Introduction

Foot and Mouth Disease (FMD) is an acute systemic vesicular disease in cloven-hoofed animals worldwide, particularly common in domesticated cattle, sheep and goats. Mortality from FMD is rare but productivity is often severely reduced [1]. In some countries like the Republic of Türkiye, Tanzania and India, FMD is continuously circulating (endemic FMD), while others are considered free of FMD [2]. FMD can present a significant economic burden on countries because meat export from endemic countries is severely restricted [3]. Livestock farmers in endemic countries risk severe loss of income, either due to reduced productivity of their livestock due to the disease or because of imposed control policies such as culling [4,5]. The negative effects of FMD are felt at the country-level as well, due to the trade barriers, costs of measures to prevent and control the disease and general reduction in food security [5].

FMD spreads to nearby farms through direct contact with infected animals, through fomites on surfaces (sharing of farm equipment, veterinarians or personnel) or by wind (rare) [1]. It may also spread to farms that receive livestock from infected farms through the shipment network, as the livestock and the vehicles that transport them may carry and spread the disease [1]. Thus, the farms with the highest risk of an FMD outbreak are likely those that are near already infected farms or are farms that receive cattle from already infected farms.

In epidemic settings, FMD has been controlled and eliminated through a mixture of movement restrictions, targeted depopulation initiatives and targeted vaccination campaigns [6,7]. Movement restrictions limit or prevent the movement of livestock between farms and to markets, significantly reducing the rate of spread of FMD in a region. Movement restrictions are often recommended to be broad [8], but some have called for them to be more targeted to reduce excess loss of profit and livestock [9,10]. Targeted depopulation initiatives and vaccination campaigns focus on livestock in farms with FMD outbreaks and farms that are a certain distance away from farms with outbreaks (ring culling/vaccination) [6]. Targeted depopulation initiatives are often found to be the most effective and rely on the existence of effective surveillance programs to find outbreaks quickly [11–13].

The vast majority of FMD cases occur in countries where FMD is endemic [14–16], and designing effective control measures for such countries can reduce the economic burden and accelerate progress towards FMD-free status. The number of FMD outbreaks in countries with endemic FMD fluctuates over time–e.g., in the Republic of Türkiye there were large outbreaks of serotype A in 2006 and 2011 and serotype O in 2007 and 2010 [17]. Control measures in endemic countries have focused on reducing the prevalence and economic burden [5] of the disease in all or parts of the country through shipment restrictions and mass (entire regions) and ring vaccination campaigns [7,18]. In the Republic of Türkiye, where FMD is endemic, control measures aided by an extensive surveillance program managed to reduce the prevalence of the disease from 45% to 5% between 2008 and 2018 [19]. However, many countries with endemic FMD have less effective surveillance programs and thus are not able to target their control measures as effectively [7,20].

If one could monitor the farms with the highest risk of an FMD outbreak in countries with endemic FMD, it would be easier to manage at low prevalence (i.e., before any particular outbreak takes hold) [21]. This would then delimit the necessary scope of control measures and prevent FMD from spreading widely. Control measures are costly [20], so pursuing targeted control measures at low prevalence is both more productive and more cost effective. This has been demonstrated in FMD-free countries –Tildesley et al. [22] found that vaccinating near known infected farms would have reduced the size of the 2001 FMD epidemic in the UK and Schley et al.[9] found that, with sufficient information, targeted movement restrictions in the UK would have been effective and led to fewer cattle culled in the 2001 and 2007 epidemics.

Surveillance of farms will help detect outbreaks early, but as surveillance is expensive and time-consuming, sentinel surveillance strategies are often prioritized. Under a sentinel surveillance strategy, farms that are thought to be at high risk of infection are identified and asked to report outbreaks [23]. Thus, it is important to identify which farms should be preferentially allocated for sentinel surveillance in countries with endemic FMD so that early outbreaks can be discovered before FMD prevalence becomes too high. One method that can help find high-risk farms is to model a country as a network of nodes (farms) and edges (shipment pathways) along which diseases may transmit and then use network theory [24–27] and past outbreak data to determine which locations might be most at risk of outbreaks. Network theory has been used before to find high risk farms in FMD-free countries experiencing FMD outbreaks. For example, Dawson et al. [28] used animal movement data from the UK to determine that targeting nodes with the highest number of animal movements allowed them to accurately predict the size and spread of simulated FMD-like epidemics.

Much of the theory on how infectious processes spread on networks that informs our understanding of which nodes are highest risk assumes an epidemic setting [29]. It is less clear which nodes are highest risk for outbreaks in an endemic setting [17]. The importance of designing active and passive surveillance strategies is highlighted by EuFMD as a core competency for a country to reach phase 2 of the Progressive Control Pathway advocated by EuFMD for countries with endemic FMD [30]. In this study, we analyze retrospective FMD incidence in an endemic setting with a highly resolved network of livestock shipments and outbreak incidence to evaluate active surveillance strategies for efficient outbreak detection. We compare the effectiveness, with respect to outbreak detection, of sentinel surveillance methods informed by shipment network and/or outbreak incidence information to help identify high risk nodes for FMD in endemic settings. By using retrospective, empirical data, rather than simulation we can assess the performance of outbreak detection under realistic spatial and temporal distributions [31].

For this study we use retrospective data (2007–2012) from the Republic of Türkiye, where FMD is endemic. During this time period there was an extensive passive surveillance system to help manage FMD outbreaks in domestic cattle. The Republic of Türkiye’s surveillance program recorded the start date and location (epiunit; defined as a geographic area containing one or more farms) of all reported outbreaks and records which cattle were shipped between which epiunits at which time. Guyver-Fletcher et al. [32] used these data to show that shipment-mediated persistence of FMD was possible and Herrera-Diestra et al. [17] used these data to show that epiunits that were more central in the network had a higher risk of an outbreak – demonstrating the usefulness of network analysis for assessing the persistence of FMD and for identifying high risk epiunits in the Republic of Türkiye.

In this study, we use these highly resolved, retrospective data on FMD outbreaks in an endemic setting as a case-study for assessing the effectiveness of sentinel surveillance strategies based on [1] network properties, [2] spatial proximity to outbreaks, and [3] network proximity to outbreaks. We assess how each of these surveillance schemes, with varying levels of effort, perform at detecting outbreaks from the Republic of Türkiye dataset as an illustration of the likely performance of these strategies in other endemic settings with minimal FMD surveillance infrastructure. We explore the value of using outbreak incidence data and shipment network data to allocate limited sentinel surveillance effort with the goal of providing some general guidelines for designing sentinel surveillance programs in countries with endemic FMD with minimal existing surveillance infrastructure. Better allocation of surveillance effort will help countries with endemic FMD better plan and utilize their resources – allowing for more efficient and less costly management protocols for controlling FMD outbreaks.

Methods

Overview

We used cattle shipment data and outbreak incidence data from January 2007 to July 2012 from the Republic of Türkiye to develop and compare general methods (Fig 1) for allocating limited sentinel surveillance effort for FMD outbreak detection in countries with endemic FMD and minimal existing FMD surveillance infrastructure. Using the cattle shipment data (edgelists with information about the origin, destination, timing and frequency of cattle shipments), we constructed overlapping two-month directed-weighted cattle shipment networks (nodes = epiunits, edges = cattle shipment routes) based on the networks calculated by Herrera-Diestra et al. [17] from the same dataset. We then used the outbreak incidence data, the geographic epiunit locations and these two-month networks to develop and compare three surveillance methods (Fig 1) across seven different levels of surveillance effort to investigate which method(s) would be the most effective at finding recorded outbreaks and if effectiveness varied by effort level.

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Fig 1. Surveillance Methods Summary.

The three different data-informed surveillance methods are shown diagrammatically with an accompanying description of which types of information they are informed by. For each surveillance method, two magnifying glasses are shown on the epiunits that would have been selected by each surveillance method at a surveillance effort of 20% (2/10 epiunits). Epiunits with outbreaks on them in the diagram indicate epiunits with outbreaks with start dates in month t (i.e., ‘outbreak epiunits’). The circle in (b) indicates the search radius chosen at a surveillance effort of 20%. Note that ‘Network’ and ‘Outbreak’ information are dynamic and require consistent collection while ‘Epiunit Location’ information (the latitude and longitude of the centroid of the epiunit) is static and does not change over time. ‘Outbreak’ information is a record of the epiunit and start date of a particular outbreak. ‘Network’ information records the start and end epiunit of all cattle shipment events. Note that the ‘Network Connectivity’ surveillance method selects sites to survey without using the information in the outbreak dataset, but the outbreak dataset is used to assess whether the epiunits selected for surveillance by this method experienced an outbreak during the relevant time period (see the ‘Network Connectivity Method’ subsection of the ‘Surveillance Methods’ section for more details). Icons are from Microsoft Powerpoint and are free to use without royalty or copyright (https://support.microsoft.com/en-us/office/insert-icons-in-microsoft-365-e2459f17-3996-4795-996e-b9a13486fa79).

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Results

Across months

All three data-informed surveillance methods (Fig 1) detect similar percentages of outbreaks at each surveillance effort level but at higher surveillance effort levels the Network Proximity method and Network Connectivity method detect 5–10% more outbreaks than the Spatial Proximity method, averaged across time (January 2007 to July 2012, Fig 2). At the lowest surveillance effort level (5%), the data-informed surveillance methods detect four to five times more outbreaks on average than were detected by the random surveillance method (Fig 2). As surveillance effort level is increased, the data-informed surveillance methods detect relatively fewer outbreaks. At 30% surveillance effort level these methods only detect approximately two times more outbreaks on average than the random surveillance method (Fig 2). Across surveillance effort levels, the Spatial Proximity method consistently finds the fewest outbreaks of the three data-informed surveillance methods on average (Fig 2). The Network Proximity method and Network Connectivity method find a similar percentage of outbreaks across surveillance effort levels on average (Fig 2). Beyond 35% surveillance effort level, the relative performance of the different surveillance methods (that were able to survey beyond 35%) did not change until >60% when the Network Connectivity method became restricted by the number of epiunits present in the two-month networks (Fig A in S1 Text).

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Fig 2. Performance of Surveillance Methods Across Surveillance Effort Levels.

The points represent the average percent of outbreaks each surveillance method detected across all t  + 1 months at each surveillance effort level (5-35%). The ribbons represent the variability across months (the interquartile range). The Network Proximity method is unable to survey over 30% of the epiunits for all of the two-month networks and so it is only plotted until the 30% surveillance effort level (as opposed to 35%).

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The Network Connectivity surveillance method detected more outbreaks on average across months than the All-Month Network Connectivity surveillance method, across all surveillance effort levels (Fig D in S1 Text). The All-Month Network Connectivity surveillance method finds the smallest percentage of outbreaks at low surveillance effort level, but the second most (on average) at the highest surveillance effort level (Fig D in S1 Text).

When we used the data-informed surveillance methods to search for only serotype A or serotype O outbreaks, they detected a similar percentage of outbreaks averaged across months as the non-serotype specific surveillance methods (Tables C and D in S1 Text). In general, the Network Connectivity method was the best at finding serotype A outbreaks on average and the Network Proximity method was the best at finding serotype O outbreaks on average but the differences in surveillance method performance are fairly minor (Tables C and D in S1 Text).

Month by month

As surveillance effort level was increased, the three different data-informed surveillance methods all detected more outbreaks per month (Fig 3 and Fig F in S1 Text). As the number of outbreaks in a month decreased, the proportion of outbreaks detected generally increased for all surveillance methods and across surveillance effort levels (Figs 3, 4 and Figs F and G in S1 Text). For example, when there were fewer than 15 outbreaks in a month, the surveillance methods detected up to 67% of outbreaks at a 5% surveillance effort level (Fig 3a) and up to 100% of outbreaks at a 35% surveillance effort level (Fig Fd in S1 Text) (across methods). In contrast, when there were more than 50 outbreaks in a month, surveillance only detected up to 44% of outbreaks at a 5% surveillance effort level (Figs 3a and 4a) and up to 89% of outbreaks at a 35% surveillance effort level (Figs Fd and Gd in S1 Text) (across methods). As the number of outbreaks increased, the spatial radius required to perform the Spatial Proximity method at the appropriate surveillance effort level decreased (and the radius increased as surveillance effort level increased; Fig E in S1 Text). In general, the three data-informed surveillance methods all showed fairly similar outbreak detection patterns to each other (Fig 3 and Fig F in S1 Text).

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Fig 3. Month-by-Month Surveillance Method Performance.

The bars in each panel show the number of outbreaks detected by each surveillance method at each surveillance effort level (5%, 15% 30% respectively) at each t  + 1 month. The black line shows the number of outbreaks reported in each t + 1 month. The t + 1 months are ordered by decreasing number of outbreaks, with the left-most month having the most outbreaks. Versions of these graphs that correspond to 10%, 20%, 25% and 35% surveillance effort levels are in the supplementary material (Fig F in S1 Text).

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Fig 4. Best Surveillance Method Each Month.

Each panel shows the percent of outbreaks detected by the data-informed surveillance method that detected the most outbreaks in each t  + 1 month at each surveillance effort level. The bars indicate the percent of outbreaks detected by the best surveillance method and the horizontal black line indicates the percent of outbreaks detected by the Random surveillance method at that surveillance effort level. The coloured bars indicate the data-informed surveillance method that detected the most outbreaks at that surveillance effort level for that t + 1 month. The t + 1 months are ordered by declining number of outbreaks, with the left-most month having the most outbreaks. Versions of these graphs that correspond to 10%, 20%, 25% and 35% surveillance effort levels are in the supplementary material (Fig G in S1 Text).

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Even though on average across months the Network Proximity method and Network Connectivity method consistently detected more outbreaks than the Spatial Proximity Method (Fig 2), none of the data-informed surveillance methods consistently performed better than the other two for all months across surveillance effort levels (Fig 4 and Fig G in S1 Text). Also, no method was consistently better at detecting outbreaks as the number of outbreaks per month decreased (Fig 4 and Fig G in S1 Text). As surveillance effort level increased, the Network Connectivity method performed better in more months compared to the Network Proximity method while the Spatial Proximity method performed better in the fewest months at every surveillance effort level (Fig 4 and Fig G in S1 Text).

Discussion

All data-informed surveillance methods detected 2 to 4.5 times more outbreaks than the random surveillance method. For example, at a 5% surveillance effort level the data-informed methods detected roughly 20% of the outbreaks (on average), while the random surveillance method only detected 20% of outbreaks when searching at a 20% surveillance effort level (Fig 2). None of the data-informed sentinel surveillance methods were consistently better across surveillance effort levels or across months than any other method (Figs 2–4). However, we determined that surveillance methods informed by network data (Network Connectivity, Network Proximity; Fig 1) performed better than the Spatial Proximity method on average across all surveillance effort levels (Fig 2; especially at high surveillance effort levels) and across months (Fig 4 and Fig G in S1 Text). As surveillance effort level increased, the surveillance method that relied exclusively on network properties (Network Connectivity method) improved in comparison to the other methods (Fig 2). Gilbert et al. [45] determined that the spread of FMD in the Republic of Türkiye had become more associated with long-range transportation of animals than with short-range transmission; a finding that these results support. The surveillance method that used both network and outbreak information (‘Network Proximity’; Fig 1) was the most effective (on average) at small surveillance effort levels, but at larger surveillance effort levels it was not much better than the less informed methods and it also became limited by the availability of edges in the network (at 35% surveillance effort level; Figs Fd and Gd in S1 Text). These results imply that knowing where and when outbreaks happen is less crucial when more epiunits are under surveillance (Fig 2). Lastly, we determined that all of the data-informed surveillance methods detected a greater proportion of outbreaks as the number of outbreaks per month decreased; even the Network Connectivity method, whose allocation method was not influenced by the number of outbreaks in a month, showed this pattern. For example, when we used the data-informed surveillance methods to survey 5% of epiunits, they detected over 60% of outbreaks when outbreaks were rare (fewer than 15 outbreaks per month) (Figs 3 and 4a).

We also found that restricting our analysis to individual serotypes did not improve our ability to find outbreaks, as we would expect if we were identifying explicit chains of transmission. For example, sentinel surveillance methods that searched for serotype O FMD outbreaks detected slightly fewer outbreaks overall than serotype A specific surveillance methods and the non-serotype specific surveillance methods (Tables C and D in S1 Text). This result implies that collecting serotype-specific information does not improve our ability to allocate sentinel surveillance locations and that informed sentinel allocation decisions could be made from non-serotype-specific information alone.

Additionally, we determined that collecting time-specific shipment network information improved our ability to find outbreaks. When we used the cattle shipment network calculated from all of the cattle shipment data (January 2007 to July 2012), the All-Month Network Connectivity surveillance method detected fewer outbreaks than the average number detected by the Network Connectivity surveillance method informed by only two months of data (Fig D in S1 Text; also holds for the serotype-specific methods– Tables C and D in S1 Text). This is likely because the two-month cattle shipment Network Connectivity surveillance method is only informed by cattle shipments that happened around the date of the potential outbreaks and is not affected by additional shipments that happened far before or far after the outbreaks being searched for. Operationally, this implies that countries with endemic FMD should prioritize collecting shipment data from around the time of the outbreak start date and should be wary of using shipment data that is from far before or far after an outbreak as it may be misleading at worst, or exhibit diminishing returns at best.

The results of this study were calculated from retrospective data from the Republic of Türkiye, and it is important to acknowledge that farming demography, animal movement networks and available resources for FMD surveillance to incentivize participation, including programmatic support from relevant industry and governmental stakeholders, may differ in other countries with endemic FMD. However, we can still extrapolate some general suggestions for designing sentinel surveillance programs (across a variety of surveillance effort levels) to detect future FMD outbreaks in countries with endemic FMD that have limited resources. Since surveillance methods informed by network data detected more outbreaks at high surveillance effort levels but roughly the same number of outbreaks as other methods at a low surveillance effort level (on average), our results would suggest that countries with endemic FMD and limited surveillance resources should prioritize collecting whichever of those data sources would require the least additional resources, while countries with endemic FMD and more resources available for surveillance might prioritize collecting network data (as, at higher surveillance efforts, these methods detect 5–10% more outbreaks which could be quite significant, practically speaking). We phrase it this way so as to not make assumptions about which types of data are already being collected by different countries (i.e., the existing surveillance infrastructure), but we do acknowledge that animal movement data is less likely to be available in low resource settings (note that India, another country with endemic FMD, has created a livestock movement database [46]). Note that Stage 1 of the Progressive Control Pathway for FMD developed by FAO and EuFMD [30] encourages building systems to describe and document outbreaks and cattle movement, thus acknowledging the importance of these two types of data. In the Republic of Türkiye, the mean shipment distance was fairly short (~5km, but note that some were >120km; [17]) which may be why the network-informed methods performed similarly to the Spatial Proximity method (Fig 2). If farms are further apart on average, network information may become even more important as there would likely be less transmission between neighbouring farms. Our results also indicate that dynamic sentinels might be ideal; we determined that the best sentinel surveillance methods were sensitive to time-specific network and outbreak information and thus the best sentinel surveillance locations changed over time. Since all of the surveillance methods detected a larger proportion of outbreaks when outbreaks were rare (Figs 2 and 3; including the ‘Network Connectivity’ method which did not use any outbreak information), it implies that outbreaks may be easier to predict and track using data-informed methods in countries with endemic FMD when they are rare, which is promising as it is often most important to find outbreaks when there are fewer of them in order to better control the disease.

Past studies of sentinel surveillance allocation for FMD and other diseases that used network data tended to focus on allocating sentinels through a Network Connectivity-type method, and found similar results. Dawson et al. [28] used livestock transport network data from the UK to determine that using a Network Connectivity-type method for surveillance allocation finds more simulated FMD-like outbreaks than various random allocation methods. Interestingly, in Dawson et al. [28]’s simulated non-endemic setting, the top 20% of nodes detected through the network connectivity-type method are sufficient to predict epidemic size to within 90% confidence; in our study, however, the top 20% of epiunits detected using the Network Connectivity method only encompassed approximately 40% of the outbreaks. This implies that Network Connectivity-type methods may perform better in simulated epidemic settings (which have perfect observation of the network and the outbreaks) than it did for realized endemic FMD outbreaks. Frössling et al. [38] combined serological survey results and cattle movement data to compare a Network Connectivity-type method and a Network Proximity-type method for allocating herds to survey for detecting BRSV (bovine respiratory syncytial virus) and BCV (bovine coronavirus) infections, also finding that surveillance methods informed by network data from the relevant time period found more infections than random methods (also finding that the Network Connectivity-type method performed slightly better than the Network Proximity-type method). Vidondo & Voelkl [39] simulated epidemic outbreaks in cattle farms in Switzerland and determined that dynamic network measures are better at detecting simulated outbreaks than static network measures, similar to our finding that the Network Connectivity method informed by the two-month networks detected more outbreaks than the All-Month Network Connectivity method. Network Connectivity-type methods have also been shown to be more effective than random methods for finding outbreaks in endemic settings for other diseases (e.g., Ribeiro-Lima et al. [47] for bovine tuberculosis).

The data that we have from the Republic of Türkiye contain very detailed information on the reported outbreaks, and on shipments of cattle between January 2007 and July 2012. However, unreported outbreaks or shipments could still limit the potential for outbreak detection in our analysis. We assume that we are missing either outbreaks or cattle movements or both because there were some outbreaks that were not detected by all three surveillance methods (Fig H in S1 Text), implying the existence of either un-reported outbreaks that could bridge the gaps between reported outbreaks or missing connections between epiunits. We also only had the exact locations of 40,746 out of 54,096 epiunits, which likely impeded the effectiveness of the Spatial Proximity method. We also did not have information about the location or shipments to or from slaughterhouses/abattoirs, markets or other non-farm hubs; such data would have been worth including in the analysis as potential surveillance targets (see Gunasekara et al. [48] and Siengsanan-Lamont et al. [49] that did explore surveillance methods based on slaughterhouses/abbatoirs, markets, etc.). However, as one is unlikely to encounter a more complete dataset from a country with endemic FMD it is important to work around these data limitations to derive findings to inform surveillance allocations in other, more resource-limited, countries with endemic FMD.

Further, we know that the Republic of Türkiye recommends certain control measures (mass vaccinations, reactive ring vaccinations, reactive movement controls) [19,33,50] but we do not know to what extent those control measures were enacted around the country from January 2007 to July 2012, so we did not factor this into our sentinel surveillance method designs. It is possible that control measures in the Republic of Türkiye managed to stop the spread of FMD outbreaks to certain epiunits between January 2007 and July 2012 that would otherwise have experienced outbreaks, which would make all of our sentinel surveillance methods appear less effective. This uncertainty may also restrict the applicability of our results to other countries with endemic FMD that have fewer control measures in place, as FMD may spread differently in them. However, it is likely that our data-informed sentinel surveillance methods would perform better in countries with no FMD control measures, because the methods we propose here do not account for the presence of control measures. Control measures known to have occurred during this time in the Republic of Türkiye, include reduced cattle shipments (reactive movement controls) and reduced transmission to nearby epiunits through vaccination after outbreak detection (reactive ring vaccination), thereby potentially directly reducing the efficacy of the Surveillance Methods we explore in this study [19,33,50]. Nonetheless, even in the presence of such control activities it remains important to identify subsequent outbreaks and we show that methods based on spatial or network properties strongly outperform random search. It is possible that, if such controls were recorded, they could be incorporated into a sentinel strategy to gain further efficiency. It would be worthwhile for future studies to use a dynamic model of both surveillance and control activities (similar to that used in [50]) to assess how the performance of surveillance strategies might interact.

In this study, we focus on using each sentinel surveillance method independently so that we can easily control the surveillance effort level and assess how well each method performs alone. However, it would be possible to develop a composite measure of outbreak risk using multiple methods at the same time (e.g., Dawson et al. [28] and Kendall et al. [51] suggest a hybrid of the Network Connectivity and Network Proximity methods) or varying the method we use over time (via an adaptive management framework, [52]). It is possible that combining surveillance methods (either across time or at one time) might also help reduce the number of outbreaks detected by no method (Fig H in S1 Text) and also allow us to survey more than 35% of epiunits consistently. In this study, to ensure direct comparison of each surveillance method, we could only explore surveillance efforts up to 35% as the Network Proximity method was unable to survey 35% of the network in certain months (Figs F and G in S1 Text). Note that when we did assess beyond 35%, the relative performance of the surveillance methods able to survey past 35% was the same until 60% surveillance effort, at 65% surveillance effort the methods that relied on network information (see Fig 1) became limited by the number of epiunits present in the two-month networks (Fig A in S1 Text). This study focused on assessing active sentinel surveillance methods for detecting outbreaks. Had we focused on other goals such as ‘early detection’ the methods we assessed would have been different and thus the relevance of collecting different types of data and the effectiveness of different surveillance methods would have been different.

Since all of the data-informed sentinel surveillance methods performed comparably and the most data-informed method was only marginally better and was limited in the number of candidate epiunits it detected, the results of this study suggest that sentinel surveillance sites in countries with endemic FMD could usefully be allocated through Spatial Proximity or Network Connectivity surveillance methods (whichever would require the least additional effort to collect the requisite information). However, based on these results, if a country is able to allocate sufficient resources to ensure a high surveillance effort, or is planning to increase the effort it allocates to surveillance in the future, it would make sense to prioritize building a network-data-motivated surveillance system. In this study we present general guidelines for designing active sentinel surveillance methods that might be useful for detecting FMD outbreaks in an endemic FMD context. However we note that developing an actual FMD surveillance system for a country would require explicit, time-specific information on farming demography and animal movement networks, FMD status, existing FMD surveillance infrastructure and available resources of the country or region of interest. Significant effort is expended on surveillance efforts, so finding the most efficient locations to survey is crucial to ensure that countries do not waste effort surveying farms that are relatively unlikely to experience outbreaks, especially if such effort could be better spent on other forms of management to help reduce future FMD outbreaks.

Supporting information

Acknowledgments

We wish to thank Glen David Guyver-Fletcher, Deepit Bhatia, Kaiyue Zou and Callum Arnold for their helpful comments on this project.