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Review Article
- COVID-19 prediction models: a systematic literature review
- Sheikh Muzaffar Shakeel, Nithya Sathya Kumar, Pranita Pandurang Madalli, Rashmi Srinivasaiah, Devappa Renuka Swamy
- Osong Public Health Res Perspect. 2021;12(4):215-229. Published online August 13, 2021
- DOI: https://doi.org/10.24171/j.phrp.2021.0100
- 10,566 View
- 221 Download
- 21 Web of Science
- 20 Crossref
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Abstract
PDF - As the world grapples with the problem of the coronavirus disease 2019 (COVID-19) pandemic and its devastating effects, scientific groups are working towards solutions to mitigate the effects of the virus. This paper aimed to collate information on COVID-19 prediction models. A systematic literature review is reported, based on a manual search of 1,196 papers published from January to December 2020. Various databases such as Google Scholar, Web of Science, and Scopus were searched. The search strategy was formulated and refined in terms of subject keywords, geographical purview, and time period according to a predefined protocol. Visualizations were created to present the data trends according to different parameters. The results of this systematic literature review show that the study findings are critically relevant for both healthcare managers and prediction model developers. Healthcare managers can choose the best prediction model output for their organization or process management. Meanwhile, prediction model developers and managers can identify the lacunae in their models and improve their data-driven approaches.
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Citations
Citations to this article as recorded by
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Original Article
- Forecasting the Number of Human Immunodeficiency Virus Infections in the Korean Population Using the Autoregressive Integrated Moving Average Model
- Hye-Kyung Yu, Na-Young Kim, Sung Soon Kim, Chaeshin Chu, Mee-Kyung Kee
- Osong Public Health Res Perspect. 2013;4(6):358-362. Published online December 31, 2013
- DOI: https://doi.org/10.1016/j.phrp.2013.10.009
- 3,396 View
- 21 Download
- 24 Crossref
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Abstract
PDF
- Objectives
From the introduction of HIV into the Republic of Korea in 1985 through 2012, 9,410 HIV-infected Koreans have been identified. Since 2000, there has been a sharp increase in newly diagnosed HIV-infected Koreans. It is necessary to estimate the changes in HIV infection to plan budgets and to modify HIV/AIDS prevention policy. We constructed autoregressive integrated moving average (ARIMA) models to forecast the number of HIV infections from 2013 to 2017.
Methods
HIV infection data from 1985 to 2012 were used to fit ARIMA models. Akaike Information Criterion and Schwartz Bayesian Criterion statistics were used to evaluate the constructed models. Estimation was via the maximum likelihood method. To assess the validity of the proposed models, the mean absolute percentage error (MAPE) between the number of observed and fitted HIV infections from 1985 to 2012 was calculated. Finally, the fitted ARIMA models were used to forecast the number of HIV infections from 2013 to 2017.
Results
The fitted number of HIV infections was calculated by optimum ARIMA (2,2,1) model from 1985–2012. The fitted number was similar to the observed number of HIV infections, with a MAPE of 13.7%. The forecasted number of new HIV infections in 2013 was 962 (95% confidence interval (CI): 889–1,036) and in 2017 was 1,111 (95% CI: 805–1,418). The forecasted cumulative number of HIV infections in 2013 was 10,372 (95% CI: 10,308–10,437) and in 2017 was14,724 (95% CI: 13,893–15,555) by ARIMA (1,2,3).
Conclusion
Based on the forecast of the number of newly diagnosed HIV infections and the current cumulative number of HIV infections, the cumulative number of HIV-infected Koreans in 2017 would reach about 15,000. -
Citations
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- Intelligent Health Care and Diseases Management System: Multi-Day-Ahead Predictions of COVID-19
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