Adaptive Susceptible-Infectious-Removed Model for Continuous Estimation of the COVID-19 Infection Rate and Reproduction Number in the United States: Modeling Study

Abstract

Background The dynamics of the COVID-19 pandemic vary owing to local population density and policy measures. During decision-making, policymakers consider an estimate of the effective reproduction number Rt, which is the expected number of secondary infections spread by a single infected individual. Objective We propose a simple method for estimating the time-varying infection rate and the Rt. Methods We used a sliding window approach with a Susceptible-Infectious-Removed (SIR) model. We estimated the infection rate from the reported cases over a 7-day window to obtain a continuous estimation of Rt. A proposed adaptive SIR (aSIR) model was applied to analyze the data at the state and county levels. Results The aSIR model showed an excellent fit for the number of reported COVID-19 cases, and the 1-day forecast mean absolute prediction error was <2.6% across all states. However, the 7-day forecast mean absolute prediction error approached 16.2% and strongly overestimated the number of cases when the Rt was rapidly decreasing. The maximal Rt displayed a wide range of 2.0 to 4.5 across all states, with the highest values for New York (4.4) and Michigan (4.5). We found that the aSIR model can rapidly adapt to an increase in the number of tests and an associated increase in the reported cases of infection. Our results also suggest that intensive testing may be an effective method of reducing Rt. Conclusions The aSIR model provides a simple and accurate computational tool for continuous Rt estimation and evaluation of the efficacy of mitigation measures.

Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model

“…When infection dynamics change and the numbers become more considerable, SIRD models, in general, tend to lead to an increase in prediction errors. Our results on medium-term forecasts are in line with other results in the recent COVID-19 forecast literature, like in [ 38 , 39 , 40 ].…”

Gauging the Impact of Artificial Intelligence and Mathematical Modeling in Response to the COVID-19 Pandemic: A Systematic Review

“…The mathematical formulae devised using statistical modeling can help predict the future course of infections which can aid in optimal policymaking. Different machine learning algorithms, viz., support vector machines (SVM), random forests (RF), gradient boosting trees (GBT), and logistic regression can work efficiently in tandem and close proximity with explicit differential equations devised through modeling that might help in future forecasting of the pandemic as shown in Figures 2 and 6, based on historical patterns of data in different settings [60,61]. Incorporating various disease-related parameters and variables into statistical models might provide insights into the dynamics of disease transmission, and this might prove helpful in future forecasting of the disease.…”

Modeling COVID-19 Incidence by the Renewal Equation after Removal of Administrative Bias and Noise

“…See also [27], who use this equation but estimate the probability distribution Φ s by a maximum entropy method. A few papers use another deterministic model, the Wallinga-Teunis formulation, to compute R t [28], or a SIR model, such as in [29], where the time variable parameter β(t) of the three ODE's of a SIR model is estimated from incidence data in a seven days sliding window.…”