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CN 34-1304/RISSN 1674-3679

Volume 28 Issue 1
Jan.  2024
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LIU Han, ZONG Huiying, HU Guoqing. Advances in consistency and periodicity across major global COVID-19 data sources[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(1): 108-111. doi: 10.16462/j.cnki.zhjbkz.2024.01.017
Citation: LIU Han, ZONG Huiying, HU Guoqing. Advances in consistency and periodicity across major global COVID-19 data sources[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(1): 108-111. doi: 10.16462/j.cnki.zhjbkz.2024.01.017

Advances in consistency and periodicity across major global COVID-19 data sources

doi: 10.16462/j.cnki.zhjbkz.2024.01.017
Funds:

The National Social Science Foundation of China 20&ZD120

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  • Corresponding author: HU Guoqing, E-mail: huguoqing009@gmail.com
  • Received Date: 2022-12-28
  • Rev Recd Date: 2023-03-20
  • Available Online: 2024-02-05
  • Publish Date: 2024-01-10
  • Using rigorous methods to identify and interpret the consistency and periodicity of multi-source COVID-19 epidemic data is the basis of properly interpreting the epidemic characteristics of the COVID-19 epidemic and developing prevention and control measures. By conducting a systematic review, we found that: (1) the existing literature on consistency studies mainly focused on global COVID-19 epidemic data released by the WHO and Johns Hopkins University, revealing inconsistent across various aspects of data collection, statistical indicators, data sharing, as well as the value of the same indicators; (2) each kind COVID-19 epidemic data had typical periodicity. Existing studies about periodicity of COVID-19 epidemic data were primarily from developed countries. The length of epidemic periodicity varied greatly across countries and regions, with a varying periodicity of 3.5 days, 5.0 days, 7.0 days, and 62.0 days. Researchers linked the periodicity of COVID-19 epidemic to multiple factors, including sample testing, data reporting, biological, social, and environmental factors. To summarize, there were no studies that quantified the consistency across major COVID-19 data sources, examined differences in data consistency over time and across countries, and explored the periodicity variations of COVID-19 epidemic across countries around the world currently.
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  • [1]
    Erdem S, Ipek F, Bars A, et al. Investigating the effect of macro-scale estimators on worldwide COVID-19 occurrence and mortality through regression analysis using online country-based data sources[J]. BMJ Open, 2022, 12(2): e055562. DOI: 10.1136/bmjopen-2021-055562.
    [2]
    Chen YY, Klein SL, Garibaldi BT, et al. Aging in COVID-19: vulnerability, immunity and intervention[J]. Ageing Res Rev, 2021, 65: 101205. DOI: 10.1016/j.arr.2020.101205.
    [3]
    Asselah T, Durantel D, Pasmant E, et al. COVID-19: discovery, diagnostics and drug development[J]. J Hepatol, 2021, 74(1): 168-184. DOI: 10.1016/j.jhep.2020.09.031.
    [4]
    Li H, Liu Z, Ge JB. Scientific research progress of COVID-19/SARS-CoV-2 in the first five months[J]. J Cell Mol Med, 2020, 24(12): 6558-6570. DOI: 10.1111/jcmm.15364.
    [5]
    Gaglione D, Braca P, Millefiori LM, et al. Adaptive bayesian learning and forecasting of epidemic evolution-data analysis of the COVID-19 outbreak[J]. IEEE Access, 2020, 8: 175244-175264. DOI: 10.1109/access.2020.3019922.
    [6]
    Dong ES, Du HR, Gardner L. An interactive web-based dashboard to track COVID-19 in real time[J]. Lancet Infect Dis, 2020, 20(5): 533-534. DOI: 10.1016/s1473-3099(20)30120-1.
    [7]
    Gallo Marin B, Aghagoli G, Lavine K, et al. Predictors of COVID-19 severity: a literature review[J]. Rev Med Virol, 2021, 31(1): 1-10. DOI: 10.1002/rmv.2146.
    [8]
    Sundar S, Schwab P, Tan JZH, et al. Forecasting the COVID-19 Pandemic: Lessons learned and future directions[J]. medRxiv, 2021: 2021.11.03.21266007. DOI: 10.1101/2021.11.06.21266007.
    [9]
    Adlhoch C, Gomes HC. Sustainability of surveillance systems for SARS-CoV-2[J]. Lancet Infect Dis, 2022, 22(7): 914-915. DOI: 10.1016/S1473-3099(22)00174-8.
    [10]
    Doornik JA, Castle JL, Hendry DF. Modeling and forecasting the COVID-19 pandemic time-series data[J]. Soc Sci Q, 2021, 102(5): 2070-2087. DOI: 10.1111/ssqu.13008.
    [11]
    Zuo X, Chen Y, Ohno-Machado L, et al. How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles[J]. Brief Bioinform, 2021, 22(2): 800-811. DOI: 10.1093/bib/bbaa331.
    [12]
    Alamo T, Reina D, Mammarella M, et al. COVID-19: open-data resources for monitoring, modeling, and forecasting the epidemic[J]. Electronics, 2020, 9(5). DOI: 10.3390/electronics9050827.
    [13]
    Akdi Y, Emre Karamanoǧlu Y, Ünlü KD, et al. Identifying the cycles in COVID-19 infection: the case of Turkey[J]. J Appl Stat, 2023, 50(11-12): 2360-2372. DOI: 10.1128/mSystems.00700-20.
    [14]
    Pavlicek T, Rehak P, Kral P. Oscillatory dnamics in infectivity and death rates of COVID-19[J]. mSystems, 2020, 5(4): e00700-20. DOI: 10.1128/mSystems.00700-20.
    [15]
    Bergman A, Sella Y, Agre P, et al. Oscillations in U.S. COVID-19 incidence and mortality data reflect diagnostic and reporting factors[J]. mSystems, 2020, 5(4): e00544-e00520. DOI: 10.1128/mSystems.00544-20.
    [16]
    Ricon-Becker I, Tarrasch R, Blinder P, et al. A seven-day cycle in COVID-19 infection and mortality rates: are intergenerational social interactions on the weekends killing susceptible people?[J]. medRxiv preprint, 2020. DOI: 10.1101/2020.05.03.20089508.
    [17]
    Kundu S, Carrasco LR, Kini RM. Lifestyle-induced stress drives the natural immune oscillations and COVID-19 infections[J]. Preprints, 2020. DOI: 10.20944/preprints202007.0009.v1.
    [18]
    Oshinubi K, Amakor A, Peter OJ, et al. Approach to COVID-19 time series data using deep learning and spectral analysis methods[J]. AIMS Bioengineering, 2021, 9(1): 1-21. DOI: 10.3934/bioeng.2022001.
    [19]
    Huang JP, Liu XY, Zhang L, et al. The oscillation-outbreaks characteristic of the COVID-19 pandemic[J]. Natl Sci Rev, 2021, 8(8): nwab100. DOI: 10.1093/nsr/nwab100.
    [20]
    耿雪倩, 常畅, 薛晓玮, 等. 中国省级行政单位新冠疫情数据的预测研究: 基于函数型数据分析方法[C]//中国统计教育学会, 教育部高等学校统计学类专业教学指导委员会, 全国应用统计专业学位研究生教育指导委员会. 2021年(第七届)全国大学生统计建模大赛获奖论文集(一). 唐山: 华北理工大学, 2021: 47.

    Geng XQ, Chang C, Xue XW, et al. Study on the prediction of epidemic data in COVID-19, a provincial administrative unit in China: Based on functional data analysis method[C]// China Statistical Education Society, the Teaching Steering Committee of Statistics Specialty in Colleges and Universities of the Ministry of Education, and the National Postgraduate Education Steering Committee of Applied Statistics Specialty. Collection of Award-winning Essays of the Seventh National Statistical Modeling Competition for College Students in 2021 (Ⅰ). Tangshan: North China University of Science and Technology, 2021: 47.
    [21]
    Moriyama M, Hugentobler WJ, Iwasaki A. Seasonality of respiratory viral infections[J]. Annu Rev Virol, 2020, 7(1): 83-101. DOI: 10.1146/annurev-virology-012420-022445.
    [22]
    Dowell SF, Ho MS. Seasonality of infectious diseases and severe acute respiratory syndrome-what we don't know can hurt us[J]. Lancet Infect Dis, 2004, 4(11): 704-708. DOI: 10.1016/S1473-3099(04)01177-6.
    [23]
    Fisman DN. Seasonality of infectious diseases[J]. Annu Rev Public Health, 2007, 28: 127-143. DOI: 10.1146/annurev.publhealth.28.021406.144128.
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