Malaria and environmental, socioeconomics and public health conditions in the municipality of São Félix do Xingu, Pará, Eastern Amazon, Brazil: An ecological and cross-sectional study.

BACKGROUND: Malaria is a parasitosis conditioned by several factors. This study sought to analyze the spatial distribution of malaria considering environmental, socioeconomic, and political variables in São Félix do Xingu, Pará, Brazil, from 2014 to 2020. METHODS: Epidemiological, cartographic, and environmental data were obtained from the Ministry of Health, Brazilian Geographical and Statistical Institute, and National Space Research Institute. Statistical and spatial distribution analyses were performed using chi-squared tests of expected equal proportions and the kernel and bivariate global Moran's techniques with Bioestat 5.0 and ArcGIS 10.5.1. RESULTS: The highest percentage of cases occurred in adult males with brown skin color, mainly placer miners, with a primary education level, living in rural areas, who were infected with Plasmodium vivax and with parasitemia of two or three crosses as diagnosed by the thick drop/smear test. The disease had a non-homogeneous distribution, with distinct annual parasite indices associated with administrative districts and clusters of cases in locations with deforestation, mining, and pastures close to Conservation Units and Indigenous Lands. Thus, a direct relationship between areas with cases and environmental degradation associated with land use was demonstrated, along with the precarious availability of health services. Pressure on protected areas and epidemiological silence in Indigenous Lands were also noted. CONCLUSIONS: Environmental and socioeconomic circuits were identified for development of diseases associated with precarious health services in the municipality. These findings highlight the need to intensify malaria surveillance and contribute to the systematic knowledge of malaria's epidemiology by considering the complexity of its conditioning factors.

Malaria and environmental, socioeconomics and public health conditions in the municipality of São Félix do Xingu, Pará, Eastern Amazon, Brazil: An ecological and cross-sectional study

ABSTRACT Background: Malaria is a parasitosis conditioned by several factors. This study sought to analyze the spatial distribution of malaria considering environmental, socioeconomic, and political variables in São Félix do Xingu, Pará, Brazil, from 2014 to 2020. Methods: Epidemiological, cartographic, and environmental data were obtained from the Ministry of Health, Brazilian Geographical and Statistical Institute, and National Space Research Institute. Statistical and spatial distribution analyses were performed using chi-squared tests of expected equal proportions and the kernel and bivariate global Moran's techniques with Bioestat 5.0 and ArcGIS 10.5.1. Results: The highest percentage of cases occurred in adult males with brown skin color, mainly placer miners, with a primary education level, living in rural areas, who were infected with Plasmodium vivax and with parasitemia of two or three crosses as diagnosed by the thick drop/smear test. The disease had a non-homogeneous distribution, with distinct annual parasite indices associated with administrative districts and clusters of cases in locations with deforestation, mining, and pastures close to Conservation Units and Indigenous Lands. Thus, a direct relationship between areas with cases and environmental degradation associated with land use was demonstrated, along with the precarious availability of health services. Pressure on protected areas and epidemiological silence in Indigenous Lands were also noted. Conclusions: Environmental and socioeconomic circuits were identified for development of diseases associated with precarious health services in the municipality. These findings highlight the need to intensify malaria surveillance and contribute to the systematic knowledge of malaria's epidemiology by considering the complexity of its conditioning factors.

COVID-PA Bulletin: reports on artificial intelligence-based forecasting in coping with COVID-19 pandemic in the state of Pará, Brazil. / Boletim COVID-PA: relatos sobre projeções baseadas em inteligência artificial no enfrentamento da pandemia de COVID-19 no estado do Pará.

OBJECTIVE: To report the university extension research result entitled 'The COVID-PA Bulletin', which presented forecasts on the behavior of the pandemic in the state of Pará, Brazil. METHODS: The artificial intelligence technique also known as 'artificial neural networks' was used to generate 13 bulletins with short-term forecasts based on historical data from the State Department of Public Health information system. RESULTS: After eight months of predictions, the technique generated reliable results, with an average accuracy of 97% (observed for147 days) for confirmed cases, 96% (observed for 161 days) for deaths and 86% (observed for 72 days) for Intensive Care Unit bed occupancy. CONCLUSION: These bulletins have become a useful decision-making tool for public managers, assisting in the reallocation of hospital resources and optimization of COVID-19 control strategies in various regions of the state of Pará.

Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon.

The first case of the novel coronavirus in Brazil was notified on February 26, 2020. After 21 days, the first case was reported in the second largest State of the Brazilian Amazon. The State of Pará presented difficulties in combating the pandemic, ranging from underreporting and a low number of tests to a large territorial distance between cities with installed hospital capacity. Due to these factors, mathematical data-driven short-term forecasting models can be a promising initiative to assist government officials in more agile and reliable actions. This study presents an approach based on artificial neural networks for the daily and cumulative forecasts of cases and deaths caused by COVID-19, and the forecast of demand for hospital beds. Six scenarios with different periods were used to identify the quality of the generated forecasting and the period in which they start to deteriorate. Results indicated that the computational model adapted capably to the training period and was able to make consistent short-term forecasts, especially for the cumulative variables and for demand hospital beds.

Boletim COVID-PA: relatos sobre projeções baseadas em inteligência artificial no enfrentamento da pandemia de COVID-19 no estado do Pará / Boletín COVID-PA: informes de proyecciones basadas en inteligencia artificial para enfrentar la pandemia COVID-19 en el estado de Pará, Brasil / COVID-PA Bulletin: reports on artificial intelligence-based forecasting in coping with COVID-19 pandemic in the state of Pará, Brazil

Objetivo: Relatar o produto de pesquisa e extensão universitária denominado Boletim COVID-PA, que apresentou projeções sobre o comportamento da pandemia no estado do Pará, Brasil. Métodos: Utilizou-se da técnica de inteligência artificial conhecida como 'redes neurais artificiais', para geração de 13 boletins com projeções de curto prazo baseadas nos dados históricos do sistema da Secretaria de Estado de Saúde Pública. Resultados: Após oito meses de projeções, a técnica gerou resultados confiáveis, com precisão média de 97% (147 dias observados) para casos confirmados, 96% (161 dias observados) para óbitos e 86% (72 dias observados) para ocupação de leitos de unidade de terapia intensiva. Conclusão: Esses boletins tornaram-se um instrumento útil para a tomada de decisão de gestores públicos, auxiliando na realocação de recursos hospitalares e otimização das estratégias de controle da COVID-19 nas diversas regiões do estado do Pará.

Objetivo: Reporte el resultado de la investigación y extensión universitaria denominada 'Boletim COVID-PA' que presentó proyecciones sobre el comportamiento de la pandemia en el estado de Pará, con un enfoque práctico y computacionalmente eficiente. Métodos: Fue utilizada una técnica de inteligencia artificial denominadas Redes Neurales para generar trece boletines con proyecciones basado en datos históricos del sistema de la Secretaría de Salud Pública. Resultados: Después de ocho meses de previsiones, la técnica genero resultados confiables con una precisión promedio de 97% (147 días observados) para casos confirmados, 96% (161 días observados) para los fallecimientos y 86% (72 días observados) para la ocupación de camas en las unidades de cuidados intensivos. Conclusión: Estos boletines se convirtieron en una herramienta para la toma de decisiones, auxiliando en la redistribución de recursos en los hospitales en el estado de Pará.

Objective: To report the university extension research result entitled 'The COVID-PA Bulletin', which presented forecasts on the behavior of the pandemic in the state of Pará, Brazil. Methods: The artificial intelligence technique also known as 'artificial neural networks' was used to generate 13 bulletins with short-term forecasts based on historical data from the State Department of Public Health information system. Results: After eight months of predictions, the technique generated reliable results, with an average accuracy of 97% (observed for147 days) for confirmed cases, 96% (observed for 161 days) for deaths and 86% (observed for 72 days) for Intensive Care Unit bed occupancy. Conclusion: These bulletins have become a useful decision-making tool for public managers, assisting in the reallocation of hospital resources and optimization of COVID-19 control strategies in various regions of the state of Pará.