Innovation in observation: a vision for early outbreak detection

Fefferman, Nina H.; Naumova, Elena N.

doi:10.3402/ehtj.v3i0.7103

Cited by 22 publications

(24 citation statements)

References 36 publications

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“…Strengthening healthcare-based surveillance in these areas should be a priority, and cost-effective approaches to achieving surveillance targets need to be identified. There is increasing recognition of the value of novel data sources to improve the sensitivity of infectious disease surveillance, some of which can provide crucial information in remote areas [20]. Novel approaches include surveillance for media reports of disease clusters, as used for various infectious diseases in Bangladesh [12,28], and training of local drug sellers to recognize and report disease symptoms, as rolled out nationally to enhance tuberculosis surveillance in Ghana [29].…”

Section: Discussionmentioning

confidence: 99%

“…For emerging infectious diseases of global health importance, such as Nipah, severe acute respiratory syndrome, or avian influenza, a single detected case may be considered an outbreak. For other disease systems (e.g., endemic diseases or diseases for which differential diagnosis is difficult), an outbreak may be declared only after more than a single case is detected over a specified period of time and within specified geographic boundaries [20]. We can extend the framework to estimate the probability of identifying an outbreak with different outbreak thresholds applied, and we provide examples for outbreaks defined as detection of at least two cases or at least five cases.…”

Section: Quantifying the Probability Of Detecting Outbreaksmentioning

confidence: 99%

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Evaluating Hospital-Based Surveillance for Outbreak Detection in Bangladesh: Analysis of Healthcare Utilization Data

et al. 2017

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BackgroundThe International Health Regulations outline core requirements to ensure the detection of public health threats of international concern. Assessing the capacity of surveillance systems to detect these threats is crucial for evaluating a country’s ability to meet these requirements.Methods and FindingsWe propose a framework to evaluate the sensitivity and representativeness of hospital-based surveillance and apply it to severe neurological infectious diseases and fatal respiratory infectious diseases in Bangladesh. We identified cases in selected communities within surveillance hospital catchment areas using key informant and house-to-house surveys and ascertained where cases had sought care. We estimated the probability of surveillance detecting different sized outbreaks by distance from the surveillance hospital and compared characteristics of cases identified in the community and cases attending surveillance hospitals.We estimated that surveillance detected 26% (95% CI 18%–33%) of severe neurological disease cases and 18% (95% CI 16%–21%) of fatal respiratory disease cases residing at 10 km distance from a surveillance hospital. Detection probabilities decreased markedly with distance. The probability of detecting small outbreaks (three cases) dropped below 50% at distances greater than 26 km for severe neurological disease and at distances greater than 7 km for fatal respiratory disease. Characteristics of cases attending surveillance hospitals were largely representative of all cases; however, neurological disease cases aged <5 y or from the lowest socioeconomic group and fatal respiratory disease cases aged ≥60 y were underrepresented.Our estimates of outbreak detection rely on suspected cases that attend a surveillance hospital receiving laboratory confirmation of disease and being reported to the surveillance system. The extent to which this occurs will depend on disease characteristics (e.g., severity and symptom specificity) and surveillance resources.ConclusionWe present a new approach to evaluating the sensitivity and representativeness of hospital-based surveillance, making it possible to predict its ability to detect emerging threats.

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Section: Discussionmentioning

confidence: 99%

Section: Quantifying the Probability Of Detecting Outbreaksmentioning

confidence: 99%

Evaluating Hospital-Based Surveillance for Outbreak Detection in Bangladesh: Analysis of Healthcare Utilization Data

et al. 2017

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“…Communicable disease control factors specific to each disease (case fatality rate, incubation period, secondary attack rate, mode of transmission) Local epidemiological factors involved in a disease outbreak (where did it begin, who has been affected, who is at risk) [17] Existing health system infrastructure to support outbreak response (including an assessment of local compliance with and functions aligning to the International Health Regulations (IHR) and capacity for different response activities, to determine which may need rapid scaling up [18]) Availability of medical interventions to treat cases (such as antibiotics or antivirals), prevent disease in susceptible populations, and the need for investment in research and development of new diagnostics/therapeutics [4] The functions, structures, and abilities of all available actors, including what and where they are, their operational structure, systems of governance and accountability, quantity and quality of the workforce, technical expertise, institutional capacity, and past performance Local social, economic, environmental, and political factors relating to specific actors, including their reputation in the community, connections and access to public infrastructure (including hospitals and schools) and key stakeholders (such as government officials and decision-makers), political and security context, geographical reach, the strength of their supply chains (for physical resources), the types and amount of financial resource currently available to them, and the projected costs of their activities.…”

Section: Leadership and Responsibilitymentioning

confidence: 99%

Financing global health emergency response: outbreaks, not agencies

Jain

2019

J Public Health Pol

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Effectively responding to global health emergencies requires substantial financial commitment from many stakeholders, including governments, multilateral agencies, and nongovernmental organizations. A major current policy challenge needs attention: how to better coordinate investment among actors aiming to address a common problem, disease outbreaks. For donors who commit colossal sums of money to outbreak response, the current model is neither efficient nor transparent. Innovative approaches to coordinate financing have recently been tested as part of a broader development agenda for humanitarian response. Adopting a system that enables donors to invest in disease outbreaks rather than actors represents an opportunity to deliver a more cost-effective, transparent, and unified global response to infectious disease outbreaks. Achieving this will be challenging, but the World Health Organization (WHO) must play a vital role. New thinking is required to improve emergency response in an increasingly crowded and financially convoluted global health arena.

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“…Furthermore, an event attracting high media attention might distort an otherwise smooth seasonal curve by a disproportionally high sudden rise or spike in tracked records if the case definition is prone to such fluctuations. With the increase in digital tools for tracking influenza cases over the internet and social media, vague definitions of influenza are currently at the core of temporal trends [51]. These new technologies pursue the noble goal of providing an early warning for influenza arrival, and their credibility depends on the quality of tracked responses and the ability to separate signal from noise.…”

Section: Influenza: the New Tower Of Babel Or The Source For Obscure mentioning

confidence: 99%

Deviations in influenza seasonality: odd coincidence or obscure consequence?

Moorthy

Castronovo²,

Abraham

et al. 2012

Clinical Microbiology and Infection

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In temperate regions, influenza typically arrives with the onset of colder weather. Seasonal waves travel over large spaces covering many climatic zones in a relatively short period of time. The precise mechanism for this striking seasonal pattern is still not well understood and the interplay of factors that influence the spread of infection and the emergence of new strains is largely unknown. The study of influenza seasonality has been fraught with problems. One of these is the ever shifting description of illness due to influenza and the use of both the historical definitions and new definitions based on actual isolation of the virus. The compilation of records describing influenza oscillations on a local and global scale is massive, but the value of these data is a function of the definitions used. In this review we argue that both observations of seasonality and deviation from the expected pattern stem from the nature of this disease. Heterogeneity in seasonal patterns may arrive from differences in behavior of specific strains, emergence of a novel strain or cross-protection from previously observed strains. Most likely the seasonal patterns emerge from interactions of individual factors behaving as coupled resonators. We emphasize that both seasonality and deviations from it may merely be a reflection of our inability to disentangle signal from noise, be it due to ambiguity in measurement and/or terminology. We conclude the review with suggestions for new promising and realistic directions with tangible consequences to model complex influenza dynamics in order to effectively control infection.

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Innovation in observation: a vision for early outbreak detection

Cited by 22 publications

References 36 publications

Evaluating Hospital-Based Surveillance for Outbreak Detection in Bangladesh: Analysis of Healthcare Utilization Data

Evaluating Hospital-Based Surveillance for Outbreak Detection in Bangladesh: Analysis of Healthcare Utilization Data

Financing global health emergency response: outbreaks, not agencies

Deviations in influenza seasonality: odd coincidence or obscure consequence?

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