A Time-dependent mathematical model for COVID-19 transmission dynamics and analysis of critical and hospitalized cases with bed requirements

Abstract

A time-dependent SEAIHCRD model is the extension of the SEIR model, which includes some new compartment that is asymptomatic infectious people, hospitalized people, critical people, and dead compartments. In this article, we analyzed six countries, namely the United States, Brazil, India, South Africa, Russia, and Mexico. A time-dependent SEAIHCRD model calculates the magnitude of peaks for exposed people, asymptomatic infectious people, symptomatic infectious people, hospitalized people, the number of people admitted to ICUs, and the number of COVID-19 deaths over time. It also computes the spread scenario and endpoints of disease. The proposed model also involves asymptomatic infectious individuals. To estimate the various parameters, we first collect the data and fit that using the Lavenberg-Marquardt model for death cases. Then we calculate infection rate, recovery rate, case fatality rate, and the basic reproduction number over time. We calculate two types of case fatality rates: one is the daily case fatality rate, and the other is the total case fatality rate. The proposed model includes the social distance parameter, various age classes, hospital beds for severe cases, and ICU beds or ventilators for critical cases. This model will be useful to determine various essential parameters such as daily hospitalization rate, daily death rates, including the requirement of normal and ICU beds during peak days of infection.

Circadian immunity, sunrise time and the seasonality of respiratory infections

“…S2C , also see ref. 17 ). We adapted our sampling models to account for these data, again allowing for a full likelihood approach.…”

“…Following the general Susceptibility-Infection-Recovery-Susceptibility (SIRS) paradigm 1 , 11 , 16 , 17 , we developed models for both influenza and COVID-19, and fitted them to available public data for infections (in influenza) and hospitalizations and deaths (in COVID-19). We adapted these models to capture the relationship between the basic reproductive number R 0 and several key covariates—including humidity, day length, and local sunrise time—allowing R 0 to change in a seasonal manner as a function of the covariates.…”

“…Following the general Susceptibility-Infection-Recovery-Susceptibility (SIRS) paradigm 1,11,16,17 , we developed models for both influenza and COVID-19, and fitted them to available public data for infections (in influenza) and hospitalizations and deaths (in COVID-19).…”

Methods, Challenges, and Practical Issues of COVID-19 Projection: A Data Science Perspective

“…For example, hospital beds and Intensive Care Units (ICU) capacity is essential, especially for mortality or recovery. Singh et al (2020) proposed the SEAIHCRD model with the addition of asymptomatic infectious (A), critical (C), death (D), hospitalized (H), and recovered (R) compartments. They focused on the basic reproduction number, the number of required hospital and ICU beds, and the case fatality rate for COVID-19 in Brazil, India, Mexico, Russia, South Africa, and the US.…”

“…Due to the flexibility and ease of extensibility of mechanistic models, they are useful in investigating the effect of various factors, such as protection, control measures, quarantine, and hospitalization. However, those mechanistic models may contain various assumptions about the dynamics of disease transmission and a large number of parameters, making their results are sensitive to a small change of model assumptions and parameters (Singh et al, 2020;. Hence, they have a high level of uncertainty, which is difficult to predict.…”