Adaptive COVID-19 Mitigation Strategies: Tradeoffs between Trigger Thresholds, Response Timing, and Effectiveness

Erinn C Sanstead; Zongbo Li; Shannon B McKearnan; Szu-Yu Zoe Kao; Pamela J Mink; Alisha Baines Simon; Karen M Kuntz; Stefan Gildemeister; Eva A Enns

doi:10.1177/23814683231202716

Adaptive COVID-19 Mitigation Strategies: Tradeoffs between Trigger Thresholds, Response Timing, and Effectiveness

MDM Policy Pract. 2023 Oct 11;8(2):23814683231202716. doi: 10.1177/23814683231202716. eCollection 2023 Jul-Dec.

Authors

Erinn C Sanstead¹, Zongbo Li², Shannon B McKearnan³, Szu-Yu Zoe Kao², Pamela J Mink¹, Alisha Baines Simon¹, Karen M Kuntz², Stefan Gildemeister¹, Eva A Enns²

Affiliations

¹ Division of Health Policy, Minnesota Department of Health, State of Minnesota, St. Paul, MN, USA.
² Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN, USA.
³ Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN, USA.

Abstract

Background. To support proactive decision making during the COVID-19 pandemic, mathematical models have been leveraged to identify surveillance indicator thresholds at which strengthening nonpharmaceutical interventions (NPIs) is necessary to protect health care capacity. Understanding tradeoffs between different adaptive COVID-19 response components is important when designing strategies that balance public preference and public health goals. Methods. We considered 3 components of an adaptive COVID-19 response: 1) the threshold at which to implement the NPI, 2) the time needed to implement the NPI, and 3) the effectiveness of the NPI. Using a compartmental model of SARS-CoV-2 transmission calibrated to Minnesota state data, we evaluated different adaptive policies in terms of the peak number of hospitalizations and the time spent with the NPI in force. Scenarios were compared with a reference strategy, in which an NPI with an 80% contact reduction was triggered when new weekly hospitalizations surpassed 8 per 100,000 population, with a 7-day implementation period. Assumptions were varied in sensitivity analysis. Results. All adaptive response scenarios substantially reduced peak hospitalizations relative to no response. Among adaptive response scenarios, slower NPI implementation resulted in somewhat higher peak hospitalization and a longer time spent under the NPIs than the reference scenario. A stronger NPI response resulted in slightly less time with the NPIs in place and smaller hospitalization peak. A higher trigger threshold resulted in greater peak hospitalizations with little reduction in the length of time under the NPIs. Conclusions. An adaptive NPI response can substantially reduce infection circulation and prevent health care capacity from being exceeded. However, population preferences as well as the feasibility and timeliness of compliance with reenacting NPIs should inform response design.

Highlights: This study uses a mathematical model to compare different adaptive nonpharmaceutical intervention (NPI) strategies for COVID-19 management across 3 dimensions: threshold when the NPI should be implemented, time it takes to implement the NPI, and the effectiveness of the NPI.All adaptive NPI response scenarios considered substantially reduced peak hospitalizations compared with no response.Slower NPI implementation results in a somewhat higher peak hospitalization and longer time spent with the NPI in place but may make an adaptive strategy more feasible by allowing the population sufficient time to prepare for changing restrictions.A stronger, more effective NPI response results in a modest reduction in the time spent under the NPIs and slightly lower peak hospitalizations.A higher threshold for triggering the NPI delays the time at which the NPI starts but results in a higher peak hospitalization and does not substantially reduce the time the NPI remains in force.

Keywords: COVID-19; adaptive interventions; compartmental modeling; non-pharmaceutical interventions.