Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases.

Deep Learning and Machine Learning are becoming more and more popular as their algorithms get progressively better, and their use is expected to have the large effect on improving the health care system. Also, the pandemic was a chance to show how adding AI to healthcare infrastructure could help, since infrastructures around the world are overworked and falling apart. These new technologies can be used to fight COVID-19 because they are flexible and can be changed. Based on these facts, we looked at how the ML and DL-based models can be used to deal with the COVID-19 pandemic problem and what the pros and cons of each are. This paper gives a full look at the different ways to find COVID-19. We looked at the COVID-19 issues in a systematic way and then rated the methods and techniques for finding it based on their availability, ease of use, accuracy, and cost. We have also shown in pictures how well each of the detection techniques works. We did a comparison of different detection models based on the above factors. This helps researchers understand the different methods and the pros and cons of using them as the basis for their research. In the last part, we talk about the open challenges and research questions that come with putting these techniques together with other detection methods.

Evaluation of symptoms along with depression, anxiety and stress levels of Sars-CoV-2 positive patients during the second wave in the city of Indore, Central India - a cross sectional study.

INTRODUCTION: The consequences of the second wave hitting India have drastically laid a huge impact on the mental state of patients. The second wave had proven to be far more dangerous and hence the psychological evaluation needed to be conducted to know the scenario of patients suffering from SARS-CoV-2. OBJECTIVE: This study was undertaken to evaluate the symptoms of SARS-CoV-2 patients along with the existing depression, anxiety and stress levels amongst them. MATERIAL AND METHODS: An observational, cross-sectional questionnaire-based survey was conducted among 351 patients infected with SARS-CoV-2 during the second wave in Indore, Central India. The questionnaire consisted of questions pertaining to socio-demographic characteristics, clinical signs and symptoms. Evaluation of depression, anxiety and stress levels were done by use of 21 item Depression, Anxiety, Stress Scale (DASS-21). RESULTS: The most common symptom amongst patients was cough (42.2%) followed by fever (40.2%). Sixty-nine (19.6%) patients were asymptomatic. Depression score was found to have significant, positive weak correlation with age (ρ-0.124, p-0.020, p value <.05). No significant difference was observed between the depression, anxiety and stress score of males and females. Based on the scores assigned to the responses, patients who tested positive were belonging to normal category with no diagnosed depression, anxiety or stress. CONCLUSION: The present study showed fever, cough, headache, weakness, and chest pain as the common sign and symptoms of COVID-19 during the second wave. There was a prevalence of low levels of anxiety, stress and depression amongst patients in Radha Saomi Covid Care Centre, Indore during the second wave.

Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning.

The Coronavirus Disease-2019 (COVID-19) pandemic persists to have a mortifying impact on the health and well-being of the global population. A continued rise in the number of patients testing positive for COVID-19 has created a lot of stress on governing bodies across the globe and they are finding it difficult to tackle the situation. We have developed an outbreak prediction system for COVID-19 for the top 10 highly and densely populated countries. The proposed prediction models forecast the count of new cases likely to arise for successive 5 days using 9 different machine learning algorithms. A set of models for predicting the rise in new cases, having an average accuracy of 87.9%  ± 3.9% was developed for 10 high population and high density countries. The highest accuracy of 99.93% was achieved for Ethiopia using Auto-Regressive Moving Average (ARMA) averaged over the next 5 days. The proposed prediction models used by us can help stakeholders to be prepared in advance for any sudden rise in outbreak to ensure optimal management of available resources.