The Role of Remote Sensing and Geospatial Analysis for Understanding COVID-19 Population Severity: A Systematic Review.

Remote sensing (RS), satellite imaging (SI), and geospatial analysis have established themselves as extremely useful and very diverse domains for research associated with space, spatio-temporal components, and geography. We evaluated in this review the existing evidence on the application of those geospatial techniques, tools, and methods in the coronavirus pandemic. We reviewed and retrieved nine research studies that directly used geospatial techniques, remote sensing, or satellite imaging as part of their research analysis. Articles included studies from Europe, Somalia, the USA, Indonesia, Iran, Ecuador, China, and India. Two papers used only satellite imaging data, three papers used remote sensing, three papers used a combination of both satellite imaging and remote sensing. One paper mentioned the use of spatiotemporal data. Many studies used reports from healthcare facilities and geospatial agencies to collect the type of data. The aim of this review was to show the use of remote sensing, satellite imaging, and geospatial data in defining features and relationships that are related to the spread and mortality rate of COVID-19 around the world. This review should ensure that these innovations and technologies are instantly available to assist decision-making and robust scientific research that will improve the population health diseases outcomes around the globe.

Remote Sensing & Modelling of Covid-19 using Satellite Imaging

The world is under continuous threat of deadly virus Covid-19 and its rapidly evolving and mutating variants. Several efforts are being made by researchers and scientists worldwide to model and predict the behavior of spreading pattern of Covid-19 virus. In this paper, relationship between weather conditions and Covid19 spread rate is modelled with the help of data acquired by satellite using MODIS. To perform all the experiments, three data sets are used: one is taken from NASA related to MODIS and weather satellite images, second data set is of Kaggle related to latest Covid-19 case reports and third data set used is related to weather. Further, Covid-19 data set is also analyzed and modeled using look up learning 2D model. From the experimental results, it is observed that the various weather parameters obtained from satellite can be used to model the impact and spread of Covid-19. The data set once prepared is fed as input to early warning system for Covid-19. It is concluded that the designed system can be an efficient technique for issuing precautionary alert in the society. © 2022 IEEE.

Emissions of nitrogen dioxide in the northeast U.S. during the 2020 COVID-19 lockdown.

We have quantified the emissions of Nitrogen dioxide (NO2) in the Northeast megalopolis of the United States during the COVID-19 lockdown. The measurement of NO2 emission serves as the indicator for the emission of the group of nitrogen oxides (NOx). Approximately 56% of NO2 emissions in the US are from mobile sources, and the remainder is from stationary sources. Since 2002, clean air regulations have resulted in approximately 5% compound annual reduction of NOx emissions in the US (8.2% in the study area). Therefore, when studying the impact of sporadic events like an epidemic on emissions, it is necessary to account for the persistent reduction of emissions due to policy driven emission reduction measures. Using spaceborne sensors, ground monitors, National Emission Inventory data, and the US Motor Vehicle Emission Simulator, we quantified the reduction of total NOx emissions, distinguishing stationary sources from on-road mobile sources (trucks and automobiles). When considering total NOx emissions (stationary and mobile combined), we find that the pandemic restrictions resulted in 3.4% reduction of total NOx emissions in the study area in 2020. This is compared to (and in addition to) the expected 8.2% policy driven reduction of NOx emissions in 2020. This somewhat low reduction of emissions is because most stationary sources (factories, power plants, etc.) were operational during the pandemic. Truck traffic, a significant source of mobile emissions, also did not decline significantly (average 4.8% monthly truck traffic reduction in the study area between March and August 2020), as they were delivering goods during the lockdown. On the other hand, automobile traffic, responsible for 24% of total NOx emissions, dropped significantly, 52% in April, returning to near normal after 5 months. While the reduction of automobile traffic was significant, especially in the early months of the pandemic, its effect on emissions was relatively insignificant.