Yasmim Barcellos Madeira Rosa, Henrique Tamanini Silva Moschen, Ana Carolina Loss, Theresa Cristina Cardoso da Silva, Ana Paula Brioschi Dos Santos, Bruna Caetano Pimenta, Julia Sthefany Nunes Zordan, Crispim Cerutti Junior, Angelica Espinosa Barbosa Miranda, Iuri Drumond Louro, Débora Dummer Meira, Creuza Rachel Vicente
{"title":"气候变化对巴西圣埃斯皮里图州登革热传播地区的影响。","authors":"Yasmim Barcellos Madeira Rosa, Henrique Tamanini Silva Moschen, Ana Carolina Loss, Theresa Cristina Cardoso da Silva, Ana Paula Brioschi Dos Santos, Bruna Caetano Pimenta, Julia Sthefany Nunes Zordan, Crispim Cerutti Junior, Angelica Espinosa Barbosa Miranda, Iuri Drumond Louro, Débora Dummer Meira, Creuza Rachel Vicente","doi":"10.1093/oxfimm/iqae011","DOIUrl":null,"url":null,"abstract":"<p><p>Espírito Santo state, in Brazil, is a dengue-endemic region predicted to suffer from an increase in temperature and drought due to climate change, which could affect the areas with active dengue virus transmission. The study objective was modeling climatic factors and climate change effects in zones suitable for dengue virus transmission in Espírito Santo state, Brazil. Data on dengue reports from 2022 were used to determine climatic variables related to spatial distribution. The climate change projections were generated for the 2030s, 2050s, 2070s, and 2090s for three distinct Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5 and SSP5-8.5. A maximum entropy algorithm was used to construct the three models and projections, and the results were used to calculate the ensemble mean. Isothermality, the maximum temperature of the warmest month, precipitation of the wettest month, precipitation of the warmest quarter, and annual precipitation impacted the model. Projections indicated a change in areas suitable for dengue virus transmission, varying from -30.44% in the 2070s (SSP1-2.6) to +13.07% in the 2070s (SSP5-8.5) compared to 2022. The coastal regions were consistently suitable in all scenarios. Urbanized and highly populated areas were predicted to persist with active dengue transmission in Espírito Santo state, posing challenges for public health response.</p>","PeriodicalId":74384,"journal":{"name":"Oxford open immunology","volume":"5 1","pages":"iqae011"},"PeriodicalIF":0.0000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11398874/pdf/","citationCount":"0","resultStr":"{\"title\":\"Climate change impacts on dengue transmission areas in Espírito Santo state, Brazil.\",\"authors\":\"Yasmim Barcellos Madeira Rosa, Henrique Tamanini Silva Moschen, Ana Carolina Loss, Theresa Cristina Cardoso da Silva, Ana Paula Brioschi Dos Santos, Bruna Caetano Pimenta, Julia Sthefany Nunes Zordan, Crispim Cerutti Junior, Angelica Espinosa Barbosa Miranda, Iuri Drumond Louro, Débora Dummer Meira, Creuza Rachel Vicente\",\"doi\":\"10.1093/oxfimm/iqae011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Espírito Santo state, in Brazil, is a dengue-endemic region predicted to suffer from an increase in temperature and drought due to climate change, which could affect the areas with active dengue virus transmission. The study objective was modeling climatic factors and climate change effects in zones suitable for dengue virus transmission in Espírito Santo state, Brazil. Data on dengue reports from 2022 were used to determine climatic variables related to spatial distribution. The climate change projections were generated for the 2030s, 2050s, 2070s, and 2090s for three distinct Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5 and SSP5-8.5. A maximum entropy algorithm was used to construct the three models and projections, and the results were used to calculate the ensemble mean. Isothermality, the maximum temperature of the warmest month, precipitation of the wettest month, precipitation of the warmest quarter, and annual precipitation impacted the model. Projections indicated a change in areas suitable for dengue virus transmission, varying from -30.44% in the 2070s (SSP1-2.6) to +13.07% in the 2070s (SSP5-8.5) compared to 2022. The coastal regions were consistently suitable in all scenarios. Urbanized and highly populated areas were predicted to persist with active dengue transmission in Espírito Santo state, posing challenges for public health response.</p>\",\"PeriodicalId\":74384,\"journal\":{\"name\":\"Oxford open immunology\",\"volume\":\"5 1\",\"pages\":\"iqae011\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11398874/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Oxford open immunology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/oxfimm/iqae011\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Oxford open immunology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/oxfimm/iqae011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
Climate change impacts on dengue transmission areas in Espírito Santo state, Brazil.
Espírito Santo state, in Brazil, is a dengue-endemic region predicted to suffer from an increase in temperature and drought due to climate change, which could affect the areas with active dengue virus transmission. The study objective was modeling climatic factors and climate change effects in zones suitable for dengue virus transmission in Espírito Santo state, Brazil. Data on dengue reports from 2022 were used to determine climatic variables related to spatial distribution. The climate change projections were generated for the 2030s, 2050s, 2070s, and 2090s for three distinct Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5 and SSP5-8.5. A maximum entropy algorithm was used to construct the three models and projections, and the results were used to calculate the ensemble mean. Isothermality, the maximum temperature of the warmest month, precipitation of the wettest month, precipitation of the warmest quarter, and annual precipitation impacted the model. Projections indicated a change in areas suitable for dengue virus transmission, varying from -30.44% in the 2070s (SSP1-2.6) to +13.07% in the 2070s (SSP5-8.5) compared to 2022. The coastal regions were consistently suitable in all scenarios. Urbanized and highly populated areas were predicted to persist with active dengue transmission in Espírito Santo state, posing challenges for public health response.