An integrated model for pre- and post-harvest aflatoxin contamination in maize

Acknowledgements: The modelling work was funded by a grant from Mars Inc to CAG at the University of Cambridge, which is gratefully acknowledged. We also thank Dr Yueju Zhao, Global Food Safety Center, Mars Inc, Beijing, for support and guidance and to the staff Ali Pinjari, Ranjith Muthoju, Rajdeep Saikia, Gangadhar Sivasubramanian, Aneesh Raizada of Mars Petcare India, 1st Floor, Ashoka Hitech Chambers, Road No. 2, Banjara Hills, Hyderabad 500034, India for the collection and provision of data used for parameter estimation and model validation. CAG also acknowledges the support of the Bill and Melinda Gates Foundation. We thank Alison Scott Brown in the Epidemiology and Modelling Group for helpful discussion and literature retrieval.Funder: MARS Inc funded research project: Predicting the risk of mycotoxin contamination of maizeAbstractAflatoxin contamination caused by colonization of maize by Aspergillus flavus continues to pose a major human and livestock health hazard in the food chain. Increasing attention has been focused on the development of models to predict risk and to identify effective intervention strategies. Most risk prediction models have focused on elucidating weather and site variables on the pre-harvest dynamics of A. flavus growth and aflatoxin production. However fungal growth and toxin accumulation continue to occur after harvest, especially in countries where storage conditions are limited by logistical and cost constraints. In this paper, building on previous work, we introduce and test an integrated meteorology-driven epidemiological model that covers the entire supply chain from planting to delivery. We parameterise the model using approximate Bayesian computation with monthly time-series data over six years for contamination levels of aflatoxin in daily shipments received from up to three sourcing regions at a high-volume maize processing plant in South Central India. The time series for aflatoxin levels from the parameterised model successfully replicated the overall profile, scale and variance of the historical aflatoxin datasets used for fitting and validation. We use the model to illustrate the dynamics of A. flavus growth and aflatoxin production during the pre- and post-harvest phases in different sourcing regions, in short-term predictions to inform decision making about sourcing supplies and to compare intervention strategies to reduce the risks of aflatoxin contamination.