Skip to Content

Biological Models of Influenza A Virus

PI: Gilles Clermont, MD

Co- Investigators: G. Bard Ermentrout, PhD; Ted Ross, MD; David Swigon, PhD

Funding: NIH R01GM083602 (Project period: 8/1/2007 – 7/31/2012)

Every year influenza virus poses a major threat to the health of US and world population, causing economical and logistical burden on health systems in many countries and potentially threatening to develop into a pandemic. Current large-scale models of Influenza A infection focus on disease dissemination and the evaluation of potential containment strategies such as vaccination and distribution of antibiotics. Such models could greatly improve adequate preparedness in the event of the emergence of a pandemic strain of Influenza A and guide the development of an effective response of the health care system to such a threat. However, the accuracy of such models is plagued by insufficient epidemiological data to adequately describe animal-to-human and human-to-human contact patterns and by the fact that these large-scale models have a relatively unsophisticated representation of the host-pathogen interaction within individual hosts, and make broad distribution assumptions on incubation time, infectivity, and disease severity, all important factors in disease dissemination.

Our main goal is to address the second of those limitations by developing models of Influenza A infection dynamics in a single host, that will greatly refine existing description of host-pathogen interaction and explore novel quantitative methods to quantify uncertainty in model predictions, including parameter estimation, stochasticity and individual predictions. Such models would not only provide biological underpinning for the current distribution assumptions, but more importantly, would individualize those distributions to host-specific factors such as age, immune status including vaccination status and existing respiratory disease and to pathogen specific factors such as intensity of exposure and virulence. Importantly, biological models could accept information from large agent-based models on individual agents and provide time-dependent information on relevant determinants of dissemination in this individual (agent) that can in turn be used by a large-scale simulation. Therefore, the proposed effort is complementary to ongoing efforts, and based on several years of work from an interdisciplinary research team composed of clinicians, virologists and mathematicians on modeling inflammation, influenza biology and acute lung injury. In a broader perspective, many of the specific models and methods developed within the framework of this proposal will be applicable to the investigation of acute lung injury beyond the specific context of Influenza virus.

View results on NIH RePORTER