ArticleDiabetes & metabolic syndrome
ARIMA modelling & forecasting of COVID-19 in top five affected countries.
Article in Diabetes & metabolic syndrome. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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47 citing papers in PubMed.
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- Google search interests and new cases of COVID-19 in Bangladesh: a vector autoregression analysis for disease surveillance.BMC research notes · 2025Article
- Spatio-temporal time series forecasting with trap catch data of oriental fruit moth (Frontiers in plant science · 2025Article
- Country-report pattern corrections of new cases allow accurate 2-week predictions of COVID-19 evolution with the Gompertz model.Scientific reports · 2024Article
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- The future of meat and dairy consumption in the UK: exploring different policy scenarios to meet net zero targets and improve population health.Global sustainability · 2023Article
- Improvement of Time Forecasting Models Using Machine Learning for Future Pandemic Applications Based on COVID-19 Data 2020-2022.Diagnostics (Basel, Switzerland) · 2023Article
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- Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters.Gondwana research : international geoscience journal · 2023Article
- Data Mining Based Techniques for Covid-19 Predictions.Procedia computer science · 2023Article
- Developing forecasting model for future pandemic applications based on COVID-19 data 2020-2022.PloS one · 2023Article
- Forecasting daily COVID-19 cases with gradient boosted regression trees and other methods: evidence from U.S. cities.Frontiers in public health · 2023Article
- Machine Learning and COVID-19: Lessons from SARS-CoV-2.Advances in experimental medicine and biology · 2023Article
- A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh.PloS one · 2023Article
- Forecasting on Covid-19 infection waves using a rough set filter driven moving average models.Applied soft computing · 2022Article
- PAN-cODE: COVID-19 forecasting using conditional latent ODEs.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Twitter conversations predict the daily confirmed COVID-19 cases.Applied soft computing · 2022Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsIn a little over six months, the Corona virus epidemic has affected over ten million and killed over half a million people worldwide as on June 30, 2020. With no vaccine in sight, the spread of the virus is likely to continue unabated. This article aims to analyze the time series data for top five countries affected by the COVID-19 for forecasting the spread of the epidemic. MATERIAL AND
methodsDaily time series data from 15th February to June 30, 2020 of total infected cases from the top five countries namely US, Brazil, India, Russia and Spain were collected from the online database. ARIMA model specifications were estimated using Hannan and Rissanen algorithm. Out of sample forecast for the next 77 days was computed using the ARIMA models.
resultsForecast for the first 18 days of July was compared with the actual data and the forecast accuracy was using MAD and MAPE were found within acceptable agreement. The graphic plots of forecast data suggest that While Russia and Spain have reached the inflexion point in the spread of epidemic, the US, Brazil and India are still experiencing an exponential curve.
conclusionOur analysis shows that India and Brazil will hit 1.38 million and 2.47 million mark while the US will reach the 4.29 million mark by 31st July. With no effective cure available at the moment, this forecast will help the governments to be better prepared to combat the epidemic by ramping up their healthcare facilities.
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