RESUMEN
African swine fever (ASF) causes greater sanitary, social and economic impacts on swine herds than many other swine diseases. Although ASF was first described in 1921 and it has affected more than fifty countries in Africa, Europe and South America, several key issues about its pathogenesis, immune evasion and epidemiology remain uncertain. This article reviews the main characteristics of the causative virus, its molecular epidemiology, natural hosts, clinical features, epidemiology and control worldwide. It also identifies and prioritizes gaps in ASF from a horizontal point of view encompassing fields including molecular biology, epidemiology, prevention, diagnosis and vaccine development. The purpose of this review is to promote ASF research and enhance its control.
Asunto(s)
Virus de la Fiebre Porcina Africana/crecimiento & desarrollo , Fiebre Porcina Africana/epidemiología , África/epidemiología , Fiebre Porcina Africana/prevención & control , Virus de la Fiebre Porcina Africana/inmunología , Animales , Control de Enfermedades Transmisibles , Reservorios de Enfermedades , Europa (Continente)/epidemiología , América del Sur/epidemiología , PorcinosRESUMEN
This study presents a multi-disciplinary decision-support tool, which integrates geo-statistics, social network analysis (SNA), spatial-stochastic spread model, economic analysis and mapping/visualization capabilities for the evaluation of the sanitary and socio-economic impact of livestock diseases under diverse epidemiologic scenarios. We illustrate the applicability of this tool using foot-and-mouth disease (FMD) in Peru as an example. The approach consisted on a flexible, multistep process that may be easily adapted based on data availability. The first module (mI) uses a geo-statistical approach for the estimation (if needed) of the distribution and abundance of susceptible population (in the example here, cattle, swine, sheep, goats, and camelids) at farm-level in the region or country of interest (Peru). The second module (mII) applies SNA for evaluating the farm-to-farm contact patterns and for exploring the structure and frequency of between-farm animal movements as a proxy for potential disease introduction or spread. The third module (mIII) integrates mI-II outputs into a spatial-stochastic model that simulates within- and between-farm FMD-transmission. The economic module (mIV) connects outputs from mI-III to provide an estimate of associated direct and indirect costs. A visualization module (mV) is also implemented to graph and map the outputs of module I-IV. After 1000 simulated epidemics, the mean (95% probability interval) number of outbreaks, infected animals, epidemic duration, and direct costs were 37 (1, 1164), 2152 (1, 13, 250), 63 days (0, 442), and US$ 1.2 million (1072, 9.5 million), respectively. Spread of disease was primarily local (<4.5km), but geolocation and type of index farm strongly influenced the extent and spatial patterns of an epidemic. The approach is intended to support decisions in the last phase of the FMD eradication program in Peru, in particular to inform and support the implementation of risk-based surveillance and livestock insurance systems that may help to prevent and control potential FMD virus incursions into Peru.