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Wang Y, Yan Z, Wang D, Yang M, Li Z, Gong X, Wu D, Zhai L, Zhang W, Wang Y. Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models. BMC Infect Dis 2022; 22:495. [PMID: 35614387 PMCID: PMC9131989 DOI: 10.1186/s12879-022-07472-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/17/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND COVID-19 poses a severe threat to global human health, especially the USA, Brazil, and India cases continue to increase dynamically, which has a far-reaching impact on people's health, social activities, and the local economic situation. METHODS The study proposed the ARIMA, SARIMA and Prophet models to predict daily new cases and cumulative confirmed cases in the USA, Brazil and India over the next 30 days based on the COVID-19 new confirmed cases and cumulative confirmed cases data set(May 1, 2020, and November 30, 2021) published by the official WHO, Three models were implemented in the R 4.1.1 software with forecast and prophet package. The performance of different models was evaluated by using root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). RESULTS Through the fitting and prediction of daily new case data, we reveal that the Prophet model has more advantages in the prediction of the COVID-19 of the USA, which could compose data components and capture periodic characteristics when the data changes significantly, while SARIMA is more likely to appear over-fitting in the USA. And the SARIMA model captured a seven-day period hidden in daily COVID-19 new cases from 3 countries. While in the prediction of new cumulative cases, the ARIMA model has a better ability to fit and predict the data with a positive growth trend in different countries(Brazil and India). CONCLUSIONS This study can shed light on understanding the outbreak trends and give an insight into the epidemiological control of these regions. Further, the prediction of the Prophet model showed sufficient accuracy in the daily COVID-19 new cases of the USA. The ARIMA model is suitable for predicting Brazil and India, which can help take precautions and policy formulation for this epidemic in other countries.
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Affiliation(s)
- Yanding Wang
- School of Public Health, China Medical University, Shenyang, 110122, China.,Chinese PLA Center for Disease Control and Prevention, Beijing, 100071, China
| | - Zehui Yan
- School of Public Health, China Medical University, Shenyang, 110122, China
| | - Ding Wang
- School of Science, Beijing University of Posts and Telecommunications, Beijing, China
| | - Meitao Yang
- School of Public Health, China Medical University, Shenyang, 110122, China.,Chinese PLA Center for Disease Control and Prevention, Beijing, 100071, China
| | - Zhiqiang Li
- School of Public Health, China Medical University, Shenyang, 110122, China.,Chinese PLA Center for Disease Control and Prevention, Beijing, 100071, China
| | - Xinran Gong
- School of Public Health, China Medical University, Shenyang, 110122, China.,Chinese PLA Center for Disease Control and Prevention, Beijing, 100071, China
| | - Di Wu
- School of Public Health, China Medical University, Shenyang, 110122, China.,Chinese PLA Center for Disease Control and Prevention, Beijing, 100071, China
| | - Lingling Zhai
- School of Public Health, China Medical University, Shenyang, 110122, China
| | - Wenyi Zhang
- Chinese PLA Center for Disease Control and Prevention, Beijing, 100071, China.
| | - Yong Wang
- School of Public Health, China Medical University, Shenyang, 110122, China. .,Chinese PLA Center for Disease Control and Prevention, Beijing, 100071, China.
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Yadav SK, Akhter Y. Response: Commentary: Statistical Modeling for the Prediction of Infectious Disease Dissemination With Special Reference to COVID-19 Spread. Front Public Health 2022; 9:783201. [PMID: 35174132 PMCID: PMC8842792 DOI: 10.3389/fpubh.2021.783201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 12/30/2021] [Indexed: 12/01/2022] Open
Affiliation(s)
- Subhash Kumar Yadav
- Department of Statistics, School of Physical and Decision Sciences, Babasaheb Bhimrao Ambedkar University, Lucknow, India
- *Correspondence: Subhash Kumar Yadav
| | - Yusuf Akhter
- Department of Biotechnology, School of Life Sciences, Babasaheb Bhimrao Ambedkar University, Lucknow, India
- Yusuf Akhter
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