Projecting COVID-19 cases and hospital burden in Ohio.

As the Coronavirus 2019 disease (COVID-19) started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at The Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: (1) A Dynamical Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. (2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology is also made publicly available. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

A Bayesian Spatiotemporal Nowcasting Model for Public Health Decision-Making and Surveillance.

As coronavirus disease 2019 (COVID-19) spread through the United States in 2020, states began to set up alert systems to inform policy decisions and serve as risk communication tools for the general public. Many of these systems included indicators based on an assessment of trends in numbers of reported cases. However, when cases are indexed by date of disease onset, reporting delays complicate the interpretation of trends. Despite a foundation of statistical literature with which to address this problem, these methods have not been widely applied in practice. In this paper, we develop a Bayesian spatiotemporal nowcasting model for assessing trends in county-level COVID-19 cases in Ohio. We compare the performance of our model with the approach used in Ohio and the approach included in decision support materials from the Centers for Disease Control and Prevention. We demonstrate gains in performance while still retaining interpretability using our model. In addition, we are able to fully account for uncertainty in both the time series of cases and the reporting process. While we cannot eliminate all of the uncertainty in public health surveillance and subsequent decision-making, we must use approaches that embrace these challenges and deliver more accurate and honest assessments to policy-makers.

Quantifying the Population-Level Effect of the COVID-19 Mass Vaccination Campaign in Israel: A Modeling Study.

Background: Estimating real-world vaccine effectiveness is challenging as a variety of population factors can impact vaccine effectiveness. We aimed to assess the population-level reduction in cumulative severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases, hospitalizations, and mortality due to the BNT162b2 mRNA coronavirus disease 2019 (COVID-19) vaccination campaign in Israel during January-February 2021. Methods: A susceptible-infected-recovered/removed (SIR) model and a Dynamic Survival Analysis (DSA) statistical approach were used. Daily counts of individuals who tested positive and of vaccine doses administered, obtained from the Israeli Ministry of Health, were used to calibrate the model. The model was parameterized using values derived from a previous phase of the pandemic during which similar lockdown and other preventive measures were implemented in order to take into account the effect of these prevention measures on COVID-19 spread. Results: Our model predicted for the total population a reduction of 648 585 SARS-CoV-2 cases (75% confidence interval [CI], 25 877-1 396 963) during the first 2 months of the vaccination campaign. The number of averted hospitalizations for moderate to severe conditions was 16 101 (75% CI, 2010-33 035), and reduction of death was estimated at 5123 (75% CI, 388-10 815) fatalities. Among children aged 0-19 years, we estimated a reduction of 163 436 (75% CI, 0-433 233) SARS-CoV-2 cases, which we consider to be an indirect effect of the vaccine. Conclusions: Our results suggest that the rapid vaccination campaign prevented hundreds of thousands of new cases as well as thousands of hospitalizations and fatalities and has probably averted a major health care crisis.

Prevalence of current and past COVID-19 in Ohio adults.

Purpose To estimate the prevalence of current and past COVID-19 in Ohio adults. Methods We used stratified, probability-proportionate-to-size cluster sampling. During July 2020, we enrolled 727 randomly-sampled adult English- and Spanish-speaking participants through a household survey. Participants provided nasopharyngeal swabs and blood samples to detect current and past COVID-19. We used Bayesian latent class models with multilevel regression and poststratification to calculate the adjusted prevalence of current and past COVID-19. We accounted for the potential effects of non-ignorable non-response bias. Results The estimated statewide prevalence of current COVID-19 was 0.9% (95% credible interval: 0.1%-2.0%), corresponding to ∼85,000 prevalent infections (95% credible interval: 6,300-177,000) in Ohio adults during the study period. The estimated statewide prevalence of past COVID-19 was 1.3% (95% credible interval: 0.2%-2.7%), corresponding to ∼118,000 Ohio adults (95% credible interval: 22,000-240,000). Estimates did not change meaningfully due to non-response bias. Conclusions Total COVID-19 cases in Ohio in July 2020 were approximately 3.5 times as high as diagnosed cases. The lack of broad COVID-19 screening in the United States early in the pandemic resulted in a paucity of population-representative prevalence data, limiting the ability to measure the effects of statewide control efforts.

The impact of the national stay-at-home order on emergency department visits for suspected opioid overdose during the first wave of the COVID-19 pandemic.

BACKGROUND: Although national syndromic surveillance data reported declines in emergency department (ED) visits after the declaration of the national stay-at-home order for COVID-19, little is known whether these declines were observed for suspected opioid overdose. METHODS: This interrupted time series study used syndromic surveillance data from four states participating in the HEALing Communities Study: Kentucky, Massachusetts, New York, and Ohio. All ED encounters for suspected opioid overdose (n = 48,301) occurring during the first 31 weeks of 2020 were included. We examined the impact of the national public health emergency for COVID-19 (declared on March 14, 2020) on trends in ED encounters for suspected opioid overdose. RESULTS: Three of four states (Massachusetts, New York and Ohio) experienced a statistically significant immediate decline in the rate of ED encounters for suspected opioid overdose (per 100,000) after the nationwide public health emergency declaration (MA: -0.99; 95 % CI: -1.75, -0.24; NY: -0.10; 95 % CI, -0.20, 0.0; OH: -0.33, 95 % CI: -0.58, -0.07). After this date, Ohio and Kentucky experienced a sustained rate of increase for a 13-week period. New York experienced a decrease in the rate of ED encounters for a 10-week period, after which the rate began to increase. In Massachusetts after a significant immediate decline in the rate of ED encounters, there was no significant difference in the rate of change for a 6-week period, followed by an immediate increase in the ED rate to higher than pre-COVID levels. CONCLUSIONS: The heterogeneity in the trends in ED encounters between the four sites show that the national stay-at-home order had a differential impact on opioid overdose ED presentation in each state.