摘要: 傳染病嚴(yán)重威脅人類生命健康，對其進(jìn)行早期預(yù)測預(yù)警是防控傳染病的關(guān)鍵。利用傳統(tǒng)方法對傳染病進(jìn)行預(yù)警已不能滿足當(dāng)前傳染病疫情早期預(yù)警的需求。隨著大數(shù)據(jù)時(shí)代的到來，基于互聯(lián)網(wǎng)大數(shù)據(jù)的傳染病預(yù)測預(yù)警技術(shù)研究成為研究熱點(diǎn)。本文結(jié)合案例，對基于互聯(lián)網(wǎng)大數(shù)據(jù)的傳染病預(yù)測預(yù)警方法和模型最新研究進(jìn)展進(jìn)行綜述，旨在為公共衛(wèi)生相關(guān)領(lǐng)域的研究人員提供參考。

關(guān)鍵詞: 傳染病  /  大數(shù)據(jù)  /  預(yù)測  /  預(yù)警  

Abstract: Infectious disease epidemics are a serious threat to human life and health. The prediction and early warning of infectious disease epidemics are the keys to the prevention of infectious diseases. The early warning of infectious disease epidemics based on traditional methods cannot meet the needs of current early warning of infectious disease epidemics. With the construction and application of big data, the researche on infectious disease epidemic prediction and early warning technology based on internet big data has become one of research hot spots. In this study, we reviewed internet big data based methods and models for the prediction and early warning of infectious disease epidemic from a technical point of view for providing references to researchers engaged in the field.

[1]

Heymann DL, Rodier GR. Hot spots in a wired world: WHO surveillance of emerging and re-emerging infectious diseases[J]. The Lancet Infectious Diseases, 2001, 1(5): 345 – 353. DOI: 10.1016/S1473-3099(01)00148-7

[2]

Grein TW, Kamara KB, Rodier G, et al. Rumors of disease in the global village: outbreak verification[J]. Emerging Infectious Diseases, 2000, 6(2): 97 – 102. DOI: 10.3201/eid0602.000201

[3]

George DB, Taylor W, Shaman J, et al. Technology to advance infectious disease forecasting for outbreak management[J]. Nature Communications, 2019, 10(1): 3932. DOI: 10.1038/s41467-019-11901-7

[4] 余艷妮, 聶紹發(fā), 廖青, 等. 傳染病預(yù)測及模型選擇研究進(jìn)展[J]. 公共衛(wèi)生與預(yù)防醫(yī)學(xué), 2018, 29(5): 89 – 92. [5]

Graham-Rowe D. Big data: the next Google[J]. Nature, 2008, 455(7209): 8 – 9. DOI: 10.1038/455008a

[6] 王妍, 王婉婷. 互聯(lián)網(wǎng)大數(shù)據(jù)時(shí)代背景下大學(xué)生碎片化學(xué)習(xí)的策略研究[J]. 中國信息化, 2021(6): 71 – 72. DOI: 10.3969/j.issn.1672-5158.2021.06.030 [7]

De Mauro A, Greco M, Grimaldi M. What is big data? A consensual definition and a review of key research topics[J]. AIP Conference Proceedings, 2015, 1644(1): 97 – 104.

[8] 劉長娜, 劉軍, 韓冬. 傳染病監(jiān)測與監(jiān)測大數(shù)據(jù)應(yīng)用的研究進(jìn)展[J]. 職業(yè)與健康, 2021, 37(6): 844 – 846, 850. [9]

France SL, Shi YY, Kazandjian B. Web Trends: a valuable tool for business research[J]. Journal of Business Research, 2021, 132: 666 – 679. DOI: 10.1016/j.jbusres.2020.10.019

[10]

Peiris JSM, Tu WW, Yen HL. A novel H1N1 virus causes the first pandemic of the 21st century[J]. European Journal of Immunology, 2009, 39(11): 2946 – 2954. DOI: 10.1002/eji.200939911

[11]

Ginsberg J, Mohebbi MH, Patel RS, et al. Detecting influenza epidemics using search engine query data[J]. Nature, 2009, 457(7232): 1012 – 1014. DOI: 10.1038/nature07634

[12]

Olson DR, Konty KJ, Paladini M, et al. Reassessing Google flu trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales[J]. PLoS Computational Biology, 2013, 9(10): e1003256. DOI: 10.1371/journal.pcbi.1003256

[13]

Butler D. When Google got flu wrong[J]. Nature, 2013, 494(7436): 155 – 156. DOI: 10.1038/494155a

[14]

Cook S, Conrad C, Fowlkes AL, et al. Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic[J]. PLoS One, 2011, 6(8): e23610. DOI: 10.1371/journal.pone.0023610

[15]

Doornik JA. Improving the timeliness of data on influenza-like illnesses using Google search data[A]. 8th OxMetrics User Con-ference[C]. Washington, D.C.: George Washington University, 2010.

[16]

Kandula S, Shaman J. Reappraising the utility of Google flu trends[J]. PLoS Computational Biology, 2019, 15(8): e1007258. DOI: 10.1371/journal.pcbi.1007258

[17] 董曉春, 李琳, 徐文體, 等. 特定關(guān)鍵詞及百度指數(shù)與流感病毒活動相關(guān)性分析[J]. 中國公共衛(wèi)生, 2016, 32(11): 1543 – 1546. DOI: 10.11847/zgggws2016-32-11-25 [18] 白寧, 郁磊, 靳禎. 基于百度指數(shù)的人感染H7N9禽流感疫情預(yù)測[J]. 公共衛(wèi)生與預(yù)防醫(yī)學(xué), 2018, 29(6): 8 – 12. [19]

Bu Y, Bai JH, Chen Z, et al. The study on China′s flu prediction model based on web search data[J]. Journal of Data Analysis and Information Processing, 2018, 6(3): 79 – 92. DOI: 10.4236/jdaip.2018.63006

[20]

Guo P, Zhang JJ, Wang L, et al. Monitoring seasonal influenza epidemics by using internet search data with an ensemble penalized regression model[J]. Scientific Reports, 2017, 7: 46469. DOI: 10.1038/srep46469

[21]

Collier N, Son NT, Nguyen NM. OMG U got flu? Analysis of shared health messages for bio-surveillance[J]. Journal of Biomedical Semantics, 2011, 2(5): S9.

[22]

Kurian SJ, Bhatti AUR, Alvi MA, et al. Correlations between COVID-19 cases and Google trends data in the United States: a state-by-state analysis[J]. Mayo Clinic Proceedings, 2020, 95(11): 2370 – 2381. DOI: 10.1016/j.mayocp.2020.08.022

[23]

Yuan XL, Xu J, Hussain S, et al. Trends and prediction in daily new cases and deaths of COVID-19 in the United States: an internet search-interest based model[J]. Exploratory Research and Hypothesis in Medicine, 2020, 5(2): 41 – 46.

[24]

Jimenez AJ, Estevez-Reboredo RM, Santed MA, et al. COVID-19 symptom-related Google searches and local COVID-19 incidence in Spain: correlational study[J]. Journal of Medical Internet Research, 2020, 22(12): e23518. DOI: 10.2196/23518

[25]

Li CL, Chen LJ, Chen XY, et al. Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020[J]. Eurosurveillance, 2020, 25(10): 2000199.

[26]

Brownstein JS, Freifeld CC. HealthMap: the development of auto-mated real-time internet surveillance for epidemic intelligence[J]. Weekly Releases (1997 – 2007), 2007, 12(48): 3322.

[27]

Cho A. AI systems aim to sniff out coronavirus outbreaks[J]. Science, 2020, 368(6493): 810 – 811. DOI: 10.1126/science.368.6493.810

[28]

Chan EH, Sahai V, Conrad C, et al. Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance[J]. PLoS Neglected Tropical Diseases, 2011, 5(5): e1206. DOI: 10.1371/journal.pntd.0001206

[29]

Althouse BM, Ng YY, Cummings DAT. Prediction of dengue incidence using search query surveillance[J]. PLoS Neglected Tropical Diseases, 2011, 5(8): e1258. DOI: 10.1371/journal.pntd.0001258

[30]

Li ZH, Liu T, Zhu GH, et al. Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: a case study in Guangzhou, China[J]. PLoS Neglected Tropical Diseases, 2017, 11(3): e0005354. DOI: 10.1371/journal.pntd.0005354

[31]

Wang J, Jiang LN, Zhang C, et al. The changes in the epidemiology of hand, foot, and mouth disease after the introduction of the EV-A71 vaccine[J]. Vaccine, 2021, 39(25): 3319 – 3323. DOI: 10.1016/j.vaccine.2021.05.009

[32]

Yang Z, Xu QN, Chen YP, et al. Using Baidu index to nowcast hand-foot-mouth disease in China: a meta learning approach[J]. BMC Infectious Diseases, 2018, 18(1): 398. DOI: 10.1186/s12879-018-3285-4

[33]

Atmar RL, Ramani S. Birth cohort studies: toward understanding protective immunity to human noroviruses[J]. Clinical Infectious Diseases, 2021, 72(2): 230 – 232. DOI: 10.1093/cid/ciaa025

[34] 吳昊澄, 吳晨, 魯琴寶, 等. 基于百度指數(shù)的諾如病毒感染暴發(fā)疫情預(yù)警研究[J/OL]. 中國預(yù)防醫(yī)學(xué)雜志: 1 – 5[2021 – 07 – 09]. http://kns.cnki.net/kcms/detail/11.4529.R.20210329.1731.002.html. [35]

Shin SY, Seo DW, An J, et al. High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea[J]. Scientific Reports, 2016, 6: 32920. DOI: 10.1038/srep32920

[36]

Milinovich GJ, Magalh?es RJS, Hu WB. Role of big data in the early detection of Ebola and other emerging infectious diseases[J]. The Lancet Global Health, 2015, 3(1): e20 – e21. DOI: 10.1016/S2214-109X(14)70356-0

[37]

McGough SF, Brownstein JS, Hawkins JB, et al. Forecasting Zika incidence in the 2016 Latin America outbreak combining traditional disease surveillance with search, social media, and news report data[J]. PLoS Neglected Tropical Diseases, 2017, 11(1): e0005295. DOI: 10.1371/journal.pntd.0005295

[38]

Milinovich GJ, Avril SMR, Clements ACA, et al. Using internet search queries for infectious disease surveillance: screening diseases for suitability[J]. BMC Infectious Diseases, 2014, 14: 690. DOI: 10.1186/s12879-014-0690-1

相關(guān)知識

國務(wù)院辦公廳關(guān)于促進(jìn)和規(guī)范健康醫(yī)療大數(shù)據(jù)應(yīng)用發(fā)展的指導(dǎo)意見
2021年中國互聯(lián)網(wǎng)女性健康管理行業(yè)市場消費(fèi)規(guī)模研究預(yù)測及未來市場發(fā)展建議分析
關(guān)于深入推進(jìn)“互聯(lián)網(wǎng)+醫(yī)療健康”“五個(gè)一”服務(wù)行動的通知
研究進(jìn)展
環(huán)境污染與先天性心臟病的研究進(jìn)展
中國健康醫(yī)療大數(shù)據(jù)研究綜述——基于期刊論文的分析
橋梁健康監(jiān)測數(shù)據(jù)分析研究綜述.pdf
健康數(shù)據(jù)分析與預(yù)測
國家健康醫(yī)療大數(shù)據(jù)研究院落戶寧夏
基于健康數(shù)據(jù)采集的健康大數(shù)據(jù)分析.pptx

網(wǎng)址: 基于互聯(lián)網(wǎng)大數(shù)據(jù)的傳染病預(yù)測預(yù)警研究進(jìn)展 http://www.u1s5d6.cn/newsview65064.html