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Prev Vet Med. 2014 Apr 1;114(1):47-63. doi: 10.1016/j.prevetmed.2014.01.013. Epub 2014 Jan 16.

A multi-analysis approach for space-time and economic evaluation of risks related with livestock diseases: the example of FMD in Peru.

Author information

1
Center for Animal Disease Modeling and Surveillance, VM: Medicine and Epidemiology, UC Davis, CA, USA; VISAVET, Veterinary School, Complutense University of Madrid, Av. Puerta de Hierro s/n, 28040, Spain; IREC (CSIC-UCLM-JCCM), Ronda de Toledo s/n, 13005 Ciudad Real, Spain. Electronic address: beamartinezlopez@ucdavis.edu.
2
Applied Mathematics Department, Mathematics School, Complutense University of Madrid, Plaza de Ciencias 3, 28040 Madrid, Spain.
3
Center for Animal Disease Modeling and Surveillance, VM: Medicine and Epidemiology, UC Davis, CA, USA.
4
Biomedical Research Network Center in Neurodegenerative Diseases, CIBERNED, ISCII, Madrid, Spain.
5
VISAVET, Veterinary School, Complutense University of Madrid, Av. Puerta de Hierro s/n, 28040, Spain.
6
IREC (CSIC-UCLM-JCCM), Ronda de Toledo s/n, 13005 Ciudad Real, Spain.

Abstract

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.

KEYWORDS:

Decision-support tool; Economic analysis; Foot-and-mouth disease; Modeling; Multi-analysis approach; Peru; Social network analysis

PMID:
24485278
DOI:
10.1016/j.prevetmed.2014.01.013
[Indexed for MEDLINE]

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