'STOP COVID-19': An Actionable Plan to Stop the Pandemic. Why Stopping the Pandemic Is Simpler, Faster and More Effective Than Just Slowing It Down

Baveja, Alok; Kapoor, Ajai; Melamed, Benjamin

doi:10.2139/ssrn.3555280

Cited by 3 publications

(1 citation statement)

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“…The current epidemic and global health crisis have worsened health supply chains and caused significant disruptions in upstream and downstream operations. Severe shortages, logistic challenges and travel restrictions have also added fuel to it 7 – 8 . Because of these constraints, accurate short-term forecasts have become an important decision-making tool and are essential to the medium and long-term forecasting supply chain processes.…”

Section: Introductionmentioning

confidence: 99%

Vaccine rate forecast for COVID-19 in Africa using hybrid forecasting models

Dhamodharavadhani¹,

Rathipriya²

2023

Afr H. Sci.

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Background: The public health sectors can use the forecasting applications to determine vaccine stock requirements to avoid excess or shortage stock. This prediction will ensure that immunization protection for COVID- 19 is well-distributed among African citizens. Objective: The aim of this study is to forecast vaccination rate for COVID-19 in Africa Methods: The method used to estimate predictions is the hybrid forecasting models which predicts the COVID-19 vaccination rate (CVR). HARIMA is a hybrid of ARIMA and the Linear Regression model and HGRNN is a hybrid of Generalized Regression Neural Network (GRNN) and the Gaussian Process Regression (GPR) model which are used to improve predictive accuracy. Results: In this study, standard and hybrid forecasting models are used to evaluate new COVID-19 vaccine cases daily in May and June 2021. To evaluate the effectiveness of the models, the COVID-19 vaccine dataset for Africa was used, which included new vaccine cases daily from 13 January 2021 to 16 May 2021. Root Mean Squared Error (RMSE) and Error Percentage (EP) are used as evaluation measures in this process. The results obtained showed that the hybrid GRNN model performed better than the hybrid ARIMA model. Conclusion: HGRNN model provides accurate daily vaccinated case forecast, which helps to maintain optimal vaccine stock toavoid vaccine wastage and save many lives. Keywords: Vaccination forecasting; ARIMA; Immunization; Time series techniques; Hybrid ARIMA; Prediction; Linear Regression; Gaussian Regression Process; Hybrid GRNN.

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Section: Introductionmentioning

confidence: 99%

Vaccine rate forecast for COVID-19 in Africa using hybrid forecasting models

Dhamodharavadhani¹,

Rathipriya²

2023

Afr H. Sci.

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Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions

Νικολόπουλος

Punia

Schäfers

et al. 2021

European Journal of Operational Research

411

292

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Highlights We provide predictive analytics tools for immediate use during COVID-19. We use data from the UK, USA, India, Germany, Singapore up to mid-April 2020. We forecast COVID-19 growth rates at country-level. We use auxiliary data (Google trends) to model excess demand. We forecast the excess demand for products and services at country-level.

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COVID-19 peak estimation and effect of nationwide lockdown in India

Belfin

Bródka

Radhakrishnan

et al. 2022

Journal of Statistics and Management Systems

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There was a fury of the pandemic because of novel coronavirus (2019-nCoV/SARS-CoV-2) that happened in Wuhan, Hubei province, in China in December 2019. Since then, many model predictions on the COVID-19 pandemic in Wuhan and other parts of China have been reported.The first incident of coronavirus disease 2019 (COVID-19) in India was reported on 30 January 2020, which was a student from Wuhan. The number of reported cases has started to increase day by day after 30 February 2020. The purpose of this investigation is to provide a prediction of the epidemic peak for COVID-19 in India by utilizing real-time data from 30 February to 14 April 2020. We apply the well-known epidemic compartmental model "SEIR" to predict the epidemic peak of COVID-19, India. Since we do not have the complete detail of the infective population, using the available infected population data, we identify the R 0 by using polynomial regression. By using the third-order polynomial equation, we estimate that the basic reproduction number for the epidemic in India is R 0 = 3.3 (95%CI, 3.1-3.5), and the epidemic peak could be reached by September 2020.

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'STOP COVID-19': An Actionable Plan to Stop the Pandemic. Why Stopping the Pandemic Is Simpler, Faster and More Effective Than Just Slowing It Down

Cited by 3 publications

References 0 publications

Vaccine rate forecast for COVID-19 in Africa using hybrid forecasting models

Vaccine rate forecast for COVID-19 in Africa using hybrid forecasting models

Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions

COVID-19 peak estimation and effect of nationwide lockdown in India

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