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  1. Article ; Online: Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning

    Clarisse Lins de Lima / Ana Clara Gomes da Silva / Giselle Machado Magalhães Moreno / Cecilia Cordeiro da Silva / Anwar Musah / Aisha Aldosery / Livia Dutra / Tercio Ambrizzi / Iuri V. G. Borges / Merve Tunali / Selma Basibuyuk / Orhan Yenigün / Tiago Lima Massoni / Ella Browning / Kate Jones / Luiza Campos / Patty Kostkova / Abel Guilhermino da Silva Filho / Wellington Pinheiro dos Santos

    Frontiers in Public Health, Vol

    A Systematic Review

    2022  Volume 10

    Abstract: Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a ...

    Abstract Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.
    Keywords digital epidemiology ; computational intelligence ; arboviruses forecast ; machine learning ; systematic review ; dengue ; Public aspects of medicine ; RA1-1270
    Subject code 306
    Language English
    Publishing date 2022-06-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting

    Cecilia Cordeiro da Silva / Clarisse Lins de Lima / Ana Clara Gomes da Silva / Eduardo Luiz Silva / Gabriel Souza Marques / Lucas Job Brito de Araújo / Luiz Antônio Albuquerque Júnior / Samuel Barbosa Jatobá de Souza / Maíra Araújo de Santana / Juliana Carneiro Gomes / Valter Augusto de Freitas Barbosa / Anwar Musah / Patty Kostkova / Wellington Pinheiro dos Santos / Abel Guilhermino da Silva Filho

    Frontiers in Public Health, Vol

    2021  Volume 9

    Abstract: Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post—the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, ... ...

    Abstract Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post—the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus.Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics.Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%.Conclusion: Spatio-temporal analysis provided a broader assessment ...
    Keywords COVID-19 ; SARS-CoV-2 ; Covid-19 pandemics forecasting ; spatio-temporal analysis ; spatio-temporal forecasting ; digital epidemiology ; Public aspects of medicine ; RA1-1270
    Language English
    Publishing date 2021-04-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Article ; Online: COVID-SGIS

    Clarisse Lins de Lima / Cecilia Cordeiro da Silva / Ana Clara Gomes da Silva / Eduardo Luiz Silva / Gabriel Souza Marques / Lucas Job Brito de Araújo / Luiz Antônio Albuquerque Júnior / Samuel Barbosa Jatobá de Souza / Maíra Araújo de Santana / Juliana Carneiro Gomes / Valter Augusto de Freitas Barbosa / Anwar Musah / Patty Kostkova / Wellington Pinheiro dos Santos / Abel Guilhermino da Silva Filho

    Frontiers in Public Health, Vol

    A Smart Tool for Dynamic Monitoring and Temporal Forecasting of Covid-19

    2020  Volume 8

    Abstract: Background: The global burden of the new coronavirus SARS-CoV-2 is increasing at an unprecedented rate. The current spread of Covid-19 in Brazil is problematic causing a huge public health burden to its population and national health-care service. To ... ...

    Abstract Background: The global burden of the new coronavirus SARS-CoV-2 is increasing at an unprecedented rate. The current spread of Covid-19 in Brazil is problematic causing a huge public health burden to its population and national health-care service. To evaluate strategies for alleviating such problems, it is necessary to forecast the number of cases and deaths in order to aid the stakeholders in the process of making decisions against the disease. We propose a novel system for real-time forecast of the cumulative cases of Covid-19 in Brazil.Methods: We developed the novel COVID-SGIS application for the real-time surveillance, forecast and spatial visualization of Covid-19 for Brazil. This system captures routinely reported Covid-19 information from 27 federative units from the Brazil.io database. It utilizes all Covid-19 confirmed case data that have been notified through the National Notification System, from March to May 2020. Time series ARIMA models were integrated for the forecast of cumulative number of Covid-19 cases and deaths. These include 6-days forecasts as graphical outputs for each federative unit in Brazil, separately, with its corresponding 95% CI for statistical significance. In addition, a worst and best scenarios are presented.Results: The following federative units (out of 27) were flagged by our ARIMA models showing statistically significant increasing temporal patterns of Covid-19 cases during the specified day-to-day period: Bahia, Maranhão, Piauí, Rio Grande do Norte, Amapá, Rondônia, where their day-to-day forecasts were within the 95% CI limits. Equally, the same findings were observed for Espírito Santo, Minas Gerais, Paraná, and Santa Catarina. The overall percentage error between the forecasted values and the actual values varied between 2.56 and 6.50%. For the days when the forecasts fell outside the forecast interval, the percentage errors in relation to the worst case scenario were below 5%.Conclusion: The proposed method for dynamic forecasting may be used to guide social policies ...
    Keywords SARS-CoV-2 spread forecast ; intelligent forecasting systems ; infectious diseases ; dynamic forecasting systems ; Covid-19 forecasting ; Public aspects of medicine ; RA1-1270
    Language English
    Publishing date 2020-11-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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