A Two-Step Polynomial and Nonlinear Growth Approach for Modeling COVID-19 Cases in Mexico

C., Rafael Pérez Abreu; Estrada, Samantha; de-la-Torre-Gutiérrez, Héctor

doi:10.3390/math9182180

Cited by 2 publications

(1 citation statement)

References 21 publications

(30 reference statements)

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“…We also found that ETS (A, AD, N) fits well the residuals from the standard model. Working on COVID-19 data, [44] used an Autoregressive (AR) process to correct the autocorrelation issue. They also found that the prediction is better with the model that considers the issue of autocorrelation.…”

Section: Discussionmentioning

confidence: 99%

On computational analysis of nonlinear regression models addressing heteroscedasticity and autocorrelation issues: An application to COVID-19 data

Atchadé

2022

Heliyon

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Section: Discussionmentioning

confidence: 99%

On computational analysis of nonlinear regression models addressing heteroscedasticity and autocorrelation issues: An application to COVID-19 data

Atchadé

2022

Heliyon

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Evaluation of COVID-19 Reported Statistical Data Using Cooperative Convolutional Neural Network Model (CCNN)

Awad

2022

COVID

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Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease was first identified in December 2019 in Wuhan, the capital of China’s Hubei province, and has since spread globally, resulting in the ongoing coronavirus pandemic. The most important type of COVID-19 confrontation is the use of polymerase chain reaction testing (PCR) to detect if a person is healthy or infected with COVID-19. Many countries released different numbers about the total cases of infected persons with the pandemic based on the number of total PCRs and other statistical numbers related to this disease. The total case numbers for some countries were very promising, such that they implied that these countries were to recover soon from these diseases. At other times, some countries overestimated the total number of cases or deaths to get financial aid. Therefore, there is a need to verify and evaluate these numbers by using machine-learning algorithms that proved to be efficient in solving many problems. The convolutional neural network (CNN) is an advanced machine-learning algorithm that was deployed to detect COVID-19 from different medical images such as X-ray images. However, CNN was not used to validate the published COVID-19 statistical data. In this paper, we adapted the self-organizing UNet (SO-UNet) in the cooperative convolutional neural network (CCNN) model to detect the accuracy of the reported COVID-19 statistics. The detection is based on using COVID-19 statistical variables that are reported by reliable and credible databases provided by known global organizations. These variables are used to create multi-dimension images to train and test the CCNN model. The results showed that reported important statistics for COVID-19 could be verified using the CCNN model. It also showed that the verified data coincided with the verification reported by the most important and trusted organizations in the world. Finally, the comparison of the new model to Benford’s law outcome proved the model’s credibility and efficiency in validating COVID-19 reported data.

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A Two-Step Polynomial and Nonlinear Growth Approach for Modeling COVID-19 Cases in Mexico

Cited by 2 publications

References 21 publications

On computational analysis of nonlinear regression models addressing heteroscedasticity and autocorrelation issues: An application to COVID-19 data

On computational analysis of nonlinear regression models addressing heteroscedasticity and autocorrelation issues: An application to COVID-19 data

Evaluation of COVID-19 Reported Statistical Data Using Cooperative Convolutional Neural Network Model (CCNN)

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