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2018 年至 2019 年巴西亚马逊地区超过 7000 例麻疹病例爆发的时空分析。

Temporal and spatial analysis of over 7,000 measles cases outbreak from 2018 to 2019 in the Brazilian Amazon.

机构信息

Escola Superior de Ciências da Saúde, Universidade do Estado do Amazonas, Manaus, AM, Brazil.

Fundação de Medicina Tropical Doutor Heitor Vieira Dourado, Manaus, AM, Brazil.

出版信息

Einstein (Sao Paulo). 2024 Mar 29;22:eAO0931. doi: 10.31744/einstein_journal/2024AO0931. eCollection 2024.

DOI:10.31744/einstein_journal/2024AO0931
PMID:38567917
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11081026/
Abstract

OBJECTIVE

This study aimed to present a temporal and spatial analysis of the 2018 measles outbreak in Brazil, particularly in the metropolitan city of Manaus in the Amazon region, and further introduce a new tool for spatial analysis.

METHODS

We analyzed the geographical data of the residences of over 7,000 individuals with measles in Manaus during 2018 and 2019. Spatial and temporal analyses were conducted to characterize various aspects of the outbreak, including the onset and prevalence of symptoms, demographics, and vaccination status. A visualization tool was also constructed to display the geographical and temporal distribution of the reported measles cases.

RESULTS

Approximately 95% of the included participants had not received vaccination within the past decade. Heterogeneity was observed across all facets of the outbreak, including variations in the incubation period and symptom presentation. Age distribution exhibited two peaks, occurring at one year and 18 years of age, and the potential implications of this distribution on predictive analysis were discussed. Additionally, spatial analysis revealed that areas with the highest case densities tended to have the lowest standard of living.

CONCLUSION

Understanding the spatial and temporal spread of measles outbreaks provides insights for decision-making regarding measures to mitigate future epidemics.

摘要

目的

本研究旨在对巴西 2018 年麻疹暴发进行时空分析，特别是对亚马逊地区首府玛瑙斯进行分析，并引入一种新的空间分析工具。

方法

我们分析了玛瑙斯 2018 年和 2019 年超过 7000 名麻疹患者的住所地理位置数据。进行时空分析以描述暴发的各个方面，包括症状的发病和流行情况、人口统计学和疫苗接种情况。还构建了一个可视化工具来显示报告的麻疹病例的地理和时间分布。

结果

约 95%的参与者在过去十年内未接种疫苗。暴发的所有方面都存在异质性，包括潜伏期和症状表现的变化。年龄分布呈现两个高峰，分别为 1 岁和 18 岁，讨论了这种分布对预测分析的潜在影响。此外，空间分析显示，病例密度最高的地区往往生活水平最低。

结论

了解麻疹暴发的时空传播情况可为制定减轻未来流行的措施提供决策依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/1176aa7ac9ef/2317-6385-eins-22-eAO0931-gf11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/e5e0afcbfe18/2317-6385-eins-22-eAO0931-gf01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/0b777701b0d6/2317-6385-eins-22-eAO0931-gf02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/adf69191a9d4/2317-6385-eins-22-eAO0931-gf03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/5adee04c7c13/2317-6385-eins-22-eAO0931-gf04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/dd63315773d6/2317-6385-eins-22-eAO0931-gf05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/ed137182eec4/2317-6385-eins-22-eAO0931-gf06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/90b3273237ee/2317-6385-eins-22-eAO0931-gf07.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/985da0e50660/2317-6385-eins-22-eAO0931-gf08.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/841f47af817d/2317-6385-eins-22-eAO0931-gf09.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/fcf7eba173be/2317-6385-eins-22-eAO0931-gf10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/1176aa7ac9ef/2317-6385-eins-22-eAO0931-gf11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/e5e0afcbfe18/2317-6385-eins-22-eAO0931-gf01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/0b777701b0d6/2317-6385-eins-22-eAO0931-gf02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/adf69191a9d4/2317-6385-eins-22-eAO0931-gf03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/5adee04c7c13/2317-6385-eins-22-eAO0931-gf04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/dd63315773d6/2317-6385-eins-22-eAO0931-gf05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/ed137182eec4/2317-6385-eins-22-eAO0931-gf06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/90b3273237ee/2317-6385-eins-22-eAO0931-gf07.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/985da0e50660/2317-6385-eins-22-eAO0931-gf08.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/841f47af817d/2317-6385-eins-22-eAO0931-gf09.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/fcf7eba173be/2317-6385-eins-22-eAO0931-gf10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ed/11081026/1176aa7ac9ef/2317-6385-eins-22-eAO0931-gf11.jpg

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