Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation

Faranda, Davide; Castillo, Isaac Pérez; Hulme, Oliver J.; Jézéquel, Aglaé; Lamb, Jeroen S. W.; Sato, Yuzuru; Thompson, Erica

doi:10.1063/5.0008834

Cited by 44 publications

(45 citation statements)

References 39 publications

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“…Asadi et al (2020) analyzed COVID-19 cases in Spain using the Gompertz model and found a growth coefficient higher than the results of this study. Faranda et al (2020) studied COVID-19 data of different countries. They applied the Logistic model to the Chinese number of infections and it was observed that both growth coefficient and inflection point were more but R-square was greater than 0.99 in both researches.…”

Section: Model Selectionmentioning

confidence: 99%

An Analysis of COVID-19 Cases in Nepal: A Modeling Approach

Nepal

2020

J. Inst. Sci. Tech.

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Unlike previous coronaviruses infections, COVID-19 has badly affected not only the health of people but also the socioeconomic activities of Nepal. It would help the government of Nepal to manage this crisis if a proper mechanism to predict COVID cases has been developed. This study aims to look for patterns of confirmed, recovery and death cases. Moreover, it tries to check whether Gompertz and Logistic model would be able to read the patterns of total confirmed and death cases. It also forecasts the total number of confirmed as well as death cases. Data from January 23, 2020 to October 30, 2020 obtained from the website of Wikipedia are used for analysis. Gompertz and Logistic models were fitted to the total number of confirmed and death cases and models are compared based on various criteria. Besides, an automatic ARIMA model was used to predict cumulative confirmed and death cases and the accuracy of the model was also checked. ARIMA model forecasted 347,812 confirmed cases and 1,754 death cases till December 31, 2020. At 95 % confidence interval, the confirmed cases were expected between 273,889 and 421,734 whereas death cases were estimated from 1,387 to 2,119. Both models were fitted well in both total confirmed cases and total death cases. It was found that the Logistic model fits better in total confirmed cases whereas in total death cases, the Gompertz model was better. ARIMA model precisely forecasted the number of confirmed and death cases.

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Section: Model Selectionmentioning

confidence: 99%

An Analysis of COVID-19 Cases in Nepal: A Modeling Approach

Nepal

2020

J. Inst. Sci. Tech.

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“…These variations occur due to extrinsic factors, such as the availability of tests for essential screening, the natural history of the disease and changes in mitigation measures. Using an S-Shape curve model, Faranda et al 11 demonstrated the high sensitivity of the estimates to the last point of COVID-19 datasets. These authors provide a simulation study, replacing the last data point of the epidemic curves in the UK, France and Italy with a random number drawn from a uniform distribution, showing that the trajectory of the curves obtained under this process have a very high variability.…”

Section: /5mentioning

confidence: 99%

“…These authors provide a simulation study, replacing the last data point of the epidemic curves in the UK, France and Italy with a random number drawn from a uniform distribution, showing that the trajectory of the curves obtained under this process have a very high variability. Faranda et al 11 also showed that long-term forecasts and predictions based on more sophisticated models, such as the Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model, are also extremely sensitive to biases in data collection and crucially depend on the last available data point.…”

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confidence: 99%

Long-term forecasts of the COVID-19 epidemic: a dangerous idea

Martinez

Aragon

Nunes

2020

Rev. Soc. Bras. Med. Trop.

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Introduction: Mathematical models have been used to obtain long-term forecasts of the COVID-19 epidemic. Methods: The daily COVID-19 case count in two Brazilian states was used to show the potential limitations of long-term forecasting through the application of a mathematical model to the data. Results: The predicted number of cases at the end of the epidemic and at the moment that the peak occurs, is highly dependent on the length of the time series used in the predictive model. Conclusions: Predictions obtained during the course of the COVID-19 pandemic need to be viewed with caution.

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“…Furthermore, as Faranda et al indicated, early estimates of COVID-19 show enormous fluctuations, despite the importance of having robust estimates of the time-asymptotic total number of infections. 18 They showed that predictions are extremely sensitive to the reporting protocol and crucially depend on the last available data point before the maximum number of daily infections is reached.…”

Section: Figurementioning

confidence: 99%

Single Parameter Estimation Approach for Robust Estimation of SIR Model With Limited and Noisy Data: The Case for COVID-19

Şenel

Özdinç

Öztürkcan

2020

Disaster med. public health prep.

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The SIR model and its variants are widely used to predict the progress of COVID-19 worldwide, despite their rather simplistic nature. Nevertheless, robust estimation of the SIR model presents a significant challenge, particularly with limited and possibly noisy data in the initial phase of the pandemic. K-means algorithm is used to perform a cluster analysis of the top ten countries with the highest number of COVID-19 cases, to observe if there are any significant differences among countries in terms of robustness. As a result of model variation tests, the robustness of parameter estimates is found to be particularly problematic in developing countries. The incompatibility of parameter estimates with the observed characteristics of COVID-19 is another potential problem. Hence, a series of research questions are visited. We propose a SPE (“Single Parameter Estimation”) approach to circumvent these potential problems if the basic SIR is the model of choice, and we check the robustness of this new approach by model variation and structured permutation tests. Dissemination of quality predictions is critical for policy and decision-makers in shedding light on the next phases of the pandemic.

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Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation

Cited by 44 publications

References 39 publications

An Analysis of COVID-19 Cases in Nepal: A Modeling Approach

An Analysis of COVID-19 Cases in Nepal: A Modeling Approach

Long-term forecasts of the COVID-19 epidemic: a dangerous idea

Single Parameter Estimation Approach for Robust Estimation of SIR Model With Limited and Noisy Data: The Case for COVID-19

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