RLIM: a recursive and latent infection model for the prediction of US COVID-19 infections and turning points

Abstract

Initially found in Hubei, Wuhan, and identified as a novel virus of the coronavirus family by the WHO, COVID-19 has spread worldwide at exponential speed, causing millions of deaths and public fear. Currently, the USA, India, Brazil, and other parts of the world are experiencing a secondary wave of COVID-19. However, the medical, mathematical, and pharmaceutical aspects of its transmission, incubation, and recovery processes are still unclear. The classical susceptible–infected–recovered model has limitations in describing the dynamic behavior of COVID-19. Hence, it is necessary to introduce a recursive, latent model to predict the number of future COVID-19 infection cases in the USA. In this article, a dynamic recursive and latent infection model (RLIM) based on the classical SEIR model is proposed to predict the number of COVID-19 infections. Given COVID-19 infection and recovery data for a certain period, the RLIM is able to fit current values and produce an optimal set of parameters with a minimum error rate according to actual reported numbers. With these optimal parameters assigned, the RLIM model then becomes able to produce predictions of infection numbers within a certain period. To locate the turning point of COVID-19 transmission, an initial value for the secondary infection rate is given to the RLIM algorithm for calculation. RLIM will then calculate the secondary infection rates of a continuous time series with an iterative search strategy to speed up the convergence of the prediction outcomes and minimize the maximum square errors. Compared with other forecast algorithms, RLIM is able to adapt the COVID-19 infection curve faster and more accurately and, more importantly, provides a way to identify the turning point in virus transmission by searching for the equilibrium between recoveries and new infections. Simulations of four US states show that with the secondary infection rate initially set to 0.5 within the selected latent period of 14 days, RLIM is able to minimize this value at 0.07 and reach an equilibrium condition. A successful forecast is generated using New York state’s COVID-19 transmission, in which a turning point is predicted to emerge on January 31, 2021. Supplementary Information The online version contains supplementary material available at 10.1007/s11071-021-06520-1.

VSHR: A Mathematical Model for the Prediction of Second-Wave COVID-19 Epidemics in Malaysia

“…In our simulation, ARIMA requires three inputs: infection records H t , vaccination records V t , and starting date T 0 (readers may refer to Table 2 ). Then, an ARIMA model is initiated with three parameters ( p , d , q ), where p is the order of autoregression, d is the degree of difference, and q is the order of the moving average [ 10 ]. In our simulation, the fitted data series in H t is expressed in …”

“…This state is treated as a balance between the number of new infections and the number of recoveries, and it can be described mathematically as a point where the accumulated infections reach their maximum speed. In [ 10 ], researchers developed a mathematical model called RLIM to locate the turning point in New York, U.S.A., using a revised classical suspected infectious recovery (SIR) model and a 4 th -order polynomial estimation to fit infection and recovery records. In [ 11 ], a segmented Poisson model was employed to predict COVID-19 outbreaks in Canada, France, Germany, Italy, the UK, and the US.…”

COVID-19 Pandemisinin Kompartman Modelleri: Sistematik Bir Literatür Taraması

“…Although some of these applications had positive impact on the prevention of this disease, some of them led to negative consequences in mental and physical health. The impacts and effectiveness of both pharmaceutical and non-pharmaceutical methods are not clear, and variants of the virus have emerged like alfa, beta, gamma, delta, and omicron [3].…”

Preface

“…As a reminder, the Spanish flu pandemic of 1918 went through four significant waves before it died down. It is hence of high interest to model the past and future waves of COVID-19, a topic thoroughly analyzed in [15,17,18]. A potential scheme is suggested in [19] to treat COVID-19 under possible analysis on 2019-nCoV.…”