scholarly journals Cost-effective Surveillance for Infectious Diseases Through Specimen Pooling and Multiplex Assays

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Christopher Bilder ◽  
Joshua Tebbs ◽  
Christopher McMahan
Keyword(s):  
Infectious Diseases ◽  
Disease Surveillance ◽  
Cost Effective ◽  
Risk Information ◽  
Individual Risk ◽  
Infectious Disease Surveillance ◽  
Risk Levels ◽  
Individual Specimen ◽  
Stage Process ◽  
Testing Algorithm

ObjectiveTo develop specimen pooling algorithms that reduce the number of tests needed to test individuals for infectious diseases with multiplex assays.IntroductionAn essential tool for infectious disease surveillance is to have a timely and cost-effective testing method. For this purpose, laboratories frequently use specimen pooling to assay high volumes of clinical specimens. The simplest pooling algorithm employs a two-stage process. In the first stage, a set number of specimens are amalgamated to form a “group” that is tested as if it were one specimen. If this group tests negatively, all individuals within the group are declared disease free. If this group tests positively, a second stage is implemented with retests performed on each individual. This testing algorithm is repeated across all individuals that need to be tested. In comparison to testing each individual specimen, large reductions in the number of tests occur when overall disease prevalence is small because most groups will test negatively.Most pooling algorithms have been developed in the context of single-disease assays. New pooling algorithms are developed in the context of multiplex (multiple-disease) assays applied over two or three hierarchical stages. Individual risk information can be employed by these algorithms to increase testing efficiency.MethodsMonte Carlo simulations are used to emulate pooling and testing processes. These simulations are based on retrospective chlamydia and gonorrhea testing data collected over a two-year period in Idaho, Iowa, and Oregon. For each simulation, the number of tests and measures of accuracy are recorded. All tests were originally performed by the Aptima Combo 2 Assay. Sensitivities and specificities for this assay are included in the simulation process.The R statistical software package is used to perform all simulations. For reproducibility of the research, programs are made available at www.chrisbilder.com/grouptesting to implement the simulations.ResultsReductions in the number of tests were obtained for all states when compared to individual specimen testing. For example, the pooling of Idaho female specimens without taking into account individual risk information resulted in a 47% and a 51% reduction in tests when using two and three stages, respectively. With the addition of individual risk information, further reductions in tests occurred. For example, the pooling of Idaho female specimens resulted in an additional 5% reduction of tests when compared directly to not using individual risk information. These reductions in tests were found to be related to the type of risk information available and the variability in risk levels. For example, males were found to have much more variability than females. For Idaho, this resulted in a 15% further reduction in tests than when not using the risk information.ConclusionsSignificant reductions in the number of tests occur through pooling. These reductions are the most significant when individual risk information is taken into account by the pooling algorithm.

scholarly journals Towards One Health Knowledge Networks: A Southern African Centre of Infectious Disease Surveillance case study

2012 ◽  
Vol 79 (2) ◽  
Author(s):  
Eric Beda
Keyword(s):  
Infectious Disease ◽  
Knowledge Management ◽  
Infectious Diseases ◽  
Disease Surveillance ◽  
One Health ◽  
Health Knowledge ◽  
Intelligent System ◽  
Scientific Information ◽  
Knowledge Network ◽  
Infectious Disease Surveillance

The dynamic nature of new information and/or knowledge is a big challenge for information systems. Early knowledge management systems focused entirely on technologies for storing, searching and retrieving data; these systems have proved a failure. Juirsica and Mylopoulos1 suggested that in order to build effective technologies for knowledge management, we need to further our understanding of how individuals, groups and organisations use knowledge. As the focus on knowledge management for organisations and consortia alike is moving towards a keen appreciation of how deeply knowledge is embedded in people’s experiences, there is a general realisation that knowledge cannot be stored or captured digitally. This puts more emphasis in creating enabling environments for interactions that stimulate knowledge sharing.Our work aims at developing an un-obtrusive intelligent system that glues together effective contemporary and traditional technologies to aid these interactions and manage the information captured. In addition this system will include tools to aid propagating a repository of scientific information relevant to surveillance of infectious diseases to complement knowledge shared and/or acts as a point of reference.This work is ongoing and based on experiences in developing a knowledge network management system for the Southern African Centre of Infectious Disease Surveillance (SACIDS), A One Health consortium of southern African academic and research institutions involved with infectious diseases of humans and animals in partnership with world-renowned centres of research in industrialised countries.

scholarly journals Optimising passive surveillance of a neglected tropical disease in the era of elimination: A modelling study

2020 ◽  
Author(s):  
Joshua Longbottom ◽  
Charles Wamboga ◽  
Paul R. Bessell ◽  
Steve J. Torr ◽  
Michelle C. Stanton
Keyword(s):  
Infectious Diseases ◽  
Disease Surveillance ◽  
Human African Trypanosomiasis ◽  
Cost Effective ◽  
Emerging Diseases ◽  
Surveillance Systems ◽  
Passive Surveillance ◽  
Resistance Surface ◽  
Western Uganda ◽  
North Western

AbstractBackgroundSurveillance is an essential component of global programs to eliminate infectious diseases and avert epidemics of (re-)emerging diseases. As the numbers of cases decline, costs of treatment and control diminish but those for surveillance remain high even after the ‘last’ case. Reducing surveillance may risk missing persistent or (re-)emerging foci of disease. Here, we use a simulation-based approach to determine the minimal number of passive surveillance sites required to ensure maximum coverage of a population at-risk (PAR) of an infectious disease.Methodology and Principal FindingsFor this study, we use Gambian human African trypanosomiasis (g-HAT) in north-western Uganda, a neglected tropical disease (NTD) which has been reduced to historically low levels (<1000 cases/year globally), as an example. To quantify travel time to diagnostic facilities, a proxy for surveillance coverage, we produced a high spatial-resolution resistance surface and performed cost-distance analyses. We simulated travel time for the PAR with different numbers (1-170) and locations (170,000 total placement combinations) of diagnostic facilities, quantifying the percentage of the PAR within 1h and 5h travel of the facilities, as per in-country targets. Our simulations indicate that a 70% reduction (51/170) in diagnostic centres still exceeded minimal targets of coverage even for remote populations, with >95% of a total PAR of ~3million individuals living ≤1h from a diagnostic centre, and we demonstrate an approach to best place these facilities, informing a minimal impact scale back.ConclusionsOur results highlight that surveillance of g-HAT in north-western Uganda can be scaled back without reducing coverage of the PAR. The methodology described can contribute to cost-effective and equable strategies for the surveillance of NTDs and other infectious diseases approaching elimination or (re-)emergence.Author SummaryDisease surveillance systems are an essential component of public health practice and are often considered the first line in averting epidemics for (re-)emerging diseases. Regular evaluation of surveillance systems ensures that they remain operating at maximum efficiency; systems that survey diseases of low incidence, such as those within elimination settings, should be simplified to reduce the reporting burden. A lack of guidance on how to optimise disease surveillance in an elimination setting may result in added expense, and/or the underreporting of disease. Here, we propose a framework methodology to determine systematically the optimal number and placement of surveillance sites for the surveillance of infectious diseases approaching elimination. By utilising estimates of geographic accessibility, through the construction of a resistance surface and a simulation approach, we identify that the number of operational diagnostic facilities for Gambian human African trypanosomiasis in north-western Uganda can be reduced by 70% without affecting existing coverage, and identify the minimum number of facilities required to meet coverage targets. Our analysis can be used to inform the number and positioning of surveillance sites for diseases within an elimination setting. Passive surveillance becomes increasingly important as cases decline and active surveillance becomes less cost-effective; methods to evaluate how best to engage this passive surveillance capacity given facility capacity and geographic distribution are pertinent for several NTDs where diagnosis is complex. Not only is this a complicated research area for diseases approaching elimination, a well-designed surveillance system is essential for the detection of emerging diseases, with this work being topical in a climate where emerging pathogens are becoming more commonplace.

scholarly journals SARS-CoV-2 genome datasets analytics for informed infectious disease surveillance

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 919
Author(s):  
Moses Effiong Ekpenyong ◽  
Ifiok James Udo ◽  
Mercy Ernest Edoho ◽  
EnoAbasi Deborah Anwana ◽  
Francis Bukie Osang ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Real Time ◽  
Disease Surveillance ◽  
Emerging Infectious Diseases ◽  
Cost Effective ◽  
Contact Tracing ◽  
Public Healthcare ◽  
Case Definitions ◽  
Smart Healthcare ◽  
Disease Case

Background: The COVID-19 pandemic has ravaged economies, health systems, and lives globally. Concerns surrounding near total economic collapse, loss of livelihood and emotional complications ensuing from lockdowns and commercial inactivity, resulted in governments loosening economic restrictions. These concerns were further exacerbated by the absence of vaccines and drugs to combat the disease, with the fear that the next wave of the pandemic would be more fatal. Consequently, integrating disease surveillance mechanism into public healthcare systems is gaining traction, to reduce the spread of community and cross-border infections and offer informed medical decisions. Methods: Publicly available datasets of coronavirus cases around the globe deposited between December, 2019 and March 15, 2021 were retrieved from GISAID EpiFluTM and processed. Also retrieved from GISAID were data on the different SARS-CoV-2 variant types since inception of the pandemic. Results: Epidemiological analysis offered interesting statistics for understanding the demography of SARS-CoV-2 and helped the elucidation of local and foreign transmission through a history of contact travels. Results of genome pattern visualization and cognitive knowledge mining revealed the emergence of high intra-country viral sub-strains with localized transmission routes traceable to immediate countries, for enhanced contact tracing protocol. Variant surveillance analysis indicates increased need for continuous monitoring of SARS-CoV-2 variants.  A collaborative Internet of Health Things (IoHT) framework was finally proposed to impact the public health system, for robust and intelligent support for modelling, characterizing, diagnosing and real-time contact tracing of infectious diseases. Conclusions: Localizing healthcare disease surveillance is crucial in emerging disease situations and will support real-time/updated disease case definitions for suspected and probable cases. The IoHT framework proposed in this paper will assist early syndromic assessments of emerging infectious diseases and support healthcare/medical countermeasures as well as useful strategies for making informed policy decisions to drive a cost effective, smart healthcare system.

scholarly journals Towards One Health disease surveillance: The Southern African Centre for Infectious Disease Surveillance approach

2012 ◽  
Vol 79 (2) ◽  
Author(s):  
Esron D. Karimuribo ◽  
Kuya Sayalel ◽  
Eric Beda ◽  
Nick Short ◽  
Philemon Wambura ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
River Basin ◽  
Disease Surveillance ◽  
One Health ◽  
Animal Health ◽  
Current Status ◽  
Animal Origin ◽  
Infectious Disease Surveillance ◽  
Custom Made

Africa has the highest burden of infectious diseases in the world and yet the least capacity for its risk management. It has therefore become increasingly important to search for ‘fit-for- purpose’ approaches to infectious disease surveillance and thereby targeted disease control. The fact that the majority of human infectious diseases are originally of animal origin means we have to consider One Health (OH) approaches which require inter-sectoral collaboration for custom-made infectious disease surveillance in the endemic settings of Africa. A baseline survey was conducted to assess the current status and performance of human and animal health surveillance systems and subsequently a strategy towards OH surveillance system was developed. The strategy focused on assessing the combination of participatory epidemiological approaches and the deployment of mobile technologies to enhance the effectiveness of disease alerts and surveillance at the point of occurrence, which often lies in remote areas. We selected three study sites, namely the Ngorongoro, Kagera River basin and Zambezi River basin ecosystems. We have piloted and introduced the next-generation Android mobile phones running the EpiCollect application developed by Imperial College to aid geo-spatial and clinical data capture and transmission of this data from the field to the remote Information Technology (IT) servers at the research hubs for storage, analysis, feedback and reporting. We expect that the combination of participatory epidemiology and technology will significantly improve OH disease surveillance in southern Africa.

scholarly journals Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Alina Deshpande ◽  
Kristin Margevicius
Keyword(s):  
Infectious Disease ◽  
Decision Support ◽  
Infectious Diseases ◽  
Disease Surveillance ◽  
Disease Modeling ◽  
National Laboratory ◽  
Decision Makers ◽  
Specific Information ◽  
Epidemiological Models ◽  
Infectious Disease Surveillance

Objective1. To develop a comprehensive model characterization frameworkto describe epidemiological models in an operational context.2. To apply the framework to characterize “operational” modelsfor specific infectious diseases and provide a web-based directory,the biosurveillance analytics resource directory (BARD) to the globalinfectious disease surveillance community.IntroductionEpidemiological modeling for infectious disease is useful fordisease management and routine implementation needs to befacilitated through better description of models in an operationalcontext. A standardized model characterization process that allowsselection or making manual comparisons of available models andtheir results is currently lacking. Los Alamos National Laboratory(LANL) has developed a comprehensive framework that can be usedto characterize an infectious disease model in an operational context.We offer this framework and an associated database to stakeholders ofthe infectious disease modeling field as a tool for standardizing modeldescription and facilitating the use of epidemiological models. Such aframework could help the understanding of diverse models by variousstakeholders with different preconceptions, backgrounds, expertise,and needs, and can foster greater use of epidemiological models astools in infectious disease surveillance.MethodsWe define, “operational” as the application of an epidemiologicalmodel to a real-world event for decision support and can be used byexperts and non-experts alike. The term “model” covers three majortypes, risk mapping, disease dynamics and anomaly detection.To develop a framework for characterizing epidemiological modelswe collected information via a three-step process: a literature searchof model characteristics, a review of current operational infectiousdisease epidemiological models, and subject matter expert (SME)panel consultation. We limited selection of operational models tofive infectious diseases: influenza, malaria, dengue, cholera andfoot-and-mouth disease (FMD). These diseases capture a varietyof transmission modes, represent high or potentially high epidemicor endemic burden, and are well represented in the literature. Wealso developed working criteria for what attributes can be used tocomprehensively describe an operational model including a model’sdocumentation, accessibility, and sustainability.To apply the model characterization framework, we built theBARD, which is publicly available (http://brd.bsvgateway.org).A document was also developed to describe the usability requirementsfor the BARD; potential users (and non-users) and use cases areformally described to explain the scope of use.Results1. Framework for model characterizationThe framework is divided into six major components (Figure 1):Model Purpose, Model Objective, Model Scope, Biosurveillance(BSV) goals, Conceptual Model and Model Utility; each of whichhas several sub-categories for characterizing each aspect of a model.2. Application to model characterizationModels for five infectious diseases—cholera, malaria, influenza,FMD and dengue were characterizedusing the framework and are included in the BARD database. Ourframework characterized disparate models in a streamlined fashion.Model information could be binned into the same categories, allowingeasy manual comparison and understanding of the models.3. Development of the BARDOur model characterization framework was implemented into anactionable tool which provides specific information about a modelthat has been systematically categorized. It allows manual categoryto-category comparison of multiple models for a single disease andwhile the tool does not rank models it provides model information ina format that allows a user to make a ranking or an assessment of theutility of the model.ConclusionsWith the model characterization framework we hope to encouragemodel developers to start describing the many features of their modelsusing a common format. We illustrate the application of the frameworkthrough the development of the BARD which is a scientific andnon-biased tool for selecting an appropriate epidemiological modelfor infectious disease surveillance. Epidemiological models are notnecessarily being developed with decision makers in mind. This gapbetween model developers and decision makers needs to be narrowedbefore modeling becomes routinely implemented in decision making.The characterization framework and the tool developed (BARD) area first step towards addressing this gap.Keywordsepidemiological models; database; decision support

scholarly journals Artificial Intelligence-based Disease Surveillance Amid COVID-19 and Beyond: A Systematic Review Protocol

2020 ◽  
Author(s):  
Zhaohui Su ◽  
Barry Bentley ◽  
Feng Shi
Keyword(s):  
Artificial Intelligence ◽  
Infectious Diseases ◽  
Disease Surveillance ◽  
Cost Effective ◽  
Surveillance Systems ◽  
Prevention Measures ◽  
Eligibility Criteria ◽  
The World ◽  
Tools And Techniques ◽  
Health Experts

Abstract Background: Infectious diseases are dangerous and deadly. As the leading causes of morbidity and mortality in all demographics across the world, infectious diseases carry substantial social, economic, and healthcare costs. Unlike previous global health crises, health experts now have access to more advanced tools and techniques to understand pandemics like COVID-19 better and faster; one such class of tools is artificial intelligence (AI) enabled disease surveillance systems. AI-based surveillance systems allow health experts to perform rapid mass infection prediction to identify potential disease cases, which is integral to understanding transmission and curbing the spread of the pandemic. However, while the importance of AI-based disease surveillance mechanisms in pandemic control is clear, what is less known is the state-of-the-art application of these mechanisms in countries across the world. Therefore, to bridge this gap, we aim to systematically review the literature to identify (1) how AI-based disease surveillance systems have been applied in counties worldwide amid COVID-19, (2) the characteristics and effects of these applications regarding the control of the spread of COVID-19, and (3) what additional disease surveillance resources such as database, AI-based tools and techniques that can be further added to the current toolbox in the fight against COVID-19. Methods: To locate research on AI-based disease surveillance amid COVID-19, we will search databases including PubMed, IEEE Explore, ACM Digital Library, and Science Direct to identify all potential records. Titles, abstracts, and full-text articles were screened against eligibility criteria developed a priori. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedures was adopted as the reporting framework.Results: NA for now Conclusions: Findings of our study will fill an important void in the literature, as no research has systematically reviewed available AI-based disease surveillance in the context of COVID-19. As the world continues to reel from COVID-19, it is important to identify cost-effective AI-based disease surveillance mechanisms that can detect COVID-19 cases and explain how one COVID-19 case turns into many cases, so that better prevention measures can be established to curb the spread of the COVID pandemic in a timely manner. Study Protocol Registration: PROSPERO CRD42020204992

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

2019 ◽  
Vol 30 (4) ◽  
pp. 639-647 ◽  
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.

scholarly journals A framework for evaluating animals as sentinels for infectious disease surveillance

2007 ◽  
Vol 4 (16) ◽  
pp. 973-984 ◽  
Author(s):  
Jo E.B Halliday ◽  
Anna L Meredith ◽  
Darryn L Knobel ◽  
Darren J Shaw ◽  
Barend M. de C Bronsvoort ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Disease Surveillance ◽  
Interdisciplinary Collaboration ◽  
Host Responses ◽  
Infectious Disease Surveillance ◽  
Potential Value ◽  
Wide Range ◽  
Animal Hosts ◽  
Collaboration And Communication

The dynamics of infectious diseases are highly variable. Host ranges, host responses to pathogens and the relationships between hosts are heterogeneous. Here, we argue that the use of animal sentinels has the potential to use this variation and enable the exploitation of a wide range of pathogen hosts for surveillance purposes. Animal sentinels may be used to address many surveillance questions, but they may currently be underused as a surveillance tool and there is a need for improved interdisciplinary collaboration and communication in order to fully explore the potential of animal sentinels. In different contexts, different animal hosts will themselves vary in their capacity to provide useful information. We describe a conceptual framework within which the characteristics of different host populations and their potential value as sentinels can be evaluated in a broad range of settings.

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