Mitigation strategies and compliance in the COVID-19 fight; how much compliance is enough?

• Published in: PloS one 2021-08, Vol.16 (8), p.e0239352-e0239352
• Main Authors: Mukerjee, Swati, Chow, Clifton M, Li, Mingfei
• Format: Article
• Language: English
• Subjects: Administrative Personnel; Basic Reproduction Number - legislation & jurisprudence; Basic Reproduction Number - prevention & control; Bears; Communicable Disease Control - legislation & jurisprudence; Communicable Disease Control - methods; Contact tracing; Control; Coronaviruses; COVID-19; COVID-19 - epidemiology; COVID-19 - prevention & control; Disease transmission; Epidemic models; Epidemics; Epidemiology; Evaluation; Humans; Infections; Investigations; Medicine and Health Sciences; Mitigation; Pandemics; Pandemics - legislation & jurisprudence; Pandemics - prevention & control; People and places; Policies; Prevention; Public health; Public health administration; SARS-CoV-2 - isolation & purification; School closures; Social Sciences; Software; United States; United States - epidemiology
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0239352

The U.S. with only 4% of the world's population, bears a disproportionate share of infections in the COVID-19 pandemic. To understand this puzzle, we investigate how mitigation strategies and compliance can work together (or in opposition) to reduce (or increase) the spread of COVID-19 infection. Building on the Oxford index, we create state-specific stringency indices tailored to U.S. conditions, to measure the degree of strictness of public mitigation measures. A modified time-varying SEIRD model, incorporating this Stringency Index as well as a Compliance Indicator is then estimated with daily data for a sample of 6 U.S. states: New York, New Hampshire, New Mexico, Colorado, Texas, and Arizona. We provide a simple visual policy tool to evaluate the various combinations of mitigation policies and compliance that can reduce the basic reproduction number to less than one, the acknowledged threshold in the epidemiological literature to control the pandemic. Understanding of this relationship by both the public and policy makers is key to controlling the pandemic. This tool has the potential to be used in a real-time, dynamic fashion for flexible policy options. Our methodology can be applied to other countries and has the potential to be extended to other epidemiological models as well. With this first step in attempting to quantify the factors that go into the "black box" of the transmission factor β, we hope that our work will stimulate further research in the dual role of mitigation policies and compliance.