Simulations for epidemiology and public health education

Abstract

Recent and potential outbreaks of infectious diseases are triggering interest in predicting epidemic dynamics on a national scale and testing the efficacies of different combinations of public health policies. Network-based simulations are proving their worth as tools for addressing epidemiology and public health issues considered too complex for field investigations and questionnaire analyses. Universities and research centres are therefore using network-based simulations as teaching tools for epidemiology and public health education students, but instructors are discovering that constructing appropriate network models and epidemic simulations are difficult tasks in terms of individual movement and contact patterns. In this paper we will describe (a) a four-category framework (based on demographic and geographic properties) to discuss ways of applying network-based simulation approaches to undergraduate students and novice researchers; (b) our experiences simulating the transmission dynamics of two infectious disease scenarios in Taiwan (HIV and influenza); (c) evaluation results indicating significant improvement in student knowledge of epidemic transmission dynamics and the efficacies of various public health policy suites; and (d) a geospatial modelling approach that integrates a national commuting network as well as multi-scale contact structures.

Keywords:

• network-based simulation
• epidemic dynamics
• public health policy
• HIV
• seasonal influenza

Acknowledgements

This work was supported in part by the Republic of China (ROC) National Science Council (NSC98-2314-B-182-043 and NSC98-2410-H-002-168-MY2), and Chang Gung Memorial Hospital (CMRPD260023). Tzai-Hung Wen also acknowledges administrative support from the Infectious Disease Research and Education Center, Department of Health, Executive Yuan, and National Taiwan University.

Notes

1 In this prediction simulation, we averaged 1000 independent experiments to obtain the mean value of error in the epidemic parameter space, then chose parameters and named the optimal parameters with minimal mean error. As shown in the figure, the prediction result corresponds to the best one (ie minimal error) among 30 simulations under the optimal parameters. The parameters did not change with time—that is, predictions are only valid for cases with no additional interventions.