This article, based on the dataset from Google Community Mobility Reports, analyzes the level of community movement in Vietnam in general and in some localities affected by the four attacks of Covid-19.
Faced with infections caused by the coronavirus, nations around the world are incessantly optimizing their policies on fighting the Covid-19 pandemic. In the initial phases of national lockdown implementation, governments are seeking intervention measures to ensure community health in more selective and locality-based ways.
How to effectively put the pandemic under control and cut economic costs for the people and the corporate sector is among the first priorities of policymakers. Due to the belatedness in gathering financial and socioeconomic information which has to rely on conventional channels, it is difficult for policymakers to answer this question in a short time. However, the readiness of “big data” can help them in this regard with monitoring responses in the pandemic. To this effect, Big Tech, such as Google and Facebook, have provided dataset on community mobility in realtime worldwide with which the impact of pandemic prevention measures can be assessed.
In this analysis of the dataset of Google Community Mobility Reports, the selection of the time frame for analyzing is based on Directive 16 or Directive 15 [of the Government] effective nationwide or locally till the end of their validity. More precisely, they were (i) first time from March 31, 2020 to April 22, 2020; (ii) second time from July 28, 2020 to September 11, 2020; (iii) third time from January 28, 2021 to March 25, 2021; and (iv) fourth time from May 31, 2021 to July 19, 2021(1).
These data partially reflect the efficiency of lockdown/social distancing policies in localities viewed from changes in the number of people visiting public places. The dataset reveals the number of people going to venues—such as (1) grocery and pharmacy, (2) parks, (3) transit stations, (4) retail and recreation, (5) workplaces, and (6) residential—has changed in localities and in the nation. The figures on P. 15 (Figures 1, 2, 3, 4 and 5) show us the quantity of people visiting public places, such as retail and recreation, parks and transit stations, grocery and pharmacy has declined significantly. Meanwhile, the decrease in the number at workplaces was not remarkable and the number of people staying at home surged dramatically. All these tallies indicate that the restriction of community mobility during social distancing has been really effective.
In addition, the dataset also reveals that Danang was the locality where mobility was the most restricted, which also exposes the economic characteristic of this city, one that relies on tourism. When Covid-19 broke out, the tourist turnout declined sharply, cutting the mobility level of communities.
Generally speaking, community mobility in Vietnam tended to decline during pandemic resurgences and was different from one locality to another after social distancing measures were imposed. In the first Covid-19 attack, the social distancing and mobility restriction were effective nationwide, which resulted in a sharp decline in the number of people visiting public places on a national scale as well as in localities. From the second wave onward, the social distancing policy and mobility restriction have been valid in localities hardest hit by the coronavirus. For instance, during the second attack in Danang, the mobility restriction took effect in this city and the number of people visiting public places there plummeted compared with other localities. Likewise, the same situation has been reported in Hai Duong in the third wave and in HCMC in the fourth wave.
The above analyses indicate that the locality-based lockdown policy is helpful when it comes to minimizing economic adverse effects versus a nationwide lockdown. Mobility restriction substantially affects the economy. According to Deb and associates (2020), such restriction lasting for 30 days would reduce 15% of industrial production value. During the first nationwide lockdown in Vietnam, her industry and retail suffered a fall of almost 15%. However, Goldstein and associates (2021) argue that the efficiency of lockdown policies would decrease as the pandemic lingers on. That means during the initial time, lockdowns would remarkably reduce the level of infections and the related death toll. Yet this effect would diminish as time goes by. Such reduction of effectiveness is understandable because it is difficult to maintain strict compliance with restriction of community mobility due to economic burdens and social and psychological stresses. The reduced compliance with lockdown regulations also comes together with more acute infections of new virus variants.
It is a proven fact if one considers the OxCGRT (The Stringency Index from Oxford’s Covid-19 Government Response Tracker) in Figure 5, which shows that compared with the first wave of Covid-19 in Vietnam, the compliance with social distancing regulations tended to fall gradually in the second, third and fourth attacks. It is crucial to emphasize that this analysis is not to imply that lockdowns are not necessary in pandemic outbreaks, but lockdowns must be stringent and as short as possible. In addition, when lockdowns and community mobility restriction are imposed to curb infection cases, the central and local authorities need to ensure social welfare to support the disadvantaged affected by the pandemic.
The use of big data will support management agencies in monitoring and appraising policy effects in realtime, from which the relevant policies can be revised to become more efficient. As a result, the Government should come up with a digital aid package which focuses on big data support to prepare for the post-Covid-19 economic recovery, such as:
– Connection enhancement: increasing wide-bandwidth to meet higher demand for Internet services; and expanding Internet access, especially in rural areas.
– Consolidation of core database infrastructure: investing in digital solutions to store, calculate and protect big data; building big data and centers of supercomputers; and optimizing Internet of Things and sensors to collect and share non-structured data.
(*) University of Economics and Law, National Univeristy – HCMC
(1) The fourth time has not ended yet, database valid until July 21, 2021.
By Nguyen Thi Hong Van & Tran Hung Son(*)