Enhancing disease surveillance with novel data streams: challenges and opportunities

EPJ Data Science

Volumn 4, Issue 1, 2015, Pages 1-8

  Althouse, Benjamin M ^a   Scarpino, Samuel V ^a   Meyers, Lauren Ancel ^a,b   Ayers, John W ^c   Bargsten, Marisa ^d   Baumbach, Joan ^d   Brownstein, John S ^e,f,g   Castro, Lauren ^h   Clapham, Hannah ⁱ   Cummings, Derek AT ⁱ   Del Valle, Sara ^h   Eubank, Stephen ^j   Fairchild, Geoffrey ^h   Finelli, Lyn ^k   Generous, Nicholas ^h   George, Dylan ^l   Harper, David R ^m   Hébert Dufresne, Laurent ^a   Johansson, Michael A ⁿ   Konty, Kevin ^o   Lipsitch, Marc ^p   Milinovich, Gabriel ^q   Miller, Joseph D ⁿ   Nsoesie, Elaine O ^e,f   Olson, Donald R ^o   Paul, Michael ^r   Polgreen, Philip M ^s   Priedhorsky, Reid ^h   Read, Jonathan M ^t,u   Rodríguez Barraquer, Isabel ⁱ   Smith, Derek J ^v   Stefansen, Christian ^w   Swerdlow, David L ^x   Thompson, Deborah ^d   Vespignani, Alessandro ^y   Wesolowski, Amy ^p   more..

^a SANTA FE INSTITUTE   (United States)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^c SAN DIEGO STATE UNIVERSITY   (United States)

^d NEW MEXICO DEPARTMENT OF HEALTH   (United States)

^e BOSTON CHILDREN S HOSPITAL   (United States)

^f HARVARD MEDICAL SCHOOL   (United States)

^g MCGILL UNIVERSITY   (Canada)

^h LOS ALAMOS NATIONAL LABORATORY   (United States)

ⁱ JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

^j VIRGINIA BIOINFORMATICS INSTITUTE   (United States)

^k CENTERS FOR DISEASE CONTROL AND PREVENTION   (United States)

^l U S DEPARTMENT OF HEALTH AND HUMAN SERVICES   (United States)

^m ROYAL INSTITUTE OF INTERNATIONAL AFFAIRS   (United Kingdom)

ⁿ NATIONAL CENTER FOR EMERGING AND ZOONOTIC INFECTIOUS DISEASES   (United States)

^o NEW YORK CITY DEPARTMENT OF HEALTH AND MENTAL HYGIENE   (United States)

^p HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^q UNIVERSITY OF QUEENSLAND   (Australia)

^r Johns Hopkins University   (United States)

^s UNIVERSITY OF IOWA   (United States)

^t UNIVERSITY OF LIVERPOOL   (United Kingdom)

^u NIHR   (United Kingdom)

^v UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^w GOOGLE INC   (United States)

^x NATIONAL CENTER FOR IMMUNIZATION AND RESPIRATORY DISEASES   (United States)

^y NORTHEASTERN UNIVERSITY   (United States)

more..

Author keywords

digital surveillance; disease surveillance; novel data streams

Indexed keywords

DATA COMMUNICATION SYSTEMS; EPIDEMIOLOGY; HEALTH; PUBLIC HEALTH; WORLD WIDE WEB;

CONCEPTUAL FRAMEWORKS; DATA STREAM; DISEASE SURVEILLANCE; PUBLIC HEALTH SURVEILLANCES; SOCIAL MEDIA; SURVEILLANCE DATA; WEB SEARCHES;

MONITORING;

EID: 84958762492     PISSN: None     EISSN: 21931127     Source Type: Journal    
DOI: 10.1140/epjds/s13688-015-0054-0     Document Type: Article

References (36)

1. Chunara R, Aman S, Smolinski M, Brownstein JS (2013) Flu Near You: an online self-reported influenza surveillance system in the USA. Online J Public Health Inform 5(1):e133
2. Paolotti D, Carnahan A, Colizza V, Eames K, Edmunds J, Gomes G, Koppeschaar C, Rehn M, Smallenburg R, Turbelin C et al. (2014) Web-based participatory surveillance of infectious diseases: the Influenzanet participatory surveillance experience. Clin Microbiol Infect 20(1):17-21
3. Olson DR, Paladini M, Lober WB, Buckeridge DL, ISDS Distribute Working Group (2011) Applying a new model for sharing population health data to national syndromic influenza surveillance: DiSTRIBuTE project proof of concept, 2006 to 2009. PLoS Curr 3:RRN1251
4. Chester KG (2013) BioSense 2.0. Online J Public Health Inform 5(1):e200
5. Althouse BM, Ng YY, Cummings DAT (2011) Prediction of dengue incidence using search query surveillance. PLoS Negl Trop Dis 5:e1258
6. Milinovich GJ, Williams GM, Clements AC, Hu W (2014) Internet-based surveillance systems for monitoring emerging infectious diseases. Lancet Infect Dis 14(2):160-168
7. Chew C, Eysenbach G (2010) Pandemics in the age of Twitter: content analysis of tweets during the 2009 H1N1 outbreak. PLoS ONE 5(11):e14118
8. Broniatowski DA, Paul MJ, Dredze M (2013) National and local influenza surveillance through Twitter: an analysis of the 2012-2013 influenza epidemic. PLoS ONE 8(12):e83672
9. McIver DJ, Brownstein JS (2014) Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time. PLoS Comput Biol 10(4):e1003581
10. Generous N, Fairchild G, Deshpande A, Del Valle SY, Priedhorsky R (2014) Global disease monitoring and forecasting with Wikipedia. arXiv:1405.3612
11. Nsoesie EO, Buckeridge DL, Brownstein JS (2014) Guess who’s not coming to dinner? Evaluating online restaurant reservations for disease surveillance. J Med Internet Res 16(1):e22
12. Harrison C, Jorder M, Stern H, Stavinsky F, Reddy V, Hanson H, Waechter H, Lowe L, Gravano L, Balter S et al. (2014) Using online reviews by restaurant patrons to identify unreported cases of foodborne illness - New York City, 2012-2013. Morb Mort Wkly Rep 63(20):441-445
13. Das D, Metzger K, Heffernan R, Balter S, Weiss D, Mostashari F et al. (2005) Monitoring over-the-counter medication sales for early detection of disease outbreaks - New York City. Morb Mort Wkly Rep 54(Suppl):41-46
14. Patwardhan A, Bilkovski R (2012) 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 7(8):e43611
15. Freifeld CC, Mandl KD, Reis BY, Brownstein JS (2008) HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports. J Am Med Inform Assoc 15:150-157
16. Polgreen PM, Nelson FD, Neumann GR, Weinstein RA (2007) Use of prediction markets to forecast infectious disease activity. Clin Infect Dis 44(2):272-279
17. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L (2008) Detecting influenza epidemics using search engine query data. Nature 457:1012-1014
18. Olson DR, Konty KJ, Paladini M, Viboud C, Simonsen L (2013) Reassessing Google flu trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales. PLoS Comput Biol 9:e1003256
19. Lazer D, Kennedy R, King G, Vespignani A (2014) The parable of Google flu: traps in big data analysis. Science 343(6176):1203-1205
20. Santillana M, Zhang DW, Althouse BM, Ayers JW (2014) What can digital disease detection learn from (an external revision to) Google flu trends? Am J Prev Med 47(3):341-347
21. Yuan Q, Nsoesie EO, Lv B, Peng G, Chunara R, Brownstein JS (2013) Monitoring influenza epidemics in China with search query from Baidu. PLoS ONE 8(5):e64323
22. Polgreen PM, Chen Y, Pennock DM, Nelson FD, Weinstein RA (2008) Using Internet searches for influenza surveillance. Clin Infect Dis 47:1443-1448
23. Scarpino SV, Dimitrov NB, Meyers LA (2012) Optimizing provider recruitment for influenza surveillance networks. PLoS Comput Biol 8(4):e1002472
24. Ayers JW, Althouse BM, Dredze M (2014) Could behavioral medicine lead the web data revolution? JAMA 311(14):1399-1400
25. Althouse BM, Allem J-P, Childers MA, Dredze M, Ayers JW (2014) Population health concerns during the United States’ Great Recession. Am J Prev Med 46(2):166-170
26. Santillana M, Nsoesie EO, Mekaru SR, Scales D, Brownstein JS (2014) Using clinicians’ search query data to monitor influenza epidemics. Clin Infect Dis. doi:10.1093/cid/ciu647
27. Ayers JW, Althouse BM, Ribisl KM, Emery S (2009) Digital detection for tobacco control: online reactions to the United States. Nicotine Tob Res. doi:10.1093/ntr/ntt186
28. Ayers JW, Ribisl K, Brownstein JS (2011) Using search query surveillance to monitor tax avoidance and smoking cessation following the United States’ 2009 “SCHIP” cigarette tax increase. PLoS ONE 6:e16777
29. Ayers JW, Althouse BM, Noar SM, Cohen JE (2014) Do celebrity cancer diagnoses promote primary cancer prevention? Prev Med 58:81-84
30. Noar SM, Ribisl KM, Althouse BM, Willoughby JF, Ayers JW (2013) Using digital surveillance to examine the impact of public figure pancreatic cancer announcements on media and search query outcomes. J Natl Cancer Inst Monographs 2013(47):188-194
31. Klaucke DN, Buehler JW, Thacker SB, Parrish RG, Trowbridge FL, Berkelman RL et al. (1988) Guidelines for evaluating surveillance systems. Morb Mort Wkly Rep 37(Suppl 5):1-18
32. Kohavi R et al. (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI’95, vol 2, pp 1137-1145
33. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning, 2nd edn. Springer, Berlin
34. Smyth P, Wolpert D (1998) Stacked density estimation. In: Advances in neural information processing systems, pp 668-674
35. Murphy KP (2012) Machine learning: a probabilistic perspective. MIT Press, Cambridge
36. Wolpert DH (2012) What the no free lunch theorems really mean; how to improve search algorithms. Working paper, Santa Fe Institute