Global Infectious Disease Surveillance And Health Intelligence
ABSTRACT Current concerns about the spread of infectious diseases, especially unexpected ("emerging") infections such as pandemic influenza or severe acute respiratory syndrome (SARS), have renewed focus on the critical importance of global early warning and rapid response. Although considerable progress has been made, many gaps remain. A number of the gaps can be addressed through increased political will, resources for reporting, improved coordination and sharing of information, raising clinicians' awareness, and additional research to develop more rigorous triggers for action. The increasing availability of communications and information technologies worldwide offers new opportunities for reporting even in low-capacity settings.
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- "In the past decade, news-based epidemic intelligence services engaged in early outbreak detection have seen growth in both number and prominence (1, 2). Organizations such as the World Health Organization (WHO) often initially discover outbreak events via informal sources (3–5), such as Global Public Health Information Network (GPHIN) (6), BioCaster (7), Pattern-based Understanding and Learning System (PULS) (8), EpiSPIDER (9), MedISys (8) and HealthMap (10). While the utility of such web-crawling systems has been well established (2, 7, 11–13), there are no publicly available evaluations quantifying the limits of news-based epidemic intelligence based on external factors and only one study descriptively examines potential limitations (14). "
ABSTRACT: 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.Emerging Health Threats Journal 11/2013; 6:21621. DOI:10.3402/ehtj.v6i0.21621
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- "Epidemic intelligence provides a new approach to address the challenges of disease globalization –. It provides an approach that is complementary to countries' national surveillance strategies. "
ABSTRACT: The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI) Early Alerting and Reporting (EAR) project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring 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 effective positive predictive values ranged from 3% to 24% and F1-scores ranged from 6% to 27%. System 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 in developing an efficient algorithm for a single pathology. However, the sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating complementarities between individual systems. The average delay between the detection of A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (95% CI: 6.7-13.8). This work illustrates the diversity in implemented epidemic intelligence activities, differences in system's designs, and the potential added values and opportunities for synergy between systems, between users and between systems and users.PLoS ONE 03/2013; 8(3):e57252. DOI:10.1371/journal.pone.0057252 · 3.23 Impact Factor
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- "It is worth emphasizing that though laboratory capacity in Sri Lanka is limited, and may not be able to identify the etiology of an EID event, previous experience with EIDs demonstrates that existing laboratory capacity is important to ruling out common causes of disease. For example, recognition of the 1995 Ebola virus outbreak in the Congo was delayed by a concurrent outbreak of Shigella, and it has been noted that the ability to rule out Shigella in cases of bloody diarrhea at the local level would have been equally as useful as the ability to rule in Ebola virus . "
ABSTRACT: The global public health community is facing the challenge of emerging infectious diseases. Historically, the majority of these diseases have arisen from animal populations at lower latitudes where many nations experience marked resource constraints. In order to minimize the impact of future events, surveillance of animal populations will need to enable prompt event detection and response. Many surveillance systems targeting animals rely on veterinarians to submit cases to a diagnostic laboratory or input clinical case data. Therefore understanding veterinarians' decision-making process that guides laboratory case submission and their perceptions of infectious disease surveillance is foundational to interpreting disease patterns reported by laboratories and engaging veterinarians in surveillance initiatives. A focused ethnographic study was conducted with twelve field veterinary surgeons that participated in a mobile phone-based surveillance pilot project in Sri Lanka. Each participant agreed to an individual in-depth interview that was recorded and later transcribed to enable thematic analysis of the interview content. Results found that field veterinarians in Sri Lanka infrequently submit cases to laboratories - so infrequently that common case selection principles could not be described. Field veterinarians in Sri Lanka have a diagnostic process that operates independently of laboratories. Participants indicated a willingness to take part in surveillance initiatives, though they highlighted a need for incentives that satisfy a range of motivations that vary among field veterinarians. This study has implications for the future of animal health surveillance, including interpretation of disease patterns reported, system design and implementation, and engagement of data providers.PLoS ONE 10/2012; 7(10):e48035. DOI:10.1371/journal.pone.0048035 · 3.23 Impact Factor