ArticleFrontiers in veterinary science2020
Prediction for Global Peste des Petits Ruminants Outbreaks Based on a Combination of Random Forest Algorithms and Meteorological Data.
Article in Frontiers in veterinary science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Is PPR Epidemiological Research Addressing Global Eradication Priorities? A Scoping Review (2015-2025).Viruses · 2026Article
- Spatial Suitability of Peste des Petits Ruminants in North Africa Using Machine-Learning Ecological Niche Modeling.Pathogens (Basel, Switzerland) · 2026Article
- Spatial Patterns of Global Peste Des Petits Ruminants Virus and Its Potential Risk Assessment for Various Wildlife Habitats.Ecology and evolution · 2025Article
- Diagnostic and vaccination challenges in the PPR pre-eradication era: Identifying gaps and potential unintended consequences.Journal of advanced veterinary and animal research · 2025Article
- Article
- QSAR analysis of VEGFR-2 inhibitors based on machine learning, Topomer CoMFA and molecule docking.BMC chemistry · 2024Article
- Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma.Biochemistry and biophysics reports · 2024Article
- Space-Time Analysis of Peste des Petits Ruminants in Mali and Identification of Risk Factors.Transboundary and emerging diseases · 2024Article
- WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases.International journal of environmental research and public health · 2023Article
- Epidemiologic and import risk analysis of Peste des petits ruminants between 2010 and 2018 in India.BMC veterinary research · 2022Article
- Assessing machine learning techniques in forecasting lumpy skin disease occurrence based on meteorological and geospatial features.Tropical animal health and production · 2022Article
- Epidemiology and Cost of Peste des Petits Ruminants (PPR) Eradication in Small Ruminants in the United Arab Emirates-Disease Spread and Control Strategies Simulations.Animals : an open access journal from MDPI · 2021Article
- Risk assessment and integrated surveillance of foot-and-mouth disease outbreaks in Russia based on Monte Carlo simulation.BMC veterinary research · 2021Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Peste des Petits Ruminants (PPR) is an acute and highly contagious transboundary disease caused by the PPR virus (PPRV). The virus infects goats, sheep and some wild relatives of small domestic ruminants, such as antelopes. PPR is listed by the World Organization for Animal Health as an animal disease that must be reported promptly. In this paper, PPR outbreak data combined with WorldClim database meteorological data were used to build a PPR prediction model. Using feature selection methods, eight sets of features were selected: bio3, bio10, bio15, bio18, prec7, prec8, prec12, and alt for modeling. Then different machine learning algorithms were used to build models, among which the random forest (RF) algorithm was found to have the best modeling effect. The ACC value of prediction accuracy for the model on the training set can reach 99.10%, while the ACC on the test sets was 99.10%. Therefore, RF algorithms and eight features were finally selected to build the model in order to build the online prediction system. In addition, we adopt single-factor modeling and correlation analysis of modeling variables to explore the impact of each variable on modeling results. It was found that bio18 (the warmest quarterly precipitation), prec7 (the precipitation in July), and prec8 (the precipitation in August) contributed significantly to the model, and the outbreak of the epidemic may have an important relationship with precipitation. Eventually, we used the final qualitative prediction model to establish a global online prediction system for the PPR epidemic.
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