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.

Epidemiological surveillance in COVID- 19 pandemic: EPIVIGILA system. / Epidemiological surveillance in COVID-19 pandemic: EPIVIGILA system.

In March 2020, the first version of EPIVIGILA was deployed in a productive environment a few days after the first local case of COVID- 19. This system is a technological integration plat-form for national epidemiological surveillance of notifiable diseases. Previously, Chile used a manual process that would probably have failed with a peak volume of more than 38 000 daily notifications; in a country with 18 million inhabitants, long and narrow geography, and centralized governance. This work highlights the importance of the national electronic surveillance system EPIVIGILA in managing the pandemic. The systems main strength is its ability to adapt to the needs of reliable, precise, timely, and real- time information. EPIVIGILA was able to include, under the circumstances, different flows, actors, data, and functionalities with high expectations of accuracy. This valuable information allowed the authorities to assess the impact of the measures to manage and control the pandemic. Its versatility positions this platform among the few globally that operates national data with a high level of granularity in a single system through a pandemic. In Chile, EPIVIGILA is the primary source of information for daily reports, epidemiological reports, and data published on government websites about COVID- 19. Thus, electronic systems prove fundamental for public health because the recording and processing of data generate clear, reliable, and timely information, helping authorities make decisions to reduce the spread of infectious diseases, prevent deaths, and improve the populations quality of life.

En marzo 2020 se despliega la primera versión de EPIVIGILA en un ambiente productivo, plataforma de integración tecnológica de vigilancia epidemiológica nacional para enfermedades de notificación obligatoria (a pocos días del caso 1 de COVID- 19 local). Anteriormente, Chile usaba un proceso manual que probablemente hubiese fracasado ante un volumen máximo superior a 38 000 notificaciones diarias, en un país con 18 millones de habitantes, de geografía larga y angosta y gobernanza centralizada. El objetivo del trabajo es relevar la importancia que tiene en el manejo de la pandemia el sistema nacional de vigilancia electrónico EPIVIGILA. La principal fortaleza del sistema es su capacidad de adaptación a las necesidades de información fidedigna, precisa, oportuna y en tiempo real. EPIVIGILA fue capaz de incluir, en el curso de las circunstancias, distintos flujos, actores, datos y funcionalidades con altas expectativas de exactitud. Ello permitió que las autoridades pudieran evaluar el impacto de las medidas implementadas para el manejo y control de la pandemia. Su versatilidad posiciona a esta plataforma entre las pocas en el mundo que opera datos nacionales en una pandemia con un alto nivel de granularidad en un único sistema. En Chile, EPIVIGILA es la principal fuente de información para los reportes diarios, informes epidemiológicos y datos publicados en sitios web gubernamentales sobre COVID- 19. Así, el uso de sistemas electrónicos muestran ser un soporte fundamental para la salud pública, porque el registro y procesamiento de los datos genera información clara, confiable y oportuna, contribuyendo a que las autoridades puedan tomar decisiones orientadas a disminuir la propagación de enfermedades transmisibles, evitar muertes y mejorar la calidad de vida de la población.

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.

A Feature-Based Analysis for Time-Series Classification of COVID-19 Incidence in Chile: A Case Study

The 2019 Coronavirus disease (COVID-19) pandemic is a current challenge for the world’s health systems aiming to control this disease. From an epidemiological point of view, the control of the incidence of this disease requires an understanding of the influence of the variables describing a population. This research aims to predict the COVID-19 incidence in three risk categories using two types of machine learning models, together with an analysis of the relative importance of the available features in predicting the COVID-19 incidence in the Chilean urban commune of Concepción. The classification results indicate that the ConvLSTM (Convolutional Long Short-Term Memory) classifier performed better than the SVM (Support Vector Machine), with results between 93% and 96% in terms of accuracy (ACC) and F-measure (F1) metrics. In addition, when considering each one of the regional and national features as well as the communal features (DEATHS and MOBILITY), it was observed that at the regional level the CRITICAL BED OCCUPANCY and PATIENTS IN ICU features positively contributed to the performance of the classifiers, while at the national level the features that most impacted the performance of the SVM and ConvLSTM were those related to the type of hospitalization of patients and the use of mechanical ventilators.

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.