Trend prediction of COVID-19 based on ARIMA model in mainland of China
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Abstract
The ongoing pandemic of COVID-19 has aroused widespread concern around the world and poses a severe threat to public health worldwide. In this paper, the autoregressive integrated moving average (ARIMA) model was used to predict the epidemic trend of COVID-19 in mainland of China. We collected the cumulative cases, cumulative deaths, and cumulative recovery in mainland of China from January 20 to June 30, 2020, and divided the data into experimental group and test group. The ARIMA model was fitted with the experimental group data, and the optimal model was selected for prediction analysis. The predicted data were compared with the test group. The average relative errors of actual cumulative cases, deaths, recovery and predicted values in each province are between −22.32%−22.66%, −9.52%−0.08%, −8.84%−1.16, the results of the comprehensive experimental group and test group show The error of fitting and prediction is small, the degree of fitting is good, the model supports and is suitable for the prediction of the epidemic situation, which has practical guiding significance for the prevention and control of the epidemic situation.
Highlights:
We predicted future COVID-19 occurrences in mainland of China based on ARIMA model.
We validated the model based on the previous outbreak data with actual data for June, 2020.
The measures taken by the government have contained spread of the epidemic
The combination of multiple models may improve the robustness of the model
Article activity feed
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SciScore for 10.1101/2020.09.04.20188235: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:However, ARIMA model also has certain limitations. It is a mathematical model built on past historical data. Therefore, ARIMA model is only suitable for short-term forecasting. If the forecasting time is too long, it will increase the forecasting error and affect the forecasting accuracy[16–18]. At present, COVID-19 has become a global …
SciScore for 10.1101/2020.09.04.20188235: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:However, ARIMA model also has certain limitations. It is a mathematical model built on past historical data. Therefore, ARIMA model is only suitable for short-term forecasting. If the forecasting time is too long, it will increase the forecasting error and affect the forecasting accuracy[16–18]. At present, COVID-19 has become a global epidemic, and the epidemic situation is still accelerating and has not yet reached its peak. The prevention and control of the epidemic situation must not be delayed. Based on the COVID-19 data of mainland of China, we found that the model has a high degree of fitting, which can predict the development trend of the epidemic situation in the future. However, in practice, other factors[19–22] will also affect the development trend of the epidemic situation, causing predictions to deviate, this needs further study. In practical applications, we can make the model have better prediction effect and accuracy by combining with multiple models.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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