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

(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.

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

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.

Protection Strategy for Edge-Weighted Graphs in Disease Spread

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-Wα 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.

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

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.

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

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.

Proyección epidemiológica de COVID-19 en Chile basado en el modelo SEIR generalizado y el concepto de recuperado

La pandemia de COVID-19 declarada por la Organización Mundial de la Salud (OMS) ha generado un amplio debate respecto de las proyecciones epidemiológicas que tendrá a nivel global en nuestro planeta. Con los datos obtenidos del Ministerio de Salud de Chile (MINSAL), se efectuó un estudio prospectivo utilizando un modelo SEIR (Susceptible-Expuesto-Infectado-Recuperado) generalizado con el objetivo de estimar la evolución del COVID-19 en Chile. La estimación se realizó bajo tres escenarios con datos oficiales del Ministerio de Salud: escenario 1 solo con datos oficiales;escenario 2 se añade el criterio de recuperados propuesto por organizaciones internacionales de salud y escenario 3 se incorpora el criterio de recuperados propuesto por organizaciones internacionales de salud, sin considerar fallecidos en el total de recuperados. Existen diferencias considerables entre el escenario 1 en comparación al 2 y 3 en número de fallecidos, enfermos activos y duración de la enfermedad. El escenario 3, considerado el más adverso, estima un total de 11 000 personas contagiadas, 1151 fallecidos y que el máximo de la enfermedad ocurriría durante los primeros días de mayo. Se concluye que el concepto de "recuperado"puede ser decisivo para las proyecciones epidemiológicas de COVID-19 en Chile. 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.

[An epidemiological forecast of COVID-19 in Chile based on the generalized SEIR model and the concept of recovered]. / Proyección epidemiológica de COVID-19 en Chile basado en el modelo SEIR generalizado y el concepto de recuperado.

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.

La pandemia de COVID-19 declarada por la Organización Mundial de la Salud (OMS) ha generado un amplio debate respecto de las proyecciones epidemiológicas que tendrá a nivel global en nuestro planeta. Con los datos obtenidos del Ministerio de Salud de Chile (MINSAL), se efectuó un estudio prospectivo utilizando un modelo SEIR (Susceptible-Expuesto-Infectado-Recuperado) generalizado con el objetivo de estimar la evolución del COVID-19 en Chile. La estimación se realizó bajo tres escenarios con datos oficiales del Ministerio de Salud: escenario 1 solo con datos oficiales; escenario 2 se añade el criterio de recuperados propuesto por organizaciones internacionales de salud y escenario 3 se incorpora el criterio de recuperados propuesto por organizaciones internacionales de salud, sin considerar fallecidos en el total de recuperados. Existen diferencias considerables entre el escenario 1 en comparación al 2 y 3 en número de fallecidos, enfermos activos y duración de la enfermedad. El escenario 3, considerado el más adverso, estima un total de 11 000 personas contagiadas, 1151 fallecidos y que el máximo de la enfermedad ocurriría durante los primeros días de mayo. Se concluye que el concepto de “recuperado” puede ser decisivo para las proyecciones epidemiológicas de COVID-19 en Chile.