Managing the Risk of COVID-19 Using Model Based Predictors: The Case of North Dakota.

OBJECTIVE: North Dakota (ND) had the highest coronavirus disease 2019 (COVID-19) case and mortality rate in the United States for nearly 2 mo. This study aims to compare 3 metrics ND used to guide public health action across its 53 counties. METHODS: Daily COVID-19 case and death totals in North Dakota were evaluated using data from the COVID-tracker website provided by the North Department of Health (NDDoH). It was reported as: active cases per 10,000, tests administered per 10,000, and test positivity rate (the North Dakota health metric). The COVID-19 Response press conferences provided data for the Governor's metric. The Harvard model used daily new cases per 100,000. A chi-squared test was used to compare differences in these 3 metrics on July 1, August 26, September 23, and November 13, 2020. RESULTS: On July 1, no significant difference between the metrics was found. By September 23, Harvard's health metric indicated critical risk while ND's health metric was moderate risk, and the Governor's metric was still low risk. CONCLUSIONS: ND's and the Governor's metric underrepresented the risk of the COVID-19 outbreak in North Dakota. The Harvard metric reflected North Dakota's increasing risk; it should be considered as a national standard in future pandemics. PUBLIC HEALTH IMPLICATIONS: Model-based predictors could guide policy-makers to effectively control spread of infectious disease; proactive models could reduce risk of disease as it progresses in vulnerable communities.

Evaluating the Association of Face Covering Mandates on COVID-19 Severity by State.

OBJECTIVE: During the COVID-19 pandemic in the United States, mitigation measures were implemented on a state-by-state basis. Governors were responsible for establishing interventions appropriate for their states and the timing of implementation. This paper evaluated the association between the presence and timing of a mask mandate and the severity of the COVID-19 epidemic by state. METHODS: The states were divided into 3 categories based on when the governors of each state implemented a mask mandate: Early (mask mandate implemented between March 2020 and June 2020), Late (July 2020-December 2020), and Never (no mask mandate implemented). The rates of hospitalizations and mortality (per 100 000) were assessed at the different time points during the pandemic across these categories from March to December 2020. RESULTS: The mortality rates across all 3 groups were observed to be highest in the beginning and toward the end of the pandemic in 2020 with the peak observed in the Early group between April and May 2020. Also, the rates of hospitalization increased steadily across all groups. The Early mask group was comprised of 86.7% and 13.3% states with Democratic and Republican governors respectively, and no states in the Never category had Democratic governors. CONCLUSION: These results support the benefit of implementing a mask mandate to minimize the impact of the COVID-19 pandemic and the role of political affiliation of governors on that impact.