Assessing the Impact of Covid-19 on Human Activities in the Greater Toronto Area by Nighttime Light Images and Active Covid-19 Cases

This paper explores the effect of COVID-19 outbreaks on human activity through nighttime light images of Greater Toronto Area (GTA), Canada. The methods used in this paper include image preprocessing, image classification, and spatial analysis. By using the nighttime light radiance data from VIIRS/NPP data products and COVID-19 cases and comparing this data from the pre-pandemic year, the impact of COVID-19 was analyzed. The result shows that during the pandemic year the monthly average nighttime light radiance has decreased about 4.3-5.0% compared to the pre-pandemic year. The classification results shows that the average percentage of changes in residential areas, public facilities, and commercial areas are 0.3%, -0.7%, and -1.2%, respectively of each corresponding month. Meanwhile, the spatial analysis results show population distribution patterns in GTA during the pandemic year. Overall, the nighttime lights (NTL) images can be used for a preliminary understanding of how COVID-19 affected human activities and is corroborated with other forms data collection used for the pandemic analysis.

Validation of a molecular assay to detect SARS-CoV-2 in saliva

AIM: To validate a reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) assay to detect SARS-CoV-2 in saliva in two independent Aotearoa New Zealand laboratories. METHODS: An RT-qPCR assay developed at University of Illinois Urbana-Champaign, USA, was validated in two New Zealand laboratories. Analytical measures, such as limit of detection (LOD) and cross-reactivity, were performed. One hundred and forty-seven saliva samples, each paired with a contemporaneously collected nasal swab, mainly of nasopharyngeal origin, were received. Positive (N=33) and negative (N=114) samples were tested blindly in each laboratory. Diagnostic sensitivity and specificity were then calculated. RESULTS: The LOD was <0.75 copy per mu L and no cross-reactivity with MERS-CoV was detected. There was complete concordance between laboratories for all saliva samples with the quantification cycle values for all three genes in close agreement. Saliva had 98.7% concordance with paired nasal samples: and a sensitivity, specificity and accuracy of 97.0%, 99.1% and 99.1%, respectively. CONCLUSION: This saliva RT-qPCR assay produces reproducible results with a low LOD. High sensitivity and specificity make it a reliable option for SARS-CoV-2 testing, including for asymptomatic people requiring regular screening.

Predictions of COVID-19 Infection Severity Based on Co-associations between the SNPs of Co-morbid Diseases and COVID-19 through Machine Learning of Genetic Data

In this research, a quantitative model is built to predict people's susceptibility to COVID-19 based on their genomes. Identifying people vulnerable to COVID-19 infections is crucial in stopping the spread of the virus. In previous studies, researchers have found that individuals with comorbid diseases have higher chances of being infected and developing more severe COVID-19 conditions. However, these patterns are only observed through correlational analyses between patient phenotypes and the severity of their COVID-19 infection. In this study, genetic variants underlying the observed comorbidity patterns are analyzed through machine learning of COVID-19 data from GWAS studies, which may reveal biological pathways underlying COVID-19 contraction that are essential to the development of effective and targeted therapeutics. Furthermore, through combining genetic variants with the individual's phenotypes, this study built a Neural Network model and Random Forest classifier to predict an individual's likelihood of COVID-19 infection. The Random Forest Classifier in this study shows that on-going symptoms are generally better predictors of COVID-19 condition (higher impurity-based feature importance) than diseases or medical histories. In addition, when trained with genomic data, the comorbid disease impact ranking deduced by the resulting RF model is highly consistent with phenotypic comorbidity patterns observed in past studies.

Traditional Chinese Medicine guidelines for coronavirus disease 2019

OBJECTIVE: To summarize the evidence from Traditional Chinese Medicine (TCM) practice in the treatment of coronavirus disease 2019 (COVID-19) and provide timely clinical practice guidance. METHODS: The guidelines were developed in accordance with the World Health Organization rapid guideline process. The evidence on TCM for COVID-19 from published guidelines, direct and indirect published clinical evidence, first hand clinical data, and expert experience and consensus were collected. The grading of recommendations assessment, development and evaluation (GRADE) method was used to grade the evidence and make the recommendations. RESULTS: Based on the available evidence, the guidelines recommended 17 Chinese medicines for COVID-19: 2 Chinese herbal granules, 7 Chinese patent medicines, and 8 Chinese herbal injections. CONCLUSION: As the literature search was conducted on March, any subsequent versions of these guidelines require an up-to-date literature review. We hope that the evidence summary in these guidelines will be helpful in global efforts to address COVID-19. (C) 2020 JTCM. All rights reserved.