Issue

Vol. 2 No. 2 (2025)

Date Log

Submitted
January 19, 2026
Published
December 31, 2025

Copyright (c) 2025 Redha Jaffar Albaqshi, Hassan Ali Ahmad Najaei, Hani Ghazi Abdulmalik, Salman Saeed Saad Alzamaa, Saeed Oudah Hammad Alshahrani, Mohammad Mosad M. Alshaarari, Abdullah Saleh Essa Aldurayhim, Ahmad Mohammad AlGhazal, Khaled Ibrahim Sohail, Ibtihal Abdullah Alotibi, Ali Essa Dallak, Ahmad Ali Ahmad Sahli, Mohammed Saleh Najdi

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Algorithmic Fairness in Clinical Predictive Models: A Review of Bias Audits and Mitigation Strategies in Epidemiological Research

https://doi.org/10.64483/202522539
Redha Jaffar Albaqshi
King Fahad Hospital – Al-Hofuf, Al-Ahsa, Ministry of Health, Saudi Arabia
Hassan Ali Ahmad Najaei
Damad General Hospital – Jazan Region, Ministry of Health, Saudi Arabia
Hani Ghazi Abdulmalik
Maternity and Children Specialized Hospital – Jeddah, Ministry of Health, Saudi Arabia
Salman Saeed Saad Alzamaa
Al-Harjah General Hospital, Aseer, KSA, Ministry of Health, Saudi Arabia
Saeed Oudah Hammad Alshahrani
Al-Maddah General Hospital, Aseer, KSA, Ministry of Health, Saudi Arabia
Mohammad Mosad M Alshsarari
Security Forces Hospital – , Al-Qurayyat, Medical Services, Ministry of Interior, Saudi Arabia
Abdullah Saleh Essa Aldurayhim
Public Health Department, Riyadh First Health Cluster, Ministry of Health, Saudi Arabia
Ahmad Mohammad AlGhazal
Salwa General Hospital, Ministry of Health, Saudi Arabia
Khaled Ibrahim Sohail
Salwa Hospital, Ministry of Health, Saudi Arabia
Ibtihal Abdullah Alotibi
Imam Abdulrahman Al-Faisal Hospital, Riyadh, Ministry of Health, Saudi Arabia
Ali Essa Dallak
Sabya General Hospital, Sabya, Ministry of Health, Saudi Arabia
Ahmad Ali Ahmad Sahli
Prince Mohammed bin Nasser Hospital, Jazan, Ministry of Health, Saudi Arabia
Mohammed Saleh Najdi
Al Mahd General Hospital, Ministry of Health, Saudi Arabia

Corresponding Author(s) : Redha Jaffar Albaqshi

REDHA360@HOTMAIL.COM

Saudi Journal of Medicine and Public Health, Vol. 2 No. 2 (2025)

Abstract

Background: Digital Epidemiology has emerged as a transformative approach to infectious disease surveillance, leveraging digital data streams such as social media, search queries, and mobility patterns. While these methods offer speed and scale, they introduce significant statistical and ethical challenges, particularly bias and fairness concerns in predictive modeling.


Aim: This review aims to examine algorithmic fairness in clinical predictive models within epidemiological research, focusing on bias audits and mitigation strategies in the context of Digital Epidemiology.


Methods: A comprehensive literature review was conducted, analyzing methodological differences between classical and digital approaches, sources of bias, and corrective strategies. Key themes include representativeness, measurement error, and algorithmic bias in machine learning models trained on digital data.


Results: Findings reveal that Digital Epidemiology offers real-time, large-scale data collection but suffers from structural biases due to self-selection, platform design, and digital divides. Bias mitigation is often retrospective, relying on weighting, normalization, and cross-validation. Ethical concerns such as privacy and informed consent intersect with fairness, as predictive models risk amplifying inequities. Integration of classical rigor with digital flexibility and continuous bias audits is essential for equitable outcomes.


Conclusion: Digital Epidemiology complements classical methods but requires robust frameworks for bias detection, ethical governance, and algorithmic transparency. Sustained collaboration, standardization, and inclusive data practices are critical to ensure predictive models support fair and actionable public health decisions.

Keywords

Digital Epidemiology, Algorithmic Fairness, Bias Mitigation, Infectious Disease Surveillance, Predictive Modeling, Public Health Ethics.
Albaqshi, R. J., Najaei, H. A. A., Abdulmalik, H. G., Alzamaa, S. S. S., Alshahrani, S. O. H., Mohammad Mosad M Alshsarari, … Najdi, M. S. (2025). Algorithmic Fairness in Clinical Predictive Models: A Review of Bias Audits and Mitigation Strategies in Epidemiological Research. Saudi Journal of Medicine and Public Health, 2(2), 3073–3084. https://doi.org/10.64483/202522539
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. 1. Rothman KJ, Huybrechts KF, Murray EJ.(2024). Epidemiology: An introduction. Oxford University Press.
  2. 2. Aiello AE, Renson A, Zivich P. Social media- and internet-based disease surveillance for public health. Annu Rev Public Health. 2020;41:101. doi: 10.1146/annurev-publhealth-040119-094402
  3. 3. Salathe M, Bengtsson L, Bodnar TJ, Brewer DD, Brownstein JS, Buckee C, et al. Digital epidemiology. PLoS Comput Biol. 2012;8(7):e1002616. doi: 10.1371/journal.pcbi.1002616
  4. 4. Salathé M. Digital epidemiology: what is it, and where is it going? Life Sci Soc Policy. 2018;14(1):1–5. doi: 10.1186/s40504-017-0065-7 [
  5. 5. Acheson ED. “Oxford record linkage study: a central file of morbidity and mortality records for a pilot population,”. Br J Prev Soc Med. 1964;18(1):8. BMJ Publishing Group.
  6. 6. Szklo M, Nieto FJ. Epidemiology: Beyond the Basics. Jones & Bartlett Publishers; 2014.
  7. 7. Park H-A, Jung H, On J, Park SK, Kang H. Digital epidemiology: use of digital data collected for non-epidemiological purposes in epidemiological studies. Healthc Inform Res. 2018;24(4):253–262. doi: 10.4258/hir.2018.24.4.253
  8. 8. Velasco E. Disease detection, epidemiology and outbreak response: the digital future of public health practice. Life Sci Soc Policy. 2018;14(1):1–6.
  9. 9. Milne R, Costa A. Disruption and dislocation in post-COVID futures for digital health. Big Data Soc. 2020;7(2):2053951720949567. doi: 10.1177/2053951720949567
  10. 10. Budd J, Miller BS, Manning EM, Lampos V, Zhuang M, Edelstein M, et al. Digital technologies in the public-health response to COVID-19. Nat Med. 2020;26(8):1183–1192. doi: 10.1038/s41591-020-1011-4
  11. 11. Valleron A-J, Bouvet E, Garnerin P, Menares J, Heard I, Letrait S, et al. A computer network for the surveillance of communicable diseases: the French experiment. Am J Public Health. 1986;76(11):1289–92. doi: 10.2105/ajph.76.11.1289
  12. 12. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457(7232):1012–1014. doi: 10.1038/nature07634
  13. 13. Lazer D, Kennedy R, King G, Vespignani A. The parable of Google Flu: traps in big data analysis. Science. 2014;343(6176):1203–1205.
  14. 14. Olson DR, Konty KJ, Paladini M, Viboud C, Simonsen L. Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales. PLoS Comput Biol. 2013;9(10):e1003256. doi: 10.1371/journal.pcbi.1003256
  15. 15. Tizzoni M, Panisson A, Paolotti D, Cattuto C. The impact of news exposure on collective attention in the United States during the 2016 Zika epidemic. PLoS Comput Biol. 2020. Mar;16(3):e1007633. doi: 10.1371/journal.pcbi.1007633
  16. 16. Freifeld CC, Mandl KD, Reis BY, Brownstein JS. HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports. J Am Med Inform Assoc. 2008;15(2):150–7. doi: 10.1197/jamia.M2544
  17. 17. 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;11(10):e1004513. doi: 10.1371/journal.pcbi.1004513
  18. 18. Federal Register. Announcement of Requirements and Registration for the Predict the Influenza Season Challenge [Internet]. 2013 [cited 2023 Aug 6].
  19. 19. Koppeschaar CE, Colizza V, Guerrisi C, Turbelin C, Duggan J, Edmunds WJ, et al. Influenzanet: citizens among 10 countries collaborating to monitor influenza in Europe. JMIR Public Health Surveill. 2017;3(3):e7429. doi: 10.2196/publichealth.7429
  20. 20. Lwin MO, Yung CF, Yap P, Jayasundar K, Sheldenkar A, Subasinghe K, et al. FluMob: enabling surveillance of acute respiratory infections in health-care workers via mobile phones. Front Public Health. 2017;5:49. doi: 10.3389/fpubh.2017.00049
  21. 21. 21.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;105(10):2124–2130. doi: 10.2105/AJPH.2015.302696
  22. 22. Moberley S, Carlson S, Durrheim D, Dalton C, et al. Flutracking: Weekly online community-based surveillance of influenza-like illness in Australia, 2017 Annual Report. Commun Dis Intell. 2019;43. doi: 10.33321/cdi.2019.43.31
  23. 23. Won M, Marques-Pita M, Louro C, Gonçalves-Sá J. Early and real-time detection of seasonal influenza onset. PLoS Comput Biol. 2017;13(2):e1005330. doi: 10.1371/journal.pcbi.1005330
  24. 24. .Danquah LO, Hasham N, MacFarlane M, Conteh FE, Momoh F, Tedesco AA, et al. Use of a mobile application for Ebola contact tracing and monitoring in northern Sierra Leone: a proof-of-concept study. BMC Infect Dis. 2019;19(1):1–2.
  25. 25. Farrahi K, Emonet R, Cebrian M. Epidemic contact tracing via communication traces. PLoS ONE. 2014;9(5):e95133. doi: 10.1371/journal.pone.0095133
  26. 26. Yoneki E. Fluphone study: Virtual disease spread using haggle. In: Proceedings of the 6th ACM Workshop on Challenged Networks. 2011. p. 65–66.
  27. 27. Vorovchenko T, Ariana P, Loggerenberg FV, Amirian P. #Ebola and Twitter. What insights can global health draw from social media? In: Big Data in Healthcare. Springer; 2017. p. 85–98.
  28. 28. Albinati J, Meira Jr W, Pappa GL, Teixeira M, Marques-Toledo C. Enhancement of epidemiological models for Dengue fever based on Twitter data. In: Proceedings of the 2017 International Conference on Digital Health; 2017. p. 109–118.
  29. 29. 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. doi: 10.1371/journal.pntd.0005295
  30. 30. de Lima CL, da Silva ACG, da Silva CC, Moreno GMM, da Silva Filho AG, Musah A, et al. Intelligent Systems for Dengue, Chikungunya, and Zika Temporal and Spatio-Temporal Forecasting: A Contribution and a Brief Review. In: Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis. Springer. 2022. p. 299–331.
  31. 31. Hargittai E. Potential biases in big data: Omitted voices on social media. Soc Sci Comput Rev. 2020;38(1):10–24.
  32. 32. Charaudeau S, Pakdaman K, Boëlle PY. Commuter mobility and the spread of infectious diseases: application to influenza in France. PLoS ONE. 2014;9(1):e83002. doi: 10.1371/journal.pone.0083002
  33. 33. Tizzoni M, Bajardi P, Decuyper A, King GKK, Schneider CM, Blondel V, et al. On the use of human mobility proxies for modeling epidemics. PLoS Comput Biol. 2014;10(7):e1003716. doi: 10.1371/journal.pcbi.1003716
  34. 34. Bharti N, Tatem AJ, Ferrari MJ, Grais RF, Djibo A, Grenfell BT. Explaining seasonal fluctuations of measles in Niger using nighttime lights imagery. Science. 2011;334(6061):1424–1427. doi: 10.1126/science.1210554
  35. 35. Shakeri Hossein Abad Z, Kline A, Sultana M, Noaeen M, Nurmambetova E, Lucini F, et al. Digital public health surveillance: a systematic scoping review. NPJ Digit Med. 2021;4(1):1–13.
  36. 36. Yavuz M, Savaskan N. A European roadmap to a digital epidemiology in public health system. Front Digit Health. 2024;6:1284426. Frontiers Media SA. doi: 10.3389/fdgth.2024.1284426
  37. 37. 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;20(1):17–21. doi: 10.1111/1469-0691.12477
  38. 38. Neto OL, Cruz O, Albuquerque J, de Sousa MN, Smolinski M, Cesse ÉAP, et al. Participatory surveillance based on crowdsourcing during the Rio 2016 Olympic Games using the guardians of health platform: descriptive study. JMIR Public Health Surveill. 2020;6(2):e16119. doi: 10.2196/16119
  39. 39. Blench M. Global public health intelligence network (GPHIN). In: Proceedings of Machine Translation Summit XI: Papers, 2007.
  40. 40. Tarkoma S, Alghnam S, Howell MD. Fighting pandemics with digital epidemiology. EClinicalMedicine. 2020. Aug;26:100497. doi: 10.1016/j.eclinm.2020.100512
  41. 41. Sridhar D. COVID-19: what health experts could and could not predict. Nat Med. 2020;26(12):1812. doi: 10.1038/s41591-020-01170-z
  42. 42. The Globe and Mail. Federal documents show sharp decline of Canada’s pandemic warning. The Globe and Mail; 2023.
  43. 43. Wagner Peter. The lasting significance of viruses: COVID-19, historical moments and social transformations. Thesis Eleven. 177(1):122–132, 2023. SAGE Publications Sage UK: London, England.
  44. 44. Dong E, Ratcliff J, Goyea TD, Katz A, Lau R, Ng TK, et al. The Johns Hopkins University Center for Systems Science and Engineering COVID-19 Dashboard: data collection process, challenges faced, and lessons learned. Lancet Infect Dis. 2022.
  45. 45. Singh B. International comparisons of COVID-19 deaths in the presence of comorbidities require uniform mortality coding guidelines. Int J Epidemiol 2021;50(2):373–377. doi: 10.1093/ije/dyaa276
  46. 46. Van Haute M, Agagon A, Gumapac FF, Anticuando MA, Coronel DN, David MC, et al. “Determinants of differences in RT-PCR testing rates among Southeast Asian countries during the first six months of the COVID-19 pandemic. PLOS Global Public Health. 2023;3(11):e0002593. Public Library of Science, San Francisco, CA, USA. doi: 10.1371/journal.pgph.0002593
  47. 47. Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of COVID-19: systematic review and critical appraisal. BMJ. 2020;369. British Medical Journal Publishing Group. doi: 10.1136/bmj.m1328
  48. 48. Vaidheeswaran S, Karmugilan K. Consumer buying behaviour on healthcare products and medical devices during COVID-19 pandemic period-a new spotlight. NVEO-NATURAL VOLATILES & ESSENTIAL OILS Journal 2021:9861–72.
  49. 49. Pandit JA, Radin JM, Quer G, Topol EJ. Smartphone apps in the COVID-19 pandemic. Nat Biotechnol. 2022;40(7):1013–22. doi: 10.1038/s41587-022-01350-x
  50. 50. Ojokoh BA, Aribisala B, Sarumi OA, Gabriel AJ, Omisore O, Taiwo AE, et al. Contact Tracing Strategies for COVID-19 Prevention and Containment: A Scoping Review. Big Data Cogn Comput. 2022;6(4):111.
  51. 51. Wymant C, Ferretti L, Tsallis D, Charalambides M, Abeler-Dörner L, Bonsall D, et al. The epidemiological impact of the NHS COVID-19 app. Nature. 2021;594(7863):408–412. Nature Publishing Group UK London. doi: 10.1038/s41586-021-03606-z
  52. 52. Sharma T, Bashir M. Use of apps in the COVID-19 response and the loss of privacy protection. Nat Med. 2020;26(8):1165–1167. doi: 10.1038/s41591-020-0928-y
  53. 53. Seto E, Challa P, Ware P, et al. Adoption of COVID-19 contact tracing apps: A balance between privacy and effectiveness. J Med Internet Res. 2021;23(3):e25726. doi: 10.2196/25726
  54. 54. Ng A. Google promised its contact tracing app was completely private—But it wasn’t. 2021.
  55. 55. Bedson J, Skrip LA, Pedi D, Abramowitz S, Carter S, Jalloh MF, et al. A review and agenda for integrated disease models including social and behavioural factors. Nat Hum Behav. 2021;5(7):834–846. doi: 10.1038/s41562-021-01136-2
  56. 56. Salathé M. Privacy-preserving contact tracing curbed COVID. Nature. 619(7968):31–33, 2023. Nature Portfolio.
  57. 57. Pullano G, Valdano E, Scarpa N, Rubrichi S, Colizza V. Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study. Lancet Digit Health. 2020;2(12):e638–e649. doi: 10.1016/S2589-7500(20)30243-0
  58. 58. Pepe E, Bajardi P, Gauvin L, Privitera F, Lake B, Cattuto C, et al. COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Sci Data. 2020;7(1):1–7.
  59. 59. Lemey P, Ruktanonchai N, Hong SL, Colizza V, Poletto C, Van den Broeck F, et al. Untangling introductions and persistence in COVID-19 resurgence in Europe. Nature. 2021;595(7869):713–717. doi: 10.1038/s41586-021-03754-2
  60. 60. Levy BL, Vachuska K, Subramanian SV, Sampson RJ. Neighborhood socioeconomic inequality based on everyday mobility predicts COVID-19 infection in San Francisco, Seattle, and Wisconsin. Sci Adv. 2022;8(7):eabl3825. doi: 10.1126/sciadv.abl3825
  61. 61. Gauvin L, Tizzoni M, Piaggesi S, Young A, Adler N, Verhulst S, et al. Gender gaps in urban mobility. Humanit Soc Sci Commun. 2020;7(1):1–13.
  62. 62. Cantor JH, McBain RK, Pera MF, Bravata DM, Whaley CM. Who is (and is not) receiving telemedicine care during the COVID-19 pandemic. Am J Prev Med. 2021. Sep;61(3):434–438. doi: 10.1016/j.amepre.2021.01.030
  63. 63. Lian W, Wen L, Zhou Q, Zhu W, Duan W, Xiao X, et al., “Digital health technologies respond to the COVID-19 pandemic in a tertiary hospital in China: development and usability study,” J Med Internet Res. 2020;22(11):e24505. JMIR Publications, Toronto, Canada. doi: 10.2196/24505
  64. 64. Kim EJ, Moretti ME, Kimathi AM, Chan SY, Wootton R. “Use of provider-to-provider telemedicine in Kenya during the COVID-19 pandemic,” Front Public Health. 2022;10:1028999. Frontiers Media SA. doi: 10.3389/fpubh.2022.1028999
  65. 65. Ganjali R, Eslami S, Samimi T, Sargolzaei M, Firouraghi N, MohammadEbrahimi S, et al. Clinical informatics solutions in COVID-19 pandemic: Scoping literature review. Inform Med Unlocked. 2022;100929. doi: 10.1016/j.imu.2022.100929
  66. 66. Rambaud K, van Woerden S, Palumbo L, Salvi C, Smallwood C, Rockenschaub G, et al. “Building a Chatbot in a Pandemic,” J Med Internet Res. 2023;25:e42960. JMIR Publications, Toronto, Canada. doi: 10.2196/42960
  67. 67. Salerno J, Coughlin SS, Goodman KW, Hlaing WM. Current ethical and social issues in epidemiology. Ann Epidemiol. 2023;80:37–42. doi: 10.1016/j.annepidem.2023.02.001
  68. 68. Zhao Y, He X, Feng Z, Bost S, Prosperi M, Wu Y, et al. Biases in using social media data for public health surveillance: A scoping review. Int J Med Inform. 2022;104804. doi: 10.1016/j.ijmedinf.2022.104804
  69. 69. Williams S. Data action: Using data for public good. MIT Press. 2022.
  70. 70. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. “Dissecting racial bias in an algorithm used to manage the health of populations,”. Science. 2019;366(6464):447–453. American Association for the Advancement of Science. doi: 10.1126/science.aax2342
  71. 71. Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. “Potential biases in machine learning algorithms using electronic health record data,” JAMA Intern Med. 2018;178(11):1544–1547. American Medical Association. doi: 10.1001/jamainternmed.2018.3763
  72. 72. Kramer ADI, Guillory JE, Hancock JT. "Experimental evidence of massive-scale emotional contagion through social networks." Proc Natl Acad Sci U S A. 2014;111(24):8788–8790. doi: 10.1073/pnas.1320040111
  73. 73. Segura A. “Epidemics and epidemiology: back to the future,” Gac Sanit. 2023;37:102277. doi: 10.1016/j.gaceta.2022.102277
  74. 74. Ferretti A, Vayena E. In the shadow of privacy: Overlooked ethical concerns in COVID-19 digital epidemiology. Epidemics. 2022;41:100652. doi: 10.1016/j.epidem.2022.100652
  75. 75. Kostkova P. Disease surveillance data sharing for public health: the next ethical frontiers. Life Sci Soc Policy. 2018;14(1):1–5.
  76. 76. Vela MB, Erondu AI, Smith NA, Peek ME, Woodruff JN, Chin MH. “Eliminating explicit and implicit biases in health care: evidence and research needs,” Annu Rev Public Health. 2022;43(1):477–501. Annual Reviews. doi: 10.1146/annurev-publhealth-052620-103528
  77. 77. Bower JK, Patel S, Rudy JE, Felix AS. “Addressing bias in electronic health record-based surveillance of cardiovascular disease risk: finding the signal through the noise,” Curr Epidemiol Rep. 2017(4):346–352. Springer. doi: 10.1007/s40471-017-0130-z
  78. 78. Chiolero A, Santschi V, Paccaud F. “Public health surveillance with electronic medical records: at risk of surveillance bias and overdiagnosis,” Eur J Public Health. 2013;23(3):350–351. Oxford University Press. doi: 10.1093/eurpub/ckt044
  79. 79. Hicks B, Kaye JA, Azoulay L, Kristensen KB, Habel LA, Pottegård A. “The application of lag times in cancer pharmacoepidemiology: a narrative review,” Ann Epidemiol. 2023;84:25–32. Elsevier.
  80. 80. Xu J, Xiao Y, Wang WH, Ning Y, Shenkman EA, Bian J, et al. “Algorithmic fairness in computational medicine,” EBioMedicine. 2022:84. doi: 10.1016/j.ebiom.2022.104250
  81. 81. Tversky A, Kahneman D. “Availability: A heuristic for judging frequency and probability,” Cogn Psychol. 1973;5(2):207–232.
  82. 82. Shaw RJ, Harron KL, Pescarini JM, Pinto Junior EP, Allik M, Siroky AN, et al. “Biases arising from linked administrative data for epidemiological research: a conceptual framework from registration to analyses,” Eur J Epidemiol. 2022;37(12):1215–1224. Springer. doi: 10.1007/s10654-022-00934-w
  83. 83. Lewin A, Brondeel R, Benmarhnia T, Thomas F, Chaix B. “Attrition bias related to missing outcome data: a longitudinal simulation study,” Epidemiology. 2018;29(1):87–95. LWW. doi: 10.1097/EDE.0000000000000755
  84. 84. Nunan D, Aronson J, Bankhead C. Catalogue of bias: attrition bias. BMJ Evid Based Med. 2018;23(1):21–22. Royal Society of Medicine. doi: 10.1136/ebmed-2017-110883
  85. 85. Lipsitch M, Tchetgen ET, Cohen T. “Negative controls: a tool for detecting confounding and bias in observational studies,” Epidemiology. 2010;21(3):383–388. LWW. doi: 10.1097/EDE.0b013e3181d61eeb
  86. 86. Stockham N, Washington P, Chrisman B, Paskov K, Jung JY, Wall DP. “Causal modeling to mitigate selection bias and unmeasured confounding in internet-based epidemiology of COVID-19: model development and validation,” JMIR Public Health Surveill. 2022;8(7):e31306. doi: 10.2196/3130
  87. 87. Engelmann L. “Digital epidemiology, deep phenotyping and the enduring fantasy of pathological omniscience,” Big Data Soc. 2022;9(1):20539517211066451. SAGE Publications Sage UK: London, England.
  88. 88. Flores L, Kim S, Young SD, “Addressing bias in artificial intelligence for public health surveillance,” J Med Ethics. 2024;50(3):190–194. doi: 10.1136/jme-2022-108875
  89. 89. European Commission. (2024). European Health Data Space. Retrieved from https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space-en.
  90. 90. European Centre for Disease Prevention and Control (ECDC). Long-term surveillance framework 2021–2027. Stockholm: ECDC; 2023. Available from: https://www.ecdc.europa.eu/sites/default/files/documents/long-term-surveillance-framework-2021-2027.pdf.
  91. 91. Andermann A, Pang T, Newton JN, Davis A, Panisset U. Evidence for Health II: Overcoming barriers to using evidence in policy and practice. Health Res Policy Syst. 2016;14:1–7. Springer.
  92. 92. Topol EJ. “Medical forecasting.” Science. 2024;384(6698):eadp7977. American Association for the Advancement of Science. doi: 10.1126/science.adp7977
  93. 93. Narayanan A, Kapoor S. AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference. Princeton University Press; 2024.
  94. 94. .Tan YR, Agrawal A, Matsoso MP, Katz R, Davis SLM, Winkler AS, et al. A call for citizen science in pandemic preparedness and response: beyond data collection. BMJ Glob Health. 2022;7(6):e009389. doi: 10.1136/bmjgh-2022-009389
  95. 95. .Chan AT, Brownstein JS. Putting the public back in public health—surveying symptoms of Covid-19. N Engl J Med. 2020;383(7):e45. doi: 10.1056/NEJMp2016259
  96. 96. Marley G, Dako-Gyeke P, Nepal P, Rajgopal R, Koko E, Chen E, et al., “Collective intelligence–based participatory COVID-19 surveillance in Accra, Ghana: pilot mixed methods study,” JMIR Infodemiology. 2024;4(1):e50125. doi: 10.2196/50125
  97. 97. .Briand SC, Cinelli M, Nguyen T, Lewis R, Prybylski D, Valensise CM, et al. Infodemics: A new challenge for public health. Cell 2021;184(25):6010–6014. doi: 10.1016/j.cell.2021.10.031
  98. 98. Bento AI, Nguyen T, Wing C, Lozano-Rojas F, Ahn YY, Simon K. Evidence from internet search data shows information-seeking responses to news of local COVID-19 cases. Proc Natl Acad Sci U S A. 2020;117(21):11220–11222. doi: 10.1073/pnas.2005335117
  99. 99. Chafetz H, Zahuranec AJ, Marcucci S, Davletov B, Verhulst S. The# Data4COVID19 Review: Assessing the Use of Non-Traditional Data During A Pandemic Crisis. SSRN. 2022;4273229.
  100. 100. European Centre for Disease Prevention and Control. RespiCast. Available from: https://respicast.ecdc.europa.eu
  101. 101. European Centre for Disease Prevention and Control. EpiPulse: European surveillance portal for infectious diseases. Available from: https://www.ecdc.europa.eu/en/publications-data/epipulse-european-surveillance-portal-infectious-diseases
  102. 102. WHO. Regional strategy for health security and emergencies 2022–2030: report of the Secretariat. 2022
  103. 103. Cohen J. ‘Cycles of panic and neglect’: Head of Pandemic Prevention Institute explains its early death. Science. 2022. Available from: https://www.science.org/content/article/cycles-panic-and-neglect-head-pandemic-prevention-institute-explains-its-early-death
Read More
Author biographies is not available.
Download
PDF
Statistic
Read Counter : 57 Download : 96