Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review.

COVID-19 has produced a global pandemic affecting all over of the world. Prediction of the rate of COVID-19 spread and modeling of its course have critical impact on both health system and policy makers. Indeed, policy making depends on judgments formed by the prediction models to propose new strategies and to measure the efficiency of the imposed policies. Based on the nonlinear and complex nature of this disorder and difficulties in estimation of virus transmission features using traditional epidemic models, artificial intelligence methods have been applied for prediction of its spread. Based on the importance of machine and deep learning approaches in the estimation of COVID-19 spreading trend, in the present study, we review studies which used these strategies to predict the number of new cases of COVID-19. Adaptive neuro-fuzzy inference system, long short-term memory, recurrent neural network and multilayer perceptron are among the mostly used strategies in this regard. We compared the performance of several machine learning methods in prediction of COVID-19 spread. Root means squared error (RMSE), mean absolute error (MAE), R2 coefficient of determination (R2), and mean absolute percentage error (MAPE) parameters were selected as performance measures for comparison of the accuracy of models. R2 values have ranged from 0.64 to 1 for artificial neural network (ANN) and Bidirectional long short-term memory (LSTM), respectively. Adaptive neuro-fuzzy inference system (ANFIS), Autoregressive Integrated Moving Average (ARIMA) and Multilayer perceptron (MLP) have also have R2 values near 1. ARIMA and LSTM had the highest MAPE values. Collectively, these models are capable of identification of learning parameters that affect dissimilarities in COVID-19 spread across various regions or populations, combining numerous intervention methods and implementing what-if scenarios by integrating data from diseases having analogous trends with COVID-19. Therefore, application of these methods would help in precise policy making to design the most appropriate interventions and avoid non-efficient restrictions.

Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review.

Coronavirus disease, first detected in late 2019 (COVID-19), has spread fast throughout the world, leading to high mortality. This condition can be diagnosed using RT-PCR technique on nasopharyngeal and throat swabs with sensitivity values ranging from 30 to 70%. However, chest CT scans and X-ray images have been reported to have sensitivity values of 98 and 69%, respectively. The application of machine learning methods on CT and X-ray images has facilitated the accurate diagnosis of COVID-19. In this study, we reviewed studies which used machine and deep learning methods on chest X-ray images and CT scans for COVID-19 diagnosis and compared their performance. The accuracy of these methods ranged from 76% to more than 99%, indicating the applicability of machine and deep learning methods in the clinical diagnosis of COVID-19.

Nanotechnology against the novel coronavirus (severe acute respiratory syndrome coronavirus 2): diagnosis, treatment, therapy and future perspectives.

COVID-19, as an emerging infectious disease, has caused significant mortality and morbidity along with socioeconomic impact. No effective treatment or vaccine has been approved yet for this pandemic disease. Cutting-edge tools, especially nanotechnology, should be strongly considered to tackle this virus. This review aims to propose several strategies to design and fabricate effective diagnostic and therapeutic agents against COVID-19 by the aid of nanotechnology. Polymeric, inorganic self-assembling materials and peptide-based nanoparticles are promising tools for battling COVID-19 as well as its rapid diagnosis. This review summarizes all of the exciting advances nanomaterials are making toward COVID-19 prevention, diagnosis and therapy.

CRISPR Systems for COVID-19 Diagnosis.

The emergence of the new coronavirus 2019 (COVID-19) was first seen in December 2019, which has spread rapidly and become a global pandemic. The number of cases of COVID-19 and its associated mortality have raised serious concerns worldwide. Early diagnosis of viral infection undoubtedly allows rapid intervention, disease management, and substantial control of the rapid spread of the disease. Currently, the standard approach for COVID-19 diagnosis globally is the RT-qPCR test; however, the limited access to kits and associated reagents, the need for specialized lab equipment, and the need for highly skilled personnel has led to a detection slowdown. Recently, the development of clustered regularly interspaced short palindromic repeats (CRISPR)-based diagnostic systems has reshaped molecular diagnosis. The benefits of the CRISPR system such as speed, precision, specificity, strength, efficiency, and versatility have inspired researchers to develop CRISPR-based diagnostic and therapeutic methods. With the global COVID-19 outbreak, different groups have begun to design and develop diagnostic and therapeutic programs based on the efficient CRISPR system. CRISPR-based COVID-19 diagnostic systems have advantages such as a high detection speed (i.e., 30 min from raw sample to reach a result), high sensitivity and precision, portability, and no need for specialized laboratory equipment. Here, we review contemporary studies on the detection of COVID-19 based on the CRISPR system.

Efficacy and safety of convalescent plasma therapy in severe COVID-19 patients with acute respiratory distress syndrome.

Since SARS-CoV-2 infection is rapidly spreading all around the world, affecting many people and exhausting health care resources, therapeutic options must be quickly investigated in order to develop a safe and effective treatment. The present study was designed to evaluate the safety and efficacy of convalescent plasma (CP) for treating severe cases of COVID-19 who developed acute respiratory distress syndrome (ARDS). Among 64 confirmed cases of severe COVID-19 with ARDS in this study, 32 patients received CP besides first line treatment. Their clinical response and outcome in regard to disease severity and mortality rate were evaluated and compared with the other 32 patients in the control group who were historically matched while randomly chosen from previous patients with the same conditions except for receiving CP therapy. Analysis of the data was performed using SPSS software. Patients with plasma therapy showed improvements in their clinical outcomes including a reduction in disease severity in terms of SOFA and APACHE II scores, the length of ICU stay, need for noninvasive ventilation and intubation and also showed an increase in oxygenation. They also showed reduction in mortality which was statistically significant in less severe cases with mild or moderate ARDS. Early administration of the convalescent plasma could successfully contribute to the treatment of severe COVID-19 patients with mild or moderate ARDS at risk of progressing to critical state.

Effect of hydroxychloroquine on COVID-19 prevention in cancer patients undergoing treatment: a structured summary of a study protocol for a randomised controlled trial.

OBJECTIVES: In this study, we investigate the effect of hydroxychloroquine on the prevention of Novel Coronavirus Disease (COVID-19) in cancer patients being treated. TRIAL DESIGN: This is a multi-centre, two-arm, parallel-group, triple-blind, phase 2-3 randomised controlled trial. PARTICIPANTS: All patients over the age of 15 from 5 types of cancer are included in the study. Patients with acute lymphoid and myeloid leukemias in the first line treated with curative intent, patients with high-grade non-Hodgkin's lymphoma treated with leukemia protocols and patients with non-metastatic breast and colon cancer in the first line of treatment will enter the study. The exclusion criteria will include known sensitivity to Hydroxychloroquine, weight below 35 kilograms, history of retinopathy, history of any cardiac disease, acute respiratory tract infection in the last 2 months, having COVID-19 in the first two weeks of entering the trial, having Diabetes Mellitus, having an immuno-suppressive disease other than cancer, having chronic pulmonary disease and taking immuno-suppressant drug other than chemotherapeutic agents for current cancer. This study is performed in five academic centres affiliated to Mashhad University of Medical Sciences, Mashhad, Iran. INTERVENTION AND COMPARATOR: Patients are randomly assigned to two groups; one being given hydroxychloroquine and the other is given placebo. During two months of treatment, the two groups are treated with either hydroxychloroquine (Amin® Pharmaceutical Company, Isfahan, Iran) or placebo (identical in terms of shape, colour, smell) as a single 200 mg tablet every other day. Patients will be monitored for COVID-19 symptoms during the follow-up period. If signs or symptoms occur (fever, cough, shortness of breath), they will be examined and investigated with a high-resolution computed tomography (CT) scan of the lungs, COVID-19 specific IgM, IgG antibody assay and a nucleic acid amplification test (NAT) for the SARS-CoV-2 virus. MAIN OUTCOMES: The primary end point of this study is to investigate the incidence of COVID-19 in patients being treated for their cancer over a 2-month period. RANDOMISATION: Randomisation will be performed using randomly permuted blocks. By using an online website (www.randomization.com) the randomization sequence will be produced by quadruple blocks. The allocation ratio in intervention and control groups is 1:1. BLINDING (MASKING): Participants and caregivers do not know whether the patient is in the intervention or the control group. The outcome assessor and the data analyst are also blinded to group assignment. NUMBERS TO BE RANDOMISED (SAMPLE SIZE): The calculated total sample size is 60 patients, with 30 patients in each group. TRIAL STATUS: The trial began on April 14, 2020 and recruitment is ongoing. Recruitment is anticipated to be completed by June 14, 2020 There has been no change in study protocol since approval, protocol version 1 was approved April 12, 2020. TRIAL REGISTRATION: This trial has been registered by the title of "Effect of Hydroxychloroquine on Novel Coronavirus Disease (COVID-19) prevention in cancer patients under treatment" in Iranian Registry of Clinical Trials (IRCT) with code "IRCT20200405046958N1", https://www.irct.ir/trial/46946. Registration date is April 14, 2020. FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol.