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Publications

Journal Article Ahead of print

PubMed: 42271042 DOI: 10.1038/s41598-026-57440-2

Spatiotemporal trends of foot and mouth disease (FMD) in Bangladesh from 2017 to 2023 and their associations with climatic factors and machine learning (ML) based prediction.

Sci Rep

Authors: Md Jisan Ahmed, Kazi Estieque Alam, Faisol Talukder, Prajwal Bhandari, Md Ismile Hossain Bhuiyan, Mustakim Mamun, Md Arifur Rahman, Ritu Chalise, Md Imran Hossain, Moheuddin Morshed, Mirza Synthia Sabrin, Md Tazul Islam Chowdhury, Md Jahangir Alam, Delower Hossain

Affiliations

  • Association of Coding, Technology, and Genomics (ACTG), Sher-e-Bangla Agricultural University (SAU), Dhaka, 1207, Bangladesh.
  • Department of Pathology, Faculty of Animal Science and Veterinary Medicine, Sher-e-Bangla Agricultural University (SAU), Dhaka, 1207, Bangladesh.
  • Association of Coding, Technology, and Genomics (ACTG), Sher-e-Bangla Agricultural University (SAU), Dhaka, 1207, Bangladesh.
  • Department of Livestock Service (DLS), Dhaka, 1212, Bangladesh.
  • Department of Microbiology and Parasitology, Faculty of Animal Science and Veterinary Medicine, Sher-e-Bangla Agricultural University (SAU), Dhaka, 1207, Bangladesh.
  • Department of Animal Production and Management, Faculty of Animal Science and Veterinary Medicine, Sher-e-Bangla Agricultural University (SAU), Dhaka, 1207, Bangladesh.
  • Association of Coding, Technology, and Genomics (ACTG), Sher-e-Bangla Agricultural University (SAU), Dhaka, 1207, Bangladesh. Department of Surgery and Theriogenology, Faculty of Animal Science and Veterinary Medicine, Sher-e-Bangla Agricultural University (SAU), Dhaka, 1207, Bangladesh.
  • Association of Coding, Technology, and Genomics (ACTG), Sher-e-Bangla Agricultural University (SAU), Dhaka, 1207, Bangladesh. Department of Pathology, Faculty of Animal Science and Veterinary Medicine, Sher-e-Bangla Agricultural University (SAU), Dhaka, 1207, Bangladesh.
  • Department of Agricultural Chemistry, Faculty of Agriculture, Sher-e-Bangla Agricultural University (SAU), Dhaka, 1207, Bangladesh.
  • Department of Medicine and Public Health, Faculty of Animal Science and Veterinary Medicine, Sher-e-Bangla Agricultural University (SAU), Dhaka, 1207, Bangladesh.

Abstract

Foot-and-mouth disease (FMD) is a highly contagious transboundary viral disease that affects cloven-hoofed livestock, causing substantial economic losses. It is caused by an Aphthovirus under the family Picornaviridae. This study aimed to analyze the spatiotemporal distribution of FMD in Bangladesh, evaluate the influence of meteorological factors, and forecast future disease trends. FMD cases and meteorological variables were obtained from DLS and BMD, respectively. Spatial autocorrelation metrics, including Getis-Ord Gi*, Moran's I, LISA, and inverse distance weighting, were applied to identify disease clusters and risk zones. Correlation and regression analyses identified significant associations between FMD incidence and climatic factors. Relative humidity and temperature exhibited positive correlations with disease occurrence. Additionally, regression modeling revealed that both relative humidity and wind speed had a significant impact on FMD incidence. Predictive models, including ARIMA, Random Forest, and XGBoost, were applied, with XGBoost providing the most accurate forecasts (RMSE = 153.64), followed by Random Forest and ARIMA. FMD incidence peaked in March, with persistent southeastern hotspots and emerging northern clusters. IDW mapping showed elevated risk in southern regions, particularly during the pre-monsoon period, influenced by climatic factors. These insights can inform targeted surveillance and control strategies to mitigate the burden of FMD in Bangladesh.

MeSH

Foot-And-Mouth Disease, Mouth Diseases, Incidence, Humidity, Foot, Machine Learning, Learning, Mouth