Epidemics forecast from SIR-modeling, verification and calculated effects of lockdown and lifting of interventions

Abstract

Due to the current COVID-19 epidemic plague hitting the worldwide population it is of utmost medical, economical and societal interest to gain reliable predictions on the temporal evolution of the spreading of the infectious diseases in human populations. Of particular interest are the daily rates and cumulative number of new infections, as they are monitored in infected societies, and the influence of non-pharmaceutical interventions due to different lockdown measures as well as their subsequent lifting on these infections. Estimating quantitatively the influence of a later lifting of the interventions on the resulting increase in the case numbers is important to discriminate this increase from the onset of a second wave. The recently discovered new analytical solutions of Susceptible-Infectious-Recovered (SIR) model allow for such forecast and the testing of lockdown and lifting interventions as they hold for arbitrary time dependence of the infection rate. Here we present simple analytical approximations for the rate and cumulative number of new infections.

Forecast for the second Covid-19 wave based on the improved SIR model with a constant ratio of recovery to infection rate

“…While the Gauss distribution can be justified both from the central limit theorem of statistics 1 * rsch@tp4.rub.de, mk@mat.etz.ch and for early times until the peak time from the susceptibleinfectious-recovered/removed (SIR) epidemic model, [6][7][8][9] it is less accurate at late times of the wave evolution as compared to monitored data. 10 The SIR model 6,7 describes the time evolution of infectious diseases in human populations, and is the simplest and most fundamental of the compartmental models and its variations. It had been solved numerically using various approaches, including Monte Carlo methods, wavelets, fuzzy control, deep learning etc.…”