Publication: A remotely sensed flooding indicator associated with cattle and buffalo leptospirosis cases in Thailand 2011-2013
Issued Date
2018-11-29
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14712334
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2-s2.0-85057536231
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Mahidol University
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SCOPUS
Bibliographic Citation
BMC Infectious Diseases. Vol.18, No.1 (2018)
Suggested Citation
Sudarat Chadsuthi, Karine Chalvet-Monfray, Anuwat Wiratsudakul, Duangjai Suwancharoen, Julien Cappelle A remotely sensed flooding indicator associated with cattle and buffalo leptospirosis cases in Thailand 2011-2013. BMC Infectious Diseases. Vol.18, No.1 (2018). doi:10.1186/s12879-018-3537-3 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/46177
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Title
A remotely sensed flooding indicator associated with cattle and buffalo leptospirosis cases in Thailand 2011-2013
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Abstract
© 2018 The Author(s). Background: Leptospirosis is an important zoonotic disease worldwide, caused by spirochetes bacteria of the genus Leptospira. In Thailand, cattle and buffalo used in agriculture are in close contact with human beings. During flooding, bacteria can quickly spread throughout an environment, increasing the risk of leptospirosis infection. The aim of this study was to investigate the association of several environmental factors with cattle and buffalo leptospirosis cases in Thailand, with a focus on flooding. Method: A total of 3571 urine samples were collected from cattle and buffalo in 107 districts by field veterinarians from January 2011 to February 2013. All samples were examined for the presence of leptospirosis infection by loop-mediated isothermal amplification (LAMP). Environmental data, including rainfall, percentage of flooded area (estimated by remote sensing), average elevation, and human and livestock population density were used to build a generalized linear mixed model. Results: A total of 311 out of 3571 (8.43%) urine samples tested positive by the LAMP technique. Positive samples were recorded in 51 out of 107 districts (47.66%). Results showed a significant association between the percentage of the area flooded at district level and leptospirosis infection in cattle and buffalo (p = 0.023). Using this data, a map with a predicted risk of leptospirosis can be developed to help forecast leptospirosis cases in the field. Conclusions: Our model allows the identification of areas and periods when the risk of leptospirosis infection is higher in cattle and buffalo, mainly due to a seasonal flooding. The increased risk of leptospirosis infection can also be higher in humans too. These areas and periods should be targeted for leptospirosis surveillance and control in both humans and animals.