Use of a Modified SIRD Model to Analyze COVID-19 Data

Sen, Devosmita; Sen, Debasis

doi:10.1021/acs.iecr.0c04754

Cited by 39 publications

(32 citation statements)

References 33 publications

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“…With the aim of taking into account the undetected infected, our model includes more compartments than a standard SIRD. In the same spirit of our work, other authors proposed compartmental models allowing for the presence of undetected asymptomatic individuals in the population [9][10][11][12][13][14][15]. Calafiore and colleagues [12] resort to a standard SIRD model and take into account the undetected infected individuals assuming that they represent a fixed fraction of the notified ones, as we do.…”

Section: Comparison With Similar Modelsmentioning

confidence: 95%

“…In most applications, key quantities on which solid evidence is not yet available, such as transition times between compartments and infection fatality rates, are actually assumed as fixed without performing any robustness or uncertainty evaluation [45]. In Sen et al [13] a sensitivity analysis is performed through comparisons among simulated scenarios corresponding to different parameter values. In our paper, we formally address this issue by performing a GSA that provides a global evaluation of the robustness of our estimates when the fixed quantities change within plausible ranges of values.…”

Section: Comparison With Similar Modelsmentioning

confidence: 99%

“…The interested reader can find an accurate description of the literature in [8]. Some of the proposed compartmental models explicitly account for the presence of undetected asymptomatic individuals, thus for the fact that part of the epidemic is submerged [9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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The first wave of the SARS-CoV-2 epidemic in Tuscany (Italy): A SI2R2D compartmental model with uncertainty evaluation

2021

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With the aim of studying the spread of the SARS-CoV-2 infection in the Tuscany region of Italy during the first epidemic wave (February-June 2020), we define a compartmental model that accounts for both detected and undetected infections and assumes that only notified cases can die. We estimate the infection fatality rate, the case fatality rate, and the basic reproduction number, modeled as a time-varying function, by calibrating on the cumulative daily number of observed deaths and notified infected, after fixing to plausible values the other model parameters to assure identifiability. The confidence intervals are estimated by a parametric bootstrap procedure and a Global Sensitivity Analysis is performed to assess the sensitivity of the estimates to changes in the values of the fixed parameters. According to our results, the basic reproduction number drops from an initial value of 6.055 to 0 at the end of the national lockdown, then it grows again, but remaining under 1. At the beginning of the epidemic, the case and the infection fatality rates are estimated to be 13.1% and 2.3%, respectively. Among the parameters considered as fixed, the average time from infection to recovery for the not notified infected appears to be the most impacting one on the model estimates. The probability for an infected to be notified has a relevant impact on the infection fatality rate and on the shape of the epidemic curve. This stresses the need of collecting information on these parameters to better understand the phenomenon and get reliable predictions.

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Section: Comparison With Similar Modelsmentioning

confidence: 95%

Section: Comparison With Similar Modelsmentioning

confidence: 99%

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The first wave of the SARS-CoV-2 epidemic in Tuscany (Italy): A SI2R2D compartmental model with uncertainty evaluation

2021

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“…ey concluded that reducing mobility and increasing social distance were the most effective measures to end the epidemic earlier. Devosmita and Debasis [31] provided a modified SIRD model to analyze data of COVID-19. e parameters of the SIRD model were estimated by different methods [32,33].…”

Section: Introductionmentioning

confidence: 99%

Long‐Term Predictions of COVID‐19 in Some Countries by the SIRD Model

Pei

Zhang

2021

Complexity

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As COVID-19 in some countries has increasingly become more severe, there have been significant efforts to develop models that forecast its evolution there. These models can help to control and prevent the outbreak of these infections. In this paper, we make long-term predictions based on the number of current confirmed cases, accumulative recovered cases, and dead cases of COVID-19 in some countries by the modeling approach. We use the SIRD (S: susceptible, I: infected, R: recovered, D: dead) epidemic model which is a nonautonomous dynamic system with incubation time delay to study the evolution of COVID-19 in some countries. From the analysis of the recent data, we find that the cure and death rates may not be constant and, in some countries, they are piecewise functions. They can be estimated from the delayed SIRD model by the finite difference method. According to the recent data and its subsequent cure and death rates, we can accurately estimate the parameters of the model and then predict the evolution of COVID-19 there. Through the predicted results, we can obtain the turning points, the plateau period, and the maximum number of COVID-19 cases. The predicted results suggest that the epidemic situation in some countries is very serious. It is advisable for the governments of these countries to take more stringent and scientific containment measures. Finally, we studied the impact of the infection rate β on COVID-19. We find that when the infection rate β decreases, the cumulative number of confirmed cases and the maximum number of currently infected cases will greatly decrease. The results further affirm that the containment techniques taken by these countries to curb the spread of COVID-19 should be strengthened further.

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“…Fernndez et al (2020) used data on death to estimate a standard SIRD model of COVID-19. Sen and Sen (2021) provided a modified SIRD model to analyze data of the pandemic and estimated and compared the key parameters. Gatta et al (2021) modeled mobility data through a graph series in order to infer the parameters of SIR and SIRD models.…”

Section: Introductionmentioning

confidence: 99%

Long-term predictions of current confirmed and dead cases of COVID-19 in China by the non-autonomous delayed epidemic models

Pei

Zhang

2021

Cogn Neurodyn

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In this paper, we make long-term predictions based on numbers of current confirmed cases, accumulative dead cases of COVID-19 in different regions in China by modeling approach. Firstly, we use the SIRD epidemic model (S-Susceptible, I-Infected, R-Recovered, D-Dead) which is a non-autonomous dynamic system with incubation time delay to study the evolution of the COVID-19 in Wuhan City, Hubei Province and China Mainland. According to the data in the early stage issued by the National Health Commission of China, we can accurately estimate the parameters of the model, and then accurately predict the evolution of the COVID-19 there. From the analysis of the issued data, we find that the cure rates in Wuhan City, Hubei Province and China Mainland are the approximately linear increasing functions of time t and their death rates are the piecewisely decreasing functions. These can be estimated by finite difference method. Secondly, we use the delayed SIRD epidemic model to study the evolution of the COVID-19 in the Hubei Province outside Wuhan City. We find that its cure rate is an approximately linear increasing function and its death rate is nearly a constant. Thirdly, we use the delayed SIR epidemic model (S-Susceptible, I-Infected, R-Removed) to predict those of Beijing, Shanghai, Zhejiang and Anhui Provinces. We find that their cure rates are the approximately linear increasing functions and their death rates are the small constants. The results indicate that it is possible to make accurate long-term predictions for numbers of current confirmed, accumulative dead cases of COVID-19 by modeling. In this paper the results indicate we can accurately obtain and predict the turning points, the end time and the maximum numbers of the current infected and dead cases of the COVID-19 in China. In spite of our simple method and small data, it is rather effective in the long-term prediction of the COVID-19.Keywords Long-term prediction Á SIRD Á SIR Á COVID-19 Á Maximum numbers Á End time Á Turning point Á Parameter estimation

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Use of a Modified SIRD Model to Analyze COVID-19 Data

Cited by 39 publications

References 33 publications

The first wave of the SARS-CoV-2 epidemic in Tuscany (Italy): A SI2R2D compartmental model with uncertainty evaluation

The first wave of the SARS-CoV-2 epidemic in Tuscany (Italy): A SI2R2D compartmental model with uncertainty evaluation

Long‐Term Predictions of COVID‐19 in Some Countries by the SIRD Model

Long-term predictions of current confirmed and dead cases of COVID-19 in China by the non-autonomous delayed epidemic models

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