MBI Emphasis Semester on Infectious Diseases: Data, Modeling, Decisions Spring 2018

Organizing Committee


Carlos Castillo-Chavez
MCMSC, Arizona State University
Carolyn Cho
Quantitative Pharmacology & Pharmacometrics, Merck, Sharp & Dohme
John Drake
Odum School of Ecology, University of Georgia
Alan Perelson
Theoretical Biology and Biophysics Group, Los Alamos National Laboratory
Larry Schlesinger
Texas Biomedical Research Institute
Joe Tien
Department of Mathematics, The Ohio State University
Pauline van den Driessche
Mathematics and Statistics, University of Victoria

The effectiveness of improved sanitation, antibiotics, and vaccination programs created a confidence in the 1960s that infectious diseases would soon be eliminated. As a result, chronic diseases such as cardiovascular disease and cancer started to receive more attention in the United States and industrialized countries. But infectious diseases have persisted and have continued to be the major causes of suffering and mortality both in developing and industrialized countries. As the infectious disease agents adapt and evolve, new infectious diseases have emerged (dengue fever in 1945, HIV in 1981, hepatitis C in 1989, hepatitis E in 1990, SARS in 2002, novel H1N1 influenza strain in 2009) and some existing diseases have recently reemerged (Zika). Antibiotic-resistant strains of tuberculosis, pneumonia, and gonorrhea have evolved and are becoming of major concern today in many parts of the world. Malaria, dengue, and yellow fever have reemerged and are spreading into new regions as climate changes occur. Diseases such as plague, cholera, and hemorrhagic fevers (Ebola, Lassa, Marburg, etc.) continue to erupt and occasionally reach dangerous thresholds of global pandemics, with the Ebola outbreak of 2014 originating in West Africa providing a recent example.

The emerging and reemerging diseases have led to a revived interest in infectious diseases, with mathematical and computational models becoming essential tools in analyzing their spread and suggesting possible mechanisms for control. Indeed, it is widely believed that better understanding of the transmission characteristics of infectious diseases at various temporal and physical scales, for instance in host-pathogen interactions, host tissues, interactions between individuals, communities, regions, and countries will lead to better approaches to decreasing the transmission of such diseases. This understanding can be greatly enhanced by the mathematical modeling effort which allows to clarify assumptions, variables, and parameters and to provide conceptual results such as thresholds, basic reproduction numbers or contact and replacement numbers. At the level of host-pathogen interactions, the mathematical models may answer questions about specific behavior of the immune systems relevant to developing effective vaccines. At the levels of individuals, the complex data from social networks may be used to build models predictive of human behavior in the face of global pandemic events. At the level of populations, the models of environmental changes may help us better understand the challenges associated with habitat loss and changing climate patterns. In order to integrate the diverse data at different scales, the multiscale mathematical models can be designed to create testable hypotheses leading to new investigational studies, identify and share gaps in knowledge requiring further research, uncover biological mechanisms, or make predictions about clinical outcome or intervention effects. These models can draw on a variety of modern information resources including relevant physical, environmental, clinical and population data. To address these numerous challenges, the scientific program at MBI will consist of four workshops focusing at the crucial areas of modeling modern infectious diseasesborn.

Events 2018


February 19, 2018 - February 23, 2018
March 05, 2018 - March 09, 2018
March 26, 2018 - March 30, 2018
April 23, 2018 - April 27, 2018
May 14, 2018 - May 18, 2018
June 11, 2018 - June 15, 2018