Data-driven surveillance: Effective collection, integration and interpretation of data to support decision-making

We searched Scopus for papers published up to December 2020 in the general area of health surveillance which contained the term “big data” (TITLE-ABS-KEY ( "big data" AND surveillance AND [health OR disease OR syndromic]). The search returned 492 papers. After reviewing title and abstract, and reading selected papers for which full-text was available and in English, we have selected a total of 44 papers which specifically discuss data science and data innovation challenges and opportunities in any area of health surveillance.

These 44 papers are listed below in alphabetical order (of first author’s last name):

You can also download the RIS or BibTex files with all references (download and then removed the ".txt" extension).

• Aiello, A.E., Renson, A., Zivich, P.N., 2019. Social media- and internet-based disease surveillance for public health. Annu. Rev. Public Health. https://doi.org/10.1146/annurev-publhealth-040119-094402
• Antoine-Moussiaux, N., Vandenberg, O., Kozlakidis, Z., Aenishaenslin, C., Peyre, M., Roche, M., Bonnet, P., Ravel, A., 2019. Valuing health surveillance as an information system: Interdisciplinary insights. Front. Public Heal. 7. https://doi.org/10.3389/fpubh.2019.00138
• Asokan, G.V., Asokan, V., 2015. Leveraging “big data” to enhance the effectiveness of “one health” in an era of health informatics. J. Epidemiol. Glob. Health 5, 311–314. https://doi.org/10.1016/j.jegh.2015.02.001
• Balicer, R.D., Luengo-Oroz, M., Cohen-Stavi, C., Loyola, E., Mantingh, F., Romanoff, L., Galea, G., 2018. Using big data for non-communicable disease surveillance. Lancet Diabetes Endocrinol. 6, 595–598. https://doi.org/10.1016/S2213-8587(17)30372-8
• Bansal, S., Chowell, G., Simonsen, L., Vespignani, A., Viboud, C., 2016. Big data for infectious disease surveillance and modeling. J. Infect. Dis. 214. https://doi.org/10.1093/infdis/jiw400
• Barrett, D., 2017. The potential for big data in animal disease surveillance in Ireland. Front. Vet. Sci. 4. https://doi.org/10.3389/fvets.2017.00150
• Bate, A., Reynolds, R.F., Caubel, P., 2018. The hope, hype and reality of Big Data for pharmacovigilance. Ther. Adv. Drug Saf. 9, 5–11. https://doi.org/10.1177/2042098617736422
• Blazes, D.L., Dowell, S.F., 2019. The role of disease surveillance in precision public health, in: Genomic and Precision Medicine: Infectious and Inflammatory Disease. pp. 257–265. https://doi.org/10.1016/B978-0-12-801496-7.00015-0
• Bragazzi, N.L., Dai, H., Damiani, G., Behzadifar, M., Martini, M., Wu, J., 2020. How big data and artificial intelligence can help better manage the covid-19 pandemic. Int. J. Environ. Res. Public Health 17. https://doi.org/10.3390/ijerph17093176
• Bu, D.D., Liu, S.H., Liu, B., Li, Y., 2020. Achieving Value in Population Health Big Data. J. Gen. Intern. Med. 35, 3342–3345. https://doi.org/10.1007/s11606-020-05869-0
• Buckee, C., 2020. Improving epidemic surveillance and response: big data is dead, long live big data. Lancet Digit. Heal. 2, e218–e220. https://doi.org/10.1016/S2589-7500(20)30059-5
• Charles-Smith, L.E., Reynolds, T.L., Cameron, M.A., Conway, M., Lau, E.H.Y., Olsen, J.M., Pavlin, J.A., Shigematsu, M., Streichert, L.C., Suda, K.J., Corley, C.D., 2015. Using social media for actionable disease surveillance and outbreak management: A systematic literature review. PLoS One 10, 1–20. https://doi.org/10.1371/journal.pone.0139701
• Chiolero, A., Chiolero, A., Chiolero, A., Chiolero, A., Buckeridge, D., 2020. Glossary for public health surveillance in the age of data science. J. Epidemiol. Community Health 74, 612–616. https://doi.org/10.1136/jech-2018-211654
• Davidson, M.W., Haim, D.A., Radin, J.M., 2015. Using Networks to Combine “Big Data” and Traditional Surveillance to Improve Influenza Predictions. Sci. Rep. 5, 8154. https://doi.org/10.1038/srep08154
• Degeling, C., Carter, S.M., Van Oijen, A.M., McAnulty, J., Sintchenko, V., Braunack-Mayer, A., Yarwood, T., Johnson, J., Gilbert, G.L., 2020. Community perspectives on the benefits and risks of technologically enhanced communicable disease surveillance systems: A report on four community juries. BMC Med. Ethics 21. https://doi.org/10.1186/s12910-020-00474-6
• Dolley, S., 2018. Big data’s role in precision public health. Front. Public Heal. 6. https://doi.org/10.3389/fpubh.2018.00068
• Eckmanns, T., Füller, H., Roberts, S.L., 2019. Digital epidemiology and global health security; An interdisciplinary conversation Tim Eckmanns, Leon Hempel, Kate Polin, Klaus Scheuermann, Edward Velasco. Life Sci. Soc. Policy 15. https://doi.org/10.1186/s40504-019-0091-8
• Flahault, A., Bar-Hen, A., Paragios, N., 2016. Public Health and Epidemiology Informatics. Yearb. Med. Inform. 240–246. https://doi.org/10.15265/iy-2016-021
• Gamache, R., Kharrazi, H., Weiner, J.P., 2018. Public and Population Health Informatics: The Bridging of Big Data to Benefit Communities. Yearb. Med. Inform. 27, 199–206. https://doi.org/10.1055/s-0038-1667081
• Garattini, C., Raffle, J., Aisyah, D.N., Sartain, F., Kozlakidis, Z., 2019. Big Data Analytics, Infectious Diseases and Associated Ethical Impacts. Philos. Technol. 32, 69–85. https://doi.org/10.1007/s13347-017-0278-y
• Gittelman, S., Lange, V., Gotway Crawford, C.A., Okoro, C.A., Lieb, E., Dhingra, S.S., Trimarchi, E., 2015. A New Source of Data for Public Health Surveillance: Facebook Likes. J. Med. Internet Res. 17, e98. https://doi.org/10.2196/jmir.3970
• Hay, S.I., George, D.B., Moyes, C.L., Brownstein, J.S., Flaxman, A., 2013. Big Data Opportunities for Global Infectious Disease Surveillance. PLoS Med. 10, e1001413. https://doi.org/10.1371/journal.pmed.1001413
• Hoffman, S., Podgurski, A., 2013. Big Bad Data: Law, Public Health, and Biomedical Databases. J. Law, Med. Ethics 41, 56–60. https://doi.org/10.1111/jlme.12040
• Huang, T., Lan, L., Fang, X., An, P., Min, J., Wang, F., 2015. Promises and Challenges of Big Data Computing in Health Sciences. Big Data Res. 2, 2–11. https://doi.org/10.1016/j.bdr.2015.02.002
• Khoury, M.J., Engelgau, M., Chambers, D.A., Mensah, G.A., 2019. Beyond Public Health Genomics: Can Big Data and Predictive Analytics Deliver Precision Public Health? Public Health Genomics 21, 244–249. https://doi.org/10.1159/000501465
• Kumar, A.T.K., Asamoah, D., Sharda, R., 2015. Can social media support public health? Demonstrating disease surveillance using big data analytics, in: 2015 Americas Conference on Information Systems, AMCIS 2015.
• Larson, E.B., 2013. Building Trust in the Power of “Big Data” Research to Serve the Public Good. JAMA 309, 2443. https://doi.org/10.1001/jama.2013.5914
• Manogaran, G., Lopez, D., 2017. Disease Surveillance System for Big Climate Data Processing and Dengue Transmission. Int. J. Ambient Comput. Intell. 8, 88–105. https://doi.org/10.4018/IJACI.2017040106
• Mavragani, A., 2020. Infodemiology and infoveillance: Scoping review. J. Med. Internet Res. 22. https://doi.org/10.2196/16206
• Milinovich, G.J., Magalhães, R.J.S., Hu, W., 2015. Role of big data in the early detection of Ebola and other emerging infectious diseases. Lancet Glob. Heal. 3, e20–e21. https://doi.org/10.1016/S2214-109X(14)70356-0
• Mooney, S.J., Pejaver, V., 2018. Big Data in Public Health: Terminology, Machine Learning, and Privacy. Annu. Rev. Public Health. https://doi.org/10.1146/annurev-publhealth-040617-014208
• O’Shea, J., O ’shea, J., O’Shea, J., 2017. Digital disease detection: A systematic review of event-based internet biosurveillance systems. Int. J. Med. Inform. 101, 15–22. https://doi.org/10.1016/j.ijmedinf.2017.01.019
• Othman, M.K., Danuri, M.S.N.M.M.S.N.M., 2016. Proposed conceptual framework of Dengue Active Surveillance System (DASS) in Malaysia, in: 2016 International Conference on Information and Communication Technology (ICICTM). IEEE, pp. 90–96. https://doi.org/10.1109/ICICTM.2016.7890783
• Ouyang, Z., Sargeant, J., Thomas, A., Wycherley, K., Ma, R., Esmaeilbeigi, R., Versluis, A., Stacey, D., Stone, E., Poljak, Z., Bernardo, T.M., 2019. A scoping review of “big data”, “informatics”, and “bioinformatics” in the animal health and veterinary medical literature. Anim. Heal. Res. Rev. 1–18. https://doi.org/10.1017/S1466252319000136
• Pollett, S., Althouse, B.M., Forshey, B., Rutherford, G.W., Jarman, R.G., 2017. Internet-based biosurveillance methods for vector-borne diseases: Are they novel public health tools or just novelties? PLoS Negl. Trop. Dis. 11. https://doi.org/10.1371/journal.pntd.0005871
• Pyne, S., Vullikanti, A.K.S., Marathe, M. V., 2015. Big Data Applications in Health Sciences and Epidemiology, in: Handbook of Statistics. pp. 171–202. https://doi.org/10.1016/B978-0-444-63492-4.00008-3
• Roberts, S.L., 2019. Big data, algorithmic governmentality and the regulation of pandemic risk. Eur. J. Risk Regul. 10, 94–115. https://doi.org/10.1017/err.2019.6
• Santillana, M., 2017. Editorial Commentary : Perspectives on the Future of Internet Search Engines and Biosurveillance Systems. Clin. Infect. Dis. 64, 42–43. https://doi.org/10.1093/cid/ciw660
• Simonsen, L., Gog, J.R., Olson, D., Viboud, C., 2016. Infectious Disease Surveillance in the Big Data Era: Towards Faster and Locally Relevant Systems. J. Infect. Dis. 214, S380–S385. https://doi.org/10.1093/infdis/jiw376
• Smith, G.E., Elliot, A.J., Lake, I., Edeghere, O., Morbey, R., Catchpole, M., Heymann, D.L., Hawker, J., Ibbotson, S., McCloskey, B., Pebody, R., Bains, A., Harcourt, S., Hughes, H., Lee, W., Loveridge, P., Smith, S., Soriano, A., 2019. Syndromic surveillance: Two decades experience of sustainable systems – Its people not just data! Epidemiol. Infect. 147. https://doi.org/10.1017/S0950268819000074
• Trifirò, G., Sultana, J., Bate, A., 2018. From Big Data to Smart Data for Pharmacovigilance: The Role of Healthcare Databases and Other Emerging Sources. Drug Saf. 41, 143–149. https://doi.org/10.1007/s40264-017-0592-4
• Vallmuur, K., Marucci-Wellman, H.R., Taylor, J.A., Lehto, M., Corns, H.L., Smith, G.S., 2016. Harnessing information from injury narratives in the “big data” era: understanding and applying machine learning for injury surveillance. Inj. Prev. 22, i34–i42. https://doi.org/10.1136/injuryprev-2015-041813
• Wong, Z.S.Y., Zhou, J., Zhang, Q., 2019. Artificial Intelligence for infectious disease Big Data Analytics. Infect. Dis. Heal. 24, 44–48. https://doi.org/10.1016/j.idh.2018.10.002
• Zhang, W., Ram, S., Burkart, M., Pengetnze, Y., 2016. Extracting signals from social media for chronic disease surveillance, in: DH 2016 - Proceedings of the 2016 Digital Health Conference. https://doi.org/10.1145/2896338.2896340