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Earlier Outbreak Detection—A Generic Model and Novel Methodology to Guide Earlier Detection Supported by Data From Low- and Mid-Income Countries

Infectious disease outbreaks can have significant impact on individual health, national economies, and social well-being. Through early detection of an infectious disease, the outbreak can be contained at the local level, thereby reducing adverse effects on populations. Significant time and funding...

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Autores principales: Steele, Lindsay, Orefuwa, Emma, Bino, Silvia, Singer, Shepherd Roee, Lutwama, Julius, Dickmann, Petra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516212/
https://www.ncbi.nlm.nih.gov/pubmed/33014967
http://dx.doi.org/10.3389/fpubh.2020.00452
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author Steele, Lindsay
Orefuwa, Emma
Bino, Silvia
Singer, Shepherd Roee
Lutwama, Julius
Dickmann, Petra
author_facet Steele, Lindsay
Orefuwa, Emma
Bino, Silvia
Singer, Shepherd Roee
Lutwama, Julius
Dickmann, Petra
author_sort Steele, Lindsay
collection PubMed
description Infectious disease outbreaks can have significant impact on individual health, national economies, and social well-being. Through early detection of an infectious disease, the outbreak can be contained at the local level, thereby reducing adverse effects on populations. Significant time and funding have been invested to improve disease detection timeliness. However, current evaluation methods do not provide evidence-based suggestions or measurements on how to detect outbreaks earlier. Key conditions for earlier detection and their influencing factors remain unclear and unmeasured. Without clarity about conditions and influencing factors, attempts to improve disease detection remain ad hoc and unsystematic. Methods: We developed a generic five-step disease detection model and a novel methodology to use for data collection, analysis, and interpretation. Data was collected in two workshops in Southeast Europe (n = 33 participants) and Southern and East Africa (n = 19 participants), representing mid- and low-income countries. Through systematic, qualitative, and quantitative data analyses, we identified key conditions for earlier detection and prioritized factors that influence them. As participants joined a workshop format and not an experimental setting, no ethics approval was required. Findings: Our analyses suggest that governance is the most important condition for earlier detection in both regions. Facilitating factors for earlier detection are risk communication activities such as information sharing, communication, and collaboration activities. Impeding factors are lack of communication, coordination, and leadership. Interpretation: Governance and risk communication are key influencers for earlier detection in both regions. However, inadequate technical capacity, commonly assumed to be a leading factor impeding early outbreak detection, was not found a leading factor. This insight may be used to pinpoint further improvement strategies.
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spelling pubmed-75162122020-10-02 Earlier Outbreak Detection—A Generic Model and Novel Methodology to Guide Earlier Detection Supported by Data From Low- and Mid-Income Countries Steele, Lindsay Orefuwa, Emma Bino, Silvia Singer, Shepherd Roee Lutwama, Julius Dickmann, Petra Front Public Health Public Health Infectious disease outbreaks can have significant impact on individual health, national economies, and social well-being. Through early detection of an infectious disease, the outbreak can be contained at the local level, thereby reducing adverse effects on populations. Significant time and funding have been invested to improve disease detection timeliness. However, current evaluation methods do not provide evidence-based suggestions or measurements on how to detect outbreaks earlier. Key conditions for earlier detection and their influencing factors remain unclear and unmeasured. Without clarity about conditions and influencing factors, attempts to improve disease detection remain ad hoc and unsystematic. Methods: We developed a generic five-step disease detection model and a novel methodology to use for data collection, analysis, and interpretation. Data was collected in two workshops in Southeast Europe (n = 33 participants) and Southern and East Africa (n = 19 participants), representing mid- and low-income countries. Through systematic, qualitative, and quantitative data analyses, we identified key conditions for earlier detection and prioritized factors that influence them. As participants joined a workshop format and not an experimental setting, no ethics approval was required. Findings: Our analyses suggest that governance is the most important condition for earlier detection in both regions. Facilitating factors for earlier detection are risk communication activities such as information sharing, communication, and collaboration activities. Impeding factors are lack of communication, coordination, and leadership. Interpretation: Governance and risk communication are key influencers for earlier detection in both regions. However, inadequate technical capacity, commonly assumed to be a leading factor impeding early outbreak detection, was not found a leading factor. This insight may be used to pinpoint further improvement strategies. Frontiers Media S.A. 2020-09-11 /pmc/articles/PMC7516212/ /pubmed/33014967 http://dx.doi.org/10.3389/fpubh.2020.00452 Text en Copyright © 2020 Steele, Orefuwa, Bino, Singer, Lutwama and Dickmann. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Steele, Lindsay
Orefuwa, Emma
Bino, Silvia
Singer, Shepherd Roee
Lutwama, Julius
Dickmann, Petra
Earlier Outbreak Detection—A Generic Model and Novel Methodology to Guide Earlier Detection Supported by Data From Low- and Mid-Income Countries
title Earlier Outbreak Detection—A Generic Model and Novel Methodology to Guide Earlier Detection Supported by Data From Low- and Mid-Income Countries
title_full Earlier Outbreak Detection—A Generic Model and Novel Methodology to Guide Earlier Detection Supported by Data From Low- and Mid-Income Countries
title_fullStr Earlier Outbreak Detection—A Generic Model and Novel Methodology to Guide Earlier Detection Supported by Data From Low- and Mid-Income Countries
title_full_unstemmed Earlier Outbreak Detection—A Generic Model and Novel Methodology to Guide Earlier Detection Supported by Data From Low- and Mid-Income Countries
title_short Earlier Outbreak Detection—A Generic Model and Novel Methodology to Guide Earlier Detection Supported by Data From Low- and Mid-Income Countries
title_sort earlier outbreak detection—a generic model and novel methodology to guide earlier detection supported by data from low- and mid-income countries
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516212/
https://www.ncbi.nlm.nih.gov/pubmed/33014967
http://dx.doi.org/10.3389/fpubh.2020.00452
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