Combining participatory influenza surveillance with modeling and forecasting: Three alternative approaches

JMIR Public Health and Surveillance

Volumn 3, Issue 4, 2017, Pages

  Brownstein, John S ^a,b   Chu, Shuyu ^c   Marathe, Achla ^c   Marathe, Madhav V ^c   Nguyen, Andre T ^a,d   Paolotti, Daniela ^e   Perra, Nicola ^f   Perrotta, Daniela ^e   Santillana, Mauricio ^a,b   Swarup, Samarth ^c   Tizzoni, Michele ^e   Vespignani, Alessandro ^g   Vullikanti, Anil Kumar S ^c   Wilson, Mandy L ^c   Zhang, Qian ^g  

^a BOSTON CHILDREN S HOSPITAL   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

^c VIRGINIA BIOINFORMATICS INSTITUTE   (United States)

^d BOOZ ALLEN HAMILTON   (United States)

^e ISI FOUNDATION   (Italy)

^f UNIVERSITY OF GREENWICH   (United Kingdom)

^g NORTHEASTERN UNIVERSITY   (United States)

Author keywords

Crowdsourcing; Disease surveillance; Forecasting; Nonresponse bias

Indexed keywords

EID: 85093428092     PISSN: None     EISSN: 23692960     Source Type: Journal    
DOI: 10.2196/publichealth.7344     Document Type: Article

References (56)

1. Overview of influenza surveillance in the United States.: Centers of Disease Control and Prevention URL:http://www. cdc.gov/flu/weekly/overview.htm [accessed 2017-10-10] [WebCite Cache ID 6mgkEts5s]
2. Hope K, Durrheim DN, d'Espaignet ET, Dalton C. Syndromic surveillance: is it a useful tool for local outbreak detection? J Epidemiol Community Health 2006 May;60(5):374-375 [FREE Full text] [Medline: 16680907]
3. National Research Council. In: Keusch GT, Pappaioanou M, Gonzalez MC, editors. Sustaining Global Surveillance and Response to Emerging Zoonotic Diseases. Washington: National Academies Press; 2009.
4. Shaman J, Karspeck A. Forecasting seasonal outbreaks of influenza. Proc Natl Acad Sci U S A 2012 Dec 11;109(50):20425-20430 [FREE Full text] [doi: 10.1073/pnas.1208772109] [Medline: 23184969]
5. Nsoesie EO, Brownstein JS, Ramakrishnan N, Marathe MV. A systematic review of studies on forecasting the dynamics of influenza outbreaks. Influenza Other Respir Viruses 2014 May;8(3):309-316 [FREE Full text] [doi: 10.1111/irv.12226] [Medline: 24373466]
6. Polgreen PM, Chen Y, Pennock DM, Nelson FD. Using internet searches for influenza surveillance. Clin Infect Dis 2008 Dec 1;47(11):1443-1448 [FREE Full text] [doi: 10.1086/593098] [Medline: 18954267]
7. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature 2009 Feb 19;457(7232):1012-1014. [doi: 10.1038/nature07634] [Medline: 19020500]
8. Santillana M, Zhang DW, Althouse BM, Ayers JW. What can digital disease detection learn from (an external revision to) google flu trends? Am J Prev Med 2014 Sep;47(3):341-347. [doi: 10.1016/j.amepre.2014.05.020] [Medline: 24997572]
9. Yang S, Santillana M, Kou SC. Accurate estimation of influenza epidemics using Google search data via ARGO. Proc Natl Acad Sci U S A 2015 Nov 24;112(47):14473-14478. [doi: 10.1073/pnas.1515373112] [Medline: 26553980]
10. 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(5):e64323 [FREE Full text] [doi: 10.1371/journal.pone.0064323] [Medline: 23750192]
11. Seo D, Jo M, Sohn CH, Shin S, Lee J, Yu M, et al. Cumulative query method for influenza surveillance using search engine data. J Med Internet Res 2014 Dec 16;16(12):e289 [FREE Full text] [doi: 10.2196/jmir.3680] [Medline: 25517353]
12. Klembczyk JJ, Jalalpour M, Levin S, Washington RE, Pines JM, Rothman RE, et al. Google flu trends spatial variability validated against emergency department influenza-related visits. J Med Internet Res 2016;18(6):e175 [FREE Full text] [doi: 10.2196/jmir.5585] [Medline: 27354313]
13. Jin F, Dougherty E, Saraf P, Cao Y, Ramakrishnan N. Epidemiological modeling of news and rumors on Twitter. 2013 Presented at: Proceedings of the 7th Workshop on Social Network Mining and Analysis; 2013 Aug 11; New York p. 1. [doi: 10.1145/2501025.2501027]
14. Chen L, Hossain T, Butler P, Ramakrishnan N, Prakesh A. Syndromic surveillance of flu on Twitter using temporal topic models. Data Min Knowl Disc 2014;30(3):681-710. [doi: 10.1109/ICDM.2014.137]
15. Nagar R, Yuan Q, Freifeld CC, Santillana M, Nojima A, Chunara R, et al. A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives. J Med Internet Res 2014;16(10):e236 [FREE Full text] [doi: 10.2196/jmir.3416] [Medline: 25331122]
16. Paul MJ, Dredze M, Broniatowski D. Twitter improves influenza forecasting. PLoS Curr 2014;6 [FREE Full text] [doi: 10.1371/currents.outbreaks.90b9ed0f59bae4ccaa683a39865d9117] [Medline: 25642377]
17. Aslam AA, Tsou M, Spitzberg BH, An L, Gawron JM, Gupta DK, et al. The reliability of tweets as a supplementary method of seasonal influenza surveillance. J Med Internet Res 2014;16(11):e250 [FREE Full text] [doi: 10.2196/jmir.3532] [Medline: 25406040]
18. Santillana M, Nsoesie EO, Mekaru SR, Scales D, Brownstein JS. Using clinicians' search query data to monitor influenza epidemics. Clin Infect Dis 2014 Nov 15;59(10):1446-1450. [doi: 10.1093/cid/ciu647] [Medline: 25115873]
19. Smolinski MS, Crawley AW, Baltrusaitis K, Chunara R, Olsen JM, Wójcik O, et al. Flu Near You: crowdsourced symptom reporting spanning 2 influenza seasons. Am J Public Health 2015 Oct;105(10):2124-2130. [doi: 10.2105/AJPH.2015.302696] [Medline: 26270299]
20. Sharpe JD, Hopkins RS, Cook RL, Striley CW. Evaluating Google, Twitter, and Wikipedia as tools for influenza surveillance using Bayesian change point analysis: a comparative analysis. JMIR Public Health Surveill 2016 Oct 20;2(2):e161 [FREE Full text] [doi: 10.2196/publichealth.5901] [Medline: 27765731]
21. Calba C, Antoine-Moussiaux N, Charrier F, Hendrikx P, Saegerman C, Peyre M, et al. Applying participatory approaches in the evaluation of surveillance systems: a pilot study on African swine fever surveillance in Corsica. Prev Vet Med 2015 Dec 01;122(4):389-398 [FREE Full text] [doi: 10.1016/j.prevetmed.2015.10.001] [Medline: 26489602]
22. Mariner JC, Jones BA, Hendrickx S, El Masry I, Jobre Y, Jost CC. Experiences in participatory surveillance and community-based reporting systems for H5N1 highly pathogenic avian influenza: a case study approach. Ecohealth 2014;11(1):22-35 [FREE Full text] [doi: 10.1007/s10393-014-0916-0] [Medline: 24643858]
23. Lester J, Paige S, Chapman CA, Gibson M, Holland JJ, Switzer WM, et al. Assessing commitment and reporting fidelity to a text message-based participatory surveillance in rural western Uganda. PLoS One 2016;11(6):e0155971 [FREE Full text] [doi: 10.1371/journal.pone.0155971] [Medline: 27281020]
24. Baltrusaitis K, Santillana M, Crawley AW, Chunara R, Smolinski M, Brownstein JS. Determinants of participants' follow-up and characterization of representativeness in Flu Near You: a participatory disease surveillance system. JMIR Public Health Surveill 2017 Apr 07;3(2):e18 [FREE Full text] [doi: 10.2196/publichealth.7304] [Medline: 28389417]
25. Blanton L, Brammer L, Finelli L, Grohskopf L, Bresee J, Klimov A, et al. VPD Surveillance Manual, 5th Edition. Influenza: Chapter 6. 2011. URL:https://www.cdc.gov/vaccines/pubs/surv-manual/chpt06-influenza.pdf [accessed 2016-12-12] [WebCite Cache ID 6mgjgvpDM]
26. Choudhury A. WiSDM: a platform for crowd-sourced data acquisition, analytics, and synthetic data generation. 2016 Aug 16. URL:https://vtechworks.lib.vt.edu/bitstream/handle/10919/72256/Choudhury_A_T_2016.pdf?sequence=1 [accessed 2017-10-10] [WebCite Cache ID 6miv3mn6P]
27. Downes-Le Guin T, Baker R, Mechling J, Ruyle E. Myths and realities of respondent engagement in online surveys. Int. J Market Res 2012;54(5):613. [doi: 10.2501/IJMR-54-5-613-633]
28. Weinberg J, Freese J, McElhattan D. Comparing data characteristics and results of an online factorial survey between a population-based and a crowdsource-recruited sample. SocScience 2014;1:292-310. [doi: 10.15195/v1.a19]
29. Beckman RJ, Baggerly KA, McKay MD. Creating synthetic baseline populations. Transport Res A-Pol 1996 Nov;30(6):415-429. [doi: 10.1016/0965-8564(96)00004-3]
30. Fienberg SE. An iterative procedure for estimation in contingency tables. Ann Math Statist 1970 Jun;41(3):907-917. [doi: 10.1214/aoms/1177696968]
31. Ireland CT, Kullback S. Contingency tables with given marginals. Biometrika 1968 Mar;55(1):179-188. [Medline: 5661043]
32. Bailey-Kellogg C, Ramakrishnan N, Marathe MV. Spatial data mining to support pandemic preparedness. SIGKDD Explor Newsl 2006 Jun 01;8(1):80-82. [doi: 10.1145/1147234.1147246]
33. Bisset K, Marathe M. A cyber environment to support pandemic planning and response. DOE SCIDAC Magazine 2009;36:47.
34. Xia H, Nagaraj K, Chen J, Marathe MV. Synthesis of a high resolution social contact network for Delhi with application to pandemic planning. Artif Intell Med 2015 Oct;65(2):113-130 [FREE Full text] [doi: 10.1016/j.artmed.2015.06.003] [Medline: 26234464]
35. Chen J, Chu S, Chungbaek Y, Khan M, Kuhlman C, Marathe A, et al. Effect of modelling slum populations on influenza spread in Delhi. BMJ Open 2016 Sep 29;6(9):e011699 [FREE Full text] [doi: 10.1136/bmjopen-2016-011699] [Medline: 27687898]
36. Halloran ME, Ferguson NM, Eubank S, Longini IM, Cummings DAT, Lewis B, et al. Modeling targeted layered containment of an influenza pandemic in the United States. Proc Natl Acad Sci U S A 2008 Mar 25;105(12):4639-4644 [FREE Full text] [doi: 10.1073/pnas.0706849105] [Medline: 18332436]
37. Eubank S, Guclu H, Kumar VSA, Marathe MV, Srinivasan A, Toroczkai Z, et al. Modelling disease outbreaks in realistic urban social networks. Nature 2004 May 13;429(6988):180-184. [doi: 10.1038/nature02541] [Medline: 15141212]
38. Marathe A, Lewis B, Barrett C, Chen J, Marathe M, Eubank S, et al. Comparing effectiveness of top-down and bottom-up strategies in containing influenza. PLoS ONE 2011 Sep 22;6(9):e25149. [doi: 10.1371/journal.pone.0025149]
39. Barrett C, Bisset K, Leidig J, Marathe A, Marathe M. Economic and social impact of influenza mitigation strategies by demographic class. Epidemics 2011 Mar;3(1):19-31 [FREE Full text] [doi: 10.1016/j.epidem.2010.11.002] [Medline: 21339828]
40. Eubank S, Barrett C, Beckman R, Bisset K, Durbeck L, Kuhlman C, et al. Detail in network models of epidemiology: are we there yet? J Biol Dyn 2010 Sep;4(5):446-455 [FREE Full text] [Medline: 20953340]
41. Yi M, Marathe A. Fairness versus efficiency of vaccine allocation strategies. Value Health 2015 Mar;18(2):278-283 [FREE Full text] [doi: 10.1016/j.jval.2014.11.009] [Medline: 25773563]
42. Paolotti D, Carnahan A, Colizza V, Eames K, Edmunds J, Gomes G, et al. Web-based participatory surveillance of infectious diseases: the Influenzanet participatory surveillance experience. Clin Microbiol Infect 2014 Jan;20(1):17-21 [FREE Full text] [doi: 10.1111/1469-0691.12477] [Medline: 24350723]
43. Zhang Q, Gioannini C, Paolotti D, Perra N, Perrotta D, Quaggiotto M, et al. The FluOutlook platform. In: Bifet A, May M, editors. Machine Learning and Knowledge Discovery in Databases. Berlin: Springer International Publishing; Dec 2015:237-240.
44. Cantarelli P, Debin M, Turbelin C, Poletto C, Blanchon T, Falchi A, et al. The representativeness of a European multi-center network for influenza-like-illness participatory surveillance. BMC Public Health 2014 Sep 20;14:984 [FREE Full text] [doi: 10.1186/1471-2458-14-984] [Medline: 25240865]
45. Balcan D, Colizza V, Gonçalves B, Hu H, Ramasco JJ, Vespignani A. Multiscale mobility networks and the spatial spreading of infectious diseases. Proc Natl Acad Sci U S A 2009 Dec 22;106(51):21484-21489 [FREE Full text] [doi: 10.1073/pnas.0906910106] [Medline: 20018697]
46. Balcan D, Gonçalves B, Hu H, Ramasco JJ, Colizza V, Vespignani A. Modeling the spatial spread of infectious diseases: the GLobal Epidemic and Mobility computational model. J Comput Sci 2010 Aug 01;1(3):132-145 [FREE Full text] [doi: 10.1016/j.jocs.2010.07.002] [Medline: 21415939]
47. European Centre for Disease Prevention and Control. Surveillance atlas of infectious diseases. URL:http://atlas.ecdc.europa.eu/ public/index.aspx [accessed 2017-10-10] [WebCite Cache ID 6u7lIH5s6]
48. Epidemic Forecasting Observatory. FluOutlook. URL:http://www.fluoutlook.org/ [accessed 2017-10-10] [WebCite Cache ID 6pPo9w64k]
49. Santillana M, Nguyen AT, Dredze M, Paul MJ, Nsoesie EO, Brownstein JS. Combining search, social media, and traditional data sources to improve influenza surveillance. PLoS Comput Biol 2015 Oct;11(10):e1004513 [FREE Full text] [doi: 10.1371/journal.pcbi.1004513] [Medline: 26513245]
50. Santillana M, Nguyen AT, Louie T, Zink A, Gray J, Sung I, et al. Cloud-based electronic health records for real-time, region-specific influenza surveillance. Sci Rep 2016 May 11;6:25732 [FREE Full text] [doi: 10.1038/srep25732] [Medline: 27165494]
51. HealthMap. Flu Trends. URL:http://www.healthmap.org/flutrends/ [accessed 2017-04-02] [WebCite Cache ID 6pPo1q9S8]
52. Perrotta D, Tizzoni M, Paolotti D. Using participatory Web-based surveillance data to improve seasonal influenza forecasting in Italy. 2017 Presented at: Proceeding of the 26th International Conference on World Wide Web (WWW); 2017; Perth.
53. Biggerstaff M, Alper D, Dredze M, Fox S, Fung IC, Hickmann KS, et al. Results from the Centers for Disease Control and Prevention's Predict the 2013-2014 Influenza Season Challenge. BMC Infect Dis 2016 Jul 22;16:357 [FREE Full text] [doi: 10.1186/s12879-016-1669-x] [Medline: 27449080]
54. Yang W, Karspeck A, Shaman J. Comparison of filtering methods for the modeling and retrospective forecasting of influenza epidemics. PLoS Comput Biol 2014 Apr;10(4):e1003583 [FREE Full text] [doi: 10.1371/journal.pcbi.1003583] [Medline: 24762780]
55. Brooks LC, Farrow DC, Hyun S, Tibshirani RJ, Rosenfeld R. Flexible modeling of epidemics with an empirical Bayes framework. PLoS Comput Biol 2015 Aug;11(8):e1004382 [FREE Full text] [doi: 10.1371/journal.pcbi.1004382] [Medline: 26317693]
56. Hickmann KS, Fairchild G, Priedhorsky R, Generous N, Hyman JM, Deshpande A. Forecasting the 2013-2014 influenza season using Wikipedia. PLoS Comput Biol 2015 May;11(5):e1004239 [FREE Full text] [doi: 10.1371/journal.pcbi.1004239] [Medline: 25974758]