Spatio-temporal Dynamics of Foot-and-Mouth Disease Virus in South America
Although foot-and-mouth disease virus (FMDV) incidence has decreased in South America over the last years, the pathogen still circulates in the region and the risk of re-emergence in previously FMDV-free areas is a veterinary public health concern. In this paper we merge environmental, epidemiological and genetic data to reconstruct spatiotemporal patterns and determinants of FMDV serotypes A and O dispersal in South America. Our dating analysis suggests that serotype A emerged in South America around 1930, while serotype O emerged around 1990. The rate of evolution for serotype A was significantly higher compared to serotype O. Phylogeographic inference identified two well-connected sub networks of viral flow, one including Venezuela, Colombia and Ecuador; another including Brazil, Uruguay and Argentina. The spread of serotype A was best described by geographic distances, while trade of live cattle was the predictor that best explained serotype O spread. Our findings show that the two serotypes have different underlying evolutionary and spatial dynamics and may pose different threats to control programmes. Key-words: Phylogeography, foot-and-mouth disease virus, South America, animal trade.
Code (1)
Similar Papers 제목 키워드 기반
Leveraging Herpangina Data to Enhance Hospital-level Prediction of Hand-Foot-and-Mouth Disease Admissions Using UPTST
Outbreaks of hand-foot-and-mouth disease(HFMD) have been associated with significant morbidity and, in severe cases, mortality. Accurate forecasting of daily admissions of pediatric HFMD patients is therefore crucial for…
PredictionRepresentation LearningTime SeriesUnifying Physics- and Data-Driven Modeling via Novel Causal Spatiotemporal Graph Neural Network for Interpretable Epidemic Forecasting
Accurate epidemic forecasting is crucial for effective disease control and prevention. Traditional compartmental models often struggle to estimate temporally and spatially varying epidemiological parameters, while deep l…
Graph Neural NetworkAdaptive Semantic-Spatio-Temporal Graph Convolutional Network for Lip Reading
The goal of this work is to recognize words, phrases, and sentences being spoken by a talking face without given the audio. Current deep learning approaches for lip reading focus on exploring the appearance and optical f…
Landmark-based LipreadingLip ReadingOptical Flow EstimationIntegrating epidemiological and economic models to estimate the cost of simulated foot-and-mouth disease outbreaks in Brazil
The introduction of foot-and-mouth disease (FMD) leads to substantial economic impacts through animal loss, decreased livestock and meat production, increased government and private spending on control and eradication me…
Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data
Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to lo…