ArticleTropical animal health and production2022
Assessing machine learning techniques in forecasting lumpy skin disease occurrence based on meteorological and geospatial features.
Article in Tropical animal health and production, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- Lumpy Skin Disease in Cattle and Buffalo in Asian Countries: A Systematic Review and Meta-Analysis.Veterinary medicine and science · 2025Pooled it
- A systematic review of epidemiological modelling in response to lumpy skin disease outbreaks.Frontiers in veterinary science · 2024Pooled it
- Climate-driven shifts in lumpy skin disease risk across Iran: Insights from ensemble species distribution models.One health (Amsterdam, Netherlands) · 2026Article
- Robust multi-class lumpy skin disease diagnosis for practical livestock applications.Scientific reports · 2026Article
- Analyzing Global Threat of Lumpy Skin Disease Virus Infection: A Comparative Study of a Time Series Model and Machine Learning Models Across 10 High-Risk Countries.Current microbiology · 2026Article
- From machine learning to digital twin integration for livestock production and research.Frontiers in veterinary science · 2026Review
- Global expansion of lumpy skin disease: transmission trends, economic consequences, and preventive measures.Veterinary research communications · 2025Review
- Development and validation of machine learning-based diagnostic models using blood transcriptomics for early childhood diabetes prediction.Frontiers in medicine · 2025Article
- A Systematic Review of Species Classification Using Deep Learning Algorithms and Gender Identification ofZoological studies · 2025Article
- Prediction of lumpy skin disease virus using customized CBAM-DenseNet-attention model.BMC infectious diseases · 2024Article
- Early Detection of Lumpy Skin Disease in Cattle Using Deep Learning-A Comparative Analysis of Pretrained Models.Veterinary sciences · 2024Article
- An extensive investigation of convolutional neural network designs for the diagnosis of lumpy skin disease in dairy cows.Heliyon · 2024Article
- Risk Factors and Spatiotemporal Distribution of Lumpy Skin Disease Occurrence in the Asian Continent during 2012-2022: An Ecological Niche Model.Transboundary and emerging diseases · 2023Article
- Understanding the research advances on lumpy skin disease: A comprehensive literature review of experimental evidence.Frontiers in microbiology · 2022Review
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lumpy skin disease virus (LSDV) causes an infectious disease in cattle. Due to its direct relationship with the survival of arthropod vectors, geospatial and climatic features play a vital role in the epidemiology of the disease. The objective of this study was to assess the ability of some machine learning algorithms to forecast the occurrence of LSDV infection based on meteorological and geological attributes. Initially, ExtraTreesClassifier algorithm was used to select the important predictive features in forecasting the disease occurrence in unseen (test) data among meteorological, animal population density, dominant land cover, and elevation attributes. Some machine learning techniques revealed high accuracy in predicting the LSDV occurrence in test data (up to 97%). In terms of area under curve (AUC) and F1 performance metric scores, the artificial neural network (ANN) algorithm outperformed other machine learning methods in predicting the occurrence of LSDV infection in unseen data with the corresponding values of 0.97 and 0.94, respectively. Using this algorithm, the model consisted of all predictive features and the one which only included meteorological attributes as important features showed similar predictive performance. According to the findings of this research, ANN can be used to forecast the occurrence of LSDV infection with high precision using geospatial and meteorological parameters. Applying the forecasting power of these methods could be a great help in conducting screening and awareness programs, as well as taking preventive measures like vaccination in areas where the occurrence of LSDV infection is a high risk.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.