Incidência de malária em Novo Repartimento, PA em relação ao gênero e idade no período entre 2010 e 2020 / Incidence of malaria in novo repartimento, PA in relation to gender and age in the period between 2010 and 2020

Este estudo teve como objetivo apresentar a situação epidemiológica da malária em relação às faixas etárias e gêneros, no período entre os anos 2010 e 2020, no município de Novo Repartimento- PA. A coleta dos dados ocorreu no Laboratório da Vigilância Epidemiológica de Novo Repartimento - PA utilizando o SIVEP/MALÁRIA. Na tabulação e análise de dados utilizou-se o software Microsoft Excel (2020), com o uso da abordagem quantitativa. Evidenciou-sesignificativamente a faixa etária de 30 a 39 anos, com reduçõese aumentos, nos anos de 2016 a 2017, respectivamente. Os dados apresentam solavancos entre os anos de 2010 a 2017, iniciando aredução de casos no ano de 2017. Observa-se que o quantitativo de casos positivos de malária entre os anos de 2010 a 2020 diminuíram gradativamente, mesmo existindo surtos com aumentos exponenciais nos anos de 2017 e 2018, diante das ações de combate atribuídas as Politicas de Saúde Publica

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost