Modelling the transmission dynamics and control of aflatoxins crops and its associated health risks in livestock and humans

Abstract

Aflatoxin contamination poses a significant challenge to food safety and security, as it affects both the health of consumers and the entire supply chain. Doses of aflatoxins beyond accept able levels are dangerous and may lead to poisoning, also called aflatoxicosis, a life-threatening illness. Liver damage or liver cancer, especially for people who may have conditions such as hepatitis B infection, is also caused by aflatoxin consumption. This study aimed to investigate the transmission dynamics and control of aflatoxin contamination in crops and its associated health risks in livestock, and humans. A deterministic mathematical model to study transmis sion dynamics was formulated and analyzed. Partial Rank Correlation Coefficients (PRCCs) for global sensitivity analysis were calculated using Latin Hypercube Sampling (LHS) to de termine how sensitive and significant the parameters are for each variable. Three controls, namely good farming practices, biological control, and public education and awareness cam paigns, were analyzed. The optimal control theory and cost-effective analysis were performed to identify the most effective strategy for aflatoxin contamination mitigation in crops, live stock, and humans. Four machine learning algorithms: Gaussian Process Classification (GPC), Support Vector Machine (SVM), Random Forest Classifier (RFC), and K Nearest Neighbors (KNN) have been used to predict aflatoxin contamination in maize and groundnuts. The anal ysis of the mathematical model formulated shows that aflatoxin contamination-free equilib rium (ACFE) and aflatoxin contamination-persistence equilibrium (ACPE) exist. The ACFE is globally asymptotically stable if the basic aflatoxin contamination number R0 < 1 whereas the ACPE is globally asymptotically stable if R0 > 1. Numerical simulations showed that a decrease in crop contamination and shedding rates and an increase in the death rate of aflatoxin fungi in the environment by 50% reduced the basic contamination number by above 92%. Re sults from the optimal control analysis suggest that implementation of all controls performs better than other strategies in controlling aflatoxin contamination in crops, livestock, and hu mans. Therefore, to control aflatoxin contamination, initiatives should focus on good farming practices, biological control, and public education and awareness campaigns. In predicting aflatoxin contamination, GPC outperformed other models with an accuracy of 96% and 95% in groundnut and maize samples, respectively. Moreover, the study revealed that humidity and rainfall have a greater influence on predicting aflatoxin contamination compared to tempera ture.