Acknowledgement
Supported by : Ministry of Health and Welfare
References
- Eckhoff PA, Tatem AJ. Digital methods in epidemiology can transform disease control. Int Health 2015;7(2):77-8. https://doi.org/10.1093/inthealth/ihv013
- Salathe M. Digital epidemiology: what is it, and where is it going? Life Sci Soc Policy 2018;14(1):1. https://doi.org/10.1186/s40504-017-0065-7
- Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature 2009;457(7232):1012-4. https://doi.org/10.1038/nature07634
- Nuti SV, Wayda B, Ranasinghe I, Wang S, Dreyer RP, Chen SI, et al. The use of google trends in health care research: a systematic review. PLoS One 2014;9(10):e109583. https://doi.org/10.1371/journal.pone.0109583
- Lazer D, Kennedy R, King G, Vespignani A. Big data: the parable of Google Flu: traps in big data analysis. Science 2014;343(6176):1203-5. https://doi.org/10.1126/science.1248506
- Signorini A, Segre AM, Polgreen PM. The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PLoS One 2011;6(5):e19467. https://doi.org/10.1371/journal.pone.0019467
- Salathe M, Khandelwal S. Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control. PLoS Comput Biol 2011;7(10):e1002199. https://doi.org/10.1371/journal.pcbi.1002199
- Freifeld CC, Brownstein JS, Menone CM, Bao W, Filice R, Kass-Hout T, et al. Digital drug safety surveillance: monitoring pharmaceutical products in twitter. Drug Saf 2014;37(5):343-50. https://doi.org/10.1007/s40264-014-0155-x
- McIver DJ, Brownstein JS. Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time. PLoS Comput Biol 2014;10(4):e1003581. https://doi.org/10.1371/journal.pcbi.1003581
- Wesolowski A, Eagle N, Tatem AJ, Smith DL, Noor AM, Snow RW, et al. Quantifying the impact of human mobility on malaria. Science 2012;338(6104):267-70. https://doi.org/10.1126/science.1223467
- Bengtsson L, Lu X, Thorson A, Garfield R, von Schreeb J. Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti. PLoS Med 2011;8(8):e1001083. https://doi.org/10.1371/journal.pmed.1001083
- Zaccai JH. How to assess epidemiological studies. Postgrad Med J 2004;80(941):140-7. https://doi.org/10.1136/pgmj.2003.012633
- Polit DF, Beck CT. Nursing research: generating and assessing evidence for nursing practice. 10th ed. Philadelphia (PA): Lippincott Williams & Wilkins; 2017.
- Sylvestre E, Bouzille G, Breton M, Cuggia M, Campillo-Gimenez B. Retrieving the vital status of patients with cancer using online obituaries. Stud Health Technol Inform 2018;247:571-5.
- Tourassi G, Yoon HJ, Xu S, Han X. The utility of web mining for epidemiological research: studying the association between parity and cancer risk. J Am Med Inform Assoc 2016;23(3):588-95. https://doi.org/10.1093/jamia/ocv141
- Edoh T. Risk prevention of spreading emerging infectious diseases using a hybridcrowdsensing paradigm, optical sensors, and smartphone. J Med Syst 2018;42(5):91. https://doi.org/10.1007/s10916-018-0937-2
- Moon RJ, Curtis EM, Davies JH, Cooper C, Harvey NC. Seasonal variation in Internet searches for vitamin D. Arch Osteoporos 2017;12(1):28. https://doi.org/10.1007/s11657-017-0322-7
- Chary M, Genes N, Giraud-Carrier C, Hanson C, Nelson LS, Manini AF. Epidemiology from Tweets: estimating misuse of prescription opioids in the USA from social media. J Med Toxicol 2017;13(4):278-86. https://doi.org/10.1007/s13181-017-0625-5
- Towers S, Afzal S, Bernal G, Bliss N, Brown S, Espinoza B, et al. Mass media and the contagion of fear: the case of Ebola in America. PLoS One 2015;10(6):e0129179. https://doi.org/10.1371/journal.pone.0129179
- Ram S, Zhang W, Williams M, Pengetnze Y. Predicting asthma-related emergency department visits using big data. IEEE J Biomed Health Inform 2015;19(4):1216-23. https://doi.org/10.1109/JBHI.2015.2404829
- Zhang X, Dang S, Ji F, Shi J, Li Y, Li M, et al. Seasonality of cellulitis: evidence from Google Trends. Infect Drug Resist 2018;11:689-93. https://doi.org/10.2147/IDR.S163290
- Miller M, Banerjee T, Muppalla R, Romine W, Sheth A. What are people tweeting about Zika? An exploratory study concerning its symptoms, treatment, transmission, and prevention. JMIR Public Health Surveill 2017;3(2):e38. https://doi.org/10.2196/publichealth.7157
- Phillips CA, Barz Leahy A, Li Y, Schapira MM, Bailey LC, Merchant RM. Relationship between state-level Google online search volume and cancer incidence in the United States: retrospective study. J Med Internet Res 2018;20(1):e6. https://doi.org/10.2196/jmir.8870
- McGough SF, Brownstein JS, Hawkins JB, Santillana M. Forecasting Zika incidence in the 2016 Latin America outbreak combining traditional disease surveillance with search, social media, and news report data. PLoS Negl Trop Dis 2017;11(1):e0005295. https://doi.org/10.1371/journal.pntd.0005295
- Generous N, Fairchild G, Deshpande A, Del Valle SY, Priedhorsky R. Global disease monitoring and forecasting with Wikipedia. PLoS Comput Biol 2014;10(11):e1003892. https://doi.org/10.1371/journal.pcbi.1003892
- Adrover C, Bodnar T, Huang Z, Telenti A, Salathe M. Identifying adverse effects of HIV drug treatment and associated sentiments using Twitter. JMIR Public Health Surveill 2015;1(2):e7. https://doi.org/10.2196/publichealth.4488
- Nsoesie EO, Buckeridge DL, Brownstein JS. Guess who's not coming to dinner? Evaluating online restaurant reservations for disease surveillance. J Med Internet Res 2014;16(1):e22. https://doi.org/10.2196/jmir.2998
- Valson JS, Soman B. Spatiotemporal clustering of dengue cases in Thiruvananthapuram district, Kerala. Indian J Public Health 2017;61(2):74-80.
- Park SE, Tang L, Bie B, Zhi D. All pins are not created equal: communicating skin cancer visually on Pinterest. Transl Behav Med 2018 Apr 17 [Epub]. https://doi.org/10.1093/tbm/iby044.
- Dettori M, Arru B, Azara A, Piana A, Mariotti G, Camerada MV, et al. In the digital era, is community outrage a feasible proxy indicator of emotional epidemiology? The case of meningococcal disease in Sardinia, Italy. Int J Environ Res Public Health 2018;15(7):E1512. https://doi.org/10.3390/ijerph15071512
- Roche B, Gaillard B, Leger L, Pelagie-Moutenda R, Sochacki T, Cazelles B, et al. An ecological and digital epidemiology analysis on the role of human behavior on the 2014 Chikungunya outbreak in Martinique. Sci Rep 2017;7(1):5967. https://doi.org/10.1038/s41598-017-05957-y
- Pollett S, Boscardin WJ, Azziz-Baumgartner E, Tinoco YO, Soto G, Romero C, et al. Evaluating Google Flu Trends in Latin America: important lessons for the next phase of digital disease detection. Clin Infect Dis 2017;64(1):34-41. https://doi.org/10.1093/cid/ciw657
- Hossain N, Househ M. Using HealthMap to analyse Middle East Respiratory Syndrome (MERS) data. Stud Health Technol Inform 2016;226:213-6.
Cited by
- Social Media- and Internet-Based Disease Surveillance for Public Health vol.41, pp.1, 2018, https://doi.org/10.1146/annurev-publhealth-040119-094402
- Digitalisierung und Gesundheitswissenschaften - White Paper Digital Public Health vol.82, pp.7, 2018, https://doi.org/10.1055/a-1191-4344
- Social media based surveillance systems for healthcare using machine learning: A systematic review vol.108, pp.None, 2018, https://doi.org/10.1016/j.jbi.2020.103500
- Digital traces of taste: methodological pathways for consumer research vol.24, pp.1, 2018, https://doi.org/10.1080/10253866.2019.1690998
- NextGen Public Health Surveillance and the Internet of Things (IoT) vol.9, pp.None, 2018, https://doi.org/10.3389/fpubh.2021.756675
- Digital public health surveillance: a systematic scoping review vol.4, pp.1, 2018, https://doi.org/10.1038/s41746-021-00407-6