Estimation of the probability risks of African swine fever outbreaks using the maximum entropy method in North Sumatra Province, Indonesia

Research (Published online: 26-07-2022)

28. Estimation of the probability risks of African swine fever outbreaks using the maximum entropy method in North Sumatra Province, Indonesia

Roza Azizah Primatika, Etih Sudarnika, Bambang Sumiarto and Chaerul Basri

Veterinary World, 15(7): 1814-1820

Roza Azizah Primatika: Department of Veterinary Public Health, Faculty of Veterinary Medicine, Universitas Gadjah Mada, Yogyakarta, Indonesia; Veterinary Public Health Study Program, Faculty of Veterinary Medicine, IPB University, Bogor, Indonesia.

Etih Sudarnika: Department of Animal Disease and Veterinary Public Health, Faculty of Veterinary Medicine, Institut Pertanian Bogor, Bogor, Indonesia.

Bambang Sumiarto: Department of Veterinary Public Health, Faculty of Veterinary Medicine, Universitas Gadjah Mada, Yogyakarta, Indonesia.

Chaerul Basri: Department of Animal Disease and Veterinary Public Health, Faculty of Veterinary Medicine, Institut Pertanian Bogor, Bogor, Indonesia.

doi: www.doi.org/10.14202/vetworld.2022.1814-1820

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Article history: Received: 04-03-2022, Accepted: 08-06-2022, Published online: 26-07-2022

Corresponding author: Roza Azizah Primatika

E-mail: roza.azizah@apps.ipb.ac.id

Citation: Primatika RA, Sudarnika E, Sumiarto B, Basri C (2022) Estimation of the Probability Risks of African Swine Fever Outbreaks Using the Maximum Entropy Method in North Sumatra Province, Indonesia, Veterinary World, 15(7): 1814–1820.

Abstract

Background and Aim: African swine fever (ASF) is an infectious disease and a major viral pig disease that threatens pork production in several locations globally. The mortality rate of ASF in domestic pigs is very high, causing a decrease in pig populations and significant economic losses for farmers. Environmental or ecological risk factors are the most important associated with the spread of the ASF virus. Environmental (or ecological) niche models are commonly used to estimate the probability of an event using the maximum entropy (Maxent) method. This study aimed to estimate the probability risk of future ASF outbreaks in North Sumatra, Indonesia.

Materials and Methods: Secondary data from the National Animal Health System Database (iSIKHNAS), including data on the ASF outbreaks of 2019–2020 in North Sumatra, Indonesia, were used in this study. The first analysis performed involved the identification of environmental risk factors using multiple regression analysis. The second analysis performed was the estimation of probability risk for future ASF outbreaks in North Sumatra, Indonesia, using the Maxent method. Data processing was performed using Microsoft Excel, ArcGIS version 10.5 software (ESRI, California, United States), Maxent version 3.4.4 software, and Rstudio (http://www.r-project.org/).

Results: The Maxent method was found to be highly accurate with a statistically significant area under the curve value of 0.860. The greatest contributing environmental factor identified by the model was the harbor, which contributed 57%. The range of high probability risk of future ASF outbreaks was found to be 0.723–0.84.

Conclusion: The estimation of the highest probability risk of future ASF outbreaks in North Sumatra, Indonesia, was 0.723–0.84. The most contributing environmental factor identified using the Maxent method was harbors, at 57%. This methodology can be used to carry out subsequent ASF analyses and contribute to developing prevention and control strategies in this area.

Keywords: African swine fever, environmental niche models, maximum entropy, probability risk.