Digital Disease Detection & Early Warning

1. Digital Disease Detection & Early Warning Ahmad Zhafir Zulkifli Nooreen Farzana Mustapha Najihah Hassan Nudin Master of Public Health Universiti Teknologi MARA

2. Outline • Evolution and main concepts of digital disease detection • Application of Early Warning Systems (EWS) in disease surveillance • Ethical, legal and future challenges in digital surveillance

3. Evolution and main concepts of digital disease detection Ahmad Zhafir Zulkifli

4. Defining digital disease detection • Digital disease detection uses digital technologies to monitor, detect and manage infectious disease outbreaks.1, 3 • The field also known as digital epidemiology or epidemic intelligence.2 • Utilizes big data from various digital sources such as search engine queries, social media trends, and digital health records to identify and track disease outbreaks in real-time.1 • Shift from traditional surveillance (passive, lab-based) to enhanced systems using real-time, multi-source digital inputs.2 • Emergence of digital epidemiology post COVID-19 pandemic.2 References: Fallatah, D. I., & Adekola, H. A. (2024). Infection Prevention in Practice, 6(3), 100382–100382. Salathé, M., Bengtsson, L., Bodnar, T. J., Brewer, D. D., Brownstein, J. S., Buckee, C., Campbell, E. M., Cattuto, C., Khandelwal, S., Mabry, P. L., & Vespignani, A. (2012. PLoS Computational Biology, 8(7), e1002616. Denecke, K. (2017). Life Sciences, Society and Policy, 13(1).

5. Major events in digital public health surveillance Abbreviations: GFT: Google Flu Trends GDT: Google Dengue Trends Reference: Aiello, A. E., Renson, A., & Zivich, P. N. (2020). Annual Review of Public Health, 41(1).

6. Main concepts of digital disease detection

7. 1. Real-time or near real-time data processing using machine learning & artificial intelligence • Machine learning (ML) which use mathematical & statistical models is the most essential data processing tools for detecting outbreaks in their early stage. • ML techniques can be used to analyze and interpret medical data to anticipate illnesses in real time. • For example, researchers used Facebook “likes” to provide estimates of health outcome and health behaviour of a population. References: Fallatah, D. I., & Adekola, H. A. (2024). Infection Prevention in Practice, 6(3), 100382–100382. Gittelman, S., Lange, V., Crawford, C. A. G., Okoro, C. A., Lieb, E., Dhingra, S. S., & Trimarchi, E. (2015). Journal of Medical Internet Research, 17(4), e98.

8. Use case: Predicting prevalence of obesity from Facebook “likes” Reference: Gittelman, S., Lange, V., Crawford, C. A. G., Okoro, C. A., Lieb, E., Dhingra, S. S., & Trimarchi, E. (2015). Journal of Medical Internet Research, 17(4), e98.

9. 2. Analysis of big & complex datasets from diverse digital sources • Natural language processing (NLP) also known as text mining enables automated analysis of unstructured texts. • NLP allows for faster and bigger scale studies than manual analysis. • Sentiment analysis and topic modelling are examples of effective NLP text analysis tools. • For example, a team of researchers examined the general people’s attitude towards COVID-19 using sentiment analysis and topic modelling, gathering over 400,000 tweet. The findings demonstrated that the general public’s attitude shifted from reasonably neutral at the start of the epidemic to increasingly negative as the pandemic became severe. References: Fallatah, D. I., & Adekola, H. A. (2024). Infection Prevention in Practice, 6(3), 100382–100382. S.V., P., & Ittamalla, R. (2021). Information Discovery and Delivery, 49(3).

10. Use case: Sentimental analysis & topic modelling April 2020 March 2020 February 2020 Reference: S.V., P., & Ittamalla, R. (2021). Information Discovery and Delivery, 49(3

11. 3. Geospatial mapping using heatmaps & hotspot identification • Geographic information systems (GIS) has been used to process health data, analyze spatial distribution, map illness prediction, conduct surveillance, and manage epidemic. • It was utilized to create epidemiological maps of viral epidemics. • It involves investigating pattern, correlations, and trends in spatial data to gain useful insights. • For example, during COVID-19 pandemic, researchers used cartograms to map the worldwide spatio-temporal dynamics of the COVID-19 infections. Reference: Fallatah, D. I., & Adekola, H. A. (2024). Infection Prevention in Practice, 6(3), 100382–100382.

12. Use case: GIS for web-based mapping for COVID-19 Reference: Isnan, S., & Shariff, A. R. M. (2022). IOP Conference Series: Earth and Environmental Science, 1064(1), 012007.

13. Traditional disease surveillance data Reference: Hu, W.-H., Sun, H.-M., Wei, Y.-Y., & Hao, Y.-T. (2024). Infectious Disease Modelling, 10.

14. Digital data source Reference: Li, L., Novillo-Ortiz, D., Azzopardi-Muscat, N., & Kostkova, P. (2021). Frontiers in Public Health, 9(9).

15. TalkWalker by Infodemiology.com Source: https://app.talkwalker.com/app/project/3d58b9f5-cd00-4f17-aa24-5101773f1022/shared_dashboard/. Retrieved 7/5/2025

16. TalkWalker by Infodemiology.com Source: https://app.talkwalker.com/app/project/3d58b9f5-cd00-4f17-aa24-5101773f1022/shared_dashboard/. Retrieved 7/5/2025

17. USA HealthMap Source: https://www.healthmap.org/en/. Retrieved 7/5/2025

18. MYSA iDengue Source: https://idengue.mysa.gov.my/pageifv2/. Retrieved: 10/5/2025

19. ProMED by ISID Source: https://www.promedmail.org/search. Retrieved 10/5/2025

20. Advantages of digital disease detection References: Salathé, M. (2016). Journal of Infectious Diseases, 214(suppl 4), S399–S403. Aiello, A. E., Renson, A., & Zivich, P. N. (2020). Annual Review of Public Health, 41(1).

21. Early Warning System (EWS)

22. Structured approach that utilizes data and indicators  > to predict and detect potential outbreaks or public health threats at an early stage. • Main purpose to provide timely information that enables prompt and effective public health interventions and minimizing the impact on morbidity, mortality, and socio-economic disruption.

24. Smoke detector for public health. • Senses early signs of fire, a disease EWS monitors various signals to detect unusual patterns or increases in disease occurrence that could indicate an impending outbreak.​

25. Types of EWS in Epidemiology

26. Indicator-Based Surveillance • Relies on the systematic collection and analysis of specific disease data, (reported cases of dengue, influenza or measles • EWS using this method often set thresholds or baselines, and alerts are triggered when these are exceeded • Event-Based Surveillance • Systematic searching, detection, and verification of information about potential health events that may pose a risk to public health.  • Events can come from formal reporting systems, news media, social media, or community report (useful for identifying novel or unexpected threats)

27. Syndromic Surveillance • Monitors non-specific, pre-diagnostic health indicators syndromes) reported in real-time or near real-time. • Include increases in reports of fever, cough, diarrhea, or rash from emergency departments, physician offices, or even OTC medication sales. • Syndromic surveillance can detect unusual increases in illness before specific diagnoses are confirmed. • Laboratory-Based Surveillance: • Analysis of biological samples to detect pathogens and monitor their  characteristics, such as antimicrobial resistance. • Changes in the prevalence or genetic makeup of pathogens can serve as early warnings of possible impending outbreak

28. Environmental and Ecological Surveillance • Monitors environmental factors (e.g., temperature, rainfall, vector populations) and ecological changes that can influence disease transmission. • For example: monitoring mosquito populations can provide early warnings for potential dengue outbreaks.

29. Digital EWS Tools and IntegrationEnhanced the capabilities of EWS. Digital tools facilitate faster data collection, real-time analysis, and wider dissemination of information.

30. Electronic Health Records (EHRs) • EHRs allow for the automated extraction of syndromic and indicator-based data, enabling near real-time monitoring of disease trends. • Integration with EWS platforms can trigger alerts based on predefined criteria. https://www.bing.com/videos/riverview/relatedvideo?q=electronic+health+records+public+health&mid=67059D806822DB3F13F067059D806822DB3F13F0&FORM=VIRE

31. The figure depicts a heat map of the proportion of women with gestational diabetes who receive nutrition counseling from a registered dietician by 3-digit zip code. The figure highlights regions of the state where disproportionately fewer women are getting counseling from a dietitian. Analyses of this sort can help public health departments develop targeted interventions for the population groups and regions at greatest need.

32. Geographic Information Systems (GIS) • GIS tools enable the visualization and analysis of spatial patterns of disease, helping to identify geographic clusters and track the spread of outbreaks. • Integrating disease data with GIS can provide valuable insights for targeted interventions. https://geospatialworld.net/dashboards/esri-covid-19-gis-hub-malaysia/ https://coronavirus-nsesrimy.hub.arcgis.com/

33. Mobile Health (mHealth) Applications • Mobile apps can be used for community-based surveillance, allowing individuals to report symptoms or health events directly.  • Also facilitate the dissemination of health information and alerts.

34. Integrated Digital Platforms​ • Combine data from various sources (e.g. surveillance systems, laboratories, environmental data) to provide a comprehensive and real-time view of public health.​ • Example : Global Digital Health Monitor (GDHM-WHO) https://data.who.int/dashboards/gdhm/overview

35. Social Media Monitoring and Analysis • Analyzing social media posts and trends can provide early signals of potential outbreaks or public health concerns. • Internet-Based Data Mining • Monitoring online search queries, news articles, and other internet sources can provide early indications of increased public interest in specific symptoms or diseases.

36. Big Data Analytics and Artificial Intelligence (AI) • Advanced analytical techniques can process large and diverse data sets to identify subtle patterns and predict potential outbreaks with greater accuracy.  • AI algorithms can be trained to detect anomalies and forecast disease trends. • Example – D-MOSS

37. Is eNotifikasi considered EWS? • It is primarily an indicator-based surveillance system, relying on manual reports from healthcare providers.​ • It does not include event-based surveillance (EBS), which uses unstructured data like news, rumors,or social media.​ • It lacks predictive toolsfor forecast potential outbreaks.​ • It is not integrated with environmental, climatic, or population movement data, which are important in modern digital EWS. • Real-time disease reporting – Enables prompt reporting of cases, which is critical for early detection. • Early identification of unusual increases – Sudden spikes in reported cases can trigger further investigations. • Faster response – Helps health authorities act quickly in response to potential outbreaks (e.g., dengue, leptospirosis).

38. Is MySejahtera considered EWS? • Self-reporting & symptom monitoring • Users could report symptoms daily, allowing authorities to detect potential clusters or outbreaks early. • Contact tracing & hotspot alerts • It tracked user movement via QR code check-ins and sent alerts if a user had been exposed to a confirmed case. • Data-driven decision-making: • Aggregated data helped the Ministry of Health identify trends, high-risk areas, and allocate resources effectively. • Vaccination status integration: • Enabled tracking of immunity coverage, crucial for monitoring potential resurgence risks. • Focused mainly on COVID-19 – Not yet generalized for multiple diseases or outbreaks. • Dependent on user compliance – Relies on users scanning QR codes and updating symptoms honestly. • Limited integration with other health or environmental data systems – Doesn’t include predictive analytics, climate/epidemiological modeling, or lab integration for other diseases. • No AI or event-based surveillance capability (e.g., doesn't scan social media, news reports, etc.).

39. WHO's Early Warning and Response Network (EWARN) • The WHO utilizes EWARN in emergency settings and for high-threat pathogens. • During outbreaks of Ebola or other infectious diseases, EWARN facilitates rapid data collection, analysis, and information sharing among partners. • EWARN relies on a network of health facilities and community reporters who use standardized reporting forms (often digital) to flag potential cases or unusual health events. • This system allows for early detection of outbreaks and rapid mobilization of resources.

40. EWARN is a network of health partners that collect and report surveillance data on selected epidemic-prone diseases, as part of establishing an early warning system for disease outbreaks in humanitarian situations. • So-called ‘syndromic surveillance system’ whereby any unusual event or disease occurrence is monitored and rapidly investigated. • Each disease is diagnosed on the basis of case definition. • For each of the epidemic diseases, a threshold value for ‘alert” of an outbreak is set. Whenever the threshold is passed, the system flags the event for rapid investigation.

43. Limitation in Current System • Underreporting & delays • Especially in rural or resource-poor settings with poor internet access. • Delays in data entry, transmission, and processing, especially in areas with poor internet connectivity or limited infrastructure. • Privacy and Security Concerns: • The collection and sharing of sensitive health data through digital systems raise important privacy and security concerns that need to be carefully addressed using data protection measures. • False alarms • Especially in EBS due to noisy or misleading data (e.g., rumors or sensationalized media reports).

44. Data Gaps and Quality Issues • The effectiveness of EWS  relies on the availability of complete, accurate, and timely data. • Gaps in reporting, data entry errors, and inconsistencies across different data sources can hinder the system's performance. • Integration Challenges • Integrating data from diverse digital systems (e.g., EHRs, surveillance platforms, environmental sensors) can be complex due to technical incompatibilities, data standardization issues, and privacy concerns. • Over-reliance on Technology • While digital tools are powerful, an over-reliance on technology without adequate human expertise for interpretation and validation can lead to false alarms or missed signals.

45. Sustainability and Infrastructure • Maintaining and upgrading digital EWS requires ongoing investment in infrastructure, technology, and human resources. • Can be a challenge, especially in resource-limited settings. • Interoperability issues • Lack of integration between different data systems and sectors. • Limited use of AI/automation in some countries due to technical capacity gaps.

46. Ethical, Legal and Future Challenges in Digital Surveillance Najihah binti Hassan Nudin

48. Ethical Challenges

49. Data protection failures in third-party sources can facilitate data breach incidents. • 24 mHealth apps and data sharing showed that personal health data were available to third parties. • Cybercriminals have the ability to utilize data mining • Risky permission to access personal data allows developers to read, modify, and delete data

50. Google's Flu Trends (GFT) prediction system has overestimated the number of influenza cases in the US for 100 of the past 108 weeks - and in February 2013 forecast twice as many cases as actually occurred.