Deep learning framework for epidemiological forecasting: A study on COVID-19 cases and deaths in the Amazon state of Pará, Brazil.

Modeling time series has been a particularly challenging aspect due to the need for constant adjustments in a rapidly changing environment, data uncertainty, dependencies between variables, volatile fluctuations, and the need to identify ideal hyperparameters. The present study presents a Framework capable of making projections from time series related to cases and deaths by COVID-19 in the Amazonian state of Pará, in Brazil. For the first time, deep learning models such as TCN, TRANSFORMER, TFT, N-BEATS, and N-HiTS were assessed for this purpose. The ARIMA statistical model was also used in post-processing for residual adjustment and short-term smoothing of the generated forecasts. The Framework generates probabilistic forecasts, with multivariate support, considering the following variables: daily cases per day of the first symptom, cases published daily, the occurrence of deaths, deaths published daily, and percentage of daily vaccination. The generated predictions are statistically evaluated by determining the best model for 7-day moving average projections using evaluating metrics such as MSE, RMSE, MAPE, sMAPE, r2, Coefficient of Variation, and residual analysis. As a result, the generated projections showed an average error of 5.4% for Cases Publication, 8.0% for Cases Symptoms, 11.12% for Deaths Publication, and 4.6% for Deaths Occurrence, with the N-HiTS and N-BEATS models obtaining better results. In general terms, the use of deep learning models to predict cases and deaths from COVID-19 has proven to be a valuable practice for analyzing the spread of the virus, which allows health managers to better understand and respond to this kind of pandemic outbreak.

Prevalence of the Human Immunodeficiency Virus and associated factors in pregnant women in the state of Pará.

OBJECTIVES: to analyze the prevalence of the Human Immunodeficiency Virus and the associated factors in pregnant women in the state of Pará. METHODS: retrospective, analytical, quantitative study with a sample of 332 medical records of HIV-positive pregnant women hospitalized at the Referral Maternity Hospital in the state of Pará between 2010 and 2019. Bivariate and multivariate statistical analysis were performed with the variables collected. RESULTS: the average prevalence in the period was 2.39% and the Metropolitan Region concentrated 66.87% of cases. There was a strong relationship between the number of antenatal consultations and lack of knowledge of serological status (p value equal to 0.01E-17) variables, and a correlation between the education and number of antenatal consultations variables. CONCLUSIONS: the increase in the infection rate during the study period revealed the need to intensify health actions, early diagnosis and strategies to improve adherence to antiretroviral treatment for maternal viral suppression and reduction of the risk of vertical transmission, contributing to improve public policies.

Prevalence of the Human Immunodeficiency Virus and associated factors in pregnant women in the state of Pará / Prevalencia del Virus de la Inmunodeficiencia Humana y factores asociados en mujeres embarazadas en el estado de Pará / Prevalência do Vírus da Imunodeficiência Humana e fatores associados em gestantes no estado do Pará

ABSTRACT Objectives: to analyze the prevalence of the Human Immunodeficiency Virus and the associated factors in pregnant women in the state of Pará. Methods: retrospective, analytical, quantitative study with a sample of 332 medical records of HIV-positive pregnant women hospitalized at the Referral Maternity Hospital in the state of Pará between 2010 and 2019. Bivariate and multivariate statistical analysis were performed with the variables collected. Results: the average prevalence in the period was 2.39% and the Metropolitan Region concentrated 66.87% of cases. There was a strong relationship between the number of antenatal consultations and lack of knowledge of serological status (p value equal to 0.01E-17) variables, and a correlation between the education and number of antenatal consultations variables. Conclusions: the increase in the infection rate during the study period revealed the need to intensify health actions, early diagnosis and strategies to improve adherence to antiretroviral treatment for maternal viral suppression and reduction of the risk of vertical transmission, contributing to improve public policies.

RESUMEN Objetivos: analizar la prevalencia del Virus de la Inmunodeficiencia Humana y los factores asociados en mujeres embarazadas en el estado de Pará. Métodos: estudio retrospectivo, analítico, cuantitativo con una muestra de 332 historias clínicas de gestantes VIH positivas hospitalizadas en la Maternidad de Referencia del estado de Pará entre 2010 y 2019. Se realizó análisis estadístico bivariado y multivariado con las variables recolectadas. Resultados: la prevalencia promedio en el período fue de 2,39% y la Región Metropolitana concentró el 66,87% de los casos. Hubo fuerte relación entre las variables número de consultas prenatales y desconocimiento del estado serológico (valor de p igual a 0,01E-17) y correlación entre las variables educación y número de consultas prenatales. Conclusiones: el aumento de la tasa de infección durante el período de estudio reveló la necesidad de intensificar las acciones de salud, el diagnóstico precoz y las estrategias para mejorar la adherencia al tratamiento antirretroviral para la supresión viral materna y la reducción del riesgo de transmisión vertical, contribuyendo a mejorar las políticas públicas.

RESUMO Objetivos: analisar a prevalência do Vírus da Imunodeficiência Humana e os fatores associados em gestantes no estado do Pará. Métodos: estudo analítico, quantitativo e retrospectivo com a amostra de 332 prontuários de gestantes HIV positivas internadas na Maternidade de Referência do estado do Pará, no período de 2010 a 2019. Com as variáveis coletadas, procedeu-se a análise estatística bivariada e multivariada. Resultados: a média de prevalência no período foi de 2,39% e a Região Metropolitana concentrou 66,87% dos casos. Houve forte relação entre as variáveis número de consultas pré-natais e desconhecimento do status sorológico (p valor igual a 0,01E-17) e correlação entre as variáveis escolaridade com o número de consultas pré-natais. Conclusões: o aumento da taxa de infecção no período estudado revelou a necessidade de intensificar as ações de saúde, o diagnóstico precoce e as estratégias para a melhoria da adesão ao tratamento antirretroviral para supressão viral materna e redução do risco de transmissão vertical, contribuindo para aprimorar as políticas públicas.

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.

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

Objective: Report the university research and extension product denominated 'Boletim COVID-PA' which presented projections about the pandemic in the State of Pará, Brazil, with practical, mathematically rigorous and computationally efficient approaches. Methods: The artificial intelligence technique known as Artificial Neural Networks was used to generate thirteen bulletins with short-term projections based on historical data from the State Department of Public Health system. Results: After eight months of projections, the technique generated reliable results with an average accuracy of 97% (147 days observed) for confirmed cases, 96% (161 observed days) for deaths and 86% (72 days observed) for occupancy of intensive care unit beds. Conclusion: These bulletins have become a useful tool for decision making by public managers, assisting in reallocating hospital resources and optimizing COVID-19 control strategies for the various regions of the State of Pará.

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 gerar 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.

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.