Skip Navigation
Skip to contents

PHRP : Osong Public Health and Research Perspectives

OPEN ACCESS
SEARCH
Search

Search

Page Path
HOME > Search
2 "Forecasting"
Filter
Filter
Article category
Keywords
Publication year
Authors
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
  • 4,205 View
  • 121 Download
  • 2 Citations
AbstractAbstract 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.

Citations

Citations to this article as recorded by  
  • Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic
    Jose M. Martin-Moreno, Antoni Alegre-Martinez, Victor Martin-Gorgojo, Jose Luis Alfonso-Sanchez, Ferran Torres, Vicente Pallares-Carratala
    International Journal of Environmental Research an.2022; 19(9): 5546.     CrossRef
  • Artificial intelligence and clinical deterioration
    James Malycha, Stephen Bacchi, Oliver Redfern
    Current Opinion in Critical Care.2022; 28(3): 315.     CrossRef
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
  • 1,892 View
  • 15 Download
  • 21 Citations
AbstractAbstract 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

Citations to this article as recorded by  
  • Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia
    Ammar H. Elsheikh, Amal I. Saba, Mohamed Abd Elaziz, Songfeng Lu, S. Shanmugan, T. Muthuramalingam, Ravinder Kumar, Ahmed O. Mosleh, F.A. Essa, Taher A. Shehabeldeen
    Process Safety and Environmental Protection.2021; 149: 223.     CrossRef
  • Forecasting future HIV infection cases: evidence from Indonesia
    Maria Dyah Kurniasari, Andrian Dolfriandra Huruta, Hsiu Ting Tsai, Cheng-Wen Lee
    Social Work in Public Health.2021; 36(1): 12.     CrossRef
  • Forecasting Confirmed Malaria Cases in Northwestern Province of Zambia: A Time Series Analysis Using 2014–2020 Routine Data
    Dhally M. Menda, Mukumbuta Nawa, Rosemary K. Zimba, Catherine M. Mulikita, Jim Mwandia, Henry Mwaba, Karen Sichinga, Hamidreza Karimi-Sari
    Advances in Public Health.2021; 2021: 1.     CrossRef
  • An Adaptive Variational Mode Decomposition Technique with Differential Evolution Algorithm and Its Application Analysis
    Yuanxin Wang, Chaoqun Duan
    Shock and Vibration.2021; 2021: 1.     CrossRef
  • A comparative study on the prediction of the BP artificial neural network model and the ARIMA model in the incidence of AIDS
    Zeming Li, Yanning Li
    BMC Medical Informatics and Decision Making.2020;[Epub]     CrossRef
  • Hybrid Decomposition Time-Series Forecasting by DirRec Strategy: Electric Load Forecasting Using Machine-Learning
    Branislav Vuksanovic, Davoud Rahimi Ardali
    International Journal of Computer and Electrical E.2019; 11(1): 1.     CrossRef
  • Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction Problem of Gas Consumption in Greece
    Papageorgiou, Poczeta, Papageorgiou, Gerogiannis, Stamoulis
    Algorithms.2019; 12(11): 235.     CrossRef
  • APLIKASI METODE DOUBLE EXPONENTIAL SMOOTHING HOLT DAN ARIMA UNTUK MERAMALKAN VOLUNTARY COUNSELING AND TESTING (VCT) ODHA DI PROVINSI JAWA TIMUR
    Suci Retno Ningtiyas
    The Indonesian Journal of Public Health.2019; 13(2): 158.     CrossRef
  • Research into the high-precision marine integrated navigation method using INS and star sensors based on time series forecasting BPNN
    Qiu Ying Wang, Kaiyue Liu, Zhiguo Sun, Minghui Zhang
    Optik.2018; 172: 494.     CrossRef
  • Real-time predictive seasonal influenza model in Catalonia, Spain
    Luca Basile, Manuel Oviedo de la Fuente, Nuria Torner, Ana Martínez, Mireia Jané, Jeffrey Shaman
    PLOS ONE.2018; 13(3): e0193651.     CrossRef
  • Using an Autoregressive Integrated Moving Average Model to Predict the Incidence of Hemorrhagic Fever with Renal Syndrome in Zibo, China, 2004–2014
    Tao Wang, Yunping Zhou, Ling Wang, Zhenshui Huang, Feng Cui, Shenyong Zhai
    Japanese Journal of Infectious Diseases.2016; 69(4): 279.     CrossRef
  • Time series analysis of influenza incidence in Chinese provinces from 2004 to 2011
    Xin Song, Jun Xiao, Jiang Deng, Qiong Kang, Yanyu Zhang, Jinbo Xu
    Medicine.2016; 95(26): e3929.     CrossRef
  • Modelling the prevalence of hepatitis C virus amongst blood donors in Libya: An investigation of providing a preventive strategy
    Mohamed A Daw
    World Journal of Virology.2016; 5(1): 14.     CrossRef
  • Forecast analysis of any opportunistic infection among HIV positive individuals on antiretroviral therapy in Uganda
    John Rubaihayo, Nazarius M. Tumwesigye, Joseph Konde-Lule, Fredrick Makumbi
    BMC Public Health.2016;[Epub]     CrossRef
  • The Use of an Autoregressive Integrated Moving Average Model for Prediction of the Incidence of Dysentery in Jiangsu, China
    Kewei Wang, Wentao Song, Jinping Li, Wu Lu, Jiangang Yu, Xiaofeng Han
    Asia Pacific Journal of Public Health.2016; 28(4): 336.     CrossRef
  • Prevalence of hemorrhagic fever with renal syndrome in Yiyuan County, China, 2005–2014
    Tao Wang, Jie Liu, Yunping Zhou, Feng Cui, Zhenshui Huang, Ling Wang, Shenyong Zhai
    BMC Infectious Diseases.2015;[Epub]     CrossRef
  • Application of an autoregressive integrated moving average model for predicting injury mortality in Xiamen, China
    Yilan Lin, Min Chen, Guowei Chen, Xiaoqing Wu, Tianquan Lin
    BMJ Open.2015; 5(12): e008491.     CrossRef
  • Back propagation neural network with adaptive differential evolution algorithm for time series forecasting
    Lin Wang, Yi Zeng, Tao Chen
    Expert Systems with Applications.2015; 42(2): 855.     CrossRef
  • Direct Medical Costs of Hospitalizations for Cardiovascular Diseases in Shanghai, China
    Shengnan Wang, Max Petzold, Junshan Cao, Yue Zhang, Weibing Wang
    Medicine.2015; 94(20): e837.     CrossRef
  • Changing Patterns of HIV Epidemic in 30 Years in East Asia
    S. Pilar Suguimoto, Teeranee Techasrivichien, Patou Masika Musumari, Christina El-saaidi, Bhekumusa Wellington Lukhele, Masako Ono-Kihara, Masahiro Kihara
    Current HIV/AIDS Reports.2014; 11(2): 134.     CrossRef
  • What is Next for HIV/AIDS in Korea?
    Hae-Wol Cho, Chaeshin Chu
    Osong Public Health and Research Perspectives.2013; 4(6): 291.     CrossRef

PHRP : Osong Public Health and Research Perspectives