Persistent Symptoms of Severe and Critical COVID‑19: A Cross‑Sectional Study after more than 120 Days from Disease Onset

Objective: The objective of this study is to assess the prevalence and predictors of symptom persistence associated with severe and critical coronavirus disease-2019 (COVID-19) after more than 120 days from the onset of the disease. Materials and Methods: This is a single-center, cross-sectional study of 125 adults who were admitted to King Khalid University Hospital (Riyadh, Saudi Arabia) with severe and critical COVID-19 between March 4 and December 1, 2020. Telephone interviews were conducted between April 1 and May 31, 2021, to collect data on COVID-19 symptoms persisting after more than 120 days from the onset of the disease. All of the participants had been discharged from the hospital and had resumed their normal lives. Symptoms of COVID-19 that had not been present before the onset of the disease were considered to be persistent if participants confirmed their continued presence at the time of the interview. The impact of chronic disease on persistent symptoms was considered. Results: About 42.4% (53/125) of patients had at least one or more persistent symptoms;27.2% (34/125) had breathlessness, 5.6% (7/125) cough, and 4.8% (6/125) chest pain. These three symptoms had been present from the first presentation. Hair loss was reported by 14.4% (18/125), forgetfulness by 8% (10/125), difficulty in concentrating by 6.4% (8/125), and lack of energy by 4% (5/125). Those had manifested after more than 120 days from the symptom's onset. The major factors in suffering from persistent symptoms were intensive care unit (ICU) admission or/and fever (temperature >38°), or/and diarrhea. There was no correlation between persistent symptoms and chronic diseases. Conclusions: After more than 120 days from the confirmation of severe and critical COVID-19, more than one-third of discharged adults were found to have one or more persistent symptoms. These were mainly associated with the need for ICU admission, fever (temperature >38°), and diarrhea. More care needs to be given to COVID-19 patients in the presence of these factors and prolonged medical care would appear to be essential. © 2023 Journal of Nature and Science of Medicine ;Published by Wolters Kluwer - Medknow.

Epidemiology of COVID-19 in the Kingdom of Saudi Arabia: An Ecological Study

Objectives: Considering the transmissible nature of COVID-19 it is important to explore the trend of the epidemiology of the disease in each country and act accordingly. This study aimed to examine the trend of COVID-19 epidemiology in the Kingdom of Saudi Arabia in term of its incidence rate, recovery rate, and mortality rate. Material(s) and Method(s): We conducted an observational study using publicly available national data taken from the Saudi Ministry of Health for the period between 3 March and 7 June 2020. The number of newly confirmed cases, active cases, critical cases, percentage of cases stratified by age group [adults, children, and elderly] and gender were extracted from the reports of the Saudi Ministry of Health. Result(s): During the study period, the total number of confirmed cases with COVID-19 rose from one on 2 March 2020 to 101,914 on 7 June, representing an average of 1,039 new cases per day, [trend test, p < 0.000]. Despite the increase in the number of newly confirmed daily cases of COVID-19, the number of reported daily active cases started to stabilize after 2 months from the start of the pandemic in the country and the overall recovery rate was 71.4%. The mortality rate decreased by 6.4% during the study period. COVID-19 was more common among adults and males compared to other demographic groups. Conclusion(s): The epidemiological status of COVID-19 in the Kingdom of Saudi Arabia showing promising improvement. Males and adults accounted for the majority of COVID-19 cases in the KSA. Further studies are recommended to be conducted at the patient level to identify other patient groups who are at higher risk of getting infected with COVID-19.

An intelligent fine-tuned forecasting technique for covid-19 prediction using neuralprophet model

COVID-19, being the virus of fear and anxiety, is one of the most recent and emergent of various respiratory disorders. It is similar to the MERS-COV and SARS-COV, the viruses that affected a large population of different countries in the year 2012 and 2002, respectively. Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty. The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution, and Random Forest Model. The analysis upon dataset has been performed considering the time duration from January 1st 2020 to16th July 2021. The model has been developed to obtain the forecast values till September 2021. This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country. In India, the cases are rapidly increasing day-by-day since mid of Feb 2021. The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave. To empower the prediction for future validation, the proposed model works effectively. © 2022 Tech Science Press. All rights reserved.

Monitoring social distancing using artificial intelligence for fighting COVID-19 virus spread

To restrict COVID-19, individuals must remain two meters away from one another in public since public health authorities find this a healthy distance. In this way, the incidence of “social distancing” keeps pace with COVID-19 spread. For this purpose, the proposed solution consists of the development of a tool based on AI technologies which takes as input videos (in real time) from streets and public spaces and gives as output the places where social distancing is not respected. Detected persons who are not respecting social distancing are surrounded with red rectangles and those who respect social distancing with green rectangles. The solution has been tested for the case of videos from the two Holy Mosques in Saudi Arabia: Makkah and Madinah. As a novel contribution compared to existent approaches in the literature, the solution allows the detection of the age, class, and sex of persons not respecting social distancing. Person detection is performed using the Faster RCNN with ResNet-50 as it is the backbone network that is pre-trained with the open source COCO dataset. The obtained results are satisfactory and may be improved by considering more sophisticated cameras, material, and techniques. Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Computational technique for effectiveness of treatments used in curing SARS-CoV-2

COVID-19 pandemic has unleashed an unprecedented humanitarian crisis in the world at present. With each passing day, the number of patients afflicted with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is rising at an alarming pace, so much so that some countries are now combating the second wave of the contagion. As the death ratio due to the Virus increases, the medical fraternity and pharmacologists are working relentlessly to identify and prescribe a standardized and effective course of treatment for treating COVID-19 patients. However, medical specialists are confused about opting for the most efficacious course of treatment because the patients infected with this virus have varied symptoms at different stages. In this league, our research study attempts to conduct an empirical analysis to identify which course of treatment is the most effective and preferred one for the treatment of SARS-CoV-2. The study proposes to achieve this objective by employing a scientific computation based symmetrical methodology. The present study has adopted a well-established and highly effective Multi Criteria Decision Making (MCDM) approach named Hesitant Fuzzy Linguistic Term Sets based Analytical Hierarchy Process-Technique for Order of Preference by Similarity to Ideal Solution (HFLTS-AHP-TOPSIS) Methodology. The computation based symmetrical methodology evaluates the various selected course of treatments identified through different research articles and guidelines suggested by different countries, and evaluates them on the basis of opinions suggested by medical experts (including doctors, industry experts, and others) and practitioners. Thus, the results drawn are highly corroborative and can be used as an authentic reference for future initiatives being undertaken in this domain. Our research investigation outlines a systematically assessed and scientifically validated ranking for various courses of treatments used in SARS-CoV-2 treatment and proposes to be an effective reckoner in the attempts to dispel the ambiguities surrounding the cure of SARS-CoV-2. Additionally, to authenticate the results of our analysis, we per-formed the sensitivity analysis (robustness analysis), marginal mean assessment and comparison analysis. © 2021, Tech Science Press. All rights reserved.