Abstract
<title>Abstract</title> <p> Background Foodborne diseases (FBD) pose a significant public health and economic threat globally. In Saudi Arabia, underreporting and a lack of predictive surveillance techniques hinder efficient management of these outbreaks. Objective This study developed a new computational probabilistic model for FBD case estimation, which provide better insights into the incidence of these infections. Methods The proposed model adapted the conceptual principles of the WHO envelope approach into a probabilistic modelling framework. Historical surveillance data from Riyadh (2015–2018) and WHO Foodborne Disease Burden Epidemiology Reference Group estimates were combined to estimate the burden of <italic>Salmonella</italic> spp., <italic>Shigella</italic> spp., <italic>Staphylococcus aureus</italic> , and <italic>Bacillus cereus</italic> . Model performance was evaluated using epidemiological surveillance data together with mean absolute error (MAE), root mean square error (RMSE), bias factor (BF), and sensitivity analysis. Results The model demonstrated robust predictive performance, with prediction errors ≤ 11% for three of the four pathogens. Overall agreement between predicted and observed values was high, yielding a mean absolute error of 2.26 cases and a bias factor of 1.015. While predictions for <italic>Bacillus cereus</italic> were associated with greater uncertainty because of limited surveillance data, sensitivity analysis confirmed that the model remained stable and robust across plausible input variations. Conclusion The proposed probabilistic model provides a practical framework for estimating foodborne disease burden under limited surveillance conditions and may support evidence-based food safety decision-making. Further validation across additional regions and pathogens is needed. </p>