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Transactions of the Royal Society of Tropical Medicine and Hygiene
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Identifying hidden Zika hotspots in Pernambuco, Brazil: A spatial analysis

Authors: Laís Picinini Freitas; Rachel Lowe; Andrew E. Koepp; Sandra Valongueiro Alves; Molly Dondero; Letícia J. Marteleto;

Identifying hidden Zika hotspots in Pernambuco, Brazil: A spatial analysis

Abstract

AbstractNortheast Brazil has the world’s highest rate of Zika-related microcephaly. Yet, in this hard-hit region, traditional case counts of Zika cannot accurately describe Zika risk. Reporting of Zika cases only became mandatory after its association with microcephaly in neonates, when the Zika epidemic was already declining in the region. To advance the study of the Brazilian Zika epidemic and its impacts, we identified hotspots of Zika in Pernambuco state, Northeast Brazil, usingAedes-borne diseases (dengue, chikungunya and Zika) and microcephaly data. We used the Kulldorff’s Poisson purely spatial scan statistic to detect low- and high-risk clusters and combined the results to identify the municipalities most affected by the Zika epidemic. Municipalities were classified as hotspots if they were part of any high-risk cluster, and classified according to a gradient of Zika burden during the epidemic, considering the strength of the evidence. In Pernambuco, officials confirmed 123,934 dengue cases, 167 Zika cases, and 32,983 chikungunya cases between 2014-2017, and 800 microcephaly cases between 2015-2017. We identified 26Aedes-borne diseases clusters (11 high-risk), and 5 microcephaly cases clusters (3 high-risk). Combining the results, sixty-three out of 184 municipalities were identified as hotspots for Zika. The northeast of Pernambuco and the Sertão region were hit hardest by the Zika epidemic. The first is the most populous area, while the second has one of the highest rates of social and economic inequality in Brazil. The identification of Sertão as a Zika hotspot was only possible because the clusters results were combined. The under-reporting of acute infectious diseases is expected to be higher in poor areas. Therefore, using onlyAedes-borne data does not correctly identify the high-risk areas. We successfully identified hidden Zika hotspots using a simple methodology combiningAedes-borne diseases and microcephaly information.

Country
Spain
Keywords

Spatial Analysis, Epidemiology, Zika Virus Infection, Àrees temàtiques de la UPC::Ciències de la salut::Medicina::Medicina comunitària i salut pública, Spatial analysis, Zika Virus, Àrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida, Dengue, Zika, Statistical analysis, Aedes, Simulació per ordinador, Microcephaly, Animals, Humans, Scan statistics, Epidemiologia, Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Desenvolupament humà::Salut, Brazil

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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