Foot-and-mouth disease (FMD) is endemic in India, where circulation of serotypes O, A and Asia1 is frequent. 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 assessing the contribution of mass vaccination campaigns in reducing the risk of FMD in India from 2008 to 2016 by evaluating sero-monitoring data and modelling the spatiotemporal dynamics of reported outbreaks. Through analyzing antibody titre data from >1 million animals sampled as part of pre-and post-vaccination monitoring, we show that the percent of animals with inferred immunological protection (based on ELISA) was highly variable across states but generally increased through time. In addition, the number of outbreaks in a state was negatively correlated with the percent of animals with inferred protection.We then analyzed the distribution of reported FMD outbreaks across states using a Bayesian space-time model. This approach provides better acuity to disentangle the effect of mass vaccination programs on outbreak occurrence, while accounting for other factors that contribute to spatiotemporal variability in outbreak counts, notably proximity to international borders and inherent spatiotemporal correlations in incidence. This model demonstrated a ∼50% reduction in the risk of outbreaks in states that were part of the vaccination program. In addition, after controlling for spatial autocorrelation in the data, states that had international borders experienced heightened risk of FMD outbreaks. These findings help inform risk-based control strategies for India as the country progresses towards reducing reported clinical disease.
Fifteen species of primates from different geographic areas are living in captivity at the National Zoological Gardens of Sri Lanka. As a result of limited space in the Zoo and ever increasing visitors, there is a possibility to increase the incidence of human animal contact. Therefore, it is important to identify potential parasitic infections that can be transferred from humans to animals and vise versa. In the present study, the primates were investigated for the gastrointestinal parasites. Total of 85 fecal samples were collected from all the species and examined for the presence of helminthes and protozoa. Balantidium sp., Entamoeba coli, Giardia sp., Blastocystis sp. and coccidial oocytes including Cryptosporidium sp. oocysts were identified. Furthermore, Nematodes and Cestodes were also recorded.
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.
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