Existing and potential infection risk zones of yellow fever worldwide: a modelling analysis.

Autor:	Shearer FM; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK., Longbottom J; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK., Browne AJ; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK., Pigott DM; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA., Brady OJ; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK., Kraemer MUG; Department of Zoology, University of Oxford, Oxford, UK; Harvard Medical School, Boston, MA, USA; Boston Children's Hospital, Boston, MA, USA., Marinho F; University of State of Rio de Janeiro, Maracana, Rio de Janeiro, Brazil., Yactayo S; World Health Organization, Infectious Hazard Management, Geneva, Switzerland., de Araújo VEM; Secretariat of Health Surveillance of the Ministry of Health of Brazil, Brazil., da Nóbrega AA; Secretariat of Health Surveillance of the Ministry of Health of Brazil, Brazil., Fullman N; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA., Ray SE; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA., Mosser JF; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Division of Pediatric Infectious Diseases, Seattle Children's Hospital/University of Washington, Seattle, WA, USA., Stanaway JD; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA., Lim SS; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA., Reiner RC Jr; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA., Moyes CL; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK., Hay SI; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA. Electronic address: sihay@uw.edu., Golding N; Quantitative & Applied Ecology Group, School of BioSciences, University of Melbourne, Parkville, VIC, Australia.
Jazyk:	angličtina
Zdroj:	The Lancet. Global health [Lancet Glob Health] 2018 Mar; Vol. 6 (3), pp. e270-e278. Date of Electronic Publication: 2018 Feb 02.
DOI:	10.1016/S2214-109X(18)30024-X
Abstrakt:	Background: Yellow fever cases are under-reported and the exact distribution of the disease is unknown. An effective vaccine is available but more information is needed about which populations within risk zones should be targeted to implement interventions. Substantial outbreaks of yellow fever in Angola, Democratic Republic of the Congo, and Brazil, coupled with the global expansion of the range of its main urban vector, Aedes aegypti, suggest that yellow fever has the propensity to spread further internationally. The aim of this study was to estimate the disease's contemporary distribution and potential for spread into new areas to help inform optimal control and prevention strategies. Methods: We assembled 1155 geographical records of yellow fever virus infection in people from 1970 to 2016. We used a Poisson point process boosted regression tree model that explicitly incorporated environmental and biological explanatory covariates, vaccination coverage, and spatial variability in disease reporting rates to predict the relative risk of apparent yellow fever virus infection at a 5 × 5 km resolution across all risk zones (47 countries across the Americas and Africa). We also used the fitted model to predict the receptivity of areas outside at-risk zones to the introduction or reintroduction of yellow fever transmission. By use of previously published estimates of annual national case numbers, we used the model to map subnational variation in incidence of yellow fever across at-risk countries and to estimate the number of cases averted by vaccination worldwide. Findings: Substantial international and subnational spatial variation exists in relative risk and incidence of yellow fever as well as varied success of vaccination in reducing incidence in several high-risk regions, including Brazil, Cameroon, and Togo. Areas with the highest predicted average annual case numbers include large parts of Nigeria, the Democratic Republic of the Congo, and South Sudan, where vaccination coverage in 2016 was estimated to be substantially less than the recommended threshold to prevent outbreaks. Overall, we estimated that vaccination coverage levels achieved by 2016 avert between 94 336 and 118 500 cases of yellow fever annually within risk zones, on the basis of conservative and optimistic vaccination scenarios. The areas outside at-risk regions with predicted high receptivity to yellow fever transmission (eg, parts of Malaysia, Indonesia, and Thailand) were less extensive than the distribution of the main urban vector, A aegypti, with low receptivity to yellow fever transmission in southern China, where A aegypti is known to occur. Interpretation: Our results provide the evidence base for targeting vaccination campaigns within risk zones, as well as emphasising their high effectiveness. Our study highlights areas where public health authorities should be most vigilant for potential spread or importation events. Funding: Bill & Melinda Gates Foundation. (Copyright © 2018 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)
Databáze:	MEDLINE
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