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

Unleashing the global potential of public health: A framework for future pandemic response

Public health emergencies, especially pandemics, need to be managed globally, and on several levels, emphasizing the importance of leadership, communication, and synchronization of measures, data, and management plans in contrast to the management of the Coronavirus-19 pandemic, which illustrated diverse strategies employed by various nations. This paper aims to review and discuss whether globalized diseases in a globalized world should be managed by globalized public health. Using a systematic literature search, followed by a non-systematic literature review, selected studies were grouped into topics, and analyzed, using content analysis to enhance the conclusive results. The results present a roadmap towards a re-envisioned framework highlighting key areas of focus: data-driven decision-making, robust technology infrastructure, global cooperation, and ongoing public health education, as part of a coordinated global response. This article reveals the weaknesses of current pandemic management systems and recommends new steps to further strengthen the management of future pandemics.

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

BACKGROUND

Coronavirus disease (COVID-19) impairs the free movement of human beings. The study aims to determine how the COVID-19 pandemic affected population mobility.

METHODS

The study obtained Google COVID-19 population mobility report and e Taiwan COVID-19 pandemic information from Our World in Data.

RESULTS

During the Alpha wave, transit decreased the most, with an average difference of >50%, followed by parks, workplaces, groceries, and pharmacies. During the Omicron wave, the average population flow in parks and transit decreased by about 20%. During the pre-existing wave, the average population visits of transit decreased by 10% at the most, followed by parks and workplaces. The peak of daily new confirmed cases per million (7-day rolling average) was 25.02, 6.39, and 0.81 for Alpha, Omicron, and the pre-existing wave, respectively. Daily new confirmed cases per million people correlated with the change in population visits of various places (all p < 0.001). The reproduction rate (7-day rolling average) correlated with the change of population visits of most places, except retail and recreation. We conclude the Alpha variant affected more individuals than Omicron and pre-existing type. Furthermore, changes in population visits in transit were most impacted. This change was consistent with daily new confirmed cases per million people and reproduction rate (7-day rolling average).

CONCLUSION

The Alpha variant affected more individuals than the Omicron and pre-existing types. Furthermore, changes in population visits in transit locations were most impacted. This change was consistent with the daily new number of confirmed cases per million people and the 7-day rolling average reproduction rate.