A Prospective Method for Generating COVID-19 Dynamics

Abstract

Generating dynamic operators are constructed here from the cumulative case function to recover all state dynamics of a Susceptible–Exposed–Infectious–Recovered (SEIR) model for COVID-19 transmission. In this study, recorded and unrecorded EIRs and a time-dependent infection rate are taken into account to accommodate immeasurable control and intervention processes. Generating dynamic operators are built and implemented on the cumulative cases. All infection processes, which are hidden in this cumulative function, can be recovered entirely by implementing the generating operators. Direct implementation of the operators on the cumulative function gives all recorded state dynamics. Further, the unrecorded daily infection rate is estimated from the ratio between IFR and CFR. The remaining dynamics of unrecorded states are directly obtained from the generating operators. The simulations are conducted using infection data provided by Worldometers from ten selected countries. It is shown that the higher number of daily PCR tests contributed directly to reducing the effective reproduction ratio. The simulations of all state dynamics, infection rates, and effective reproduction ratios for several countries in the first and second waves of transmissions are presented. This method directly measures daily transmission indicators, which can be effectively used for the day-to-day control of the epidemic.

Computation to Fight SARS-CoV-2 (COVID-19)

“…Simulations of all state dynamics, infection rates, and effective reproduction ratios were performed for a number of countries during the first and second waves of transmission. With this method, daily transmission indicators are directly measured and can be utilized to successfully control the epidemic on a daily basis [10]. Aslam published a further article of interest.…”

Infectious Disease Modeling with Socio-Viral Behavioral Aspects—Lessons Learned from the Spread of SARS-CoV-2 in a University

“…Recovery rates denote the quantity representing how fast infected individuals recover from the disease and, hence, build their immunity [ 43 ]. For some infectious diseases, the absence of healthcare might cause a longer infection period [ 27 , 44 , 45 ], specifically for COVID-19. Not limited to this disease, we define the implicitly time-dependent recovery rate as follows: where denotes the state variable for infectious individuals and denotes the constant healthcare capacity .…”

“…As a case study, we applied the constructed models to the SARS-CoV-2 spread data that were collected in a university environment (Institut Teknologi Bandung College) in January 2022. The choice to use data from a university was made to ensure homogeneous socio-behavioral aspects for the whole society; no demographic is taken into account due to the homogeneity assumption [ 25 , 26 , 27 ]. The small scale of a university environment also ensures the involvement of pathogens in the air; the larger the scale of the observation, the smaller the effect of pathogens in the air.…”