A Multi-Period Curve Fitting Model for Short-Term Prediction of the COVID-19 Spread in the U.S. Metropolitans

Abstract

The COVID-19 has wreaked havoc upon the world with over 248 million confirmed cases and a death toll of over 5 million. It is alarming that the United States contributes over 18% of these confirmed cases and 14% of the deaths. Researchers have proposed many forecasting models to predict the spread of COVID-19 at the national, state, and county levels. However, due to the large variety in the mitigation policies adopted by various state and local governments; and unpredictable social events during the pandemic, it is incredibly challenging to develop models that can provide accurate long-term forecasting for disease spread. In this paper, to address such a challenge, we introduce a new multi-period curve fitting model to give a short-term prediction of the COVID-19 spread in Metropolitan Statistical Areas (MSA) within the United States. Since most counties/cities within a single MSA usually adopt similar mitigation strategies, this allows us to substantially diminish the variety in adopted mitigation strategies within an MSA. At the same time, the multi-period framework enables us to incorporate the impact of significant social events and mitigation strategies in the model. We also propose a simple heuristic to estimate the COVID-19 fatality based on our spread prediction. Numerical experiments show that the proposed multi-period curve model achieves reasonably high accuracy in the prediction of the confirmed cases and fatality.

Comprehensive Review on Statistical Modeling Approach to Predict the COVID-19 Transmission

“…Based on our spread forecast, we suggest a straightforward heuristic to estimate the COVID-19 fatality. The suggested multi-period curve model yields a respectably high level of accuracy in the prediction of the confirmed cases and fatality, according to numerical trials [32]. Artificial neural network-based curve fitting techniques in prediction and forecasting of the Covid-19 number of rising cases and death cases [33].…”

Prediction of cross-border spread of the COVID-19 pandemic: A predictive model for imported cases outside China

“…To predict the epidemic situation of COVID-19 in a timely, accurate, and reliable manner, scholars have conducted numerous studies on the prediction, prevention and control of COVID-19 transmission [9][10][11][12][13], and an infectious disease dynamics model has been proposed. As a tool aimed at epidemic prediction and as well as actual application, this model considers the transmission speed, transmission mode, and various prevention and control measures of infectious diseases as well as other factors as a whole [14], and thus has significant application value for early warning of infectious diseases as well as for assessing prevention and control effects on the diseases.…”

Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach

“…These models can compare several scenarios depending on the available data in order to forecast the path of the pandemic, as well as propose measures for managing it. Several such models have been reported in the literature, with some of the widely known ones being the susceptible-infected-recovered model [29][30][31], curve-fitting model [32], extended-susceptible-infected-recovered model [33], susceptibleexposed-infected-quarantined-dead-hospitalized-recovered model [27], susceptible-unascertained-cases-pre-symptomatic infectiousness-exposed-infectious-recovered model [34], susceptible-infected-diagnosed-ailing-recognized-threatened-healed-extinct model [35], and susceptible-exposed-asymptomatic-infected-hospitalized-recovered-dead due to COVID-19 infection-susceptible model [36]. Although these are all mathematical models, their complexity increases and applicability decreases as the amount of data increases, necessitating an exponential growth in computational power.…”