Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data

Abstract

African swine fever (ASF) is a virulent infectious disease of pigs. As there is no effective vaccine and treatment method at present, it poses a great threat to the pig industry once it breaks out. In this paper, we used ASF outbreak data and the WorldClim database meteorological data and selected the CfsSubset Evaluator‐Best First feature selection method combined with the random forest algorithms to construct an African swine fever outbreak prediction model. Subsequently, we also established a test set for data other than modelling, and the accuracy accuracy value range of the model on the independent test set was 76.02%–84.64%, which indicated that the modelling effect was better and the prediction accuracy was higher than previous estimates. In addition, logistic regression analysis was conducted on 12 features used for modelling and the ROC curves were drawn. The results showed that the bio14 features (precipitation of driest month) had the largest contribution to the outbreak of ASF, and it was speculated that the outbreak of the epidemic was significantly related to precipitation. Finally, we used this qualitative prediction model to build a global online prediction system for ASF outbreaks, in the hope that this study will help to decision‐makers who can then take the relevant prevention and control measures in order to prevent the further spread of future epidemics of the disease.

Modelling the transmission and persistence of African swine fever in wild boar in contrasting European scenarios

“…Model studies have also been used to undertake a risk assessment for the spread of ASF. These studies have used statistical data fitting approaches to determine the risk of ASF introduction through contaminated pork products 24,25 , have linked ASF infection data to meteorological records to make global predictions of ASF outbreaks 26 , and have identified risk factor indicators to predict the spread of ASF in Europe 27,28 , with wild boar density classified as a key indicator. These model approaches have not focused on determining the underlying epidemiological processes responsible for infection.…”

Enhanced Gaussian process regression-based forecasting model for COVID-19 outbreak and significance of IoT for its detection

“…Various machine learning models were used to predict multiple pandemics such as Ebola, SARS, Swine flu, Cholera, H1N1 influenza, Zika, Dengue fever, Oyster norovirus, and MARS [20][21][22][23][24][25][26][27][28][29][30]. These machine learning methods are limited to some basic models such as Neural Networks, Random Forest Regression, Bayesian Networks, Genetic Programming, Naïve Bayes, Classification and Regression Tree, Linear Regression, and Support Vector Regression.…”

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

“…Application of ML in outbreak prediction includes several algorithms, e.g., random forest for swine fever [39] [40], neural network for H1N1 flu, dengue fever, and Oyster norovirus [41] [11] [42], genetic programming for Oyster norovirus [43], classification and regression tree (CART) for Dengue [44], Bayesian Network for Dengue and Aedes [45], LogitBoost for Dengue [46], multi-regression and Naïve Bayes for Dengue outbreak prediction [47]. Although ML methods were used in modeling former pandemics (e.g., Ebola, Cholera, swine fever, H1N1 influenza, dengue fever, Zika, oyster norovirus [11,[39][40][41][42][43][44][45][46][47][48]), there is a gap in the literature for peer-reviewed papers dedicated to COVID-19. Nevertheless, machine learning has been strongly proposed as a great potential for the fight against COVID-19 [49,50].…”