Epidemiological Modeling

Diseases like malaria, tuberculosis, polio or HIV effect communities differently across the globe; in some countries the disease may be completely eradicated and in others it may be an epidemic. Different geographic locations vary in terms of the burden of disease, patterns of transmission, and which public health strategies are most applicable. For example: malaria has extensive geographic heterogeneity in transmission intensity, transmission patterns, and mosquito species ecology, behavior, and health.  Systems which limit the applicability of a one-size-fits-all elimination policy.

As a part of IV’s Global Good effort, the epidemiological modeling (EMOD) program at Intellectual Ventures Lab develops detailed, geographically-specific, and mechanistic stochastic simulations of disease transmission simulations through the use of extensive and complex software modeling. EMOD will help enable broad accessibility of modeling and quantitative analysis tools to achieve acceptance and utilization of data-driven computer models as decision making tools in the eradication and control of infectious diseases.

The goal of the EMOD program is to determine the combination of health policies and intervention strategies that can lead to disease eradication. EMOD’s model calculates how diseases may spread in particular areas and is used to analyze the effects of current and future health policies and intervention strategies. The model supports infectious disease campaign planning, data gathering, new product development and policy decisions for four generic transmission types: vector-borne, water-borne, airborne, and sexually transmitted.

The EMOD team is composed of research scientists and software professionals who focus on creating powerful and innovative disease modeling and data analysis tools to help researchers and policy makers understand diseases, their causes, the way they spread, and the best types of interventions to use for the particular situation faced. EMOD partners with selected universities, NGOs, government ministries, and other research and public health institutions focused on researching new ways to understand and combat diseases both locally and globally.

For more information on EMOD’s work, please visit EMODGlobalHealth.com.

Published Papers

Bayati BS, Eckhoff, PA (2012) Influence of high-order nonlinear fluctuations in the multivariate susceptible-infectious-recovered master equation. Physical Review E 86, 062103 doi:10.1103/PhysRevE.86.062103

Bershteyn A, Klein DJ, Wenger E, Eckhoff P (2012) Description of the EMOD-HIV Model v0.7 arXiv:1206.3720 [q-bio.QM]

Eaton JW, Johnson LF, Salomon JA, Bärnighausen T, Bendavid E, et al. (2012) HIV Treatment as Prevention: Systematic Comparison of Mathematical Models of the Potential Impact of Antiretroviral Therapy on HIV Incidence in South Africa. PLoS Med 9(7):e1001245. doi:10.1371/journal.pmed.1001245

Eckhoff P (2011) A malaria transmission-directed model of mosquito life cycle and ecology. Malaria Journal 10:303. doi:10.1186/1475-2875-10-303

Eckhoff P (2012) Malaria parasite diversity and transmission intensity affect development of parasitological immunity in a mathematical model. Malaria Journal 11:419. doi:10.1186/1475-2875-11-419

Eckhoff P (2012) P. falciparum Infection Durations and Infectiousness Are Shaped by Antigenic Variation and Innate and Adaptive Host Immunity in a Mathematical Model. PLoS ONE 7(9):e44950. doi:10.1371/journal.pone.0044950

The HIV Modelling Consortium Treatment as Prevention Editorial Writing Group (2012) HIV Treatment as Prevention: Models, Data, and Questions—Towards Evidence-Based Decision-Making. PLoS Med 9(7):e1001259. doi:10.1371/journal.pmed.1001259

The malERA Consultative Group on Modeling (2011) A Research Agenda for Malaria Eradication: Modeling. PLoS Med 8(1):e1000403. doi:10.1371/journal.pmed.1000403