Decision support for evidence-based integration of disease control: A proof of concept for malaria and schistosomiasis.
Designing and implementing effective programs for infectious disease control requires complex decision-making, informed by an understanding of the diseases, the types of disease interventions and control measures available, and the disease-relevant characteristics of the local community. Though dise...
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ftdoajarticles:oai:doaj.org/article:2a8a3613f18b4e5e9cf5520e8912c6e0 2023-05-15T15:07:27+02:00 Decision support for evidence-based integration of disease control: A proof of concept for malaria and schistosomiasis. Claire J Standley Ellie Graeden Justin Kerr Erin M Sorrell Rebecca Katz 2018-04-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0006328 https://doaj.org/article/2a8a3613f18b4e5e9cf5520e8912c6e0 EN eng Public Library of Science (PLoS) http://europepmc.org/articles/PMC5896906?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0006328 https://doaj.org/article/2a8a3613f18b4e5e9cf5520e8912c6e0 PLoS Neglected Tropical Diseases, Vol 12, Iss 4, p e0006328 (2018) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2018 ftdoajarticles https://doi.org/10.1371/journal.pntd.0006328 2022-12-31T03:22:24Z Designing and implementing effective programs for infectious disease control requires complex decision-making, informed by an understanding of the diseases, the types of disease interventions and control measures available, and the disease-relevant characteristics of the local community. Though disease modeling frameworks have been developed to address these questions and support decision-making, the complexity of current models presents a significant barrier to on-the-ground end users. The picture is further complicated when considering approaches for integration of different disease control programs, where co-infection dynamics, treatment interactions, and other variables must also be taken into account. Here, we describe the development of an application available on the internet with a simple user interface, to support on-the-ground decision-making for integrating disease control, given local conditions and practical constraints. The model upon which the tool is built provides predictive analysis for the effectiveness of integration of schistosomiasis and malaria control, two diseases with extensive geographical and epidemiological overlap. This proof-of-concept method and tool demonstrate significant progress in effectively translating the best available scientific models to support pragmatic decision-making on the ground, with the potential to significantly increase the impact and cost-effectiveness of disease control. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 12 4 e0006328 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
spellingShingle |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Claire J Standley Ellie Graeden Justin Kerr Erin M Sorrell Rebecca Katz Decision support for evidence-based integration of disease control: A proof of concept for malaria and schistosomiasis. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Designing and implementing effective programs for infectious disease control requires complex decision-making, informed by an understanding of the diseases, the types of disease interventions and control measures available, and the disease-relevant characteristics of the local community. Though disease modeling frameworks have been developed to address these questions and support decision-making, the complexity of current models presents a significant barrier to on-the-ground end users. The picture is further complicated when considering approaches for integration of different disease control programs, where co-infection dynamics, treatment interactions, and other variables must also be taken into account. Here, we describe the development of an application available on the internet with a simple user interface, to support on-the-ground decision-making for integrating disease control, given local conditions and practical constraints. The model upon which the tool is built provides predictive analysis for the effectiveness of integration of schistosomiasis and malaria control, two diseases with extensive geographical and epidemiological overlap. This proof-of-concept method and tool demonstrate significant progress in effectively translating the best available scientific models to support pragmatic decision-making on the ground, with the potential to significantly increase the impact and cost-effectiveness of disease control. |
format |
Article in Journal/Newspaper |
author |
Claire J Standley Ellie Graeden Justin Kerr Erin M Sorrell Rebecca Katz |
author_facet |
Claire J Standley Ellie Graeden Justin Kerr Erin M Sorrell Rebecca Katz |
author_sort |
Claire J Standley |
title |
Decision support for evidence-based integration of disease control: A proof of concept for malaria and schistosomiasis. |
title_short |
Decision support for evidence-based integration of disease control: A proof of concept for malaria and schistosomiasis. |
title_full |
Decision support for evidence-based integration of disease control: A proof of concept for malaria and schistosomiasis. |
title_fullStr |
Decision support for evidence-based integration of disease control: A proof of concept for malaria and schistosomiasis. |
title_full_unstemmed |
Decision support for evidence-based integration of disease control: A proof of concept for malaria and schistosomiasis. |
title_sort |
decision support for evidence-based integration of disease control: a proof of concept for malaria and schistosomiasis. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2018 |
url |
https://doi.org/10.1371/journal.pntd.0006328 https://doaj.org/article/2a8a3613f18b4e5e9cf5520e8912c6e0 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 12, Iss 4, p e0006328 (2018) |
op_relation |
http://europepmc.org/articles/PMC5896906?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0006328 https://doaj.org/article/2a8a3613f18b4e5e9cf5520e8912c6e0 |
op_doi |
https://doi.org/10.1371/journal.pntd.0006328 |
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PLOS Neglected Tropical Diseases |
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12 |
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4 |
container_start_page |
e0006328 |
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1766338954536157184 |