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

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

Frontiers in public health

2021 Volume 9, Page(s) 641253

Abstract: Background: ...

Abstract	Background:
MeSH term(s)	Brazil/epidemiology ; COVID-19/epidemiology ; Epidemiological Monitoring ; Forecasting ; Humans ; Linear Models ; Neural Networks, Computer ; Spatio-Temporal Analysis ; Support Vector Machine
Language	English
Publishing date	2021-04-08
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2711781-9
ISSN	2296-2565 ; 2296-2565
ISSN (online)	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2021.641253
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Covid-19 rapid test by combining a Random Forest-based web system and blood tests.

Barbosa, Valter Augusto de Freitas / Gomes, Juliana Carneiro / de Santana, Maíra Araújo / de Lima, Clarisse Lins / Calado, Raquel Bezerra / Bertoldo Júnior, Cláudio Roberto / Albuquerque, Jeniffer Emidio de Almeida / de Souza, Rodrigo Gomes / de Araújo, Ricardo Juarez Escorel / Mattos Júnior, Luiz Alberto Reis / de Souza, Ricardo Emmanuel / Dos Santos, Wellington Pinheiro

Journal of biomolecular structure & dynamics

2021 Volume 40, Issue 22, Page(s) 11948–11967

Abstract: The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already ... ...

Abstract	The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already caused over 2 million deaths to date. In this work, we propose a web solution, called Heg.IA, to optimize the diagnosis of Covid-19 through the use of artificial intelligence. Our system aims to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU based on decision a Random Forest architecture with 90 trees. The main idea is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. The system reached good results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891%±0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19.Communicated by Ramaswamy H. Sarma.
MeSH term(s)	Humans ; COVID-19/diagnosis ; SARS-CoV-2 ; COVID-19 Testing ; Random Forest ; Artificial Intelligence ; Hematologic Tests
Language	English
Publishing date	2021-08-31
Publishing country	England
Document type	Journal Article
ZDB-ID	49157-3
ISSN	1538-0254 ; 0739-1102
ISSN (online)	1538-0254
ISSN	0739-1102
DOI	10.1080/07391102.2021.1966509
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: COVID-SGIS: A Smart Tool for Dynamic Monitoring and Temporal Forecasting of Covid-19.

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

Frontiers in public health

2020 Volume 8, Page(s) 580815

Abstract: Background: ...

Abstract	Background:
MeSH term(s)	Brazil/epidemiology ; COVID-19 ; Coronavirus Infections/epidemiology ; Forecasting ; Humans ; Pandemics ; Population Surveillance/methods ; Search Engine
Language	English
Publishing date	2020-11-17
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2711781-9
ISSN	2296-2565 ; 2296-2565
ISSN (online)	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2020.580815
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: COVID-SGIS

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

Frontiers in Public Health

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

2020 Volume 8

Keywords	covid19
Publisher	Frontiers Media SA
Publishing country	ch
Document type	Article ; Online
ZDB-ID	2711781-9
ISSN	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2020.580815
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: IKONOS: An intelligent tool to support diagnosis of Covid-19 by texture analysis of x-ray images

Gomes, Juliana Carneiro / Barbosa, Valter Augusto de Freitas / de Santana, Maira Araujo / Bandeira, Jonathan / Valenca, Meuser Jorge Silva / de Souza, Ricardo Emmanuel / Ismael, Aras Masood / dos Santos, Wellington Pinheiro

medRxiv

Abstract: In late 2019, the SARS-Cov-2 spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. There is still no specific treatment and diagnosis for the disease. The standard diagnostic method for ... ...

Abstract	In late 2019, the SARS-Cov-2 spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. There is still no specific treatment and diagnosis for the disease. The standard diagnostic method for pneumonia is chest X-ray image. There are many advantages to using Covid-19 diagnostic X-rays: low cost, fast and widely available. We propose an intelligent system to support diagnosis by X-ray images.We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Support vector machines stood out, reaching an average accuracy of 89:78%, average recall and sensitivity of 0:8979, and average precision and specificity of 0:8985 and 0:9963 respectively. The system is able to differentiate Covid-19 from viral and bacterial pneumonia, with low computational cost.
Keywords	covid19
Language	English
Publishing date	2020-05-09
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.05.05.20092346
Database	COVID19

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Article ; Online: Heg.IA: An intelligent system to support diagnosis of Covid-19 based on blood tests

Barbosa, Valter Augusto de Freitas / Gomes, Juliana Carneiro / de Santana, Maira Araujo / Albuquerque, Jeniffer Emidio de Almeida / de Souza, Rodrigo Gomes / de Souza, Ricardo Emmanuel / dos Santos, Wellington Pinheiro

medRxiv

Abstract: A new kind of coronavirus, the SARS-Cov2, started the biggest pandemic of the century. It has already killed more than 250,000 people. Because of this, it is necessary quick and precise diagnosis test. The current gold standard is the RT-PCR with DNA ... ...

Abstract	A new kind of coronavirus, the SARS-Cov2, started the biggest pandemic of the century. It has already killed more than 250,000 people. Because of this, it is necessary quick and precise diagnosis test. The current gold standard is the RT-PCR with DNA sequencing and identification, but its results takes too long to be available. Tests base on IgM/IgG antibodies have been used, but their sensitivity and specificity may be very low. Many studies have been demonstrating the Covid-19 impact in hematological parameters. This work proposes an intelligent system to support Covid-19 diagnosis based on blood testing. We tested several machine learning methods, and we achieved high classification performance: 95.159% +- 0.693 of overall accuracy, kappa index of 0.903 +- 0.014, sensitivity of 0.968 +- 0.007, precision of 0.938 +- 0.010 and specificity of 0.936 +- 0.011. These results were achieved using classical and low computational cost classifiers, with Bayes Network being the best of them. In addition, only 24 blood tests were needed. This points to the possibility of a new rapid test with low cost. The desktop version of the system is fully functional and available for free use.
Keywords	covid19
Language	English
Publishing date	2020-05-18
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.05.14.20102533
Database	COVID19

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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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Article ; Online: Rapid protocols to support Covid-19 clinical diagnosis based on hematological parameters

Gomes, Juliana Carneiro / Barbosa, Valter Augusto de Freitas / de Santana, Maira Araujo / de Lima, Clarisse Lins / Calado, Raquel Bezerra / Bertoldo Junior, Claudio Roberto / Albuquerque, Jeniffer Emidio de Almeida / de Souza, Rodrigo Gomes / de Araujo, Ricardo Juarez Escorel / Moreno, Giselle Machado Magalhaes / Soares, Luiz Alberto Lira / Mattos Junior, Luiz Alberto Reis / de Souza, Ricardo Emmanuel / dos Santos, Wellington Pinheiro

medRxiv

Abstract: Purpose In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in ... ...

Abstract	Purpose In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in the construction of specific treatments for the disease. Objective In this work, we propose rapid protocols for clinical diagnosis of Covid-19 through the automatic analysis of hematological parameters using Evolutionary Computing and Machine Learning. These hematological parameters are obtained from blood tests common in clinical practice. Method We investigated the best classifier architectures. Then, we applied the particle swarm optimization algorithm (PSO) to select the most relevant attributes: serum glucose, troponin, partial thromboplastin time, ferritin, D-dimer, lactic dehydrogenase, and indirect bilirubin. Finally, we used decision trees to build four rapid protocols for Covid-19 clinical diagnosis. Results We developed a web system for Covid-19 diagnosis support. Using a 100-tree Random Forest, we obtained results for accuracy, sensitivity and specificity superior to 99%. Conclusion By using a reduced set of hematological parameters common in clinical practice, it was possible to achieve results of accuracy, sensitivity and specificity comparable to those obtained with RT-PCR. It was also possible to automatically generate clinical decision protocols, allowing relatively accurate clinical diagnosis even without the aid of the web decision support system.
Keywords	covid19
Language	English
Publishing date	2021-06-28
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2021.06.21.21259252
Database	COVID19

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Article ; Online: Covid-19 rapid test by combining a random forest based web system and blood tests

Barbosa, Valter Augusto de Freitas / Gomes, Juliana Carneiro / de Santana, Maira Araujo / de Lima, Clarisse Lins / Calado, Raquel Bezerra / Bertoldo Junior, Claudio Roberto / Albuquerque, Jeniffer Emidio de Almeida / de Souza, Rodrigo Gomes / de Araujo, Ricardo Juarez Escorel / de Souza, Ricardo Emmanuel / dos Santos, Wellington Pinheiro

medRxiv

Abstract: The disease caused by the new type of coronavirus, the Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-Cov2 has already ... ...

Abstract	The disease caused by the new type of coronavirus, the Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-Cov2 has already caused over 400 thousand deaths to date. The diagnosis of the disease has an important role in combating Covid-19. Proposed method In this work, we propose a web system, Heg.IA, which seeks to optimize the diagnosis of Covid-19 through the use of artificial intelligence. The main ideia is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. It will indicate if the patient is infected with SARS-Cov2 virus, and also predict the type of hospitalization (regular ward, semi-ICU, or ICU). We developed a web system called Heg.IA to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU. This application is based on decision trees in a Random Forest architecture with 90 trees. The system showed to be highly efficient, with great results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891% ± 0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19. We also expect the system will provide wide access to Covid-19 effective diagnosis and thereby reach and help saving lives.
Keywords	covid19
Language	English
Publishing date	2020-06-16
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.06.12.20129866
Database	COVID19

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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)

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