Responding to and managing infectious disease outbreaks is a persistent challenge. With limited resources, maximizing efficiency and efficacy of these control efforts is essential. Here, I present three examples across an array of systems to demonstrate how we can make better use of management resources, especially in the face of uncertainty. I show that governance structure (i.e., the allocation of resources across geo-political units) can have a significant effect on the equity of epidemiological outcomes, demonstrate an approach for designing control strategies that leverage multiple interventions, and discuss how we can combine predictions from multiple models to better represent uncertainty for planning.
Emily Howerton is a Ph.D. candidate in the Department of Biology at Penn State University. She uses
mathematical models to design strategies for controlling infectious diseases in the face of uncertainty,
most recently focusing on the COVID-19 pandemic. For example, Emily has modeled the interaction
between diagnostic testing and non-pharmaceutical interventions to identify optimal combination
management strategies. Emily is also a member of COVID-19 Scenario Modeling Hub coordination team,
where she develops methods to capture uncertainty from multiple models in 6-month projections of
COVID-19 outcomes. In particular, she has adapted aggregation and evaluation methods from multiple
fields to accommodate the unique needs of infectious disease scenario projections.
Emily Howerton is a Ph.D. candidate in the Department of Biology at Penn State University. She uses
mathematical models to design strategies for controlling infectious diseases in the face of uncertainty,
most recently focusing on the COVID-19 pandemic. For example, Emily has modeled the interaction
between diagnostic testing and non-pharmaceutical interventions to identify optimal combination
management strategies. Emily is also a member of COVID-19 Scenario Modeling Hub coordination team,
where she develops methods to capture uncertainty from multiple models in 6-month projections of
COVID-19 outcomes. In particular, she has adapted aggregation and evaluation methods from multiple
fields to accommodate the unique needs of infectious disease scenario projections.
