Vampire bat-transmitted rabies has recently become the leading cause of rabies mortality in both humans and livestock in Latin America. Evaluating risk of transmission from bats to other animal species has thus become a priority in the region. An integrated bat-rabies dynamic modeling framework quantifying spillover risk to cattle farms was developed. The model is spatially explicit, and is calibrated to the state of São Paulo, using real roost and farm locations. Roosts and farms characteristics, as well as environmental data through ecological niche model, are used to modulate rabies transmission. Interventions in roosts (such as culling or vaccination) and in farms (vaccination) where considered as control strategies implemented to reduce risk. Both interventions significantly reduce the number of outbreaks in farms and disease spread (based on distance from source), with control in roosts being a significantly better intervention. High risk areas where also identified, which can support ongoing programs, leading to more effective control interventions.

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.

We introduce a new R package ‘MrIML’ (Multi-response Interpretable Machine Learning). MrIML provides a powerful and interpretable framework that enables users to harness recent advances in machine learning to map multi-locus genomic relationships, to identify loci of interest for future landscape genetics studies and to gain new insights into adaptation across environmental gradients. Relationships between genetic change and environment are often non-linear, interactive and autocorrelated. Our package helps capture this complexity and offers functions that construct, fit and conduct inference on a wide range of highly flexible models that are routinely used for single-locus landscape genetics studies but are rarely extended to estimate response functions for multiple loci. To demonstrate the package’s broad functionality, we test its ability to recover landscape relationships from simulated genomic data. We also apply the package to two empirical case studies. In the first we estimate variation in the population-level genetic composition of North American balsam poplar (Populus balsamifera, Salicaceae) and in the second we recover individual-level landscapes while estimating host drivers of feline immunodeficiency virus genetic spread in bobcats (Lynx rufus). The ability to model thousands of loci collectively and compare models from linear regression to extreme gradient boosting, within the same analytical framework, has the potential to be transformative. The MrIML framework is also extendable and not limited to mapping genetic change, for example, it can be used to quantify the environmental driver sof microbiomes and coinfection dynamics.