Evaluating the utility of national‐scale data to estimate the local risk of foot‐and‐mouth disease in endemic regions. Issue 1 (29th August 2019)

• Record Type: Journal Article
• Authors: van Andel, Mary; Zaari, Scott; Bernard, Phiri; McFadden, Andrew; Dacre, Ian; Bingham, Paul; Heuer, Cord; Binney, Barbara; Buckle, Kelly; Abila, Ronel; Win, Htun Htun; Lwin, Khin Ohnmar; Gates, M. Carolyn
• Is Part Of: Transboundary and emerging diseases. Volume 67:Issue 1(2020)
• Page Start: 108
• Page End: 120
• Publication Date: 2019-08-29
• Subjects: Veterinary medicine -- Periodicals; 636.089
• DOI: 10.1111/tbed.13329
• Languages: English
• ISSNs: 1865-1674

Abstract

Abstract: Knowledge of the distribution of foot‐and‐mouth disease (FMD) is required if control programmes are to be successful. However, data on the seroprevalence and incidence of affected villages in developing countries with endemic disease are scarce. This is partly due to resource constraints as well as the logistical challenges of conducting intensive surveys and diagnostic testing in remote locations. In this study, we evaluated the performance of low resolution national‐scale data against high resolution local survey data to predict the FMD serological status of 168 villages in the Mandalay and Sagaing Regions of central Myanmar using both logistic regression and random forest modelling approaches. Blood samples for ELISA testing were collected from approximately 30 cattle per village in both the 6 to 18 month age range and in the over 18 month age range to distinguish between recent and historical exposure, respectively. The results of the animal level tests were aggregated to the village level to provide the outcome of interest (village positive or not positive for FMD), and three explanatory data sets were constructed: using only nationally available data, using only data collected by survey and using the combined survey and nationally available data. The true seroprevalence of FMD at the village level was 61% when only young animals were included, but increased to 87% when all animals were included. The best performing model was a logistic regression model using the combined national and survey data to predict recent infection in villages. However, this still incorrectly classified 40% of villages, which suggests that using national‐level data were not reliable enough for extrapolating seroprevalence in regions where conducting detailed surveys is impractical. Other methods for collected data on FMD such as the use of local reporting should be explored.