A Data Driven Analysis and Forecast of COVID-19 Dynamics during the Third Wave Using SIRD Model in Bangladesh

Abstract

In this study, we developed a compartmental SIRD model to analyze and forecast the transmission dynamics of the COVID-19 pandemic in Bangladesh during the third wave caused by the Indian delta variant. With the help of the nonlinear system of differential equations, this model can analyze the trends and provide reliable predictions regarding how the epidemic would evolve. The basic reproduction number regarding the pandemic has been determined analytically. The parameters used in this model have been estimated by fitting our model to the reported data for the months of May, June, and July 2021 and the goodness of fit of the parameter’s value has been found by the respective regression coefficients. Further, we conducted a sensitivity analysis of the basic reproduction number and observed that decreasing the transmission rate is the most significant factor in disease prevention. Our proposed model’s appropriateness for the available COVID-19 data in Bangladesh has been demonstrated through numerical simulations. According to the numerical simulation, it is evident that a rise in the transmission rate leads to a significant increase in the infected number of the population. Numerical simulations have also been performed by using our proposed model to forecast the future transmission dynamics for COVID-19 over a longer period of time. Knowledge of these forecasts may help the government in adopting appropriate measures to prepare for unforeseen situations that may arise in Bangladesh as well as to minimize detrimental impacts during the outbreak.

All-People-Test-Based Methods for COVID-19 Infectious Disease Dynamics Simulation Model: Towards Citywide COVID Testing

“…Recent works [ 10 , 11 , 12 ] for the spread of COVID-19 mainly include SEIRD, SIRD-RM, and SEIRDV models, and there have been studies on the spread of novel coronavirus pneumonia using these models. A standard model of disease spread is the SEIRD model, in which each individual is either susceptible ( S ), exposed ( E ), infected ( I ), recovered ( R ), or dead ( D ).…”

“…Reference [ 11 ] proposed a compartmental SIRD model with time-dependent parameters that can be used to give epidemiological interpretations to the phenomenological parameters of the Richards growth model. It illustrates the use of the map between these two models by fitting the fatality curves of the COVID-19 epidemic data in Italy, Germany, Sweden, the Netherlands, Cuba, and Japan.…”

Determination of a Key Pandemic Parameter of the SIR-Epidemic Model from Past Covid-19 Mutant Waves and Its Variation for the Validity of the Gaussian Evolution

“…The infection (a(t)) and recovery (µ(t)) rates then regulate the transition probability between the compartment fractions. Later refinements of the SIR-model such as the SEIR [3][4][5][6][7][8][9][10][11][12], SVEIR [13,14], SEIRD [15], SIRD [16][17][18], SIRS [19,20] and SIRV [21][22][23][24] have introduced additional compartments (for reviews see refs. [25][26][27][28][29][30][31]).…”

Key Epidemic Parameters of the SIRV Model Determined from Past COVID-19 Mutant Waves

“…The SIR and SIRV epidemic models provide a good explanation for the temporal evolution of COVID-19 waves caused by different mutants [8][9][10]. Later refinements of these models, such as the SEIR [11][12][13][14][15][16][17][18][19][20], SVEIR [21,22], SEIRD [23], SIRD [24][25][26] and SIRS [27,28], have introduced additional compartments (for reviews, see refs. [29][30][31][32][33][34][35]).…”