Please join us on Thursday, February 12 from 4:15pm to 5:15pm in the LETS Lab for an upcoming, hybrid, presentation by Thomas McAndrew, titled-Why Data and Expertise Matter: Infectious Disease Modeling for Public Health Decision Making. 

This event is part of Love Data Week. Love Data Week is an international celebration of all-things data, connecting over 200 institutions. This year’s theme is “Where’s the Data?”, focusing on how data travels from collection through storage and preservation

Registration link = https://lehigh.zoom.us/meeting/register/W00L5o_7QE6j7DuwKWTj9g

Abstract:

Presidential actions on Jan 20, 2025, including executive orders, delayed access to or led to the removal of crucial public health data sources in the USA. Starting February 14, 2025, the United States Government has—via the cancellation of COVID-era grants, policy shifts, and rebudgeting—reduced staff positions related to public health services. Here, we present two recent projects that have shown the value that public health officials, and the data that they steward, add to infectious disease modeling for public health decision making. The first project compared two mechanistic models that forecast US national hospitalizations due to influenza: a data-rich model that inputted past hospitalization data plus ten government datasets and a data-poor model that used only past hospitalization data. The data-rich model generated reliable forecasts useful for public health decision making, while the data-poor model produced unreliable and uncertain predictions. The second project asked 114 experts in public health, epidemiology, and mathematical modeling to predict two influenza targets of interest: (1) “Assign a probability to this upcoming season being characterized as a H3 season (i.e. a season with worse symptoms) in Pennsylvania” and (2) “What will be the peak number of influenza hospitalizations in PA for the 2024/25 season?”. Compared to a computational model, experts better predicted H3 dominance and made similar predictions of peak hospitalizations. Our findings highlight the critical role of public health data in service of modeling efforts and underline the value that expertise, as a data source, plays in evidence-based decision making.