Internet-based surveillance systems for monitoring emerging infectious diseases

Gabriel J Milinovich, Gail M Williams, Archie C A Clements, Wenbiao Hu, Gabriel J Milinovich, Gail M Williams, Archie C A Clements, Wenbiao Hu

Abstract

Emerging infectious diseases present a complex challenge to public health officials and governments; these challenges have been compounded by rapidly shifting patterns of human behaviour and globalisation. The increase in emerging infectious diseases has led to calls for new technologies and approaches for detection, tracking, reporting, and response. Internet-based surveillance systems offer a novel and developing means of monitoring conditions of public health concern, including emerging infectious diseases. We review studies that have exploited internet use and search trends to monitor two such diseases: influenza and dengue. Internet-based surveillance systems have good congruence with traditional surveillance approaches. Additionally, internet-based approaches are logistically and economically appealing. However, they do not have the capacity to replace traditional surveillance systems; they should not be viewed as an alternative, but rather an extension. Future research should focus on using data generated through internet-based surveillance and response systems to bolster the capacity of traditional surveillance systems for emerging infectious diseases.

Copyright © 2014 Elsevier Ltd. All rights reserved.

Figures

Figure 1
Figure 1
Internet access in resource-rich and resource-poor countries Data taken from the International Telecommunications Union.
Figure 2
Figure 2
Proportions of the population assessable by traditional and internet-based surveillance systems during an influenza epidemic in high-income and low-income countries Light blue sections correspond with fractions visible to internet-based surveillance systems in high-income countries, dark blue corresponds to low-income countries. Red sections indicate background noise. Grey sections indicate fractions not visible to internet-based surveillance systems. Adapted from Watson and Pebody.
Figure 3
Figure 3
Percentage of population who use the internet, by country 2012 data were used for all countries, except the British Virgin Islands (2010).

References

    1. Jones KE, Patel NG, Levy MA. Global trends in emerging infectious diseases. Nature. 2008;451:990–993.
    1. Wilson K, Brownstein JS. Early detection of disease outbreaks using the internet. Can Med Assoc J. 2009;180:829–831.
    1. Brachman PS. Public health surveillance. In: Brachman PS, Abrutyn E, editors. Bacterial infections of humans. Springer; US: 2009. pp. 51–67.
    1. Van Beneden CA, Lynfield R. Public health surveillance for infectious diseases. In: Lee LM, Teutsch SM, Thacker SB, St Louis ME, editors. Principles and practice of public health surveillance. 3rd edn. Oxford University Press; 2010. pp. 236–254.
    1. O'Connell EK, Zhang GY, Leguen F, Llau A, Rico E. Innovative uses for syndromic surveillance. Emerg Infect Dis. 2010;16:669–671.
    1. Doyle TJ, Glynn MK, Groseclose SL. Completeness of notifiable infectious disease reporting in the United States: an analytical literature review. Am J Epidemiol. 2002;155:866–874.
    1. Madoff LC, Fisman DN, Kass-Hout T. A new approach to monitoring dengue activity. PLoS Negl Trop Dis. 2011;5:e1215.
    1. Cheng CK, Lau EH, Ip DK, Yeung AS, Ho LM, Cowling BJ. A profile of the online dissemination of national influenza surveillance data. BMC Public Health. 2009;9:339.
    1. International Telecommunications Union World Telecommunication/ICT Indicators Database 2013 (17th edition) 2013. (accessed Julu 19, 2013).
    1. Rice RE. Influences, usage, and outcomes of internet health information searching: multivariate results from the Pew surveys. Int J Med Inform. 2006;75:8–28.
    1. Leung L. Internet embeddedness: links with online health information seeking, expectancy value/quality of health information websites, and internet usage patterns. Cyberpsychol Behav. 2008;11:565–569.
    1. Malik MT, Gumel A, Thompson LH, Strome T, Mahmud SM. “Google flu trends” and emergency department triage data predicted the 2009 pandemic H1N1 waves in Manitoba. Can J Public Health. 2011;102:294–297.
    1. Morse SS. Public health surveillance and infectious disease detection. Biosecur Bioterror. 2012;10:6–16.
    1. Keller M, Blench M, Tolentino H. Use of unstructured event-based reports for global infectious disease surveillance. Emerg Infect Dis. 2009;15:689–695.
    1. Mykhalovskiy E, Weir L. The Global Public Health Intelligence Network and early warning outbreak detection: a Canadian contribution to global public health. Can J Public Health. 2006;97:42–44.
    1. Freifeld CC, Mandl KD, Ras BY, Bronwnstein JS. HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports. J Am Med Inform Assoc. 2008;15:150–157.
    1. Madoff LC. ProMED-mail: an early warning system for emerging diseases. Clin Infect Dis. 2004;39:227–232.
    1. Pollack MP, Pringle C, Madoff LC, Memish ZA. Latest outbreak news from ProMED-mail: novel coronavirus—Middle East. Int J Infect Dis. 2013;17:e143–e144.
    1. Brownstein JS, Freifeld CC, Madoff LC. Digital disease detection—harnessing the web for public health surveillance. N Engl J Med. 2009;360:2153–2155.
    1. The World Bank Internet users (per 100 people) 2012. (accessed Dec 10, 2012).
    1. Fox S. Online health search 2006. Pew Internet and American Life Project; 2006.
    1. Sadasivam RS, Kinney RL, Lemon SC, Shimada SL, Allison JJ, Houston TK. Internet health information seeking is a team sport: analysis of the Pew Internet Survey. Int J Med Inform. 2013;82:193–200.
    1. Fox S. The social life of health information, 2011. Pew Internet and American Life Project; 2011.
    1. Carneiro HA, Mylonakis E. Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clin Infect Dis. 2009;49:1557–1564.
    1. Desai R, Hall AJ, Lopman BA. Norovirus disease surveillance using Google internet query share data. Clin Infect Dis. 2012;55:E75–E78.
    1. Desai R, Lopman BA, Shimshoni Y, Harris JP, Patel MM, Parashar UD. Use of internet search data to monitor impact of rotavirus vaccination in the United States. Clin Infect Dis. 2012;54:CP8–C11.
    1. Dukic VM, David MZ, Lauderdale DS. Internet queries and methicillin-resistant Staphylococcus aureus surveillance. Emerg Infect Dis. 2011;17:1068–1070.
    1. Pelat C, Turbelin C, Bar-Hen A, Flahault A, Valleron A. More diseases tracked by using Google Trends. Emerg Infect Dis. 2009;15:1327–1328.
    1. Samaras L, Garcia-Barriocanal E, Sicilia MA. Syndromic surveillance models using web data: the case of scarlet fever in the UK. Inform Health Soc Care. 2012;37:106–124.
    1. Seifter A, Schwarzwalder A, Geis K, Aucott J. The utility of “Google Trends” for epidemiological research: Lyme disease as an example. Geospatial Health. 2010;4:135–137.
    1. Valdivia A, Monge-Corella S. Diseases tracked by using Google trends, Spain. Emerg Infect Dis. 2010;16:168.
    1. Zhou X, Ye J, Feng Y. Tuberculosis surveillance by analyzing Google Trends. IEEE Trans Biomed Eng. 2011;58:2247–2254.
    1. Zhou XC, Shen HB. Notifiable infectious disease surveillance with data collected by search engine. J Zhejiang Univ-SCI C. 2010;11:241–248.
    1. Breyer BN, Sen S, Aaronson DS, Stoller ML, Erickson BA, Eisenberg ML. Use of Google Insights for search to track seasonal and geographic kidney stone incidence in the United States. Urology. 2011;78:267–271.
    1. Willard SD, Nguyen MM. Internet search trends analysis tools can provide real-time data on kidney stone disease in the United States. Urology. 2011;81:37–42.
    1. Reilly S, Richey S, Taylor JB. Using Google search data for state politics research: an empirical validity test using roll-off data. State Politics Policy Q. 2012;12:146–159.
    1. Chen YD, Brown SA, Hu PJH, King CC, Chen HC. Managing emerging infectious diseases with information systems: reconceptualizing outbreak management through the lens of loose coupling. Info Sys Res. 2011;22:447–468.
    1. Yin S, Ho M. Monitoring a toxicological outbreak using internet search query data. Clin Toxicol. 2012;50:818–822.
    1. Reis BY, Brownstein JS. Measuring the impact of health policies using internet search patterns: the case of abortion. BMC Public Health. 2010;10:514.
    1. Deutsch CM, Bronstein AC, Spyker DA. A spoonful of cinnamon: The “cinnamon challenge” — Google Trends and the National Poison Data System. Clin Toxicol. 2012;50:645.
    1. Polgreen PM, Chen Y, Pennock DM, Nelson FD. Using internet searches for influenza surveillance. Clin Infect Dis. 2008;47:1443–1448.
    1. Hulth A, Rydevik G, Linde A. Web queries as a source for syndromic surveillance. PLoS One. 2009;4:e4378.
    1. Hulth A, Rydevik G. Web query-based surveillance in Sweden during the influenza A(H1N1)2009 pandemic, April 2009 to February 2010. Euro Surveill. 2011;16 PII:19856.
    1. Yuan Q, Nsoesie EO, Lv B, Peng G, Chunara R, Brownstein JS. Monitoring influenza epidemics in China with search query from Baidu. PLoS One. 2013;8:e64323.
    1. Choi HY, Varian H. Predicting the present with Google Trends. Economic Record. 2012;88:2–9.
    1. Kang M, Zhong H, He J, Rutherford S, Yang F. Using Google Trends for influenza surveillance in South China. PLoS One. 2013;8:e55205.
    1. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457:1012–1014.
    1. Chan EH, Sahai V, Conrad C, Brownstein JS. Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance. PLoS Negl Trop Dis. 2011;5:e1206.
    1. Althouse BM, Ng YY, Cummings DA. Prediction of dengue incidence using search query surveillance. PLoS Negl Trop Dis. 2011;5:e1258.
    1. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd edn. Springer; New York: 2009.
    1. Kelly H, Grant K. Interim analysis of pandemic influenza (H1N1) 2009 in Australia: surveillance trends, age of infection and effectiveness of seasonal vaccination. Euro Surveill. 2009;14 PII:19288.
    1. Wilson N, Mason K, Tobias M, Peacey M, Huang QS, Baker M. Interpreting Google Flu Trends data for pandemic H1N1 influenza: the New Zealand experience. Euro Surveill. 2009:14.
    1. Boyle JR, Sparks RS, Keijzers GB, Crilly JL, Lind JF, Ryan LM. Prediction and surveillance of influenza epidemics. Med J Aust. 2011;194:S28–S33.
    1. Valdivia A, Lopez-Alcalde J, Vicente M, Pichiule M, Ruiz M, Ordobas M. Monitoring influenza activity in Europe with Google Flu Trends: comparison with the findings of sentinel physician networks — results for 2009–10. Euro Surveill. 2010;15 PII:19261.
    1. Pattie DC, Cox KL, Burkom HS, Lombardo JS, Gaydos JC. A public health role for internet search engine query data? Mil Med. 2009;174:11–12.
    1. Vandendijck Y, Faes C, Hens N. Eight years of the great influenza survey to monitor influenza-like illness in Flanders. PLoS One. 2013;8:e64156.
    1. Ortiz JR, Zhou H, Shay DK, Neuzil KM, Fowlkes AL, Goss CH. Monitoring influenza activity in the United States: a comparison of traditional surveillance systems with Google Flu Trends. PLoS One. 2011;6:e18687.
    1. Dugas AF, Hsieh YH, Levin SR. Google Flu Trends: correlation with emergency department influenza rates and crowding metrics. Clin Infect Dis. 2012;54:463–469.
    1. Patwardhan A, Bilkovski R. Comparison: flu prescription sales data from a retail pharmacy in the US with Google Flu Trends and US ILINet (CDC) data as flu activity indicator. PLoS One. 2012;7:e43611.
    1. Watts G. Google watches over flu. BMJ. 2008;337:a3076.
    1. McDonnell WM, Nelson DS, Schunk JE. Should we fear “flu fear” itself? Effects of H1N1 influenza fear on ED use. Am J Emerg Med. 2012;30:275–282.
    1. Cook S, Conrad C, Fowlkes AL, Mohebbi MH. Assessing Google Flu Trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PLoS One. 2011;6:e23610.
    1. Dukic V, Lopes HF, Polson NG. Tracking epidemics with Google Flu Trends data and a state-space SEIR model. J Am Stat Assoc. 2012;107:1410–1426.
    1. Pervaiz F, Pervaiz M, Abdur Rehman N, Saif U. FluBreaks: early epidemic detection from Google Flu Trends. J Med Internet Res. 2012;14:e125.
    1. Zhou XC, Li Q, Zhu ZL, Zhao H, Tang H, Feng YJ. Monitoring epidemic alert levels by analyzing internet search volume. IEEE Trans Biomed Eng. 2013;60:446–452.
    1. Shaman J, Karspeck A. Forecasting seasonal outbreaks of influenza. Proc Natl Acad Sci USA. 2012;109:20425–20430.
    1. Dugas AF, Jalalpour M, Gel Y. Influenza forecasting with Google Flu Trends. PLoS One. 2013;8:e56176.
    1. Scarpino SV, Dimitrov NB, Meyers LA. Optimizing provider recruitment for influenza surveillance networks. PLoS Comp Biol. 2012;8:e1002472.
    1. St Louis C, Zorlu G. Can Twitter predict disease outbreaks? BMJ. 2012;344:e2353.
    1. Sofean M, Smith M. A real-time disease surveillance architecture using social networks. Stud Health Technol Inform. 2012;180:823–827.
    1. Corley CD, Cook DJ, Mikler AR, Singh KP. Using web and social media for influenza surveillance. Adv Exp Med Biol. 2010;680:559–564.
    1. Corley CD, Cook DJ, Mikler AR, Singh KP. Text and structural data mining of influenza mentions in web and social media. Int J Environ Res Public Health. 2010;7:596–615.
    1. Collier N, Son NT, Nguyen NM. OMG U got flu? Analysis of shared health messages for bio-surveillance. J Biomed Semantics. 2011;2(suppl 5):S9.
    1. Chew C, Eysenbach G. Pandemics in the age of Twitter: content analysis of tweets during the 2009 H1N1 outbreak. PLoS One. 2010;5:e14118.
    1. Lampos V, Cristianini N. Nowcasting events from the social web with statistical learning. ACM Trans Intell Syst Technol. 2012;3:72.
    1. Culotta A. Lightweight methods to estimate influenza rates and alcohol sales volume from Twitter messages. Lang Resources Eval. 2013;47:217–238.
    1. Zeng X, Wagner M. Modeling the effects of epidemics on routinely collected data. J Am Med Inform Assoc. 2002;9:S17–S22.
    1. Oum S, Chandramohan D, Cairncross S. Community-based surveillance: a pilot study from rural Cambodia. Trop Med Int Health. 2005;10:689–697.
    1. Watson JM, Pebody RG. Influenza surveillance and pandemic requirements. In: Van-Tam J, Sellwood C, editors. Pandemic influenza. 2nd edn. CABI; 2013. pp. 9–18.
    1. Hale TM, Cotten SR, Drentea P, Goldner M. Rural–urban differences in general and health-related internet use. Am Behav Sci. 2010;53:1304–1325.
    1. Kiciman E. OMG, I have to tweet that! a study of factors that influence tweet rates. Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media; Trinity College, Dublin, Ireland; 2012.
    1. Mohebbi M, Vanderkam D, Kodysh J, Schonberger R, Choi H, Kumar S. Google correlate whitepaper. 2011. (accessed Jan 1, 2013).
    1. Eysenbach G. Infodemiology and infoveillance tracking online health information and cyberbehavior for public health. Am J Prev Med. 2011;40:S154–S158.
    1. Chunara R, Freifeld CC, Brownstein JS. New technologies for reporting real-time emergent infections. Parasitology. 2012;139:1843–1851.
    1. Hulth A, Rydevik G. GET WELL: an automated surveillance system for gaining new epidemiological knowledge. BMC Public Health. 2011;11:252.
    1. Khan AS, Fleischauer A, Casani J, Groseclose SL. The next public health revolution: public health information fusion and social networks. Am J Public Health. 2010;100:1237–1242.
    1. Barclay E. Predicting the next pandemic. Lancet. 2008;372:1025–1026.

Source: PubMed

3
Prenumerera