Chest radiograph findings in children with COVID-19-A retrospective analysis from a tertiary care paediatric hospital in South India.

OBJECTIVE: The primary aim of this study is to document the chest X-ray findings in children with COVID-19 pneumonia. The secondary aim is to correlate chest X-ray findings to patient outcome. METHODS: We performed a retrospective analysis of children (0-18 years) with SARS-CoV-2 admitted to our hospital from June 2020 to December 2021. The chest radiographs were assessed for: peribronchial cuffing, ground-glass opacities (GGOs), consolidation, pulmonary nodules and pleural effusion. The severity of the pulmonary findings was graded using a modification of the Brixia score. RESULTS: There were a total of 90 patients with SARS-CoV-2 infection; the mean age was 5.8 years (age range 7 days to 17 years). Abnormalities were seen on the CXR in 74 (82%) of the 90 patients. Bilateral peribronchial cuffing was seen in 68% (61/90), consolidation in 11% (10/90), bilateral central GGOs in 2% (2/90) and unilateral pleural effusion in 1% (1/90). Overall the average CXR score in our cohort of patients was 6. The average CXR score in patients with oxygen requirement was 10. The duration of hospital stay was significantly longer in those patients with CXR score >9. CONCLUSION: The CXR score has the potential to serve as tool to identify children at high risk and may aid planning of clinical management in such patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) created a global pandemic in early March 2020. There are very few studies describing the lung changes in affected children. We performed a retrospective study in children, aged between 0 days and 18 years, who tested positive for this virus. This study was conducted in a paediatric tertiary care hospital in South India. Chest X-ray (CXR) was done in children with moderate and severe SARS-CoV-2 infection; these X-rays were reviewed and scoring was done to assess the degree of abnormality. It was seen that the duration of hospital stay was longer in children with a high CXR score. Amongst the children with score >9, 60% needed oxygen support during their treatment. Thus, CXR score can play a role in the prediction of disease outcome in SARS-CoV-2 infection.

Optimal Vaccination and Treatment Strategies in Reduction of COVID-19 Burden (preprint)

In this study, we formulate a mathematical model incorporating age specific transmission dynamics of COVID-19 to evaluate the role of vaccination and treatment strategies in reducing the size of COVID-19 burden. Initially, we establish the positivity and boundedness of the solutions of the model and calculate the basic reproduction number. We then formulate an optimal control problem with vaccination and treatment as control variables. Optimal vaccination and treatment policies are analysed for different values of the weight constant associated with the cost of vaccination and different transmissibility levels. Findings from these suggested that the combined strategies(vaccination and treatment) worked best in minimizing the infection and disease induced mortality. In order to reduce COVID-19 infection and COVID-19 induced deaths to maximum, it was observed that optimal control strategy should be prioritized to population with age greater than 40 years. Not much difference was found between individual strategies and combined strategies in case of mild epidemic ($R_0 \in (0, 2)$). For higher values of $R_0 (R_0 \in (2, 10))$ the combined strategies was found to be best in terms of minimizing the overall infection. The infection curves varying the efficacies of the vaccines were also analysed and it was found that higher efficacy of the vaccine resulted in lesser number of infection and COVID induced death.

Landmark Guidance Independent Spatio-channel Attention and Complementary Context Information based Facial Expression Recognition (preprint)

A recent trend to recognize facial expressions in the real-world scenario is to deploy attention based convolutional neural networks (CNNs) locally to signify the importance of facial regions and, combine it with global facial features and/or other complementary context information for performance gain. However, in the presence of occlusions and pose variations, different channels respond differently, and further that the response intensity of a channel differ across spatial locations. Also, modern facial expression recognition(FER) architectures rely on external sources like landmark detectors for defining attention. Failure of landmark detector will have a cascading effect on FER. Additionally, there is no emphasis laid on the relevance of features that are input to compute complementary context information. Leveraging on the aforementioned observations, an end-to-end architecture for FER is proposed in this work that obtains both local and global attention per channel per spatial location through a novel spatio-channel attention net (SCAN), without seeking any information from the landmark detectors. SCAN is complemented by a complementary context information (CCI) branch. Further, using efficient channel attention (ECA), the relevance of features input to CCI is also attended to. The representation learnt by the proposed architecture is robust to occlusions and pose variations. Robustness and superior performance of the proposed model is demonstrated on both in-lab and in-the-wild datasets (AffectNet, FERPlus, RAF-DB, FED-RO, SFEW, CK+, Oulu-CASIA and JAFFE) along with a couple of constructed face mask datasets resembling masked faces in COVID-19 scenario. Codes are publicly available at https://github.com/1980x/SCAN-CCI-FER