Health policy response to mobility during the pandemic: Evaluating the effectiveness using location‐based services big data

Abstract

As an emergent health policy response, the population mobility restriction policy was implemented to cope with the unprecedented pandemic that outbroke in early 2020, but its effectiveness showed vast disparities even within a single country. Using multisource data from Baidu mobility big data and the statistics of novel coronavirus disease in China, mobility restrictions (including restrictions on inflow-mobility, outflow-mobility, and intra-city mobility) were examined.It was found that the mobility restriction had contained the development of pandemic, but such effect would gradually recede over time. Moreover, there existed region-specific policy effectiveness. Specifically, outflow-mobility restrictions were ineffective in reducing death cases in population influx areas, and restrictions on inflow-mobility (or intra-city mobility) were ineffective in reducing confirmed cases (or death cases) in population outflow areas. It was concluded that the mobility restriction policy can be effective in epidemic prevention and control in spatial-temporal pattern.However, there was a remarkable disparity in policy effectiveness between different regions with different population mobility patterns.

How Did the COVID-19 Pandemic Affect Population Mobility in Taiwan?

“…COVID-19 has indeed caused many countries to take urgent public health measures, and limiting people’s population mobility is the most common and possible and effective mitigation strategy [ 15 , 16 , 17 , 18 ]. However, there may be some adverse social and economic consequences for the most vulnerable groups [ 17 ].…”

“…However, there may be some adverse social and economic consequences for the most vulnerable groups [ 17 ]. However, there are also studies that suggest that there are significant differences in the effectiveness of policies between different regions with different population mobility patterns [ 18 ]. For example, outflow mobility restrictions were ineffective in reducing death cases in population influx areas, and restrictions on inflow mobility (or intra-city mobility) were ineffective in reducing confirmed cases (or death cases) in population outflow areas [ 18 ].…”

“…The first wave is mainly the pre-existing variant, the second is the Alpha variant, and the third wave is the Omicron variant [ 14 ]. There is a strong correlation between the COVID-19 pandemic and population mobility [ 15 , 16 , 17 , 18 ]. Some may be in isolation due to the fear of patients in self-defense, or due to the government’s public health policy to prevent the spread of large-scale infections.…”