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Comparison of Statistical Algorithms for the Detection of Infectious Disease Outbreaks in Large Multiple Surveillance Systems.

ABSTRACT: A large-scale multiple surveillance system for infectious disease outbreaks has been in operation in England and Wales since the early 1990s. Changes to the statistical algorithm at the heart of the system were proposed and the purpose of this paper is to compare two new algorithms with the original algorithm. Test data to evaluate performance are created from weekly counts of the number of cases of each of more than 2000 diseases over a twenty-year period. The time series of each disease is separated into one series giving the baseline (background) disease incidence and a second series giving disease outbreaks. One series is shifted forward by twelve months and the two are then recombined, giving a realistic series in which it is known where outbreaks have been added. The metrics used to evaluate performance include a scoring rule that appropriately balances sensitivity against specificity and is sensitive to variation in probabilities near 1. In the context of disease surveillance, a scoring rule can be adapted to reflect the size of outbreaks and this was done. Results indicate that the two new algorithms are comparable to each other and better than the algorithm they were designed to replace.

SUBMITTER: Enki DG

PROVIDER: S-EPMC4981474 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Comparison of Statistical Algorithms for the Detection of Infectious Disease Outbreaks in Large Multiple Surveillance Systems.

Enki Doyo G DG Garthwaite Paul H PH Farrington C Paddy CP Noufaily Angela A Andrews Nick J NJ Charlett Andre A

PloS one 20160811 8

A large-scale multiple surveillance system for infectious disease outbreaks has been in operation in England and Wales since the early 1990s. Changes to the statistical algorithm at the heart of the system were proposed and the purpose of this paper is to compare two new algorithms with the original algorithm. Test data to evaluate performance are created from weekly counts of the number of cases of each of more than 2000 diseases over a twenty-year period. The time series of each disease is sep ...[more]

PMID: 27513749

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Project description:There is growing concern in Sub-Saharan Africa about the spread of the Ebola virus disease (EVD), formerly known as Ebola haemorrhagic fever, and the public health burden that it ensues. Since 1976, there have been 885,343 suspected and laboratory confirmed cases of EVD and the disease has claimed 2,512 cases and 932 fatality in West Africa. There are certain requirements that must be met when responding to EVD outbreaks and this process could incur certain challenges. For the purposes of this paper, five have been identified: (i) the deficiency in the development and implementation of surveillance response systems against Ebola and others infectious disease outbreaks in Africa; (ii) the lack of education and knowledge resulting in an EVD outbreak triggering panic, anxiety, psychosocial trauma, isolation and dignity impounding, stigmatisation, community ostracism and resistance to associated socio-ecological and public health consequences; (iii) limited financial resources, human technical capacity and weak community and national health system operational plans for prevention and control responses, practices and management; (iv) inadequate leadership and coordination; and (v) the lack of development of new strategies, tools and approaches, such as improved diagnostics and novel therapies including vaccines which can assist in preventing, controlling and containing Ebola outbreaks as well as the spread of the disease. Hence, there is an urgent need to develop and implement an active early warning alert and surveillance response system for outbreak response and control of emerging infectious diseases. Understanding the unending risks of transmission dynamics and resurgence is essential in implementing rapid effective response interventions tailored to specific local settings and contexts.(i) national and regional inter-sectorial and trans-disciplinary surveillance response systems that include early warnings, as well as critical human resources development, must be quickly adopted by allied ministries and organisations in African countries in epidemic and pandemic responses; (ii) harnessing all stakeholders commitment and advocacy in sustained funding, collaboration, communication and networking including community participation to enhance a coordinated responses, as well as tracking and prompt case management to combat challenges; (iii) more research and development in new drug discovery and vaccines; and (iv) understanding the involvement of global health to promote the establishment of public health surveillance response systems with functions of early warning, as well as monitoring and evaluation in upholding research-action programmes and innovative interventions.

Project description:BackgroundThe COVID-19 pandemic has increased the importance of the deployment of digital detection surveillance systems to support early warning and monitoring of infectious diseases. These opportunities create a "double-edge sword," as the ethical governance of such approaches often lags behind technological achievements.ObjectiveThe aim was to investigate ethical issues identified from utilizing artificial intelligence-augmented surveillance or early warning systems to monitor and detect common or novel infectious disease outbreaks.MethodsIn a number of databases, we searched relevant articles that addressed ethical issues of using artificial intelligence, digital surveillance systems, early warning systems, and/or big data analytics technology for detecting, monitoring, or tracing infectious diseases according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, and further identified and analyzed them with a theoretical framework.ResultsThis systematic review identified 29 articles presented in 6 major themes clustered under individual, organizational, and societal levels, including awareness of implementing digital surveillance, digital integrity, trust, privacy and confidentiality, civil rights, and governance. While these measures were understandable during a pandemic, the public had concerns about receiving inadequate information; unclear governance frameworks; and lack of privacy protection, data integrity, and autonomy when utilizing infectious disease digital surveillance. The barriers to engagement could widen existing health care disparities or digital divides by underrepresenting vulnerable and at-risk populations, and patients' highly sensitive data, such as their movements and contacts, could be exposed to outside sources, impinging significantly upon basic human and civil rights.ConclusionsOur findings inform ethical considerations for service delivery models for medical practitioners and policymakers involved in the use of digital surveillance for infectious disease spread, and provide a basis for a global governance structure.Trial registrationPROSPERO CRD42021259180; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=259180.

Project description:Benchmarking surveillance systems requires realistic simulations of disease outbreaks. However, obtaining these data in sufficient quantity, with a realistic shape and covering a sufficient range of agents, size and duration, is known to be very difficult. The dataset of outbreak signals generated should reflect the likely distribution of authentic situations faced by the surveillance system, including very unlikely outbreak signals. We propose and evaluate a new approach based on the use of historical outbreak data to simulate tailored outbreak signals. The method relies on a homothetic transformation of the historical distribution followed by resampling processes (Binomial, Inverse Transform Sampling Method-ITSM, Metropolis-Hasting Random Walk, Metropolis-Hasting Independent, Gibbs Sampler, Hybrid Gibbs Sampler). We carried out an analysis to identify the most important input parameters for simulation quality and to evaluate performance for each of the resampling algorithms. Our analysis confirms the influence of the type of algorithm used and simulation parameters (i.e. days, number of cases, outbreak shape, overall scale factor) on the results. We show that, regardless of the outbreaks, algorithms and metrics chosen for the evaluation, simulation quality decreased with the increase in the number of days simulated and increased with the number of cases simulated. Simulating outbreaks with fewer cases than days of duration (i.e. overall scale factor less than 1) resulted in an important loss of information during the simulation. We found that Gibbs sampling with a shrinkage procedure provides a good balance between accuracy and data dependency. If dependency is of little importance, binomial and ITSM methods are accurate. Given the constraint of keeping the simulation within a range of plausible epidemiological curves faced by the surveillance system, our study confirms that our approach can be used to generate a large spectrum of outbreak signals.

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Comparison of Statistical Algorithms for the Detection of Infectious Disease Outbreaks in Large Multiple Surveillance Systems.

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Comparison of Statistical Algorithms for the Detection of Infectious Disease Outbreaks in Large Multiple Surveillance Systems.

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