Climate, social vulnerability, and dengue occurrence in Pernambuco, Brazil

Sara Nasário Tenório Alves; Camila  Portela Marinho do Rego Barros; Edivan Rodrigues da Silva; Ricardo Brauer Vigoderis; Romero Luiz Mendonça Sales Filho; Werônica Meira de Souza

doi:10.5327/z2176-94782814

Authors

Sara Nasário Tenório Alves Universidade Federal do Agreste de Pernambuco (UFAPE) - Brazil https://orcid.org/0000-0001-6058-0578
Camila Portela Marinho do Rego Barros Universidade Católica de Pernambuco (UNICAP) - Brazil https://orcid.org/0009-0002-0828-3052
Edivan Rodrigues da Silva Universidade Federal do Agreste de Pernambuco (UFAPE) - Brazil https://orcid.org/0009-0006-5590-3102
Ricardo Brauer Vigoderis Universidade Federal do Agreste de Pernambuco (UFAPE) - Brazil https://orcid.org/0000-0001-8775-972X
Romero Luiz Mendonça Sales Filho Universidade Federal do Agreste de Pernambuco (UFAPE) - Brazil https://orcid.org/0000-0001-6491-9161
Werônica Meira de Souza Universidade Federal do Agreste de Pernambuco (UFAPE) - Brazil https://orcid.org/0000-0002-6270-2345

DOI:

https://doi.org/10.5327/z2176-94782814

Keywords:

Aedes aegypti; seasonality; rainfall; epidemiology; Poisson regression.

Abstract

Dengue remains one of the main public health challenges in Brazil. This study aimed to analyze the relationship between rainfall and dengue incidence in the state of Pernambuco, as well as to understand how socioeconomic indicators explain the heterogeneity of the disease’s seasonal risk. Historical rainfall series from the National Institute of Meteorology (1961–2024), dengue notifications from the Department of Informatics of the Brazilian Unified Health System (2007–2024), and basic sanitation and Municipal Human Development Index (MHDI) indicators from the Brazilian Institute of Geography and Statistics (2010–2022) were used for municipalities in the Sertão, Agreste, and the Metropolitan Region of Recife. Climatology and mean values were calculated to characterize seasonality, and Poisson regression was applied to estimate climatic effects and simulate scenarios with rainfall increases of 10, 50, 100, and 200 mm. Rainfall was identified as the main trigger, with lags of one to three months. No trends were observed in the time series; however, the frequency of notifications increased after 2014. The model indicated that monthly increases of 50 mm raised cases by 56%, and 100 mm by 113%, particularly in Recife. The Sertão and Agreste regions, with lower MHDI and insufficient sanitation, showed higher risks; in the Metropolitan Region of Recife, unequal urbanization sustained elevated levels. In the absence of effective sanitation policies and under irregular rainfall patterns, outbreaks tend to evolve into regional epidemics. An integrated approach is recommended, including investments in water supply, sewage, and solid waste management, health education, entomological surveillance, climate monitoring, and preventive actions during the rainy season.