Spatiotemporal transmission dynamics of the COVID-19 pandemic and its impact on critical healthcare capacity

Abstract

The role of geospatial disparities in the dynamics of the COVID-19 pandemic is poorly understood. We developed a spatially-explicit mathematical model to simulate transmission dynamics of COVID-19 disease infection in relation with the uneven distribution of the healthcare capacity in Ohio, U.S. The results showed substantial spatial variation in the spread of the disease, with localized areas showing marked differences in disease attack rates. Higher COVID-19 attack rates experienced in some highly connected and urbanized areas (274 cases per 100,000 people) could substantially impact the critical health care response of these areas regardless of their potentially high healthcare capacity compared to more rural and less connected counterparts (85 cases per 100,000). Accounting for the spatially uneven disease diffusion linked to the geographical distribution of the critical care resources is essential in designing effective prevention and control programmes aimed at reducing the impact of COVID-19 pandemic.

Keywords: COVID-19; Critical healthcare capacity; Spatial epidemiology; Spatially-explicit mathematical model; Transport connectivity.

Fig. 1

Temporal dynamics of the cumulative number of COVID-19 cases (A), hospitalizations (B), ICU admissions (C), and deaths (D). Red lines illustrate model estimations, with their corresponding 95% credible interval (grey areas). Blue marks illustrate data values. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2

Map in the left illustrates the distribution of the counties in the different spatial risk groups, counties in Group 1 are illustrated on red color, Group 2 in dark pink, Group 3 in blue, and Group 4 in grey. Figure in the right illustrate the temporal dynamics of the cumulative number of COVID-19 confirmed cases in each of the different spatial risk groups. Maps were created using ArcGIS® by ESRI version 10.5 (http://www.esri.com) (ESRI, 2004). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3

Spatiotemporal dynamics of the cumulative number of COVID-19 cases estimated from the spatial adjusted and non-spatial adjusted models. Maps were created using ArcGIS® by ESRI version 10.5 (http://www.esri.com) (ESRI, 2004).

Fig. 4

Spatiotemporal dynamics of the projected proportion of ICU beds occupied under different scenarios of relaxation of the non-pharmaceutical interventions in Ohio. Maps were created using ArcGIS® by ESRI version 10.3 (http://www.esri.com) (ESRI, 2004).