Marisa Eisenberg, PhD, MS
- Associate Professor, Epidemiology
- Associate Professor, Complex Systems
- Director, Center for the Study of Complex Systems
- Associate Professor, Mathematics
Marisa Eisenberg's research program is in mathematical epidemiology, and is focused on using and developing parameter estimation and identifiability techniques to model disease dynamics. Much of her research group's work is in modeling and analysis of infectious disease epidemiology, including examining COVID-19, environmentally driven diseases, and the dynamics of HPV and HPV-related cancers. Her collaborative work in this area also extends to developing tools, dashboards, and visualizations to understand infectious diseases (e.g. the mistartmap.info dashboard used for monitoring COVID-19). She also jointly works on several projects related to wastewater monitoring for a range of pathogens, including SARS-CoV-2. Her research blends mathematics, statistics, and epidemiology to inform optimal intervention strategies, improve forecasting, and understand transmission dynamics.
- PhD, Biomedical Engineering, University of California, Los Angeles, 2009
- MS, Biomedical Engineering, University of California, Los Angeles, 2007
- BS, Cybernetics, University of California, Los Angeles, 2003
Mathematical modeling, parameter identifiability and estimation, infectious diseases, COVID-19, environmentally-driven diseases, wastewater monitoring, systems science, public health technology
Eisenberg's work includes a range of collaborative projects with the state of Michigan focused on understanding the COVID-19 pandemic. These include the mistartmap.info dashboard, as well as a range of other tools (more information available at dataepi.org)
Eisenberg is also involved in several projects focused on wastewater surveillance of a range of pathogens (much of our public-facing data can be found at: https://um.wastewatermonitoring.dataepi.org/)
Many of Prof. Eisenberg's research group's projects involve developing systems models to understand infectious disease dynamics, explore alternative intervention approaches, and generate predictions or counterfactuals
From the more theoretical/methodological side, Eisenberg and her research group work on a wide range of parameter estimation, identifiability, and uncertainty quantification problems. This work includes developing mathematical and computational methods for understanding parameter identifiability.
Additionally, Eisenberg's research includes projects related to social network data and analysis, cancer (particularly HPV-related cancers), multi-scale modeling, and developing public health technology
Brouwer AF, Eisenberg JN, Pomeroy CD, Shulman LM, Hindiyeh M, Manor Y, Grotto I, Koopman
JS, Eisenberg MC. Epidemiology of the silent polio outbreak in Rahat, Israel, based on modeling of environmental
surveillance data. Proceedings of the National Academy of Sciences. 2018 Nov 6;115(45):E10625-33.
Zelner J, Eisenberg M. Rapid response modeling of SARS-CoV-2 transmission. Science. 2022 May 6;376(6593):579-80.
Havumaki J, Eisenberg JN, Mattison CP, Lopman BA, Ortega-Sanchez IR, Hall AJ, Hutton
DW, Eisenberg MC. Immunologic and epidemiologic drivers of norovirus transmission in daycare and school
outbreaks. Epidemiology. 2021 May 1;32(3):351-9.
Havumaki J, Meza R, Phares CR, Eisenberg MC. Comparing alternative cholera vaccination strategies in Maela refugee camp: using
a transmission model in public health practice. BMC infectious diseases. 2019 Dec;19(1):1-7.
Kao YH, Eisenberg MC. Practical unidentifiability of a simple vector-borne disease model: Implications for
parameter estimation and intervention assessment. Epidemics. 2018 Dec 1;25:89-100.
Eisenberg MC, Campredon LP, Brouwer AF, Walline HM, Marinelli BM, Lau YK, Thomas TB, Delinger
RL, Sullivan TS, Yost ML, Goudsmit CM. Dynamics and determinants of HPV infection: the Michigan HPV and Oropharyngeal Cancer
(M-HOC) study. BMJ open. 2018 Oct 1;8(10):e021618.
And for the full list of publications, please see: https://scholar.google.com/citations?user=RhyNk3EAAAAJandhl=enandoi=ao