Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model

Abstract

The global COVID-19 pandemic has taken a heavy toll on health, social, and economic costs since the end of 2019. Predicting the spread of a pandemic is essential to developing effective intervention policies. Since the beginning of this pandemic, many models have been developed to predict its pathways. However, the majority of these models assume homogeneous dynamics over the geographic space, while the pandemic exhibits substantial spatial heterogeneity. In addition, spatial interaction among territorial entities and variations in their magnitude impact the pandemic dynamics. In this study, we used a spatial extension of the SEIR-type epidemiological model to simulate and predict the 4-week number of COVID-19 cases in the Charlotte–Concord–Gastonia Metropolitan Statistical Area (MSA), USA. We incorporated a variety of covariates, including mobility, pharmaceutical, and non-pharmaceutical interventions, demographics, and weather data to improve the model’s predictive performance. We predicted the number of COVID-19 cases for up to four weeks in the 10 counties of the studied MSA simultaneously over the time period 29 March 2020 to 13 March 2021, and compared the results with the reported number of cases using the root-mean-squared error (RMSE) metric. Our results highlight the importance of spatial heterogeneity and spatial interactions among locations in COVID-19 pandemic modeling.

Spatial Interaction Analysis of Infectious Disease Import and Export Between Regions

“…Simulating infectious disease transmission with the SEIR model deepens the understanding of disease dynamics. Compartment models have been useful in modeling cases of H1N1 [33], Covid-19 [30,34], and measles [35] in the United States. Prior studies generally applied a standard SEIR model, which adopts a constant basic reproduction number.…”

“…Levin et al investigated the patterns of Covid-19 transmission, including short-term travel between counties in Minnesota with a modified SEIR model [29]. Hatami et al studied the spatio-temporal dynamics of Covid-19 in 10 counties in the Charlotte–Concord–Gastonia Metropolitan Statistical Area [30]. The spatially structured model is also effective when implemented in mobile and high-density populations, such as military or refugee camps.…”

“…Such compartment models provide insights for understanding infectious disease dynamics. Vrabac [30]. The spatially structured model is also effective when implemented in mobile and high-density populations, such as military or refugee camps.…”

Quantifying Social Interventions for Combating COVID-19 via a Symmetry-Based Model

“…However, the reliability of the model is unclear when new parameters are introduced [8,18]. Second, the originally assumed constant transition rates were adjusted to a time-dependent function to fit the real data [7,[19][20][21][22][23][24][25][26][27]. For example, Cheynet et al [20][21][22][23][24], expanding on the work of Peng et al [7], used multiple functional forms to fit cure and mortality rates.…”

Public Risk Perception Explains the Mitigation of COVID-19

“…Susceptible-exposed-infectious-removed (SEIR) models (4,5) are the most common classic models. With the Exposed (E) state of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the SEIR models consider the complicated situation of COVID-19 and portray its transmission mechanism more practically (6).…”