# A decision tool to inform the optimal use of non-pharmaceutical interventions during the COVID-19 pandemic

> **NIH NIH R21** · YALE UNIVERSITY · 2024 · $235,692

## Abstract

PROJECT SUMMARY/ABSTRACT
As the prospect for the elimination of COVID-19 in the near future remains uncertain, non-pharmaceutical
interventions (NPIs) such as limiting social gatherings, quarantine after exposure to the virus, and school
closure, will continue to play important roles in mitigating the morbidity and mortality associated with the
pandemic. Since these interventions impose immense economic, social, and health-related costs, their use
should be recommended only when epidemic control benefits outweigh their adverse consequences. Our
overall objective in this proposal is to develop an analytical decision tool to optimize the use of NPIs based on
latest information related to the local epidemiology of COVID-19, the effectiveness of different NPIs, and the
population’s stated disutility associated with these interventions. This decision tool is structured to provide a
transparent mechanism to communicate the rationale for the current policy regarding the use of NPIs and the
conditions under which the policy would change. To develop our decision tools, this proposal has three specific
aims: 1) to develop state-level decision models that identify the optimal combination of NPIs, in real-time, and
based on the projected loss in the quality-adjusted life-years (QALYs) and the disutility borne by the population
under various combinations of NPIs under various combinations of NPIs; 2) to design, conduct, and analyze
discrete-choice experiments to estimate the disutility weights of different NPIs as borne by population members
due to social, economic, and health consequences of these programs; and 3) to estimate the societal tolerance
for loss in QALYs due to existing infectious diseases without triggering NPIs. This tolerance threshold can be
estimated using historical data related to past pandemic and seasonal influenza and will serve as a benchmark
to decide when the burden of COVID-19 is low enough to lift all NPIs, at least for a short term. The research
proposed in this project is innovative as it develops a novel, principled approach to consolidate real-time data
from three different sources to optimize the use of NPIs: 1) COVID-19 cases, hospitalizations, and deaths as
projected by existing and new predictive models of COVID-19 pandemic, 2) effectiveness of various NPIs in
breaking the transmission of SARS-CoV-2, and 3) disutility weights of NPIs directly elicited from target
populations. The proposed research is significant because it meets the critical needs of policymakers to
identify evidence-based and real-time recommendations regarding the efficient use of NPIs to contain the
burden of COVID-19. The methods and decision tools developed as part of this project could also be used in
responding to other existing and future infectious threats where NPIs are employed.

## Key facts

- **NIH application ID:** 10893484
- **Project number:** 5R21AI173746-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** THEODORE H COHEN
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $235,692
- **Award type:** 5
- **Project period:** 2023-07-25 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10893484

## Citation

> US National Institutes of Health, RePORTER application 10893484, A decision tool to inform the optimal use of non-pharmaceutical interventions during the COVID-19 pandemic (5R21AI173746-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10893484. Licensed CC0.

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