scholarly journals Internet crawler uses unconventional information sources to track infectious disease outbreaks

BMJ ◽  
2008 ◽  
Vol 337 (jul09 1) ◽  
pp. a742-a742 ◽  
Author(s):  
S. Mayor
Keyword(s):  
Infectious Disease ◽  
Information Sources ◽  
Disease Outbreaks ◽  
Infectious Disease Outbreaks

scholarly journals The More the Better? A Comparison of the Information Sources Used by the Public during Two Infectious Disease Outbreaks

PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0140028 ◽  
Author(s):  
Cynthia G. Jardine ◽  
Franziska U. Boerner ◽  
Amanda D. Boyd ◽  
S. Michelle Driedger
Keyword(s):  
Infectious Disease ◽  
Information Sources ◽  
Disease Outbreaks ◽  
The Public ◽  
Infectious Disease Outbreaks

scholarly journals A Generalizable Data Assembly Algorithm for Infectious Disease Outbreaks

2021 ◽  
Author(s):  
Maimuna S. Majumder ◽  
Sherri Rose
Keyword(s):  
Infectious Disease ◽  
Information Sources ◽  
Disease Outbreaks ◽  
Data Sets ◽  
Validation Data ◽  
Data Set ◽  
Assembly Algorithm ◽  
Infectious Disease Outbreaks ◽  
Health Agencies ◽  
Machine Readable

AbstractBackground & ObjectiveDuring infectious disease outbreaks, health agencies often share text-based information about cases and deaths. This information is usually text-based and rarely machine-readable, thus creating challenges for outbreak researchers. Here, we introduce a generalizable data assembly algorithm that automatically curates text-based, outbreak-related information and demonstrate its performance across three outbreaks.MethodsAfter developing an algorithm with regular expressions, we automatically curated data from health agencies via three information sources: formal reports, email newsletters, and Twitter. A validation data set was also curated manually for each outbreak.FindingsWhen compared against the validation data sets, the overall cumulative missingness and misidentification of the algorithmically curated data were ≤2% and ≤1%, respectively, for all three outbreaks.ConclusionsWithin the context of outbreak research, our work successfully addresses the need for generalizable tools that can transform text-based information into machine-readable data across varied information sources and infectious diseases.

scholarly journals A generalizable data assembly algorithm for infectious disease outbreaks

JAMIA Open ◽  
2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Maimuna S Majumder ◽  
Sherri Rose
Keyword(s):  
Infectious Disease ◽  
Information Sources ◽  
Disease Outbreaks ◽  
Implementation Process ◽  
Validation Data ◽  
Data Set ◽  
Assembly Algorithm ◽  
Infectious Disease Outbreaks ◽  
Health Agencies ◽  
Machine Readable

Abstract During infectious disease outbreaks, health agencies often share text-based information about cases and deaths. This information is rarely machine-readable, thus creating challenges for outbreak researchers. Here, we introduce a generalizable data assembly algorithm that automatically curates text-based, outbreak-related information and demonstrate its performance across 3 outbreaks. After developing an algorithm with regular expressions, we automatically curated data from health agencies via 3 information sources: formal reports, email newsletters, and Twitter. A validation data set was also curated manually for each outbreak, and an implementation process was presented for application to future outbreaks. When compared against the validation data sets, the overall cumulative missingness and misidentification of the algorithmically curated data were ≤2% and ≤1%, respectively, for all 3 outbreaks. Within the context of outbreak research, our work successfully addresses the need for generalizable tools that can transform text-based information into machine-readable data across varied information sources and infectious diseases.

scholarly journals Infectious disease outbreaks in the African region: overview of events reported to the World Health Organization in 2018

2019 ◽  
Vol 147 ◽  
Author(s):  
F. Mboussou ◽  
P. Ndumbi ◽  
R. Ngom ◽  
Z. Kassamali ◽  
O. Ogundiran ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Disease Onset ◽  
Disease Outbreaks ◽  
Case Fatality ◽  
World Health ◽  
African Region ◽  
Regional Office ◽  
Member States ◽  
Infectious Disease Outbreaks ◽  
The World

Abstract The WHO African region is characterised by the largest infectious disease burden in the world. We conducted a retrospective descriptive analysis using records of all infectious disease outbreaks formally reported to the WHO in 2018 by Member States of the African region. We analysed the spatio-temporal distribution, the notification delay as well as the morbidity and mortality associated with these outbreaks. In 2018, 96 new disease outbreaks were reported across 36 of the 47 Member States. The most commonly reported disease outbreak was cholera which accounted for 20.8% (n = 20) of all events, followed by measles (n = 11, 11.5%) and Yellow fever (n = 7, 7.3%). About a quarter of the outbreaks (n = 23) were reported following signals detected through media monitoring conducted at the WHO regional office for Africa. The median delay between the disease onset and WHO notification was 16 days (range: 0–184). A total of 107 167 people were directly affected including 1221 deaths (mean case fatality ratio (CFR): 1.14% (95% confidence interval (CI) 1.07%–1.20%)). The highest CFR was observed for diseases targeted for eradication or elimination: 3.45% (95% CI 0.89%–10.45%). The African region remains prone to outbreaks of infectious diseases. It is therefore critical that Member States improve their capacities to rapidly detect, report and respond to public health events.

scholarly journals Statistical methods for the prospective detection of infectious disease outbreaks: a review

2011 ◽  
Vol 175 (1) ◽  
pp. 49-82 ◽  
Author(s):  
Steffen Unkel ◽  
C. Paddy Farrington ◽  
Paul H. Garthwaite ◽  
Chris Robertson ◽  
Nick Andrews
Keyword(s):  
Infectious Disease ◽  
Statistical Methods ◽  
Disease Outbreaks ◽  
Infectious Disease Outbreaks

scholarly journals Dot map cartograms for detection of infectious disease outbreaks: an application to Q fever, the Netherlands and pertussis, Germany

2017 ◽  
Vol 22 (26) ◽  
Author(s):  
Loes Soetens ◽  
Susan Hahné ◽  
Jacco Wallinga
Keyword(s):  
Infectious Disease ◽  
The Netherlands ◽  
Q Fever ◽  
Disease Outbreaks ◽  
Point Pattern ◽  
Infectious Disease Outbreaks ◽  
Public Health Professionals ◽  
Potential Source ◽  
High Incidence ◽  
And Control

Geographical mapping of infectious diseases is an important tool for detecting and characterising outbreaks. Two common mapping methods, dot maps and incidence maps, have important shortcomings. The former does not represent population density and can compromise case privacy, and the latter relies on pre-defined administrative boundaries. We propose a method that overcomes these limitations: dot map cartograms. These create a point pattern of cases while reshaping spatial units, such that spatial area becomes proportional to population size. We compared these dot map cartograms with standard dot maps and incidence maps on four criteria, using two example datasets. Dot map cartograms were able to illustrate both incidence and absolute numbers of cases (criterion 1): they revealed potential source locations (Q fever, the Netherlands) and clusters with high incidence (pertussis, Germany). Unlike incidence maps, they were insensitive to choices regarding spatial scale (criterion 2). Dot map cartograms ensured the privacy of cases (criterion 3) by spatial distortion; however, this occurred at the expense of recognition of locations (criterion 4). We demonstrate that dot map cartograms are a valuable method for detection and visualisation of infectious disease outbreaks, which facilitates informed and appropriate actions by public health professionals, to investigate and control outbreaks.

scholarly journals SurvNet Electronic Surveillance System for Infectious Disease Outbreaks, Germany

2007 ◽  
Vol 13 (10) ◽  
pp. 1548-1555 ◽  
Author(s):  
Gérard Krause ◽  
Doris Altmann ◽  
Daniel Faensen ◽  
Klaudia Porten ◽  
Justus Benzler ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Surveillance System ◽  
Disease Outbreaks ◽  
Electronic Surveillance ◽  
Infectious Disease Outbreaks
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