Understanding the linkage between the lockdown dynamics and COVID-19 death incidents is highly important for balancing between health, welfare, and economy. It is currently unknown which aspects of the lockdown (e.g., duration, strictness, timing from onset of death cases) affect mortality rates. These predictions were based on a neural-network model, in which the connection between mobility data and infection rates is hard to interpret and thus, practically, cannot be converted into tangible measures for the arms race against the disease. Soures et al (preprint: 2020) used data collected from navigation applications on mobile cellphones together with past infection rates to predict future infection rates. While this model may be useful when governmental decisions are made, it does not reflect whether social distancing has been implemented de facto (preprint: Kohanovski et al, 2020). This score was used to predict future infections depending on the intervention level. For example, Hu et al (preprint: 2020) suggested a score that takes into account various governmental interventions in the United States. How can social distancing be quantified? One could measure governmental regulations such as the permitted walking distance from the residence, limitations on mass gatherings, school closures, and whether people were allowed to attend their workplaces. In contrast, several countries, such as Sweden and Japan, advocated social distancing but did not enforce a lockdown as a means of coronavirus spread prevention. Israel, for instance, reached the strict lockdown on March 19 with a relatively low number of 648 confirmed cases and no deaths to that day. In other countries, lockdown policies were embraced at earlier stages in attempt to prevent severe outbreaks. For example, Italy enforced a severe, nationwide, lockdown on March 10, when over 35,000 confirmed cases and almost 3,000 deaths had already been recorded. Governments that used lockdown to enforce social distancing varied in their policy, timing, and duration, in particular relative to the mortality rate in their country. However, the rapid person-to-person transmission rate of the virus (Chan et al, 2020 Li et al, 2020) required that more severe measures be taken to plummet infection frequencies. At first, many governments attempted to minimize exposure to the virus by limiting cross-border arrivals. In 2020, the coronavirus pandemic has rapidly spread around the globe, threatening health and economical systems.

As a direct consequence, the socioeconomic damage of a strict lockdown could have been less severe. ImpactĬountries that enforced a very strict lockdown could have obtained similar mortality figures with less stringent mobility restrictions as long as social distancing is initiated as early as possible after the first incidents are recorded. This is in contrast to other parameters such as the lockdown strictness that had negligible impact on mortality.

Our analysis suggests that the time at which social distancing was initiated had a critical and long-term effect: a delay of 7.49 days in lockdown commencement is associated with a doubling of the expected number of deaths. We crossed the different characteristics with the observed mortality rate of each country.

We analyzed mobility volume obtained from cellular usage of Apple users from many countries around the world to quantify country-specific lockdown characteristics, such as, social distancing start time, lockdown timing, lockdown strictness, lockdown duration, and lockdown release rate. We wished to understand the impact of mobility restriction on mortality rate, by comparing mobility and mortality data across countries around the world. These mobility restrictions included, for example, closure of non-essential businesses and prevention of public gatherings and led to serious socioeconomic consequences. In order to curb the spread of the COVID-19 pandemic, governments around the world have enforced mobility restrictions on their citizens.

Covid lockdown