Correlating dynamic climate conditions and socioeconomic-governmental factors to spatiotemporal spread of COVID-19 via semantic segmentation deep learning analysis

Data-driven multiscale modelling and analysis of COVID-19 spatiotemporal evolution using explainable AI

“… Pan et al, 2021 ; Sobral et al, 2020 ). Similar to the global study carried out by Chew et al, (2021) , Wu et al (2020) and Guo et al (2021) examined the impacts of ambient temperature on the transmission rates of COVID-19 worldwide by too reporting negative correlations between ambient temperature and the transmission rate(s) of COVID-19.…”

“… Guo et al (2021) leveraged on multiple meteorological parameters, which are first weighted via population density, for the defined modelling objective at the city- or country-level. Chew et al (2021) exploited the use of climate-related satellite images to forecast the growth rate in the total number of confirmed COVID-19 cases globally via semantic segmentation analysis. A similar study has recently performed by Zhou et al (2021) where the authors leveraged on time-series earth observation data for land surface temperature (LST) to quantificationally explore the impacts of the spatiotemporal variations of multiple COVID-19 control measures on industrial production in Wuhan, China.…”

“… Şahin (2020) observed a correlation between the spread of COVID-19 in several cities of Turkey with wind speed values recorded 14 days ago and the temperature of the actual day for the total number of confirmed cases reported. Chew et al, (2021) reported that land surface temperature day has the strongest negative correlation with the growth rate in the number of confirmed COVID-19 cases globally. Other studies for specific countries, such as China, Spain, and the United States ( Abdollahi & Rahbaralam, 2020 ; Liu et al, 2020 ; Runkle et al, 2020 ; Shi et al, 2020 ), have also reported that temperature does affect, to an extent, the transmission rate of COVID-19.…”

“… Pan et al, 2021 ; Sannigrahi et al, 2020 ) for either investigating the relationship between specific control factors (work resumption policies, home quarantines) and COVID-19, or to determine the optimal policies to control the spread of COVID-19, respectively. For works pertaining to the global scale analysis ( Chew et al, 2021 ; Guo et al, 2021 ; Wu et al, 2020 ), they are mostly related to investigating the most important environmental factor(s) which have the strongest correlations with the transmission rates of COVID-19 globally. As described in-detail previously, climate-related environmental conditions do contribute complexly to COVID-19’s spatiotemporal dynamics which, however, cannot be optimally regulated by humanity due to (1) lack of complete quantitative understanding of their complex relationship, and (2) inability of humanity to optimize environmental conditions that can best control the spread of COVID-19 and its associated death rates.…”

Knowledge-based normative safety measure approach: systematic assessment of capabilities to conquer COVID-19

“…Mouratidis [ 23 ] studied this information and reported the findings of time-varying reproductive data and doubling time in the most affected regions, which support governments towards control measures. Also, [ 6 ] have analyzed information statistically in order to extract the pattern of Spatio-temporal clusters along with the duration of clusters, resulting in the positivity of countries containment policies. In the meantime, [ 26 ] estimating the break-point of the pattern on the spread of the novel COVID-19 and setting an uncertain growth rate transmission point, although it is critical to consider.…”