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

AbstractThe upsurge in the number of people affected by the COVID-19 is likely to lead to increased rates of emotional trauma and mental illnesses. This article systematically reviewed the available data on the benefits of interventions to reduce adverse mental health sequelae of infectious disease outbreaks, and to offer guidance for mental health service responses to infectious disease pandemic. PubMed, Web of Science, Embase, PsycINFO, WHO Global Research Database on infectious disease, and the preprint server medRxiv were searched. Of 4278 reports identified, 32 were included in this review. Most articles of psychological interventions were implemented to address the impact of COVID-19 pandemic, followed by Ebola, SARS, and MERS for multiple vulnerable populations. Increasing mental health literacy of the public is vital to prevent the mental health crisis under the COVID-19 pandemic. Group-based cognitive behavioral therapy, psychological first aid, community-based psychosocial arts program, and other culturally adapted interventions were reported as being effective against the mental health impacts of COVID-19, Ebola, and SARS. Culturally-adapted, cost-effective, and accessible strategies integrated into the public health emergency response and established medical systems at the local and national levels are likely to be an effective option to enhance mental health response capacity for the current and for future infectious disease outbreaks. Tele-mental healthcare services were key central components of stepped care for both infectious disease outbreak management and routine support; however, the usefulness and limitations of remote health delivery should also be recognized.

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Italian Adults’ Likelihood of Getting COVID-19 Vaccine: A Second Online Survey

10.20944/preprints202102.0353.v1 ◽

2021 ◽

Author(s):

Luigi Roberto Biasio ◽

Guglielmo Bonaccorsi ◽

Chiara Lorini ◽

Daniela Mazzini ◽

Sergio Pecorelli

Keyword(s):

Infectious Disease ◽

Online Survey ◽

Disease Outbreaks ◽

Online Surveys ◽

The Public ◽

Infectious Disease Outbreaks ◽

Early Phases

Rapid online surveys are an important tool in tracking the public’s knowledge and perceptions during infectious disease outbreaks. In June 2020, during the early phases of COVID-19 vaccines development, a survey had been conducted, aimed at assessing attitudes and opinions about vaccination of 885 Italian adults, in addition to their vaccine literacy levels (i.e. skills of finding, understanding and using information about vaccines). In January 2021, the same questionnaire has been administered to a similar population (n=160). Interactive vaccine literacy was significantly higher than in June 2020 (mean score 3.38 vs 3.27 respectively, P=.0021). The percentage of participants willing to be vaccinated against COVID-19 was assessed by the means of either-or questions, and was equally high in both surveys (>90%), which is quite reassuring, despite metrics based on categorical scales cannot identify hesitant subjects.

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A Generalizable Data Assembly Algorithm for Infectious Disease Outbreaks

10.1101/2021.04.21.21255862 ◽

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.

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A generalizable data assembly algorithm for infectious disease outbreaks

JAMIA Open ◽

10.1093/jamiaopen/ooab058 ◽

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.

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Classifying Infectious Disease Outbreaks to Improve Timeliness and Efficiency of Response

Disaster Medicine and Public Health Preparedness ◽

10.1017/dmp.2014.2 ◽

2014 ◽

Vol 8 (1) ◽

pp. 89-94 ◽

Cited By ~ 1

Author(s):

Joseph M. Posid ◽

Richard A. Goodman ◽

Ali S. Khan

Keyword(s):

Public Health ◽

Infectious Disease ◽

Disease Outbreaks ◽

Response Strategy ◽

Postal Service ◽

Public Health Community ◽

The Public ◽

Infectious Disease Outbreaks ◽

Naturally Occurring ◽

Health Community

AbstractFollowing the intentional dissemination of B.anthracis through the U.S. Postal Service in 2001, use of the term “naturally occurring” to classify some infectious disease outbreaks has become more evident. However, this term is neither a scientific nor an epidemiologic classification that is helpful in describing either the source or the mode of transmission in outbreaks. In this paper, the authors provide examples of how and when the public health community has recognized potentially flawed or misleading taxonomy in the past and taken steps to improve the taxonomy's accuracy and usefulness. We also offer examples of alternative terms for classifying outbreaks since inaccurate descriptions of outbreaks could potentially lead to a flawed or incomplete set of underlying assumptions about the outbreak's causal factors. This, in turn, could lead to implementing a flawed or incomplete intervention or response strategy which could extend the duration of the outbreak, resulting in avoidable morbidity and mortality. (Disaster Med Public Health Preparedness. 2014;0:1–6)

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Internet crawler uses unconventional information sources to track infectious disease outbreaks

BMJ ◽

10.1136/bmj.a742 ◽

2008 ◽

Vol 337 (jul09 1) ◽

pp. a742-a742 ◽

Cited By ~ 2

Author(s):

S. Mayor

Keyword(s):

Infectious Disease ◽

Information Sources ◽

Disease Outbreaks ◽

Infectious Disease Outbreaks

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

Epidemiology and Infection ◽

10.1017/s0950268819001912 ◽

2019 ◽

Vol 147 ◽

Cited By ~ 6

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.

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