Digital Epidemiology In Urban Health Monitoring: A Narrative Review Of Smart Technologies And Public Health Integration

Authors

  • Arya V R
  • Sagar K S

DOI:

https://doi.org/10.69980/ajpr.v28i5.656

Keywords:

Digital epidemiology, urban health, Smart technology, Public health Scenario, Informatic, Real-time surveillance, mHealth, IoT

Abstract

Background:

Digital epidemiology that is, the use of data generated by digital devices that has health-related content to infer health-related outcomes is becoming a central strategy for meeting complex urban health challenges. High rates of urbanisation lead to population density, mobility, and environmental pressures that can predispose cities to outbreaks of infectious disease, burdens of chronic illness, and environmental health threats. Smart tools such as mobile health apps, wearables, Internet of Things (IoT)–based sensors, and artificial intelligence (AI)–based analytics allow real-time monitoring and early warning and targeted interventions, offering an unprecedented opportunity to improve public health promotion efforts in urban settings.

Materials and Methods:

For a narrative review in PubMed, Scopus and Web of Science since 2010 up to 2024. It encompassed keywords such as "digital epidemiology," "urban health," "smart technologies," "mHealth," "IoT" and "public health informatics." Studies and reports pertaining to digital urban health monitoring tools and their integration into public health systems were included. We excluded rural health studies, non-digital interventions, and certain reports such as a non-peer-reviewed report.

Results:

The report finds an increasing emphasis on wearable devices to achieve continuous monitoring of biometrics, the use of mobile health apps to engage citizens, AI-driven analytics to drive predictive modelling, combinations of IoT-based environmental sensors to monitor pollution and climate, and the use of GIS mapping for spatio-temporal disease tracking. Many smart city projects have successfully proved the value of integrating with public health systems, enabling faster outbreak detection, better chronic disease management, and enhanced community-based health interventions.

Conclusion:

Smart technologies are increasingly used for urban and health monitoring, and integrating such technologies into public health policies may not only enable accessing many such systems but also facilitate a transition from reactive to proactive urban health monitoring systems. This transition encourages prevention of diseases earlier, an evidence-based approach on policies, along with more durable and healthier urban populations.

Author Biographies

Arya V R

Senior Resident, Department of Community Medicine, Pondicherry Institute of Medical Sciences, Puducherry.

Sagar K S

Senior Resident, Department of Community Medicine, ESIC medical college and PGIMSR, KK Nagar, Chennai

References

1. Salathé M. Digital epidemiology: what is it, and where is it going? Life Sci Soc Policy. 2018. https://lsspjournal.biomedcentral.com/articles/10.1186/s40504-017-0065-7

2. Lippi G, Mattiuzzi C. Is digital epidemiology the future of clinical epidemiology? Eur J Clin Invest. 2019. https://pmc.ncbi.nlm.nih.gov/articles/PMC7310749/

3. Salathé M, et al. Digital epidemiology. PLoS Comput Biol. 2012. https://journals.plos.org/ ploscompbiol/article?id=10.1371/journal.pcbi.1002616

4. Bansal S, et al. Big data for infectious disease surveillance and modeling. J Infect Dis. 2016. (overview) https://academic.oup.com/jid/article/214/suppl_4/S375/2386880

5. Khoury MJ, Ioannidis JPA. Big data meets public health. JAMA. 2014. https://pubmed.ncbi. nlm.nih.gov/25005661/

6. Brownstein JS, Freifeld CC, Madoff LC. Digital disease detection. N Engl J Med. 2009. https://www.nejm.org/doi/full/10.1056/NEJMp0900702

7. Lazer D, et al. The parable of Google Flu: traps in big data analysis. Science. 2014. https://www. science.org/doi/10.1126/science.1248506

8. Paul MJ, Dredze M. You Are What You Tweet: Analyzing Twitter for Public Health. ICWSM. 2011. https://www.cs.jhu.edu/~mdredze/publications/icwsm_2011.pdf

9. Eysenbach G. Infodemiology and infoveillance. Am J Prev Med. 2011. https://www. sciencedirect.com/science/article/pii/S0749379711002303

10. World Health Organization. Public health surveillance: action planning for digitalization. 2022. https://www.who.int/publications/i/item/9789240040786

11. Piwek L, et al. The rise of consumer health wearables: promises and barriers. PLoS Med. 2016. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001953

12. Patel S, et al. A review of wearable sensors and systems with application in rehabilitation. J NeuroEng Rehabil. 2012.

https://jneuroengrehab.biomedcentral.com/articles/10.1186/1743-0003-9-21

13. Majumder S, et al. Wearable sensors for remote health monitoring. Sensors. 2017. https://pmc.ncbi.nlm.nih.gov/articles/PMC5298703/

14. Mishra T, et al. Pre-symptomatic detection of COVID-19 from smartwatch data. Nat Biomed Eng. 2020.

https://www.nature.com/articles/s41551-020-00640-6

15. Zhu T, et al. Smartwatch data help detect COVID-19. Nat Biomed Eng. 2020 (News & Views). https://www.nature.com/articles/s41551-020-00659-9

16. Boulos MNK, et al. Mobile medical and health apps: state of the art. JMIR. 2014. https://pmc.ncbi.nlm.nih.gov/articles/PMC3959919/

17. Lewis TL, Wyatt JC. mHealth app safety framework. JMIR. 2014. https://www.jmir. org/2014/9/e210/

18. Gagnon M-P, et al. Healthcare professional adoption of m-health. JMIR. 2016. https://pmc. ncbi.nlm.nih.gov/articles/PMC7814918/

19. Kitsiou S, et al. Effectiveness of mHealth for diabetes. PLoS One. 2017. https://journals.plos. org/plosone/article?id=10.1371/journal.pone.0173160

20. Bashi N, et al. Remote monitoring in heart failure—overview of reviews. JMIR. 2017. https://www.jmir.org/2017/1/e18/

21. Chow CK, et al. mHealth in cardiovascular care. Heart Lung Circ. 2016. https://pubmed. ncbi.nlm.nih.gov/27262389/

22. Wang Y, et al. mHealth interventions for diabetes & obesity—review. JMIR mHealth uHealth. 2020.

https://mhealth.jmir.org/2020/4/e15400/

23. Lee JA, et al. Effective behavioral strategies via mHealth. BMC Med Inform Decis Mak. 2018. https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-018-0591-0

24. Son Y-J, et al. Mobile phone–based interventions in HF. Int J Environ Res Public Health. 2020. https://www.mdpi.com/16604601/17/5/1749

25. Kitsiou S, et al. mHealth for heart failure—systematic review & meta-analysis. Can J Cardiol. 2021. https://onlinecjc.ca/article/ S0828-282X%2821%2900118-5/fulltext

26. Freifeld CC, et al. Participatory epidemiology via mobile phones. PLoS Med. 2010. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1000376

27. Signorini A, et al. Twitter to track H1N1 activity. PLoS One. 2011. https://journals.plos. org/plosone/article?id=10.1371/journal.pone.0019467

28. Paul MJ, Dredze M, Broniatowski DA. Social monitoring for public health. Synthesis Lectures. 2014. https://www.morganclaypool.com/doi/abs/10.2200/S00533ED1V01Y201404HLT025

29. Charles-Smith LE, et al. Social media for actionable surveillance—systematic review. PLoS One. 2015. https://journals.plos.org/ plosone/article?id=10.1371/journal.pone.0139701

30. Chunara R, et al. Flu Near You participatory surveillance. Am J Public Health. 2015. https://ajph.aphapublications.org/doi/full/10.2105/AJPH.2015.302696

31. Baltrusaitis K, et al. Follow-up in Flu Near You. JMIR Public Health Surveill. 2017. https://pmc. ncbi.nlm.nih.gov/articles/PMC5400887/

32. Menni C, et al. App-based symptom tracking for COVID-19. Nat Med. 2020. https://www. nature.com/articles/s41591-020-0916-2

33. Paul MJ, Dredze M. Discovering health topics from Twitter. ACL. 2012. https://aclanthology. org/P12-2017.pdf

34. Fung ICH, et al. Social media in public health surveillance—narrative review. PMJ. 2015. https://pmc.ncbi.nlm.nih.gov/articles/PMC4542478/

35. Eisen L, Eisen RJ. GIS & decision support for vector-borne diseases. Annu Rev Entomol. 2011. https://www.annualreviews.org/content/journals/10.1146/annurev-ento-120709-144847

36. Kamel Boulos MN, Geraghty EM. Geographical tracking & mapping of COVID-19. Int J Health Geogr. 2020. https://ij-healthgeographics. biomedcentral.com/articles/10.1186/s12942-020-00202-8

37. Franch-Pardo I, et al. Spatial analysis & GIS for COVID-19—review. Sci Total Environ. 2020. https://www.sciencedirect.com/science/article/pii/S0048969720335531

38. Ahasan R, et al. GIS applications in COVID-19—systematic review. BMC Public Health. 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC8822139/

39. Dong E, et al. JHU COVID-19 dashboard. Lancet Infect Dis. 2020. https://www.thelancet. com/journals/laninf/article/PIIS1473-3099(20)30120-1/fulltext

40. Ozdenerol E. GIS/RS in Lyme disease epidemiology. Int J Environ Res Public Health. 2015. https://pmc.ncbi.nlm.nih.gov/articles/PMC4690907/

41. Kumar P, et al. Low-cost sensing for city air pollution. Environ Int. 2015. https://www. sciencedirect.com/science/article/abs/pii/S0160412014003547

42. Morawska L, et al. Applications of low-cost sensing tech for air quality. Environ Int. 2018. https://pmc.ncbi.nlm.nih.gov/articles/PMC6145068/

43. Popoola OAM, et al. Networks of low-cost sensors for urban air. Atmos Environ. 2018. https://www.sciencedirect.com/science/article/pii/S1352231018306241

44. Khreis H, et al. Evaluating low-cost air quality sensors. Atmosphere. 2022. https://pmc. ncbi.nlm.nih.gov/articles/PMC8835131/

45. Tariq H, et al. State-of-the-art low-cost air quality sensors—review. Atmosphere. 2024. https://www.mdpi.com/2073-4433/15/4/471

46. OGC SensorThings API Standard (IoT + geospatial). Open Geospatial Consortium. https://www.ogc.org/publications/standard/sensorthings/

47. Deo RC. Machine learning in medicine. Circulation. 2015. https://www.ahajournals. org/doi/10.1161/CIRCULATIONAHA.115.001593

48. Jiang F, et al. AI in healthcare: past, present, future. Stroke Vasc Neurol. 2017. https://pubmed. ncbi.nlm.nih.gov/29507784/

49. Topol EJ. High-performance medicine: human + AI. Nat Med. 2019. https://www. nature.com/ articles/s41591-018-0300-7

50. Ardabili SF, et al. COVID-19 forecasting with ML/DL. Chaos Solitons Fractals. 2020. https://www.sciencedirect.com/science/article/pii/S0960077920305998

51. Sim JZT, et al. Machine learning in medicine—what clinicians should know. Ann Acad Med Singap. 2021. https://pmc.ncbi.nlm.nih.gov/ articles/PMC10071847/

52. Wang L, et al. Urban transfer learning for smart cities. Computer (IEEE). 2018. https://dl.acm. org/doi/10.1109/MC.2018.2880015

53. Park YJ, et al. Contact tracing during COVID-19, South Korea. Emerg Infect Dis. 2020. https://pmc.ncbi.nlm.nih.gov/articles/PMC7510731/

54. Sun K, et al. Impact of contact tracing on SARS-CoV-2. Lancet Infect Dis. 2020. https://www. thelancet.com/journals/laninf/article/PIIS1473-3099(20)30357-1/fulltext

55. Lee SW, et al. Nationwide results of contact tracing (Korea). JMIR Med Inform. 2020. https://medinform.jmir.org/2020/8/e20992/

56. Menni C, et al. Real-time symptom tracking (ZOE). Nat Med. 2020. https://pubmed.ncbi. nlm.nih.gov/32393804/

57. HL7. FHIR Specification—Overview. https://www. hl7.org/fhir/overview.html

58. FHIR Foundation—Implementers’ resources. https://fhir.org/

59. Mandel JC, et al. SMART on FHIR platform. JAMIA. 2016. https://pubmed.ncbi. nlm.nih. gov/26911829/

60. Wagholikar KB, et al. SMART-on-FHIR implemented over i2b2. JAMIA. 2017. https:// academic.oup.com/jamia/article/24/2/398/2631471

61. U.S. ONC. HL7 FHIR overview (HealthIT.gov). https://www.healthit.gov/topic/standards-technology/standards/fhir

62. CMS. Learn About FHIR (training hub). https://www.cms.gov/priorities/burden-reduction/ overview/interoperability/learn-about-fhir

63. OGC API & SensorThings (overview). https://ogcapi.ogc.org/sensorthings/

64. WHO. Ethics and governance of AI for health. 2021. https://www.who.int/ publications/i/ item/9789240029200

65. WHO Guidance PDF (full).https://iris.who. int/bitstream/handle/10665/341996/ 9789240029200-eng.pdf

66. Gasser U, et al. Digital tools against COVID-19: taxonomy & ethics. Lancet Digit Health. 2020. https://pmc.ncbi.nlm.nih.gov/articles/PMC7324107/

67. Mello MM, Wang CJ. Ethics & governance for digital disease surveillance. Science. 2020. https://www.science.org/doi/10.1126/science.abb9045

68. Vayena E, et al. Health research with big data: systemic oversight. J Law Med Ethics. 2018. https://pmc.ncbi.nlm.nih.gov/articles/PMC6052857/

69. Mittelstadt B. Is there a duty to participate in digital epidemiology? Life Sci Soc Policy. 2018. https://pmc.ncbi.nlm.nih.gov/articles/PMC5943201/

70. Floridi L, et al. AI4People—ethical framework for a good AI society. Minds Mach. 2018. https://link.springer.com/article/10.1007/s11023-018-9482-5

71. GDPR—Regulation (EU) 2016/679 (Official Journal). https://eur-lex.europa.eu/eli/ reg/2016/679/oj/eng

72. GDPR (official PDF). https://eur-lex.europa. eu/legal-content/EN/TXT/PDF/?uri=CELEX: 32016R0679

73. HIPAA Privacy Rule (HHS overview). https://www.hhs.gov/hipaa/for-professionals/ privacy/index.html

74. CDC. HIPAA: resources & overview. https://www.cdc.gov/phlp/php/resources/health-insurance-portability-and-accountability-act-of-1996-hipaa.html

75. Samuel G, et al. Ethical debates around UK contact-tracing app. Health Gov. 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC8802538/

76. Lucivero F, et al. Normative positions toward COVID apps. Crit Public Health. 2022. https://www.tandfonline.com/doi/full/10.1080/09581596.2021.1925634

77. Scheerder A, van Deursen A, van Dijk J. Determinants of internet skills/uses/outcomes—systematic review. Telemat Informatics. 2017. https://www. sciencedirect.com/science/ article/abs/pii/S0736585317303192

78. van Dijk J. The Digital Divide. Polity; 2020. (Book info) https://asistdl.onlinelibrary. wiley.com/doi/10.1002/asi.24355

79. Robinson L, et al. Digital inequalities and why they matter. Info Commun Soc. 2015. https://www.tandfonline.com/doi/abs/10.1080/1369118X.2015.1012532

80. Fourman M, et al. Measuring the digital divide. 2015. https://homepages.inf.ed.ac.uk/ mfourman/research/publications/pdf/fourman2015-measuring-the-digital-divide.pdf

81. Parker S, et al. eHealth for vulnerable patients—realist synthesis. BMJ Open. 2018. https://bmjopen.bmj.com/content/8/8/e019192

82. Greenwood DA, et al. Technology-enabled DSME/S—review of reviews. Diabetes Educ. 2017. https://www.welldoc.com/wp-content/uploads/2021/04/2017_Greenwood_et_al_Systmatic_Review_of_Reviews_Evaluating_Technology_Enabled_DSMES.pdf

83. Thomas EE, et al. Effectiveness of RPM—meta-review. BMJ Open. 2021. https://bmjopen. bmj.com/content/11/8/e051844

84. El-Rashidy N, et al. Mobile health + AI for remote monitoring. Sensors. 2021. https:// pmc.ncbi. nlm.nih.gov/articles/PMC8067150/

85. Al-Arkee S, et al. Apps to improve medication adherence in CVD. JMIR. 2021. https://www. jmir.org/2021/5/e24190/

86. Komninos N. The age of intelligent cities (policy synthesis). 2015.

https://www.researchgate.net/publication/281631559

87. Ratti C, Claudel M. The City of Tomorrow. Yale; 2016.

https://yalebooks.yale.edu/ 9780300211462/the-city-of-tomorrow/

88. Goodman E, Powles J. Urban data governance in Sidewalk Toronto. Int Data Priv Law. 2019. https://academic.oup.com/idpl/article/9/4/293/5677154

89. Ng LC, et al. Singapore dengue surveillance (spatial/IoT). Lancet. 2017 (perspective on smart nation). https://www.thelancet.com/ journals/lancet/article/PIIS0140-6736(17)31698-0/fulltext

90. Ahas R, et al. Mobile positioning data for urban analytics & health. Int J Health Geogr. 2010. https://ij-healthgeographics. biomedcentral. com/articles/10.1186/1476-072X-9-10

91. Haines A, Ebi K. The imperative for climate action for health. N Engl J Med. 2019. https://www.nejm.org/doi/full/10.1056/NEJMra1807873

92. Watts N, et al. The 2023 Lancet Countdown on health & climate. Lancet. 2023. https://www. thelancet.com/countdown-health-climate

93. Robinson CL, et al. Heat health warning systems—WHO guidance. 2015. https:// www.who. int/publications/i/item/heat-waves-and-health-guidance-on-warning-system-development

94. Hystad P, et al. GIS-modeled air pollution & cardio-resp outcomes. Environ Health Perspect. 2013. https://ehp.niehs.nih.gov/ doi/full/10.1289/ehp.1205687

95. World Meteorological Organization. Integrated urban services (WMO). 2019. https://public. wmo.int/en/our-mandate/ focus-areas/urban

96. Lazer D, et al. (GFT) methodological lessons. Science. 2014. https://gking.harvard. edu/files/gking/files/0314policyforumff.pdf

97. Broniatowski DA, et al. Weaponized health communication on social media. Am J Public Health. 2018.

https://ajph.aphapublications.org/doi/10.2105/AJPH.2018.304567

98. Chan MS, et al. Misinformation & corrective strategies—COVID-19. Curr Opin Psychol. 2020. https://www.sciencedirect.com/science/article/pii/S2352250X20300839

99. Olteanu A, Castillo C, et al. Social data: biases, methodological pitfalls. Big Data & Society. 2019. https://journals.sagepub.com/doi/10.1177/2053951719874239

100. Ioannidis JPA. Why most discovered true associations are inflated. Int J Epidemiol. 2008. https://academic.oup.com/ije/article/37/3/641/743354

101. WHO. Global strategy on digital health 2020–2025. https://www.who.int/publications/ i/item/9789240020924

102. OECD. Health in the 21st century: digital health adoption. 2019. https://www.oecd.org /health/health-in-the-21st-centurye3b23fdf-en.htm

103. European Commission. eHealth Network—Interoperability guidelines. 2021.

https://health.ec.europa.eu/ehealth-digital-health-and-care/eu-ehealth-network_en

104. Public Health England. Data and analysis tools for health inequalities. 2020. https://www. gov.uk/government/collections/health-inequalities-data-and-analysis

105. UN-Habitat. People-centered smart cities playbook. 2022. https://unhabitat.org/ programme/people-centred-smart-cities Real-time Surveillance Platforms & Tools

106. HealthMap (global event-based surveillance). https://www.healthmap.org/en/

107. ProMED-mail (expert-moderated outbreak reports). https://promedmail.org/

108. EIOS (WHO Epidemic Intelligence from Open Sources). https://www.who.int/initiatives/eios

109. GPHIN (Global Public Health Intelligence Network). https://www.canada.ca/en/publichealth/services/emergencypreparednessresponse/global-public-health-intelligence-network.html

110. BlueDot (mobility-enhanced analytics). https://bluedot.global/Urban Mobility, Human Dynamics & Health

111. Wesolowski A, et al. Human mobility & infectious disease dynamics. Trends Parasitol. 2016. https://www.sciencedirect.com/science/article/pii/S1471492216300904

112. Buckee CO, et al. Aggregated mobility data to combat COVID-19. Science. 2020. https://www.science.org/doi/10.1126/science.abb8021

113. Oliver N, et al. Mobile phone data for pandemic response. Sci Adv. 2020. https://www. science.org/doi/10.1126/sciadv.abc0764

114. Kraemer MUG, et al. Real-time surveillance of disease via mobility. Science. 2020. https://www.science.org/doi/10.1126/science.abb4218 Citizen Engagement & Risk Communication

115. Fung IC-H, et al. Social media as risk communication tool. Infect Dis Health. 2017. https://www.sciencedirect.com/science/article/pii/S2468045117301048

116. Venkatraman A, et al. Social media engagement for public health. Front Public Health. 2017. https://www.frontiersin. org/articles/10.3389/fpubh.2017.00222/full

117. WHO. Communicating risk in public health emergencies. 2017. https://www.who.int/ publications/i/item/9789241550208

118. Gasparrini A, et al. Mortality risk attributable to heat. Lancet. 2015. https://www. thelancet.com/journals/lancet/article/PIIS0140-6736(14)62114-0/fulltext

119. Basu R. High ambient temperature and mortality—review. Environ Health. 2009. https://ehjournal.biomedcentral.com/articles/10.1186/1476-069X-8-40

120. WHO/WMO. Heatwaves and Health: Guidance on Warning Systems. 2015. https://www. who.int/publications/i/item/heat-waves-and-health-guidance-on-warning-system-development

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2025-09-09