In response to the COVID-19 epidemic, global governments have implemented substantial physical isolation measures to prevent and regulate viral transmission. Outside of Europe and the United States, there is a severe lack of quantitative, spatially disaggregated data on population-scale changes in activity as a result of these initiatives.
Public health organizations are frequently forced to make control plan decisions based on social behavior, exposure patterns, and sickness outcomes due to limited data for individual regions. The Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB), a next-generation space-borne low-light imager capable of capturing changes in human activity, can be used to follow COVID-19 reactions.
The Middle East faced distinct obstacles with the advent of the COVID-19 pandemic. The outbreak response was complicated by the fact that several nations in the region had political divides, a lack of openness, and underfunded public health institutions. Ramadan, which began in April, has the potential to be a “super-spreader” event for COVID-19, encompassing a diverse range of countries.
The clash between traditional religious traditions built on community solidarity and shared meals was visible, with public health advice to commercialize gatherings, close public spaces, or cancel iftars (a shared meal). In reaction to economic and religious pressure, some governments strengthened curfews and physical separation orders, while others reduced the rules and lightened regulations.
In terms of speed and thoroughness, as well as public reaction, efforts to reduce COVID-19 transmission across the region varied substantially. The Coronet database, HIT-COVID, and Oxford-19 Government all track COVID-19 government control measures, but there is no comparable data on how people’s activity patterns changed as a result of physical distancing measures.
Mobility and NTL patterns are strongly synchronized in the majority of cities and nations; nevertheless, there is substantial heterogeneity in these patterns within and across countries. Cities in Jordan, Oman, and the UAE have the most consistent synchronization, according to the Euclidean distance analysis. Tarsus has virtually little in common with the other four, however its Black Marble and mobility time series are very similar. Tel Aviv, Israel, has a much larger dataset than Haifa, Israel. Iraq, Libya, and Yemen have the lowest national synchronization, and all have recently been embroiled in combat.