Data generation and modeling during COVID-19: utility, barriers, and priorities for future investments in public health response

Overview

This study examined the perceived utility, operational barriers, and investment priorities for data-generation and infectious disease modeling infrastructure during the COVID-19 pandemic response in the United States. The research synthesized perspectives from 112 respondents across data collection, epidemiological modeling, and policy implementation roles to identify systemic gaps and opportunities for strengthening public health information systems.

Methods and approach

A survey instrument was administered to 112 individuals directly engaged in COVID-19 response activities in the United States. The sample included representatives from data collection operations, epidemiological modeling teams, and end-user organizations responsible for translating evidence into policy decisions. Respondents were queried on the utility of different data-driven tools, the magnitude and nature of implementation challenges, and their assessments of the most promising investment opportunities for enhanced future pandemic preparedness.

Key Findings

Respondents demonstrated broad consensus regarding the value of data infrastructure, epidemiological models, and academic-practitioner collaborations for informing response decisions. Data availability and quality emerged as both the most significant operational constraint and the highest-priority domain for future investment. Specific needs included increased granularity of data collection, expanded diversity of data types, and improvements in data completeness and accuracy. Insufficient human resources, particularly within public health institutions, constituted the second major challenge. Academic institutions contributed substantively to response efforts despite structural incentives that frequently misaligned with real-time operational demands. Translational barriers, encompassing deficiencies in science communication and navigation of political influences, were identified as persistent obstacles to effective evidence integration in policy processes.

Implications

The findings establish empirical support for sustained investment in data collection infrastructure and epidemiological modeling capacity as core components of pandemic preparedness. Data quality and accessibility should be designated as primary targets for institutional and financial resources, with particular emphasis on enabling granular, timely, and multifaceted data generation systems. Public health workforce expansion, particularly at local and state levels, represents a critical enabling condition for operational effectiveness during public health emergencies.