A Novel Framework for Modeling Person-to-Person Transmission of Respiratory Diseases.

From the beginning of the COVID-19 pandemic, researchers assessed the impact of the disease in terms of loss of life, medical load, economic damage, and other key metrics of resiliency and consequence mitigation; these studies sought to parametrize the critical components of a disease transmission model and the resulting analyses were informative but often lacked critical parameters or a discussion of parameter sensitivities. Using SARS-CoV-2 as a case study, we present a robust modeling framework that considers disease transmissibility from the source through transport and dispersion and infectivity. The framework is designed to work across a range of particle sizes and estimate the generation rate, environmental fate, deposited dose, and infection, allowing for end-to-end analysis that can be transitioned to individual and population health models. In this paper, we perform sensitivity analysis on the model framework to demonstrate how it can be used to advance and prioritize research efforts by highlighting critical parameters for further analyses.

Operational Considerations in Global Health Modeling.

Epidemiological modeling and simulation can contribute cooperatively across multifaceted areas of biosurveillance systems. These efforts can be used to support real-time decision-making during public health emergencies and response operations. Robust epidemiological modeling and simulation tools are crucial to informing risk assessment, risk management, and other biosurveillance processes. The Defense Threat Reduction Agency (DTRA) has sponsored the development of numerous modeling and decision support tools to address questions of operational relevance in response to emerging epidemics and pandemics. These tools were used during the ongoing COVID-19 pandemic and the Ebola outbreaks in West Africa and the Democratic Republic of the Congo. This perspective discusses examples of the considerations DTRA has made when employing epidemiological modeling to inform on public health crises and highlights some of the key lessons learned. Future considerations for researchers developing epidemiological modeling tools to support biosurveillance and public health operations are recommended.