Epidemic versus economic performances of the COVID-19 lockdown: A big data driven analysis

Abstract

Lockdown measures have been a "panacea" for pandemic control but also a violent "poison" for economies. Lockdown policies strongly restrict human mobility but mobility reduce does harm to economics. Governments meet a thorny problem in balancing the pros and cons of lockdown policies, but lack comprehensive and quantified guides. Based on millions of financial transaction records, and billions of mobility data, we tracked spatio-temporal business networks and human daily mobility, then proposed a high-resolution two-sided framework to assess the epidemiological performance and economic damage of different lockdown policies. We found that the pandemic duration under the strictest lockdown is less about two months than that under the lightest lockdown, which makes the strictest lockdown characterize both epidemiologically and economically efficient. Moreover, based on the two-sided model, we explored the spatial lockdown strategy. We argue that cutting off intercity commuting is significant in both epidemiological and economical aspects, and finally helped governments figure out the Pareto optimal solution set of lockdown strategy.

Keywords: COVID-19; Economic damage; Epidemiological performance; Metropolitan lockdown.

Fig. 1

Mechanism of the two-sided metropolitan lockdown modeling for analyzing epidemiological performance and economic damage. The colors of the arrows vary according to different data sources and methods. The orange panes illustrate the data sources and how the data were utilized in this research. The grey panes show the specific impacts on each component under the COVID-19 lockdown policy. The green panes represent the scenarios we set to give a comprehensive analysis of different situations. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Framework for Tracking propagation of the economic impact of lockdown (Zhang et al., 2021).

Fig. 3

Metropolitan activity characteristics under different lockdown policies before April 7th 2020. a. Classification of lockdown pattern of the 48 global major metropolitan areas. The authoritative information on their lockdown policies is from the official government websites. The metropolitan areas in red are under hard lockdown. Those in yellow are under medium lockdown, and those in green are under soft lockdown. The metropolitan areas whose names are highlighted by a dashed box represent no data on their mobility changes. b. Mobility changes of different travel purposes and under different lockdown policies. The retail & recreation line represents mobility trends for places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters. Groceries line represents mobility trends for places like grocery markets, food warehouses, farmers markets, specialty food shops, drug stores, and pharmacies. The Parks line represents mobility trends for places like national parks, public beaches, marinas, dog parks, plazas, and public gardens. The transit stations line represents mobility trends for places like public transport hubs such as subway, bus, and train stations. Workplaces line represents changes in mobility trends for places of work. Furthermore, the Residential line represents changes in mobility trends for places of residence. The transparent squares represent the confidence intervals. The blue and orange light lines represent the time when the retail and groceries mobilities reduce. The dotted lines represent the average decreased values. The transparent boxes represent the confidence intervals. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Mobility intensity and epidemiological performances of different lockdown policies. a. Mobility intensity in the center area of the Greater Tokyo Area under the different lockdown policies. The dark color represents the high mobility intensity, and the light represents low intensity. The number in the legend represents the percentage of people who have ever stayed at (or passed by) a specific cell. For example, when one cell's color is the darkest one, that means more than 21% of the total population have ever stayed at (or passed by) this cell during the lockdown period. If the value is less than 0.1%, we will not show the cell's color anymore. The blue circle represents the Greater Tokyo Area center area where also has a high resident population density. The three yellow circles represent the main commuting channel areas from the surrounding cities to the Tokyo center. It is clear that with the increase of the strengthening of lockdown policy, the mobility in the center area of the Greater Tokyo Area trends to be sparser. b. Epidemiological performances of different lockdown policies. Different colors represent different lockdown policies or baselines. The ending color point of each curve represents the pandemic ending time under the lockdown policy when the TCN does not increase anymore. The value in the bracket and nearby the ending color point represents the ending time and TCN of each policy. For example, the value of the medium lockdown (87, 8.68) means the pandemic duration is 87 days, and TCN is 8.68 thousand people. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Economic damage caused by fixed schedule lockdown and dynamic adjusting lockdown under different intensity level policies. a. The monthly value-added losses in different economic regions. Based on the real case of the Tokyo lockdown, we simulated the economic damage caused by a one-month lockdown (fixed schedule) under different intensity level policies. We took the value-added loss as the measurement of economic damage. b. The monthly value-added losses in 11 different industries under one-month lockdown. Black boxes represent the confidence interval of value-added loss values. c. The annual value-added losses under dynamic adjusting lockdown policies. The ending color point of each curve represents the pandemic ending time under the lockdown policy when the TCN does not increase anymore. The color points with a black frame represent the ending time under the risky decision to lift the lockdown policy as early as the TCN increase rate is less than 100 per day. The values nearby the points show the corresponding values of annual value-added losses.

Fig. 6

Epidemiological performance and economic damage of different lockdown policy mixes. a. The exhaustive examples of the policy mix. Each point represents a policy mix plan, and the data label of the point shows the description of the policy mix. S represents soft lockdown, M is for medium lockdown, and H is for hard lockdown. The first letter represents the policy level in Tokyo city, the second is for Chiba prefecture, the third is for Saitama prefecture, and the last is for Kanagawa prefecture. For example, H-M-S-S means Tokyo city implements hard lockdown, Chiba prefecture implements medium lockdown, Saitama and Kanagawa implement soft lockdown. The orange boxes show the confidence intervals for both epidemiological performance and economic damage. The red dotted line connects the Pareto optimal policy mix solutions. The maps show the implementations of the optimal policy mixes. As the same, red is for hard lockdown, orange is for medium lockdown, and green is for soft lockdown. b. Epidemiological performances of different lockdown policies in the Pareto optimal solution set. Different colors represent different lockdown policies. c. The annual value-added losses under dynamic adjusting lockdown policies. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 7

Ground-truth Study of the Proposed Model. We validate our epidemic simulation result with the ground-truth accumulated case number from January 24th to May 8th, 2020, in the Greater Tokyo area. There is a soft lockdown policy in Greater Tokyo Metropolis before April 8th, when the Japanese government announced the state of emergency (orange points). After April 8th, the policy came to medium lockdown (blue points). Thus, we conduct the simulation corresponding to reality. The result reaches high accuracy with a 5.67% relative mean deviation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)