Projecting COVID-19 Cases and Subsequent Hospital Burden in Ohio

Abstract

As the Coronavirus 2019 (COVID-19) disease 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 Dynamic 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 has been made available publicly.

Highlights: We present a novel statistical approach called Dynamic Survival Analysis (DSA) to model an epidemic curve with incomplete data. The DSA approach is advantageous over standard statistical methods primarily because it does not require prior knowledge of the size of the susceptible population, the overall prevalence of the disease, and also the shape of the epidemic curve.The principal motivation behind the study was to obtain predictions of case counts of COVID-19 and the resulting hospital burden in the state of Ohio during the early phase of the pandemic.The proposed methodology was applied to the COVID-19 incidence data in the state of Ohio to support the Ohio Department of Health (ODH) and the Ohio Hospital Association (OHA) with predictions of hospital burden in each of the Hospital Catchment Areas (HCAs) of the state.

Figure 4:

Estimated density of infection times in the four segments of the epidemic.

Figure 5:

Posterior densities of the fitted parameters in the first two segments of the pandemic.

Figure 6:

Posterior densities of the fitted parameters in the last two segments of the pandemic.

Figure 7:

Trace plots of the fitted parameters in the four segments.

Figure 1:

Proportion of the population over the age of 55 in A) counties and B) hospital catchment areas

Figure 2:

Estimates of hospital burden mapped to hospital catchment areas for 12 days during the pandemic

Figure 3:

Predictions based on the fitted model vs actual daily incidence in Ohio as reported by the Ohio Department of Health. We have assumed three change points here corresponding to four density segments. Day zero corresponds to March 1, 2020. The first change point is on March 17, 2020. The second change point is on June 1, 2020 after which the state observed another phase of exponential growth. The third change point is on October 1, 2021, which marks the beginning of second wave characterized by a severe exponential growth phase. The broader (outer) confidence bound corresponds to the variance adjusted trajectories.