe collected data and reviewed the draft manuscript. Lars Eik Olav reviewed the draft manuscript. Doreen Ndossi contributed to data analysis and reviewed the draft manuscript. Ann‐Katrin Llarena reviewed the draft manuscript. Calvin Sindato conceived the idea, designed the study and reviewed the draft manuscript

Conflicts of Interest

The authors declare no conflicts of interest.

Peer Review

The peer review history for this article is available at https://publons.com/publon/10.1002/vms3.70483.

Acknowledgements

We would like to thank the Ending Pandemics for financial support (Grant reference # TC2109‐101853) during data collection. Dr. Mark Smolinski and Dr. Nomita Divi of the Ending Pandemics played a valuable role in conceptualisation of the One Health timeliness metrics analysis. We recognise support of the EU‐funded HIGHLANDS.3 to EDK for secondment at the Noragric/Norwegian University of Life Sciences (NMBU), which provided time to complete data analysis and writing of the manuscript. The authors would also like to acknowledge and thank officials in the Northern and Southern Highlands zones for their help during data collection. The authors thank the SACIDS Foundation for One Health Secretariat and the Sokoine University of Agriculture for support during the study.

Funding: Financial support was provided by the Ending Pandemics (Grant reference # TC2109‐101853).

Data Availability Statement

The data that support the findings of this study are available upon reasonable request.

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Associated Data

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Data Availability Statement

The data that support the findings of this study are available upon reasonable request.

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