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dc.contributor.author
Cohen, Joel E  
dc.contributor.author
zu Dohna, Heinrich  
dc.contributor.author
Gurtler, Ricardo Esteban  
dc.date.available
2022-06-25T02:58:46Z  
dc.date.issued
2019-12  
dc.identifier.citation
Cohen, Joel E; zu Dohna, Heinrich; Gurtler, Ricardo Esteban; Insights from quantitative and mathematical modelling on the proposed WHO 2030 goals for Chagas disease; Bill and Melinda Gates Foundation; Gates Open Research; 3; 12-2019; 1539-1539  
dc.identifier.issn
2572-4754  
dc.identifier.uri
http://hdl.handle.net/11336/160563  
dc.description.abstract
Chagas disease (CD) persists as one of the neglected tropical diseases (NTDs) with a particularly large impact in the Americas. The World Health Organization (WHO) recently proposed goals for CD elimination as a public health problem to be reached by 2030 by means of achieving intradomiciliary transmission interruption (IDTI), blood transfusion and transplant transmission interruption, diagnostic and treatment scaling-up and prevention and control of congenital transmission. The NTD Modelling Consortium has developed mathematical models to study Trypanosoma cruzi transmission dynamics and the potential impact of control measures. Modelling insights have shown that IDTI is feasible in areas with sustained vector control programmes and no presence of native triatomine vector populations. However, IDTI in areas with native vectors it is not feasible in a sustainable manner. Combining vector control with trypanocidal treatment can reduce the timeframes necessary to reach operational thresholds for IDTI (<2% seroprevalence in children aged <5 years), but the most informative age groups for serological monitoring are yet to be identified. Measuring progress towards the 2030 goals will require availability of vector surveillance and seroprevalence data at a fine scale, and a more active surveillance system, as well as a better understanding of the risks of vector re-colonization and disease resurgence after vector control cessation. Also, achieving scaling-up in terms of access to treatment to the expected levels (75%) will require a substantial increase in screening asymptomatic populations, which is anticipated to become very costly as CD prevalence decreases. Further modelling work includes refining and extending mathematical models (including transmission dynamics and statistical frameworks) to predict transmission at a sub-national scale, and developing quantitative tools to inform IDTI certification, post-certification and re-certification protocols. Potential perverse incentives associated with operational thresholds are discussed. These modelling insights aim to inform discussions on the goals and treatment guidelines for CD.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Bill and Melinda Gates Foundation  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/  
dc.subject
Chagas disease  
dc.subject
mathematical model  
dc.subject
disease control  
dc.subject
vector control  
dc.subject.classification
Ecología  
dc.subject.classification
Ciencias Biológicas  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Insights from quantitative and mathematical modelling on the proposed WHO 2030 goals for Chagas disease  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2020-11-20T14:43:40Z  
dc.journal.volume
3  
dc.journal.pagination
1539-1539  
dc.journal.pais
Estados Unidos  
dc.journal.ciudad
Seattle  
dc.description.fil
Fil: Cohen, Joel E. The Rockefeller University; Estados Unidos  
dc.description.fil
Fil: zu Dohna, Heinrich. The Rockefeller University; Estados Unidos  
dc.description.fil
Fil: Gurtler, Ricardo Esteban. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ecología, Genética y Evolución; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; Argentina  
dc.journal.title
Gates Open Research  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.21956/gatesopenres.14202.r27887  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://gatesopenresearch.org/articles/3-1539/v1#referee-response-27887