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Purpose The aim of this study was to carry out a systematic review of telemedicine technology and to discuss the role of legislation in the application of telemedicine technology within the scope of COVID-19. Methods The search for papers was conducted in the databases Cochrane Central, MEDLINE, and Embase. The keywords considered for this systematic review were (coronavirus OR “2019-nCoV” OR 2019nCoV OR nCoV2019 OR “nCoV-2019” OR “COVID-19” OR COVID19 OR “HCoV-19” OR HCoV19 OR CoV OR “2019 novel*” OR Ncov OR “n-cov” OR “SARS-CoV-2” OR “SARSCoV-2” OR “SARSCoV2” OR “SARSCoV2” OR SARSCov19 OR “SARS-Cov19” OR “SARS-Cov-19” OR “severe acute respiratory syndrome*” OR ((corona* OR corono*) AND (virus* OR viral* OR virinae*))) AND ((telemedicine OR telehealth OR “remote assessment” OR telemonitoring)). The review process was carried out by six independent reviewers, using the PRISMA-P method. Results As a result, a total of 34 papers were selected, which addressed the study of telemedicine technologies used in times of endemics, epidemics, and pandemics, focusing on COVID-19. Conclusion Telemedicine allows for care in remote areas and at a lower cost to the patient and, in the current situation, it can reduce the number of contagions as well as the occupation of beds in health facilities. Telemedicine may suffer from limitations, mainly due to current legislation, but in this pandemic era of COVID-19, several countries around the world have made their laws more flexible to allow more widespread use of telemedicine.

Purpose COVID-19 causes lung inflammation and lesions, and chest X-ray and computed tomography images are remarkably suitable for differentiating the new disease from patients with other lung diseases. In this paper, we propose a computer model to classify X-ray images of patients diagnosed with COVID-19. Chest X-ray exams were chosen over computed tomography scans because they are low cost, results are quickly obtained, and X-ray equipment is readily available. Methods A new CNN network, called CNN-COVID, has been developed to classify X-ray patient’s images. Images from two different datasets were used. The images of Dataset I is originated from the COVID-19 image data collection and the ChestXray14 repository, and the images of Dataset II belong to the BIMCV COVID-19+ repository. To assess the accuracy of the network, 10 training and testing sessions were performed in both datasets. A confusion matrix was generated to evaluate the model’s performance and calculate the following metrics: accuracy (ACC), sensitivity (SE), and specificity (SP). In addition, Receiver Operating Characteristic (ROC) curves and Areas Under the Curve (AUCs) were also considered. Results After running 10 tests, the average accuracy for Dataset I and Dataset II was 0.9787 and 0.9839, respectively. Since the weights of the best test results were applied in the validation, it was obtained the accuracy of 0.9722 for Dataset I and 0.9884 for Dataset II. Conclusions The results showed that the CNN-COVID is a promising tool to help physicians classify chest images with pneumonia, considering pneumonia caused by COVID-19 and pneumonia due to other causes.

Purpose Coronavirus disease is an irresistible infection caused by the respiratory disease coronavirus 2 (SARS-CoV-2). It was first found in Wuhan, China, in December 2019, and has since spread universally, causing a constant pandemic. On June 3, 2020, 6.37 million cases were found in 188 countries and regions. During pandemic prevention, this can minimize the impact of the disease on individuals and groups. A study was carried out on coronavirus to observe the number of cases, deaths, and recovery cases worldwide within a specific time period of 5 months. Based on this data, this research paper will predict the future spread of this infectious disease in human society. Methods In our study, the dataset was taken from WHO “Data WHO Coronavirus Covid-19 cases and deaths-WHO-COVID-19-global-data”. This dataset contains information about the observation date, provenance/state, country/region, and latest updates. In this article, we implemented several forecasting techniques: naive method, simple average, moving average, single exponential smoothing, Holt linear trend method, Holt-Winters method and ARIMA, for comparison, and how these methods improve the Root mean square error score. Results The naive method is best suited as described over all other methods. In the ARIMA model, utilizing grid search, we recognized a lot of boundaries that delivered the best-fit model for our time series data. By continuing the model, future predictions of death cases indicate that the number of deaths will increased by more than 600,000 by January 2021. Conclusion This survey will support the government and experts in making arrangements for what is about to happen. Based on the findings of instantaneous model, these models can be adjusted to guide long time.

In December 2019, China reported a series of atypical pneumonia cases caused by a new Coronavirus, called COVID-19. In response to the rapid global dissemination of the virus, on the 11th of Mars, the World Health Organization (WHO) has declared the outbreak a pandemic. In light of this situation, this paper intends to analyze and improve the current SEIR models to better represent the behavior of the COVID-19 and accurately predict the outcome of the pandemic in a given social, economic and political scenario. We present a novel generalized Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model and test it using a global optimization algorithm with data collected from the WHO. Our main results were: (a) our model was able to accurately fit the data of all countries tested (b) it is possible to predict values for one week ahead with errors in the order of 15% for the number of cases and 30% in the number of deaths for all countries; (c) predictions are better for countries where the active cases curve already reached the maximum; the error being in the order of 10% in the number of cases and 20% in the number of deaths; (d) for countries where the active curve is still growing, different optimization solutions can be found that fit the data; so, to predict future behavior in this scenarios some of the model coefficients should be estimated from outside sources or based on generalized results from other countries according to their health policies of social distance, quarantining and case test and tracing.