Real-time Estimation of Epidemiologic Parameters from Contact Tracing Data During an Emerging Infectious Disease Outbreak

AbstractWhole genome sequencing (WGS) is a powerful tool for public health infectious disease investigations owing to its higher resolution, greater efficiency, and cost-effectiveness over traditional genotyping methods. Implementation of WGS in routine public health microbiology laboratories is impeded by a lack of user-friendly automated and semi-automated pipelines, restrictive jurisdictional data sharing policies, and the proliferation of non-interoperable analytical and reporting systems. To address these issues, we developed the Integrated Rapid Infectious Disease Analysis (IRIDA) platform (irida.ca), a user-friendly, decentralized, open-source bioinformatics and analytical web platform to support real-time infectious disease outbreak investigations using WGS data. Instances can be independently installed on local high-performance computing infrastructure, enabling private and secure data management and analyses according to organizational policies and governance. IRIDA’s data management capabilities enable secure upload, storage and sharing of all WGS data and metadata. The core platform currently includes pipelines for quality control, assembly, annotation, variant detection, phylogenetic analysis, in silico serotyping, multi-locus sequence typing, and genome distance calculation. Analysis pipeline results can be visualized within the platform through dynamic line lists and integrated phylogenomic clustering for research and discovery, and for enhancing decision-making support and hypothesis generation in epidemiological investigations. Communication and data exchange between instances are provided through customizable access controls. IRIDA complements centralized systems, empowering local analytics and visualizations for genomics-based microbial pathogen investigations. IRIDA is currently transforming the Canadian public health ecosystem and is freely available at https://github.com/phac-nml/irida and www.irida.ca.Impact StatementWhole genome sequencing (WGS) is revolutionizing infectious disease analysis and surveillance due to its cost effectiveness, utility, and improved analytical power. To date, no “one-size-fits-all” genomics platform has been universally adopted, owing to differences in national (and regional) health information systems, data sharing policies, computational infrastructures, lack of interoperability and prohibitive costs. The Integrated Rapid Infectious Disease Analysis (IRIDA) platform is a user-friendly, decentralized, open-source bioinformatics and analytical web platform developed to support real-time infectious disease outbreak investigations using WGS data. IRIDA empowers public health, regulatory and clinical microbiology laboratory personnel to better incorporate WGS technology into routine operations by shielding them from the computational and analytical complexities of big data genomics. IRIDA is now routinely used as part of a validated suite of tools to support outbreak investigations in Canada. While IRIDA was designed to serve the needs of the Canadian public health system, it is generally applicable to any public health and multi-jurisdictional environment. IRIDA enables localized analyses but provides mechanisms and standard outputs to enable data sharing. This approach can help overcome pervasive challenges in real-time global infectious disease surveillance, investigation and control, resulting in faster responses, and ultimately, better public health outcomes.DATA SUMMARYData used to generate some of the figures in this manuscript can be found in the NCBI BioProject PRJNA305824.

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Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak

JMIR Public Health and Surveillance ◽

10.2196/publichealth.5814 ◽

2016 ◽

Vol 2 (1) ◽

pp. e30 ◽

Cited By ~ 65

Author(s):

Maimuna S Majumder ◽

Mauricio Santillana ◽

Sumiko R Mekaru ◽

Denise P McGinnis ◽

Kamran Khan ◽

...

Keyword(s):

Real Time ◽

Zika Virus ◽

Virus Disease ◽

Time Estimation ◽

Disease Outbreak ◽

Transmission Dynamics ◽

Data Sources ◽

Real Time Estimation

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Infectious disease outbreak prediction using media articles with machine learning models

Scientific Reports ◽

10.1038/s41598-021-83926-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Juhyeon Kim ◽

Insung Ahn

Keyword(s):

Infectious Disease ◽

Infectious Diseases ◽

Human Health ◽

Emerging Infectious Diseases ◽

Disease Outbreak ◽

Emerging Infectious Disease ◽

Health Conditions ◽

News Article ◽

Support Vector ◽

Infectious Disease Outbreak

AbstractWhen a newly emerging infectious disease breaks out in a country, it brings critical damage to both human health conditions and the national economy. For this reason, apprehending which disease will newly emerge, and preparing countermeasures for that disease, are required. Many different types of infectious diseases are emerging and threatening global human health conditions. For this reason, the detection of emerging infectious disease pattern is critical. However, as the epidemic spread of infectious disease occurs sporadically and rapidly, it is not easy to predict whether an infectious disease will emerge or not. Furthermore, accumulating data related to a specific infectious disease is not easy. For these reasons, finding useful data and building a prediction model with these data is required. The Internet press releases numerous articles every day that rapidly reflect currently pending issues. Thus, in this research, we accumulated Internet articles from Medisys that were related to infectious disease, to see if news data could be used to predict infectious disease outbreak. Articles related to infectious disease from January to December 2019 were collected. In this study, we evaluated if newly emerging infectious diseases could be detected using the news article data. Support Vector Machine (SVM), Semi-supervised Learning (SSL), and Deep Neural Network (DNN) were used for prediction to examine the use of information embedded in the web articles: and to detect the pattern of emerging infectious disease.

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What factors influence symptom reporting during an emerging infectious disease outbreak? A rapid review of the evidence

10.1101/2020.07.23.20159897 ◽

2020 ◽

Cited By ~ 1

Author(s):

Patrice Carter ◽

Odette Megnin-Viggars ◽

Gideon James Rubin

Keyword(s):

Infectious Disease ◽

Healthcare Facility ◽

Disease Outbreak ◽

Emerging Infectious Disease ◽

Symptom Reporting ◽

Rapid Review ◽

Infectious Disease Outbreak ◽

The Public ◽

Ease Of Access ◽

Informative Communication

Introduction During any emerging infectious disease outbreak, people with symptoms of the illness are often asked to report their symptoms to the health service in a timely manner, to facilitate contract tracing. Numerous factors may influence an individuals willingness to report these symptoms. Understanding these factors has become urgent during the COVID-19 pandemic Objective To determine which factors influence symptom reporting during an emerging infectious disease outbreak Methods We conducted a rapid review of the evidence. We included papers based on primary research, published in peer-reviewed journals, written in English, included factors associated with symptom reporting or accessing healthcare, and related to a major public health incident involving an infectious disease outbreak Results Five themes were identified as facilitators of symptom reporting or accessing healthcare (accurate and informative communication, symptom severity, concern about exposure, ease of access, and relationship with the healthcare provider). Seven themes were identified as barriers of symptom reporting or accessing healthcare (lack of knowledge, fear, stigmatization, invasion of privacy, low concerns about symptoms, economics, and practicalities of attending a healthcare facility) Discussion If contract tracing services are to be effective, members of the public need to have the capability, opportunity and motivation to use them. The themes identified should be used to evaluate the information provided to the public about such a service, the routes of access, and the underlying polices relating to the service, in order to ensure that as many people as possible with relevant symptoms will make contact

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SORMAS-Stats: A Novel Statistical Application for Real-time Estimation of Epidemiological Parameters of Communicable Diseases using Contact-tracing Data. (Preprint)

10.2196/preprints.34438 ◽

2021 ◽

Author(s):

Bernard C Silenou ◽

Carolin Verset ◽

Basil B Kaburi ◽

Olivier Leuci ◽

Juliane Doerrbecker ◽

...

Keyword(s):

Real Time ◽

Time Estimation ◽

Dispersion Parameter ◽

Contact Tracing ◽

Network Data ◽

Transmission Network ◽

Outbreak Response ◽

Network Diagram ◽

Real Time Estimation ◽

Epidemiological Parameters

BACKGROUND The Surveillance Outbreak Response Management and Analysis System (SORMAS) contains a management module to support countries in epidemic response. It consists of documentation, linkage and follow-up of cases, contacts, and events. To allow SORMAS users to visualise, compute key surveillance indicators and estimate epidemiological parameters from such a network data in real time, we developed the SORMAS Statistics (SORMAS-Stats) application. OBJECTIVE The aim of this study is to describe the key visualisations, surveillance indicators and epidemiological parameters implemented in the SORMAS-Stats application, and illustrate the application of SORMAS-Stats to COVID-19 outbreak response. METHODS Based on literature review and user requests, we included the following visualisation and estimation of parameters in SORMAS-Stats: transmission network diagram, serial interval (SI), time-varying reproduction number (Rt), dispersion parameter (k) and additional surveillance indicators presented in graphs and tables. We estimated SI by fitting a lognormal, gamma, and Weibull distributions to the observed distribution of the number of days between symptoms onset dates of infector-infectee pairs. We estimated k by fitting a negative binomial distribution to the observed number of infectees per infector. We applied the Markov Chain Monte Carlo approach and estimated Rt using the incidence data and the observed SI data, computed from the transmission network data. RESULTS Using COVID-19 contact tracing data of confirmed cases reported between July 31, and October 29, 2021 in Bourgogne-Franche-Comté region of France, we constructed a network diagram containing 63570 nodes comprising 1.75% (1115/63570) events, 19.59% (12452/63570) case persons, and 78.66% (50003/63570) exposed persons, 1238 infector-infectee pairs, 3860 transmission chains with 24.69% (953/3860) having events as the index infector. The distribution with best fit to the observed SI data was lognormal distribution with mean 4.32 days (95% CI, 4.10–4.53 days). We estimated the dispersion parameter, k of 21.11 (95% CI, 7.57–34.66) and a reproductive number, R of 0.9 (95% CI, 0.58–0.60). The weekly estimated Rt values ranged from 0.80 to 1.61. CONCLUSIONS We provide an application for real-time estimation of epidemiological parameters, which are essential for informing outbreak response strategies. These estimates are commensurate with findings from previous studies. SORMAS-Stats application would greatly assist public health authorities in the regions using SORMAS or similar applications by providing extensive visualisations and computation of surveillance indicators.

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What Factors Influence Symptom Reporting and Access to Healthcare During an Emerging Infectious Disease Outbreak? A Rapid Review of the Evidence

Health Security ◽

10.1089/hs.2020.0126 ◽

2021 ◽

Author(s):

Patrice Carter ◽

Odette Megnin-Viggars ◽

G. James Rubin

Keyword(s):

Infectious Disease ◽

Disease Outbreak ◽

Emerging Infectious Disease ◽

Access To Healthcare ◽

Symptom Reporting ◽

Rapid Review ◽

Infectious Disease Outbreak

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A machine for real-time estimation of the tribological characteristics of materials

Industrial laboratory Diagnostics of materials ◽

10.26896/1028-6861-2020-86-4-61-65 ◽

2020 ◽

Vol 86 (4) ◽

pp. 61-65

Author(s):

M. V. Abramchuk ◽

R. V. Pechenko ◽

K. A. Nuzhdin ◽

V. M. Musalimov

Keyword(s):

Real Time ◽

Time Estimation ◽

Tribological Characteristics ◽

Continuous Processing ◽

Friction Machine ◽

Dynamic Friction Coefficient ◽

Displacement Sensors ◽

Frictional Interaction ◽

Real Time Estimation ◽

Automated Quality Control

A reciprocating friction machine Tribal-T intended for automated quality control of the rubbing surfaces of tribopairs is described. The distinctive feature of the machine consists in implementation of the forced relative motion due to the frictional interaction of the rubbing surfaces fixed on the drive and conjugate platforms. Continuous processing of the signals from displacement sensors is carried out under conditions of continuous recording of mutual displacements of loaded tribopairs using classical approaches of the theory of automatic control to identify the tribological characteristics. The machine provides consistent visual real time monitoring of the parameters. The MATLAB based computer technologies are actively used in data processing. The calculated tribological characteristics of materials, i.e., the dynamic friction coefficient, damping coefficient and measure of the surface roughness, are presented. The tests revealed that a Tribal-T reciprocating friction machine is effective for real-time study of the aforementioned tribological characteristics of materials and can be used for monitoring of the condition of tribo-nodes of machines and mechanisms.

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