Current Artificial Intelligence (AI) Techniques, Challenges, and Approaches in Controlling and Fighting COVID-19: A Review.

SARS-CoV-2 (COVID-19) has been one of the worst global health crises in the 21st century. The currently available rollout vaccines are not 100% effective for COVID-19 due to the evolving nature of the virus. There is a real need for a concerted effort to fight the virus, and research from diverse fields must contribute. Artificial intelligence-based approaches have proven to be significantly effective in every branch of our daily lives, including healthcare and medical domains. During the early days of this pandemic, artificial intelligence (AI) was utilized in the fight against this virus outbreak and it has played a major role in containing the spread of the virus. It provided innovative opportunities to speed up the development of disease interventions. Several methods, models, AI-based devices, robotics, and technologies have been proposed and utilized for diverse tasks such as surveillance, spread prediction, peak time prediction, classification, hospitalization, healthcare management, heath system capacity, etc. This paper attempts to provide a quick, concise, and precise survey of the state-of-the-art AI-based techniques, technologies, and datasets used in fighting COVID-19. Several domains, including forecasting, surveillance, dynamic times series forecasting, spread prediction, genomics, compute vision, peak time prediction, the classification of medical imaging-including CT and X-ray and how they can be processed-and biological data (genome and protein sequences) have been investigated. An overview of the open-access computational resources and platforms is given and their useful tools are pointed out. The paper presents the potential research areas in AI and will thus encourage researchers to contribute to fighting against the virus and aid global health by slowing down the spread of the virus. This will be a significant contribution to help minimize the high death rate across the globe.

Increased IL-10-producing regulatory T cells are characteristic of severe cases of COVID-19.

OBJECTIVES: The pandemic spread of the coronavirus SARS-CoV-2 is due, in part, to the immunological properties of the host-virus interaction. The clinical presentation varies from individual to individual, with asymptomatic carriers, mild-to-moderate-presenting patients and severely affected patients. Variation in immune response to SARS-CoV-2 may underlie this clinical variation. METHODS: Using a high-dimensional systems immunology platform, we have analysed the peripheral blood compartment of 6 healthy individuals, 23 mild-to-moderate and 20 severe COVID-19 patients. RESULTS: We identify distinct immunological signatures in the peripheral blood of the mild-to-moderate and severe COVID-19 patients, including T-cell lymphopenia, more consistent with peripheral hypo- than hyper-immune activation. Unique to the severe COVID-19 cases was a large increase in the proportion of IL-10-secreting regulatory T cells, a lineage known to possess anti-inflammatory properties in the lung. CONCLUSION: As IL-10-secreting regulatory T cells are known to possess anti-inflammatory properties in the lung, their proportional increase could contribute to a more severe COVID-19 phenotype. We openly provide annotated data (https://flowrepository.org/experiments/2713) with clinical correlates as a systems immunology resource for the COVID-19 research community.