Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review

Objectives: Using participatory health informatics (PHI) to detect disease outbreaks or learn about pandemics has gained interest in recent years. However, the role of PHI in understanding and managing pandemics, citizens’ role in this context, and which methods are relevant for collecting and processing data are still unclear, as is which types of data are relevant. This paper aims to clarify these issues and explore the role of PHI in managing and detecting pandemics. Methods: Through a literature review we identified studies that explore the role of PHI in detecting and managing pandemics. Studies from five databases were screened: PubMed, CINAHL (Cumulative Index to Nursing and Allied Health Literature), IEEE Xplore, ACM (Association for Computing Machinery) Digital Library, and Cochrane Library. Data from studies fulfilling the eligibility criteria were extracted and synthesized narratively. Results: Out of 417 citations retrieved, 53 studies were included in this review. Most research focused on influenza-like illnesses or COVID-19 with at least three papers on other epidemics (Ebola, Zika or measles). The geographic scope ranged from global to concentrating on specific countries. Multiple processing and analysis methods were reported, although often missing relevant information. The majority of outcomes are reported for two application areas: crisis communication and detection of disease outbreaks. Conclusions: For most diseases, the small number of studies prevented reaching firm conclusions about the utility of PHI in detecting and monitoring these disease outbreaks. For others, e.g., COVID-19, social media and online search patterns corresponded to disease patterns, and detected disease outbreak earlier than conventional public health methods, thereby suggesting that PHI can contribute to disease and pandemic monitoring

Quantifying the effect of media limitations on outbreak data in a global online web-crawling epidemic intelligence system, 2008–2011

Background: This is the first study quantitatively evaluating the effect that media-related limitations have on data from an automated epidemic intelligence system. Methods: We modeled time series of HealthMap's two main data feeds, Google News and Moreover, to test for evidence of two potential limitations: first, human resources constraints, and second, high-profile outbreaks “crowding out” coverage of other infectious diseases. Results: Google News events declined by 58.3%, 65.9%, and 14.7% on Saturday, Sunday and Monday, respectively, relative to other weekdays. Events were reduced by 27.4% during Christmas/New Years weeks and 33.6% lower during American Thanksgiving week than during an average week for Google News. Moreover data yielded similar results with the addition of Memorial Day (US) being associated with a 36.2% reduction in events. Other holiday effects were not statistically significant. We found evidence for a crowd out phenomenon for influenza/H1N1, where a 50% increase in influenza events corresponded with a 4% decline in other disease events for Google News only. Other prominent diseases in this database – avian influenza (H5N1), cholera, or foodborne illness – were not associated with a crowd out phenomenon. Conclusions: These results provide quantitative evidence for the limited impact of editorial biases on HealthMap's web-crawling epidemic intelligence

Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events

Web-based expert systems dedicated to epidemic intelligence were developed to detect health threats. The Early Alerting and Reporting (EAR) project, launched under the Global Health Initiative, aimed at assessing the feasibility and opportunity of pooling seven of those expert systems. A qualitative survey was carried out with EAR participants to document epidemic intelligence strategies and to assess perceptions regarding the performance of participating systems. Timeliness and sensitivity were rated with high scores illustrating the overall perceived value of all systems while weaknesses were underlined especially in terms of representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events which occurred in March 2010. For the six systems for which this information was available; the detection rate ranged from 31% to 38%, and increased to 72% when considering the virtual combined system. The positive predictive values (PPV) ranged from 3% to 24% and the F1-score ranged from 6% to 27%. These low scores point out false positive signals related to varying abilities of the systems to efficiently sort-out information and reduce background noise. For the seven systems sensitivity ranged from 38% to 72%. An average difference of 23% was observed between the sensitivities calculated for human cases and epizootics, underlining the difficulties to develop an efficient algorithm or a single pathology. The sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating the systems’ complementarities. The average delay between the detection of the A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (CI95%, 6.7; 13.8). This work illustrates the diversity in implemented epidemic intelligence activities, differences in systems designs and the potential added values and opportunities for synergy: between systems, between users and between systems and users.JRC.G.2-Global security and crisis managemen