COVID-19 in Bangladesh: An Exploratory Data Analysis and Prediction of Neurological Syndrome Using Machine Learning Algorithms Based on Comorbidity

COVID-19 is caused by the SARS-CoV-2 virus, which has infected millions of people worldwide and claimed many lives. This highly contagious virus can infect people of all ages, but the symptoms and fatality are higher in elderly and comorbid patients. Many COVID-19 survivors have experienced a number of clinical consequences following their recovery. In order to have better knowledge about the long-COVID effects, we focused on the immediate and post-COVID-19 consequences in healthy and comorbid individuals and developed a statistical model based on comorbidity in Bangladesh. The dataset was gathered through a phone conversation with patients who had been infected with COVID-19 and had recovered. The results demonstrated that out of 705 patients, 66.3% were comorbid individuals prior to COVID-19 infection. Exploratory data analysis showed that the clinical complications are higher in the comorbid patients following COVID-19 recovery. Comorbidity-based analysis of long-COVID neurological consequences was investigated and risk of mental confusion was predicted using a variety of machine learning algorithms. On the basis of the accuracy evaluation metrics, decision trees provide the most accurate prediction. The findings of the study revealed that individuals with comorbidity have a greater likelihood of experiencing mental confusion after COVID-19 recovery. Furthermore, this study is likely to assist individuals dealing with immediate and post-COVID-19 complications and its management. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

DeepTrack: An ML-based Approach to Health Disparity Identification and Determinant Tracking for Improving Pandemic Health Care

The Coronavirus disease 2019 (COVID-19) pandemic has severely impacted countries around the world with unprecedented mortality and economic devastation and has disproportionately and negatively impacted different communities - especially racial and ethnic minorities who are at a particular disadvantage. Black Americans have a long-standing history of disadvantage (e.g., long-standing disparities in health outcomes) and are in a vulnerable position to experience the impact of this pandemic. Some studies indicate high-risk and vulnerability of the elderly and patients with underlying co-morbidities, however, little research paid attention to leveraging geographic information and machine learning (ML) to track the social and structural health determinants, which can provide a lower level of granularity. In this paper, we propose DeepTrack, a geospatial and ML-based approach to identify diverse determinants (including the structural, social, and constructural determinants) of health disparities in COVID-19 pandemic, which provides a lower level of granularity. We provide a thorough analysis of health disparities and diets based on multiple COVID-19 datasets and examine the structural, social, and constructural health determinants to assist in ascertaining why disparities (in racial and ethnic minorities who are particularly disadvantaged) occur in infection and death rates due to COVID-19 pandemic. We track determinants of nutrition and obesity through diet examination. Extensive experimental results show the effectiveness of our approach. The research provides new strategies for health disparity identification and determinant tracking with a goal to improve pandemic health care. © 2021 IEEE.

Difference in Clinical Patterns between COVID-19 Affected Children and Adults

Coronavirus has created a major global health problem since December 2019. People of all age groups were affected by this virus though children showed milder clinical characteristics and initially less number of children was affected by this virus. It is very important to know the difference in clinical patterns between COVID-19 affected children and adults. This cross-sectional prospective study was carried out in Kurmitola General Hospital, Dhaka from April to September 2020 to compare the clinical pattern and laboratory findings between COVID-19 positive children and adults. Total 150 COVID-19 positive patients were enrolled in this study, among them 100 patients were adults (>18 year) mean+/-SD age (49.9+/-14.33) and 50 patients were children (Day 1-18 year) mean+/-SD age (8.7+/-4.79). The adult group had 66 males and 34 females and the pediatric group had 27 males and 23 females. No significant sex difference was seen between the two groups (0.153). Most of the children were affected by family contact and they showed a mild type of illness but adult patients had contact from different sources. Fever and cough were the main symptoms of both groups but fever was more common in adults (81%) than children (36%), p-value (0.001). In children no severe or critical cases were found. But asymptomatic cases were 8%, mild cases (68%) and moderate cases (24%) in children. In adults no asymptomatic patients were found. Moderate cases were 72%, severe 14% and critical 5% (p value 0.001). Leucopenia, Lymphopenia and raised CRP and increased ferritin were found more in adults than children. Chest X-ray showed 42% of children had pneumonia and 83% adults had pneumonia. There was significant difference between the two groups (p value 0.0001). This study concludes that corona virus affects children like adults but their presentation is not so severe and children show mild clinical symptoms in comparison with adults.

Role-Framework of Artificial Intelligence in Combating the COVID-19 Pandemic

COVID-19 caused by the SARS-CoV-2 (Corona) virus, first detected in China in December 2019 is now a worldwide pandemic due to its rapid spreading. People all over the world are fighting against it to combat the pandemic. Maintaining social aloofness and lockdowns can prevent the infection of COVID-19 but if this situation continues then the whole world must be confronted with economic catastrophe. Technology governed by artificial intelligence (AI) is a promising logistic that confirms its effectiveness for the benefits in different sectors like spread prediction, population screening, social awareness, hospital management, healthcare logistics, vaccine and drug delivery, surveillance and tracking, continuation of education and industrial production, etc. This article describes a framework of the role of AI in combating the effects of COVID-19 pandemic in dividing into nine sectors: i) Early trace-out, detection and diagnosis, ii) Disease surveillance, control, awareness build-up, and disease prevention, iii) Monitoring the treatment and predicting the risk of developing severe cases, iv) Screening and helping patients through chatbots, v) Service management through intelligence drones and robots, vi) Management of stress and the spread of rumors through social networks, vii) Understanding the virus through analysis of protein–protein interactions, viii) Speeding up the vaccine and drug discoveries and development, ix) Continuation of education and prediction of economic loss. In addition, an overview of commercialization of the AI strategies by highlighting some success stories is presented. © 2021, Springer Nature Switzerland AG.