Biosurveillance: a systematic review of global infectious disease surveillance systems from 1900 to 2016

Infectious disease surveillance systems provide vital data for guiding disease prevention and control policies, yet the formalization of methods to optimize surveillance networks has largely been overlooked. Decisions surrounding surveillance design parameters—such as the number and placement of surveillance sites, target populations, and case definitions—are often determined by expert opinion or deference to operational considerations, without formal analysis of the influence of design parameters on surveillance objectives. Here we propose a simulation framework to guide evidence-based surveillance network design to better achieve specific surveillance goals with limited resources. We define evidence-based surveillance design as an optimization problem, acknowledging the many operational constraints under which surveillance systems operate, the many dimensions of surveillance system design, the multiple and competing goals of surveillance, and the complex and dynamic nature of disease systems. We describe an analytical framework—the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework—for the identification of optimal surveillance designs through mathematical representations of disease and surveillance processes, definition of objective functions, and numerical optimization. We then apply the framework to the problem of selecting candidate sites to expand an existing surveillance network under alternative objectives of: (1) improving spatial prediction of disease prevalence at unmonitored sites; or (2) estimating the observed effect of a risk factor on disease. Results of this demonstration illustrate how optimal designs are sensitive to both surveillance goals and the underlying spatial pattern of the target disease. The findings affirm the value of designing surveillance systems through quantitative and adaptive analysis of network characteristics and performance. The framework can be applied to the design of surveillance systems tailored to setting-specific disease transmission dynamics and surveillance needs, and can yield improved understanding of tradeoffs between network architectures.

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New eyes: improving Europe’s infectious disease surveillance

Eurosurveillance ◽

10.2807/esm.12.05.00704-en ◽

2007 ◽

Vol 12 (5) ◽

pp. 1-2

Author(s):

D O’Flanagan

Keyword(s):

Public Health ◽

Infectious Disease ◽

Disease Surveillance ◽

Public Health Intervention ◽

Health Intervention ◽

Sufficient Information ◽

Surveillance Systems ◽

Infectious Disease Surveillance ◽

Microbiological Data

This edition of Eurosurveillance contains reports of infectious disease surveillance systems from all corners of Europe. In some instances, routinely collected notifiable data coupled with microbiological data can provide sufficient information to allow appropriate public health intervention.

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Utility of emergency call centre, dispatch and ambulance data for syndromic surveillance of infectious diseases: a scoping review

European Journal of Public Health ◽

10.1093/eurpub/ckz177 ◽

2019 ◽

Vol 30 (4) ◽

pp. 639-647 ◽

Cited By ~ 2

Author(s):

Janneke W Duijster ◽

Simone D A Doreleijers ◽

Eva Pilot ◽

Wim van der Hoek ◽

Geert Jan Kommer ◽

...

Keyword(s):

Infectious Disease ◽

Infectious Diseases ◽

Scoping Review ◽

Disease Surveillance ◽

Syndromic Surveillance ◽

Surveillance Systems ◽

Call Centre ◽

Infectious Disease Surveillance ◽

Respiratory Illnesses ◽

Key Informants

Abstract Background Syndromic surveillance can supplement conventional health surveillance by analyzing less-specific, near-real-time data for an indication of disease occurrence. Emergency medical call centre dispatch and ambulance data are examples of routinely and efficiently collected syndromic data that might assist in infectious disease surveillance. Scientific literature on the subject is scarce and an overview of results is lacking. Methods A scoping review including (i) review of the peer-reviewed literature, (ii) review of grey literature and (iii) interviews with key informants. Results Forty-four records were selected: 20 peer reviewed and 24 grey publications describing 44 studies and systems. Most publications focused on detecting respiratory illnesses or on outbreak detection at mass gatherings. Most used retrospective data; some described outcomes of temporary systems; only two described continuously active dispatch- and ambulance-based syndromic surveillance. Key informants interviewed valued dispatch- and ambulance-based syndromic surveillance as a potentially useful addition to infectious disease surveillance. Perceived benefits were its potential timeliness, standardization of data and clinical value of the data. Conclusions Various dispatch- and ambulance-based syndromic surveillance systems for infectious diseases have been reported, although only roughly half are documented in peer-reviewed literature and most concerned retrospective research instead of continuously active surveillance systems. Dispatch- and ambulance-based syndromic data were mostly assessed in relation to respiratory illnesses; reported use for other infectious disease syndromes is limited. They are perceived by experts in the field of emergency surveillance to achieve time gains in detection of infectious disease outbreaks and to provide a useful addition to traditional surveillance efforts.

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Big Data, Algorithmic Governmentality and the Regulation of Pandemic Risk

European Journal of Risk Regulation ◽

10.1017/err.2019.6 ◽

2019 ◽

Vol 10 (1) ◽

pp. 94-115

Author(s):

Stephen L ROBERTS

Keyword(s):

Infectious Disease ◽

Big Data ◽

Disease Surveillance ◽

Disease Outbreaks ◽

Surveillance Systems ◽

Infectious Disease Surveillance ◽

Novel Technologies ◽

Infectious Disease Outbreaks ◽

The Government ◽

Processing Techniques

This article investigates the rise of algorithmic disease surveillance systems as novel technologies of risk analysis utilised to regulate pandemic outbreaks in an era of big data. Critically, the article demonstrates how intensified efforts towards harnessing big data and the application of algorithmic processing techniques to enhance the real-time surveillance and regulation infectious disease outbreaks significantly transform practices of global infectious disease surveillance; observed through the advent of novel risk rationalities which underpin the deployment of intensifying algorithmic practices to increasingly colonise and patrol emergent topographies of data in order to identify and govern the emergence of exceptional pathogenic risks. Conceptually, this article asserts further howthe rise of these novel risk regulating technologies within a context of big data transforms the government and forecasting of epidemics and pandemics: illustrated by the rise of emergent algorithmic governmentalties of risk within contemporary contexts of big data, disease surveillance and the regulation of pandemic.

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School-based surveillance of acute infectious disease in children: a systematic review

BMC Infectious Diseases ◽

10.1186/s12879-021-06444-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

A. L. Donaldson ◽

J. L. Hardstaff ◽

J. P. Harris ◽

R. Vivancos ◽

S. J. O’Brien

Keyword(s):

Infectious Disease ◽

Systematic Review ◽

Disease Surveillance ◽

Syndromic Surveillance ◽

Lead Time ◽

School Attendance ◽

Data Extraction ◽

Surveillance Systems ◽

Eligibility Criteria ◽

Lag Times

Abstract Background Syndromic surveillance systems are an essential component of public health surveillance and can provide timely detection of infectious disease cases and outbreaks. Whilst surveillance systems are generally embedded within healthcare, there is increasing interest in novel data sources for monitoring trends in illness, such as over-the-counter purchases, internet-based health searches and worker absenteeism. This systematic review considers the utility of school attendance registers in the surveillance of infectious disease outbreaks and occurrences amongst children. Methods We searched eight databases using key words related to school absence, infectious disease and syndromic surveillance. Studies were limited to those published after 1st January 1995. Studies based in nursery schools or higher education settings were excluded. Article screening was undertaken by two independent reviewers using agreed eligibility criteria. Data extraction was performed using a standardised data extraction form. Outcomes included estimates of absenteeism, correlation with existing surveillance systems and associated lead or lag times. Results Fifteen studies met the inclusion criteria, all of which were concerned with the surveillance of influenza. The specificity of absence data varied between all-cause absence, illness absence and syndrome-specific absence. Systems differed in terms of the frequency of data submissions from schools and the level of aggregation of the data. Baseline rates of illness absence varied between 2.3–3.7%, with peak absences ranging between 4.1–9.8%. Syndrome-specific absenteeism had the strongest correlation with other surveillance systems (r = 0.92), with illness absenteeism generating mixed results and all-cause absenteeism performing the least well. A similar pattern of results emerged in terms of lead and lag times, with influenza-like illness (ILI)-specific absence providing a 1–2 week lead time, compared to lag times reported for all-cause absence data and inconsistent results for illness absence data. Conclusion Syndrome-specific school absences have potential utility in the syndromic surveillance of influenza, demonstrating good correlation with healthcare surveillance data and a lead time of 1–2 weeks ahead of existing surveillance measures. Further research should consider the utility of school attendance registers for conditions other than influenza, to broaden our understanding of the potential application of this data for infectious disease surveillance in children. Systematic review registration PROSPERO 2019 CRD42019119737.

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