Bayesian space-time regression models are helpful tools to describe and predict the number and distribution of infectious disease outbreaks, identify risk factors, and delineate high-risk areas for disease prevention or control. In these models, structured and unstructured spatial and temporal effects account for various forms of non-independence amongst case counts reported across spatial units. For example, structured spatial effects are used to capture correlations in case counts amongst neighboring provinces that may stem from shared risk factors or population connectivity. For highly mobile populations, spatial adjacency is an imperfect measure of population connectivity due to frequent long-distance movements. In many instances, we lack data on host movement and population connectivity, hindering the application of space-time risk models that inform disease control efforts. Phylogeographic models that infer routes of viral dissemination across a region could serve as a proxy for historical patterns of population connectivity. The objective of this study was to investigate whether the effects of population connectivity in space-time regressions of case counts were better captured by spatial adjacency or by inferences from phylogeographic analyses. To compare these two approaches, we used foot-and-mouth disease virus (FMDV) in Vietnam as an example. We explored whether the distribution of reported clinical FMD outbreaks across space and time was better explained by models that incorporate population connectivity based upon FMDV movement (inferred by discrete phylogeographic analysis) as opposed to spatial adjacency and showed that the best-fit model utilized phylogeographic-based connectivity. Therefore, accounting for virus movement through phylogeographic analysis serves as a superior proxy for population connectivity in spatial-temporal risk models when movement data are not available. This approach may contribute to the design of surveillance and control activities in countries in which movement data are lacking or insufficient.

Since the first outbreak was identified in July 2021, the African swine fever (ASF) epidemic in pigs in the Dominican Republic (DR) has generated much discourse on various measures for its control. Strategies range from complete depopulation of the swine population, as was done in 1978, to a system of passive surveillance with endemicity, with many in-between. Currently, ASF-decision makers need an evaluation of these potential strategies that incorporates both private and public perspectives. To achieve this goal, we used strengths, weaknesses, opportunities, and threats (SWOT) analysis to evaluate three different theoretical ASF control scenarios with the aim of contributing to the discussion of different alternatives to mitigate the epidemic’s impact. These included total depopulation of all pigs in the DR, partial depopulation, and continuation of current control measures. Relevant experts from the DR private swine industry sector were identified through “snowball sampling” techniques. First, relevant stakeholders within the DR private swine industry were asked to identify individuals that they would consider experts for ASF in the DR. Experts identified through this process were contacted to participate. Of these, 5 experts completed the SWOT questionnaire for each of the scenarios, with additional questions considering aspects of financial cost, social impact, feasibility, animal welfare, and regional policy. The responses were summarized for an overall evaluation of each scenario and presented to the full group of experts initially nominated for final review and later to representatives of the DR government for feedback. The SWOT analysis highlighted that although there are certain benefits associated with each of the proposed strategies, there are also important drawbacks and disadvantages for all. This may explain in part why 6 months after the epidemic was first reported, there are still uncertainty about the most effective control strategy to be implemented. This analysis is a tool for discussions at the private-public interface and facilitate cooperation between the DR government and swine industry. Ultimately, this work supports the development of strategies that will reduce ASF burden in the DR in a way suitable for all relevant stakeholders.

Foot-and-mouth disease (FMD) is endemic in India, where circulation of serotypes O, A and Asia 1 is frequent. In the past two decades, many of the most widespread and significant FMD lineages globally have emerged from the South Asia region. Here, we provide an epidemiological assessment of the ongoing mass vaccination programs in regard to post-vaccination monitoring and outbreak occurrence. The objective of this study was to quantify the spatiotemporal dynamics of FMD outbreaks and to assess the impact of the mass vaccination program between 2008 to 2016 with available antibody titer data from the vaccination monitoring program, alongside other risk factors that facilitate FMD spread in the country. We first conducted a descriptive analysis of epidemiological outcomes of governmental vaccination programs in India, focusing on antibody titer data from >1 million animals sampled as part of pre- and post-vaccination monitoring and estimates of standardized incidence ratios calculated from reported outbreaks per state/administrative unit. The percent of animals with inferred immunological protection (based on ELISA) was highly variable across states, but there was a general increase in the overall percent of animals with inferred protection through time. In addition, the number of outbreaks in a state was negatively correlated with the percent of animals with inferred protection. Because standardized incidence ratios of outbreaks were heterogeneously distributed over the course of eight years, we analyzed the distribution of reported FMD outbreaks using a Bayesian space-time model to map high-risk areas. This model demonstrated a ~50% reduction in the relative risk of outbreaks in states that were part of the vaccination program. In addition, states that did not have an international border experienced reduced risk of FMD outbreaks. These findings help inform risk-based control strategies for India as the country progresses towards reducing reported clinical disease.

Peste des petits ruminants (PPR) is a viral transboundary disease of small ruminants that causes significant damage to agriculture. This disease has not been previously registered in the Republic of Kazakhstan (RK). This paper presents an assessment of the susceptibility of the RK’s territory to the spread of the disease in the event of its importation from infected countries. The Generalized Linear Negative Binomial regression model that was trained on the PPR outbreaks in China was used to rank municipal districts in the RK in terms of PPR spread risk. The outbreaks count per administrative district was used as a risk indicator, while a number of socio-economic, landscape and climatic factors were considered as explanatory variables. Summary road length, altitude, the density of small ruminants, the maximum green vegetation fraction, cattle density and the Engel coefficient were the most significant factors. The model demonstrated a good performance in training data (R 2 = 0.69) and was transferred to the RK, suggesting a significantly lower susceptibility of this country to the spread of PPR. Hot Spot analysis identified three clusters of districts at the highest risk, located in the western, eastern and southern parts of Kazakhstan. As part of the study, a countrywide survey was conducted to collect data on the distribution of livestock populations, which resulted in the compilation of a complete geo-database of small ruminant holdings in the RK. The research results may be used to formulate a national strategy for preventing the importation and spread of PPR in Kazakhstan through targeted monitoring in high-risk areas.

Peste des petits ruminants (PPR) is a viral transboundary disease of small ruminants that causes significant damage to agriculture. The disease has not been previously registered in the Republic of Kazakhstan (RK). This paper presents an assessment of the susceptibility of the RK territory to the spread of this disease in case of its importation from infected countries. Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models trained on the PPR outbreaks in China were used to rank municipal districts of the RK in terms of the risk of PPR spread. Spatial density of outbreaks was used as a risk indicator while a number of socio-economic, landscape and climatic indicators were considered as explanatory variables. The Exploratory Regression tool was used to reveal a best combination of independent variables based on specified thresholds of R-squared, variables’ multicollinearity and residuals’ normality and autocorrelation. The small ruminants’ density, the maximum green vegetation fraction, the annual mean temperature, the road length and density as well as the cattle density were the most significant factors. Both OLS and GWR demonstrated nearly similar model performance providing a global adjusted R-squared of 0.61. Applied to the RK, the models show the greatest risk of PPR spread in the south-eastern and northern regions of the country, especially within Almaty, Zhambyl, Turkistan, West Kazakhstan and East Kazakhstan regions. As part of the study, a country-wise survey was carried out to collect data on the distribution of livestock population the RK, which resulted in compiling a complete geo-database of small ruminants’ holdings in the country. The research results can be used to form a national strategy for the prevention of the importation and spread of PPR in Kazakhstan through targeted monitoring in high-risk areas.

Bovine tuberculosis (bTB) prevalence substantially increased over the past two decades with relatively high impact on large dairy herds, raising the concern of regulatory authorities and industry stakeholders, and threatening animal and public health. Lack of resources, together with the economic and social consequences of whole-herd stamping-out, makes depopulation an impractical disease control alternative in these herds. The increase in bTB-prevalence was associated with demographic and management changes in the dairy industry in Uruguay, reducing the efficacy of the current control program (i.e. status quo) based on intradermal serial testing with caudal fold- and comparative cervical- tuberculin test-and slaughter of reactors (CFT-CCT). Here, we aimed to assess the epidemiological effectiveness of six alternative control scenarios based on test-and-slaughter of positive animals, using mathematical modeling to infer bTB-within-herd dynamics. We simulated six alternative control strategies consisting of testing adult cattle (>1 year) in the herd every three months using one test (in-vivo or in-vitro) or a combination in parallel of two tests (CFT, interferon-gamma release assay –IGRA- or Enzyme-linked immunosorbent assay). Results showed no significant differences overall in the time needed to reach bTB-eradication (median ranging between 61 to 82 months) or official bovine tuberculosis-free status (two consecutive negative herd-tests) between any of the alternative strategies and the status quo (median ranging between 50 and 59 months). However, we demonstrate how alternative strategies can significantly reduce bTB-prevalence when applied for restricted periods (6, 12, or 24 months), and in the case of IGRAc (IGRA using peptide-cocktail antigens), without incurring on higher unnecessary slaughter of animals (false-positives) than the status quo in the first 6 months of the program (P-value <0.05). Enhanced understanding bTB-within-herd dynamics with the application of different control strategies help to identify optimal strategies to ultimately improve bTB-control and -eradication from dairies in Uruguay and similar endemic settings.