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Open Access Research article

Spatial epidemiology of porcine reproductive and respiratory syndrome in Thailand

Weerapong Thanapongtharm12*, Catherine Linard23, Nutavadee Pamaranon1, Sarayuth Kawkalong4, Tanom Noimoh1, Karoon Chanachai15, Tippawon Parakgamawongsa5 and Marius Gilbert23

Author Affiliations

1 Department of Livestock Development (DLD), Ratchatewi, Bangkok, Thailand

2 Biological Control and Spatial Ecology, University of Brussels, Brussels, Belgium

3 Fonds National de la Recherche Scientifique (FNRS), University of Brussels, Brussels, Belgium

4 National Institute of Animal Health, Ladyao, Chatuchak, Bangkok, Thailand

5 Field Epidemiology Training Program for Veterinarian (FETPV), Department of Livestock Development, Ratchatewi, Bangkok, Thailand

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BMC Veterinary Research 2014, 10:174  doi:10.1186/s12917-014-0174-y

Published: 5 August 2014

Abstract

Background

Porcine reproductive and respiratory syndrome (PRRS) has become a worldwide endemic disease of pigs. In 2006, an atypical and more virulent PRRS (HP-PRRS) emerged in China and spread to many countries, including Thailand. This study aimed to provide a first description of the spatio-temporal pattern of PRRS in Thailand and to quantify the statistical relationship between the presence of PRRS at the sub-district level and a set of risk factors. This should provide a basis for improving disease surveillance and control of PRRS in Thailand.

Results

Spatial scan statistics were used to detect clusters of outbreaks and allowed the identification of six spatial clusters covering 15 provinces of Thailand. Two modeling approaches were used to relate the presence or absence of PRRS outbreaks at the sub-district level to demographic characteristics of pig farming and other epidemiological spatial variables: autologistic multiple regressions and boosted regression trees (BRT). The variables showing a statistically significant association with PRRS presence in the autologistic multiple regression model were the sub-district human population and number of farms with breeding sows. The predictive power of the model, as measured by the area under the curve (AUC) of the receiver operating characteristics (ROC) plots was moderate. BRT models had higher goodness of fit the metrics and identified the sub-district human population and density of farms with breeding sows as important predictor variables.

Conclusions

The results indicated that farms with breeding sows may be an important group for targeted surveillance and control. However, these findings obtained at the sub-district level should be complemented by farm-level epidemiological investigations in order to obtain a more comprehensive view of the factors affecting PRRS presence. In this study, the outbreaks of PRRS could not be differentiated from the potential novel HP-PPRS form, which was recently discovered in the country.

Keywords:
Spatial epidemiology; PRRS; Autologistic multiple regression; BRT; Breeding sows; Thailand