Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting
Copyright © 2021 da Silva, de Lima, da Silva, Silva, Marques, de Araújo, Albuquerque Júnior, de Souza, de Santana, Gomes, Barbosa, Musah, Kostkova, dos Santos and da Silva Filho..
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 of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:9 |
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| Enthalten in: |
Frontiers in public health - 9(2021) vom: 23., Seite 641253 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
da Silva, Cecilia Cordeiro [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 30.04.2021 Date Revised 01.04.2024 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.3389/fpubh.2021.641253 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a Date Revised 01.04.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021 da Silva, de Lima, da Silva, Silva, Marques, de Araújo, Albuquerque Júnior, de Souza, de Santana, Gomes, Barbosa, Musah, Kostkova, dos Santos and da Silva Filho. | ||
| 520 | |a 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 of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Covid-19 pandemics forecasting | |
| 650 | 4 | |a SARS-CoV-2 | |
| 650 | 4 | |a digital epidemiology | |
| 650 | 4 | |a spatio-temporal analysis | |
| 650 | 4 | |a spatio-temporal forecasting | |
| 700 | 1 | |a de Lima, Clarisse Lins |e verfasserin |4 aut | |
| 700 | 1 | |a da Silva, Ana Clara Gomes |e verfasserin |4 aut | |
| 700 | 1 | |a Silva, Eduardo Luiz |e verfasserin |4 aut | |
| 700 | 1 | |a Marques, Gabriel Souza |e verfasserin |4 aut | |
| 700 | 1 | |a de Araújo, Lucas Job Brito |e verfasserin |4 aut | |
| 700 | 1 | |a Albuquerque Júnior, Luiz Antônio |e verfasserin |4 aut | |
| 700 | 1 | |a de Souza, Samuel Barbosa Jatobá |e verfasserin |4 aut | |
| 700 | 1 | |a de Santana, Maíra Araújo |e verfasserin |4 aut | |
| 700 | 1 | |a Gomes, Juliana Carneiro |e verfasserin |4 aut | |
| 700 | 1 | |a Barbosa, Valter Augusto de Freitas |e verfasserin |4 aut | |
| 700 | 1 | |a Musah, Anwar |e verfasserin |4 aut | |
| 700 | 1 | |a Kostkova, Patty |e verfasserin |4 aut | |
| 700 | 1 | |a Dos Santos, Wellington Pinheiro |e verfasserin |4 aut | |
| 700 | 1 | |a da Silva Filho, Abel Guilhermino |e verfasserin |4 aut | |
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