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Article ; Online: Participación social en el sistema de salud de Chile: aportes reflexivos desde la bioética.

Guerrero-Nancuante, Camilo / Melo, Andrea / Gundelach, Paulina / Fuster, Nicolás

2023 Volume 19, Page(s) e4486

Abstract: Social participation in health is related to the ability of collectives to intervene in the healthcare system. From a bioethical perspective, the relevance of social participation in health has been emphasized due to its positive effects at the level of ... ...

Title translation	Social participation in Chile's healthcare system: Reflective contributions from bioethics.
Abstract	Social participation in health is related to the ability of collectives to intervene in the healthcare system. From a bioethical perspective, the relevance of social participation in health has been emphasized due to its positive effects at the level of social groups, the healthcare structure, and democratic political systems. To ensure social participation in health, bioethics advocates for the incorporation of deliberation as a tool for making binding decisions. The aim of this essay is to reflect on social participation in the history of Chile's healthcare system from a bioethical perspective. The main reflections indicate that participation is consultative in nature, lacking deliberation and, therefore, the distribution of power. Additionally, social participation has been redefined under the label of "citizen," promoting an instrumental, individual, and client-oriented character in healthcare. To subvert this situation, it is necessary to incorporate bioethical reflections into the healthcare structure to enable communities to consistently influence the healthcare system.
MeSH term(s)	Humans ; Chile ; Social Participation ; Bioethical Issues ; Delivery of Health Care ; Bioethics
Language	Spanish
Publishing date	2023-10-30
Publishing country	Argentina
Document type	Journal Article ; English Abstract
ZDB-ID	2394313-0
ISSN	1851-8265 ; 1669-2381
ISSN (online)	1851-8265
ISSN	1669-2381
DOI	10.18294/sc.2023.4486
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Protection Strategy against an Epidemic Disease on Edge-Weighted Graphs Applied to a COVID-19 Case.

Manríquez, Ronald / Guerrero-Nancuante, Camilo / Taramasco, Carla

Biology

2021 Volume 10, Issue 7

Abstract: Among the diverse and important applications that networks currently have is the modeling of infectious diseases. Immunization, or the process of protecting nodes in the network, plays a key role in stopping diseases from spreading. Hence the importance ... ...

Abstract	Among the diverse and important applications that networks currently have is the modeling of infectious diseases. Immunization, or the process of protecting nodes in the network, plays a key role in stopping diseases from spreading. Hence the importance of having tools or strategies that allow the solving of this challenge. In this paper, we evaluate the effectiveness of the DIL-Wα ranking in immunizing nodes in an edge-weighted network with 3866 nodes and 6,841,470 edges. The network is obtained from a real database and the spread of COVID-19 was modeled with the classic SIR model. We apply the protection to the network, according to the importance ranking list produced by DIL-Wα, considering different protection budgets. Furthermore, we consider three different values for α; in this way, we compare how the protection performs according to the value of α.
Language	English
Publishing date	2021-07-15
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2661517-4
ISSN	2079-7737
ISSN	2079-7737
DOI	10.3390/biology10070667
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Sensitivity and Specificity of Patient-Reported Clinical Manifestations to Diagnose COVID-19 in Adults from a National Database in Chile: A Cross-Sectional Study.

Martinez, Felipe / Muñoz, Sergio / Guerrero-Nancuante, Camilo / Taramasco, Carla

Biology

2022 Volume 11, Issue 8

Abstract: 1) Background: The diagnosis of COVID-19 is frequently made on the basis of a suggestive clinical history and the detection of SARS-CoV-2 RNA in respiratory secretions. However, the diagnostic accuracy of clinical features is unknown. (2) Objective: To ... ...

Abstract	(1) Background: The diagnosis of COVID-19 is frequently made on the basis of a suggestive clinical history and the detection of SARS-CoV-2 RNA in respiratory secretions. However, the diagnostic accuracy of clinical features is unknown. (2) Objective: To assess the diagnostic accuracy of patient-reported clinical manifestations to identify cases of COVID-19. (3) Methodology: Cross-sectional study using data from a national registry in Chile. Infection by SARS-CoV-2 was confirmed using RT-PCR in all cases. Anonymised information regarding demographic characteristics and clinical features were assessed using sensitivity, specificity, and diagnostic odds ratios. A multivariable logistic regression model was constructed to combine epidemiological risk factors and clinical features. (4) Results: A total of 2,187,962 observations were available for analyses. Male participants had a mean age of 43.1 ± 17.5 years. The most common complaints within the study were headache (39%), myalgia (32.7%), cough (31.6%), and sore throat (25.7%). The most sensitive features of disease were headache, myalgia, and cough, and the most specific were anosmia and dysgeusia/ageusia. A multivariable model showed a fair diagnostic accuracy, with a ROC AUC of 0.744 (95% CI 0.743-0.746). (5) Discussion: No single clinical feature was able to fully confirm or exclude an infection by SARS-CoV-2. The combination of several demographic and clinical factors had a fair diagnostic accuracy in identifying patients with the disease. This model can help clinicians tailor the probability of COVID-19 and select diagnostic tests appropriate to their setting.
Language	English
Publishing date	2022-07-29
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2661517-4
ISSN	2079-7737
ISSN	2079-7737
DOI	10.3390/biology11081136
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Detection of COVID-19 Patients Using Machine Learning Techniques: A Nationwide Chilean Study.

Ormeño, Pablo / Márquez, Gastón / Guerrero-Nancuante, Camilo / Taramasco, Carla

International journal of environmental research and public health

2022 Volume 19, Issue 13

Abstract: Epivigila is a Chilean integrated epidemiological surveillance system with more than 17,000,000 Chilean patient records, making it an essential and unique source of information for the quantitative and qualitative analysis of the COVID-19 pandemic in ... ...

Abstract	Epivigila is a Chilean integrated epidemiological surveillance system with more than 17,000,000 Chilean patient records, making it an essential and unique source of information for the quantitative and qualitative analysis of the COVID-19 pandemic in Chile. Nevertheless, given the extensive volume of data controlled by Epivigila, it is difficult for health professionals to classify vast volumes of data to determine which symptoms and comorbidities are related to infected patients. This paper aims to compare machine learning techniques (such as support-vector machine, decision tree and random forest techniques) to determine whether a patient has COVID-19 or not based on the symptoms and comorbidities reported by Epivigila. From the group of patients with COVID-19, we selected a sample of 10% confirmed patients to execute and evaluate the techniques. We used precision, recall, accuracy, F1-score, and AUC to compare the techniques. The results suggest that the support-vector machine performs better than decision tree and random forest regarding the recall, accuracy, F1-score, and AUC. Machine learning techniques help process and classify large volumes of data more efficiently and effectively, speeding up healthcare decision making.
MeSH term(s)	COVID-19/epidemiology ; Chile/epidemiology ; Humans ; Machine Learning ; Pandemics ; Support Vector Machine
Language	English
Publishing date	2022-06-30
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph19138058
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Article ; Online: Proyección epidemiológica de COVID-19 en Chile basado en el modelo SEIR generalizado y el concepto de recuperado.

Guerrero-Nancuante, Camilo / Manríquez P, Ronald

Medwave

2020 Volume 20, Issue 4, Page(s) e7898

Abstract: The COVID-19 pandemic declared by the World Health Organization (WHO) has generated a wide-ranging debate regarding epidemiological forecasts and the global implications. With the data obtained from the Chilean Ministry of Health (MINSAL), a prospective ... ...

Title translation	An epidemiological forecast of COVID-19 in Chile based on the generalized SEIR model and the concept of recovered.
Abstract	The COVID-19 pandemic declared by the World Health Organization (WHO) has generated a wide-ranging debate regarding epidemiological forecasts and the global implications. With the data obtained from the Chilean Ministry of Health (MINSAL), a prospective study was carried out using the generalized SEIR model to estimate the course of COVID-19 in Chile. Three scenarios were estimated: Scenario 1 with official MINSAL data; scenario 2 with official MINSAL data and recovery criteria proposed by international organizations of health; and scenario 3 with official MINSAL data, recovery criteria proposed by international organizations of health, and without considering deaths in the total recovered. There are considerable differences between scenario 1 compared to 2 and 3 in the number of deaths, active patients, and duration of the disease. Scenario 3, considered the most adverse, estimates a total of 11,000 infected people, 1,151 deaths, and that the peak of the disease will occur in the first days of May. We concluded that the concept of recovered may be decisive for the epidemiological forecasts of COVID-19 in Chile.
MeSH term(s)	Betacoronavirus ; COVID-19 ; Chile/epidemiology ; Coronavirus Infections/epidemiology ; Coronavirus Infections/mortality ; Coronavirus Infections/transmission ; Disease Susceptibility ; Forecasting ; Global Health ; Government Agencies ; Guidelines as Topic ; Humans ; Models, Theoretical ; Pandemics ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/mortality ; Pneumonia, Viral/transmission ; Prospective Studies ; Recovery of Function ; SARS-CoV-2 ; Time Factors
Keywords	covid19
Language	Spanish
Publishing date	2020-05-15
Publishing country	Chile
Document type	Journal Article
ZDB-ID	2818022-7
ISSN	0717-6384 ; 0717-6384
ISSN (online)	0717-6384
ISSN	0717-6384
DOI	10.5867/medwave.2020.04.7898
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Spread of Epidemic Disease on Edge-Weighted Graphs from a Database: A Case Study of COVID-19.

Manríquez, Ronald / Guerrero-Nancuante, Camilo / Martínez, Felipe / Taramasco, Carla

International journal of environmental research and public health

2021 Volume 18, Issue 9

Abstract: The understanding of infectious diseases is a priority in the field of public health. This has generated the inclusion of several disciplines and tools that allow for analyzing the dissemination of infectious diseases. The aim of this manuscript is to ... ...

Abstract	The understanding of infectious diseases is a priority in the field of public health. This has generated the inclusion of several disciplines and tools that allow for analyzing the dissemination of infectious diseases. The aim of this manuscript is to model the spreading of a disease in a population that is registered in a database. From this database, we obtain an edge-weighted graph. The spreading was modeled with the classic SIR model. The model proposed with edge-weighted graph allows for identifying the most important variables in the dissemination of epidemics. Moreover, a deterministic approximation is provided. With database COVID-19 from a city in Chile, we analyzed our model with relationship variables between people. We obtained a graph with 3866 vertices and 6,841,470 edges. We fitted the curve of the real data and we have done some simulations on the obtained graph. Our model is adjusted to the spread of the disease. The model proposed with edge-weighted graph allows for identifying the most important variables in the dissemination of epidemics, in this case with real data of COVID-19. This valuable information allows us to also include/understand the networks of dissemination of epidemics diseases as well as the implementation of preventive measures of public health. These findings are important in COVID-19's pandemic context.
MeSH term(s)	COVID-19 ; Chile/epidemiology ; Communicable Diseases/epidemiology ; Epidemics ; Humans ; Pandemics ; SARS-CoV-2
Language	English
Publishing date	2021-04-22
Publishing country	Switzerland
Document type	Journal Article
ISSN	1660-4601
ISSN (online)	1660-4601
DOI	10.3390/ijerph18094432
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Article ; Online: Protection Strategy for Edge-Weighted Graphs in Disease Spread

Ronald Manríquez / Camilo Guerrero-Nancuante / Carla Taramasco

Applied Sciences, Vol 11, Iss 5115, p

2021 Volume 5115

Abstract: Fake news, viruses on computer systems or infectious diseases on communities are some of the problems that are addressed by researchers dedicated to study complex networks. The immunization process is the solution to these challenges and hence the ... ...

Abstract	Fake news, viruses on computer systems or infectious diseases on communities are some of the problems that are addressed by researchers dedicated to study complex networks. The immunization process is the solution to these challenges and hence the importance of obtaining immunization strategies that control these spreads. In this paper, we evaluate the effectiveness of the DIL-W <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mi>α</mi></msup></semantics></math> ranking in the immunization of nodes that are attacked by an infectious disease that spreads on an edge-weighted graph using a graph-based SIR model. The experimentation was done on real and scale-free networks and the results illustrate the benefits of this ranking.
Keywords	edge-weighted graph ; SIR model ; graph protection ; immunization strategy ; complex networks ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	511
Language	English
Publishing date	2021-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Detection of COVID-19 Patients Using Machine Learning Techniques

Pablo Ormeño / Gastón Márquez / Camilo Guerrero-Nancuante / Carla Taramasco

International Journal of Environmental Research and Public Health, Vol 19, Iss 8058, p

A Nationwide Chilean Study

2022 Volume 8058

Abstract	Epivigila is a Chilean integrated epidemiological surveillance system with more than 17,000,000 Chilean patient records, making it an essential and unique source of information for the quantitative and qualitative analysis of the COVID-19 pandemic in Chile. Nevertheless, given the extensive volume of data controlled by Epivigila, it is difficult for health professionals to classify vast volumes of data to determine which symptoms and comorbidities are related to infected patients. This paper aims to compare machine learning techniques (such as support-vector machine, decision tree and random forest techniques) to determine whether a patient has COVID-19 or not based on the symptoms and comorbidities reported by Epivigila. From the group of patients with COVID-19, we selected a sample of 10% confirmed patients to execute and evaluate the techniques. We used precision, recall, accuracy, <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>F</mi><mn>1</mn></msub></semantics></math> -score, and AUC to compare the techniques. The results suggest that the support-vector machine performs better than decision tree and random forest regarding the recall, accuracy, <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>F</mi><mn>1</mn></msub></semantics></math> -score, and AUC. Machine learning techniques help process and classify large volumes of data more efficiently and effectively, speeding up healthcare decision making.
Keywords	Epivigila ; machine learning ; symptoms ; comorbidities ; Medicine ; R
Subject code	006
Language	English
Publishing date	2022-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Protection Strategy against an Epidemic Disease on Edge-Weighted Graphs Applied to a COVID-19 Case

Ronald Manríquez / Camilo Guerrero-Nancuante / Carla Taramasco

Biology, Vol 10, Iss 667, p

2021 Volume 667

Abstract	Among the diverse and important applications that networks currently have is the modeling of infectious diseases. Immunization, or the process of protecting nodes in the network, plays a key role in stopping diseases from spreading. Hence the importance of having tools or strategies that allow the solving of this challenge. In this paper, we evaluate the effectiveness of the DIL-W <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mi>α</mi></msup></semantics></math> ranking in immunizing nodes in an edge-weighted network with 3866 nodes and 6,841,470 edges. The network is obtained from a real database and the spread of COVID-19 was modeled with the classic SIR model. We apply the protection to the network, according to the importance ranking list produced by DIL-W <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mi>α</mi></msup></semantics></math> , considering different protection budgets. Furthermore, we consider three different values for <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math> in this way, we compare how the protection performs according to the value of <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math> .
Keywords	edge-weighted graph ; SIR model ; graph protection ; COVID-19 ; Biology (General) ; QH301-705.5
Subject code	511
Language	English
Publishing date	2021-07-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Protection Strategy against an Epidemic Disease on Edge-Weighted Graphs Applied to a COVID-19 Case

Manríquez, Ronald / Guerrero-Nancuante, Camilo / Taramasco, Carla

Biology. 2021 July 15, v. 10, no. 7

2021

Abstract	Among the diverse and important applications that networks currently have is the modeling of infectious diseases. Immunization, or the process of protecting nodes in the network, plays a key role in stopping diseases from spreading. Hence the importance of having tools or strategies that allow the solving of this challenge. In this paper, we evaluate the effectiveness of the DIL-Wα ranking in immunizing nodes in an edge-weighted network with 3866 nodes and 6,841,470 edges. The network is obtained from a real database and the spread of COVID-19 was modeled with the classic SIR model. We apply the protection to the network, according to the importance ranking list produced by DIL-Wα, considering different protection budgets. Furthermore, we consider three different values for α; in this way, we compare how the protection performs according to the value of α.
Keywords	Biological Sciences ; COVID-19 infection ; databases ; immunization ; models
Language	English
Dates of publication	2021-0715
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2661517-4
ISSN	2079-7737
ISSN	2079-7737
DOI	10.3390/biology10070667
Database	NAL-Catalogue (AGRICOLA)

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