Modified SIRD Model for COVID-19 Spread Prediction for Northern and Southern States of India

Shringi, Sakshi; Sharma, Harish; Rathie, Pushpa N.; Bansal, Jagdish Chand; Nagar, Atulya K.

doi:10.1016/j.chaos.2021.111039

Cited by 15 publications

(10 citation statements)

References 19 publications

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“…Therefore, the analysis with the modified SIRD model was suitable for the Ebola virus study [ 28 ]. Similar to Calafiore et al [ 16 ], Osemwinyen et al [ 28 ], Basti et al [ 29 ], Shringi et al [ 30 ], and Devosmita et al [ 31 ] were unable to reflect on policy and population movement. Furthermore, in the case of COVID-19, reinfection cases are sporadic in Korea; thus, a modified SIRD model was considered inappropriate.…”

Section: Related Workmentioning

confidence: 81%

Hybrid Model-Based Simulation Analysis on the Effects of Social Distancing Policy of the COVID-19 Epidemic

Kang

Park

Jang

et al. 2021

IJERPH

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This study utilizes modeling and simulation to analyze coronavirus (COVID-19) infection trends depending on government policies. Two modeling requirements are considered for infection simulation: (1) the implementation of social distancing policies and (2) the representation of population movements. To this end, we propose an extended infection model to combine analytical models with discrete event-based simulation models in a hybrid form. Simulation parameters for social distancing policies are identified and embedded in the analytical models. Administrative districts are modeled as a fundamental simulation agent, which facilitates representing the population movements between the cities. The proposed infection model utilizes real-world data regarding suspected, infected, recovered, and deceased people in South Korea. As an application, we simulate the COVID-19 epidemic in South Korea. We use real-world data for 160 days, containing meaningful days that begin the distancing policy and adjust the distancing policy to the next stage. We expect that the proposed work plays a principal role in analyzing how social distancing effectively affects virus prevention and provides a simulation environment for the biochemical field.

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Section: Related Workmentioning

confidence: 81%

Hybrid Model-Based Simulation Analysis on the Effects of Social Distancing Policy of the COVID-19 Epidemic

Kang

Park

Jang

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…An interesting aspect of this methodology is the ability to evaluate the effect of the lockdown and other restrictions. In order to include the effect of social distancing measures, [ 26 ] proposed to replace the number of susceptible individuals by a non-linear function of S t that depended on the social distance adopted at time t. Other strategies were made based on dividing the considered sample period into stages and change the parameters depending on each stage, as in [ 27 , 28 ].…”

Section: Discussionmentioning

confidence: 99%

Forecasting Covid-19 in the United Kingdom: A dynamic SIRD model

Athayde

Alencar

2022

PLoS ONE

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Making use of a state space framework, we present a stochastic generalization of the SIRD model, where the mortality, infection, and underreporting rates change over time. A new format to the errors in the Susceptible-Infected-Recovered-Dead compartments is also presented, that permits reinfection. The estimated trajectories and (out-of-sample) forecasts of all these variables are presented with their confidence intervals. The model only uses as inputs the number of reported cases and deaths, and was applied for the UK from April, 2020 to Sep, 2021 (daily data). The estimated infection rate has shown a trajectory in waves very compatible with the emergence of new variants and adopted social measures. The estimated mortality rate has shown a significant descendant behaviour in 2021, which we attribute to the vaccination program, and the estimated underreporting rate has been considerably volatile, with a downward tendency, implying that, on average, more people are testing than in the beginning of the pandemic. The evolution of the proportions of the population divided into susceptible, infected, recovered and dead groups are also shown with their confidence intervals and forecast, along with an estimation of the amount of reinfection that, according to our model, has become quite significant in 2021. Finally, the estimated trajectory of the effective reproduction rate has proven to be very compatible with the real number of cases and deaths. Its forecasts with confident intervals are also presented.

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“…These models offer a precious tool to public health and government authorities for the control of the pandemic vis-a-vis prevention and control. They also offer long and short term predictions which can assist stakeholders optimize control strategies, minimize constraints and make the most effective and efficient decisions [33,34]. These models are typically constructed in the form of ODE and are characterized by a set of parameters whose values are not known a-priori and have to be experimentally determined from the data [35].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Basic Reproduction Number for the Second Wave of COVID–19 Pandemic in Nigeria

Wusu¹,

Olabanjo²,

Mazzara³

2022

Preprint

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It is no news that the COVID–19 pandemic has affected many persons in different ways. As the number of reported cases rises across the globe, efforts are geared towards production and administration of effective vaccines for the disease. However, many developing countries are faced with the dilemma of how to slow the spread and flatten the curves of the disease as the available vaccines are not enough. Interestingly, the dynamics of the disease can be analysed to get useful insights to enhance the making of suitable preventive policies that will slow the spread, ultimately flatten the curves of the disease and also help in managing any future occurrence. In this work, the aim is to analyse the dynamics, and estimate the basic reproduction number of the second wave of the pandemic in Nigeria using a Susceptible-Infected-Recovered-Deceased (SIRD) compartmental–based model. The dynamics of the disease is described by a system of nonlinear ordinary differential equations. The model takes into consideration the current control policies in place - social distancing, mask usage, personal hygiene and quarantine. Available data provided by Nigeria Centre for Disease Control (NCDC), World Health Organization (WHO) and Wolfram Data Repository were used for the computations. The Quasi–Newton algorithm was implemented in fitting the proposed model to the available data and a sensitivity analysis was presented. Major parameters - effective contact rate, average recovery time, average mortality rate, and overall effectiveness of the control policies - influencing the dynamics of the disease, and the basic reproduction numbers were estimated. The turning points of the disease during the second wave were also obtained. The proposed model gave estimated values for the parameters influencing the spread of the disease. Also, the measure of the overall effectiveness of the current control policies gave insight into how effective the measures are.

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Modified SIRD Model for COVID-19 Spread Prediction for Northern and Southern States of India

Cited by 15 publications

References 19 publications

Hybrid Model-Based Simulation Analysis on the Effects of Social Distancing Policy of the COVID-19 Epidemic

Hybrid Model-Based Simulation Analysis on the Effects of Social Distancing Policy of the COVID-19 Epidemic

Forecasting Covid-19 in the United Kingdom: A dynamic SIRD model

Estimating the Basic Reproduction Number for the Second Wave of COVID–19 Pandemic in Nigeria

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