Simulation tool for predicting classical epidemiology data for infectious diseases using a probabilistic model

A computational tool for predicting classical epidemiological data simulating the spread of an infectious disease is under development at Los Alamos National Laboratory. This tool performs spatial and temporal transport of the infectious disease using a Monte Carlo based model. The human transport method used in the tool draws much of its origin from Monte Carlo based approaches in radiation transport. Transmission and viral infectivity are represented by probability distribution functions derived from literature data. The tool allows for the grouping of populations based on sex, age, social group, geographic locations, and ethnicity. The methodology used in the simulations and a plan for model validation are described in detail in the paper. The tool is computationally intensive; however, the approach used here has several advantages over deterministic models including the ability to readily include numerous parameters and the ability to effectively simulate the early stages of an epidemic. The tool's other advantages and disadvantages are compared to the characteristics of previous modeling efforts. Upon completion, the tool will be used to aid in estimating the impact of central measures on disease spread, devising effective strategies to minimize the impact and spread of infectious diseases, increasing the database available for infectious diseases through an inverse problem analysis of known disease spreads, and understanding the development and spread of resistant strains.