SEIHCRD Model for COVID-19 spread scenarios, disease predictions and estimates the basic reproduction number, case fatality rate, hospital, and ICU beds requirement

Abstract

We have proposed a new mathematical method, SEIHCRD-Model that is an extension of the SEIR-Model adding hospitalized and critical twocompartments. SEIHCRD model has seven compartments: susceptible (S), exposed (E), infected (I), hospitalized (H), critical (C), recovered (R), and deceased or death (D), collectively termed SEIHCRD. We have studied COVID- 19 cases of six countries, where the impact of this disease in the highest are Brazil, India, Italy, Spain, the United Kingdom, and the United States. SEIHCRD model is estimating COVID-19 spread and forecasting under uncertainties, constrained by various observed data in the present manuscript. We have first collected the data for a specific period, then fit the model for death cases, got the values of some parameters from it, and then estimate the basic reproduction number over time, which is nearly equal to real data, infection rate, and recovery rate of COVID-19. We also compute the case fatality rate over time of COVID-19 most affected countries. SEIHCRD model computes two types of Case fatality rate one is CFR daily and the second one is total CFR. We analyze the spread and endpoint of COVID-19 based on these estimates. SEIHCRD model is time-dependent hence we estimate the date and magnitude of peaks of corresponding to the number of exposed cases, infected cases, hospitalized cases, critical cases, and the number of deceased cases of COVID-19 over time. SEIHCRD model has incorporated the social distancing parameter, different age groups analysis, number of ICU beds, number of hospital beds, and estimation of how much hospital beds and ICU beds are required in near future.

One Year of the COVID-19 Pandemic in Galicia: A Global View of Age-Group Statistics during Three Waves

“…Since the beginning of the pandemic of COVID-19, a number of attempts to predict its evolution have been published, helping the health authorities with their strategies. The mathematical tools used vary from compartmental models [ 1 , 2 , 3 , 4 , 5 ], including fractional derivatives [ 6 , 7 ], to statistical tools [ 8 , 9 , 10 ]. With the aim of helping health authorities to avoid hospital resource collapses, some works have been used, e.g., to predict the numbers of required beds at intensive care units [ 11 ].…”

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

“…Singh A et al (2020) described the basic compartment model SIR, the SEIR model, and the SEIRD model [27]. Our proposed model, a time-dependent SEAIHCRD model, is a new mathematical method that extends the SEIR model by adding asymptomatic infectious, death, hospitalized, and critical compartments.…”

“…Since Kermack and Mckendrick's paper, stochastic models, discretetime models, continuous-time models, and diffusion models have been developed for many diseases. Some manuscripts provide an excellent approach to mathematical models that provide a comprehensive understanding of the disease outbreak scenario [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. The SEIR model has not been able to estimate spread where preventive measures such as social distances, different age groups, number of ICU beds, number of hospital beds, and mortality rates have been adopted.…”

Forecasting infection fatality rate of COVID-19, Measuring the effciency of several hybrid models.

“…The deep learning tools are also used to predict the new cases such as the work of Alazab et al [3], Tamang et al [16] Kapoor et al [11] and Namasudra et al [12] use neural networks, Zeroual et al [20] make a compartive study between different deep learning models, ArunKumar et al [5] compared between statistical models ARIMA, seasonal ARIMA model and machine learning models Gated Recurrent unit (GRU), Long-Short term memory (LSTM). IFR stands for infection fatality rate, which is the proportion of people who die from an infectious disease among all those who have been infected, was also estimated and forecasted in many works such as Singh et al [15], Vattay et al [19] Forecast the outcome and estimating the epidemic model parameters from the fatality time series. Ahmar et al [2] use ARIMA and nonlinear AR model.…”