Abstract
The Alternaria disease complex has emerged as a major concern in tomato cropping systems, posing significant threats to both tomato production and human health due to the co-occurrence of multiple species on the same plants and the production of mycotoxins. Predictive tools are instrumental in advancing food security and mitigating risks to food safety. Therefore, this study aimed to synthesize current knowledge on Alternaria spp. affecting tomato and develop a mechanistic, weather-driven model capable of predicting both epidemics and mycotoxin contamination. A systematic literature review was conducted to compile quantitative information on the ecology, biology, epidemiology, and mycotoxin production of Alternaria species associated with the disease complex. These data were used to construct a logical and mathematical framework describing interactions among major Alternaria species, tomato, and environmental drivers. The resulting model comprises interconnected compartments representing: (i) conidia production from overwintering and in-season sources, (ii) conidia dispersal, (iii) infection by conidia, (iv) symptom development, and (v) mycotoxin (alternariol, alternariol monomethyl ether, and tenuazonic acid) production and accumulation within tomato fruit tissues. Model parameterization was performed for three major species affecting tomato (A. alternata, A. solani, and A. tenuissima). Model validation using eight epidemics recorded in Italy, India, and Canada demonstrated strong agreement between predicted and observed outcomes, with a high concordance (CCC = 0.98) and a low average prediction error (RMSE = 0.069). Results suggest that the proposed framework may capture the complexity of the Alternaria-tomato pathosystem and could serve as the basis for a tool to support more informed and sustainable disease management strategies; however, its applicability to mycotoxin risk management remains to be validated.