The emerging new area referred to as computational science or science done on a computer adds a third dimension to the traditional methods of theoretical and experimental approaches. Counterparts to computational science such as computational linguistice, computational engineering and others arc beginning to take roots. Naturally, new research paths and opportunities in computational epidemiology must also be explored. One of the major challenges in epidemiologic research is the issue of how to realistically and effectively handle complex bioepidemiologic dynamics involving interactions between humans or animals, etiological agents and the multiple array of environmental and socioeconomic determinants which affect these populations. To understand the behavior of such complex biological systems, it is useful to devise computer based simulation models. Computational epidemiologic approaches now provide alternative avenues to classical laboratory and/or field experimental methods. Systems which may be impractical because they are too large, or, not feasible because the cost is too prohibitive can now be simulated realistically. In the past obtaining solutions to biomathematical equations with any degree of complexity was impossible. However, the availability of powerful computers now makes the quantitative analysis of such systems feasible and indeed practical. With this in mind our research at Tuskegee University has focused on: a) Epidemiologic modelling and expert systems, and, b) Hypertext/hypermedia based epidemiologic knowledge management. The case studies for our research involve the bioepidemiologic dynamics of two complex host-parasite systems of trypanosoma and schistosoma. The ultimate goal is to develop resources and methodologies based on computational technology to advance epidemiologic research. The paper will address the methodological issues and findings as well as questions related to configuring an appropriate research workstation for computational epidemiology. © 1990.
Habtemariam, T., Oryang, D., Gabreab, F., Robnett, V., & Trammell, G. (1990). Research in computational epidemiology. Mathematical and Computer Modelling, 14(C), 653–658. https://doi.org/10.1016/0895-7177(90)90263-M