Population Flows Drive the Spread and Intensity of COVID-19 in China
Modeling of population flows in China enables the forecasting of the temporal-spatial distribution of confirmed cases of COVID-19 and the early identification of areas at high risk
Sudden, large-scale and diffuse human migration can amplify localized outbreaks into widespread epidemics. Rapid and accurate tracking of aggregate population flows may therefore be epidemiologically informative. The authors used 11,478,484 mobile-phone-data-based counts of individuals leaving or transiting through the prefecture of Wuhan between 1 January and 24 January 2020 as they moved to 296 prefectures throughout mainland China.
First, they document the efficacy of quarantine in ceasing movement. Second, they show that the distribution of population outflow from Wuhan accurately predicts the relative frequency and geographical distribution of infections with SARS-CoV-2 until 19 February 2020, across mainland China. Third, they develop a spatio-temporal ‘risk source’ model that leverages population flow data (which operationalizes the risk that emanates from epidemic epicentres) not only to forecast the distribution of confirmed cases, but also to identify regions that have a high risk of transmission at an early stage. Fourth, they use this risk source model to statistically derive the geographical spread of COVID-19 and the growth pattern based on the population outflow from Wuhan; the model yields a benchmark trend and an index for assessing the risk of community transmission of COVID-19 over time for different locations.
This approach can be used by policy-makers in any nation with available data to make rapid and accurate risk assessments and to plan the allocation of limited resources ahead of ongoing outbreaks.
Public Disclosure of COVID-19 Cases Is More Effective than Lockdowns
South Korea’s public disclosure plan effectively protects the vulnerable while preserving economic stability during the pandemic.
South Korea’s success in battling COVID-19 is largely due to its widespread testing and contact tracing, but its key innovation is to publicly disclose detailed information on the individuals who test positive for COVID-19. This new research reveals that public disclosure measures are more effective at reducing deaths than comprehensive stay-at-home orders.
The COVID-19 outbreak was identified in South Korea on January 13, and since then South Koreans have received text messages whenever new cases were discovered in their neighborhood, as well as information and timelines of infected persons’ travel. The authors combined detailed foot-traffic data in Seoul with publicly disclosed information on the location of individuals who had tested positive. The results reveal that public disclosure can help people target their social distancing, which proves especially helpful for vulnerable populations who can more easily avoid areas with a higher rate of infection.
The authors estimate that over the next two years, the current strategy in Seoul will lead to a cumulative 925,000 cases, 17,000 deaths (10,000 for those 60 and older and 7,000 for ages 20 to 59), and economic losses that average 1.2 percent of GDP. In a model representing partial lockdown, the authors estimate the same number of cases, but deaths increase from 17,000 to 21,000 (14,000 for those 60 and older and 7,000 for ages 20 to 59) and economic losses increase from 1.2 to 1.6 percent of GDP.
Importantly, while death rates among older populations are significantly higher under lockdowns, those under 60 suffer economic losses twice as high, compared to South Korea’s current strategy.
In the absence of a vaccine, the authors conclude that targeted social distancing is much more effective in reducing the transmission of the disease, while minimizing the economic cost of social isolation. However, they also note that these benefits come with a cost: Disclosure of public information infringes upon the privacy of affected individuals. The authors anticipate the day when cost measures for privacy loss are available, after which a full cost/benefit analysis is possible.
The Shocking Supply-Side Effects of COVID-19
The negative economic shock caused by COVID-19 is similar to a supply shock that causes a reduction in aggregate demand larger than the original reduction in labor supply.
Understanding the nature of a negative economic shock is key to getting the policy prescription right. After ensuring that households have enough short-term resources, policymakers are confronted with the following conundrum: Should the aim of policy be to encourage people to spend more, that is to provide stimulus, or should policy focus purely on providing forms of social insurance?
The authors’ key insight is that the coronavirus shock is a supply shock of a special nature, as it affects different sectors unevenly. The central argument of their work is that the coronavirus shock will likely cause a reduction in aggregate demand larger than the original reduction in labor supply, something that the authors coin a “Keynesian supply shock.” Their work describes two forces that propagate the shock from those it directly affects, or those in affected (or contact-intensive) sectors, to those in less affected sectors: complementarities across sectors and incomplete markets. In the first case, when people are restricted from spending on certain goods, like restaurants and events, they do not spend the same amount on other complementary goods and services, and there is less overall spending
In the second case, the overall reduction in spending spreads to unaffected sectors because those who retain their jobs do not spend enough to prevent this occurrence (in economists’ parlance, the marginal propensity to consume of those in the unaffected sectors is less than those in affected sectors). Together, these two forces transform the original supply shock into a demand shock.
The authors’ findings pose challenges for policymakers, as a “typical” increase in government consumption may be less powerful in a pandemic shock. The reason is that government spending can only lift incomes in the unaffected sectors, not in the affected sectors, but it’s the workers in the affected sectors who have the highest propensity to consume, and they are exactly those who cannot benefit from an aggregate spending increase. On the other hand, fiscal stimulus can be desirable when combined with polices more targeted towards the workers in the affected sectors.
COVID-19 Infection Externalities: Trading Off Lives vs. Livelihoods
Individuals internalize only one quarter of the social cost of COVID-19 because the infected impose significant externalities on others.
Should we let individuals decide how much social distancing to engage in, or are there good reasons why governments should infringe upon civil liberties and order citizens to stay at home? The authors show that infectious diseases such as COVID-19 lead to significant externalities, i.e. adverse effects that individuals do not internalize when they engage in their personal cost-benefit analysis. These externalities therefore call for mandatory public health interventions.
The authors develop an epidemiological model that captures the main features of COVID-19 in the US economy. They show that individuals perceive the cost of an additional infection to be around $80k, whereas the social cost including infection externalities is more than three times higher, around $286k. This misvaluation has stark implications for how the pandemic evolves: in the absence of public health interventions, individuals act cautiously to “flatten the curve” of infections, but the disease still spreads quickly which induces a sharp recession and a slow recovery over several years. By contrast, the optimal public health intervention will contain the disease, producing a short-lived and much milder recession. This holds even if the infected and susceptible cannot be targeted independently, although the economic cost is greater.
Can Big Data Solve the Pandemic Dilemma?
Exploiting staggered adoption of contact-tracing apps in 322 Chinese cities, the author finds that cities that adopt contact-tracing apps experience a significant increase in economic activities without suffering from higher infection rates.
Pandemics such as COVID-19 present an impossible choice to policymakers between saving lives and saving livelihoods. On the one hand, population movement restrictions such as social distancing and lockdown are deemed necessary to contain the rapid spread of the disease. On the other hand, such restrictions inflict steep economic costs as normal activities are disrupted.Analyzing economic indicators and daily COVID-19 cases in China– the first country that successfully contained the outbreak – this paper suggests that big data technology may be the solution.
This study explores the staggered implementation of contact-tracing apps called “health code” in 322 Chinese cities during the COVID-19 pandemic. Using high-frequency variations in population movements and greenhouse emission across cities, the study finds that cities that adopt health code experience a significant increase in economic activities without suffering from higher infection rates. In fact, big data technology created an economic value of 0.5%-0.75% of China’s GDP during this period. The economic benefits of big data technology seem to outweigh the potential costs on privacy.
Measuring the Impact of Mobility Restrictions on Virus Transmission
There would have been 65 percent more cases of COVID-19 in the 347 Chinese cities outside Hubei province had Wuhan not been locked down on January 23.
Human mobility contributes to the transmission of infectious diseases that threaten global health. A principal response of many countries to the COVID-19 pandemic has been to impose restrictions on people’s movements. However, such policies are controversial because they have a negative economic impact, and they also limit personal freedoms. To strike the right balance, it is essential to understand the effect of lockdowns on the spread of pandemics.
In Human Mobility Restrictions and the Spread of the Novel Coronavirus (2019-nCoV) in China, Hanming Fang, Long Wang, and Yang Yang quantify the effectiveness of human mobility restrictions on efforts to control the spread of the disease and reduce health risks in Hubei province, the area of China in which the virus emerged. The researchers conclude that the Wuhan lockdown reduced inflow into the city by 77 percent, outflow by 56 percent, and within-Wuhan movement by 54 percent. They find that the lockdown significantly contributed to reduction in the total cases of infection outside of Wuhan, even with the social distancing measures later imposed by other cities.
The study estimates that there would have been 65 percent more COVID-19 cases in the 347 Chinese cities outside Hubei province, and 53 percent more in 16 Hubei province cities other than Wuhan, had Wuhan not been locked down on January 23. Imposing enhanced social distancing policies in 63 cities outside Hubei province effectively reduced the impact of population inflows from the epicenter cities in Hubei province on the spread of the virus in destination cities elsewhere.
China’s Consumption in the Three-month Post-outbreak Period
High frequency transaction data reveal offline consumption in China declined by as much as 66% and ended 16% below baseline by mid-April 2020.
We focus on the impact of COVID-19 on consumption, which accounts for over 42% of China’s GDP in the last decade. We use data on the universe of consumer spending transactions at offline merchants using bank cards and QR codes (i.e., linked to e-wallets in Alipay and WeChat pay), captured by UnionPay’s POS machines and QR scanners that cover 30% of China’s total offline consumer spending. While E-Commerce has experienced accelerating growth in recent years, offline consumption still constitutes 76% of China’s overall retail consumption in 2019. We collect total offline consumption for 214 Chinese cities on a daily basis from January 1, 2020 to April 14, 2020 and conduct difference-in-differences analyses (using the corresponding period in 2019 as the benchmark period).
During the twelve-week period, offline consumption fell by 6.6% during the immediate week after Wuhan lockdown, before reaching the largest decline (59%-66%) in the next three weeks after the outbreak. Notably, the consumption change became less negative starting from the fifth week, when the epidemic curve showed signs of flattening and mobility restrictions had yet to be relaxed. By the end of March, consumption had fully rebounded. However, consumption fell again, ending at 16% below the baseline level in mid-April. This retreat is responsive to the one-day lagged number of new infections (including asymptomatic cases), echoing the rising concern over a potential second wave of infections. The recovery is evident for both the goods and services consumption types yet spending on dining & entertainment as well as on travel-related show much weaker rebounds than spending on discretionary items and durable goods.
Social Connections Increase Compliance with Mobility Restrictions
Counties with one-standard-deviation more social connections to China or Italy have a 50% higher compliance with mobility restrictions like “shelter-in-place” policies.
Mobility restrictions play a crucial role in mitigating the spread of pandemics. As local governments may be limited in their capacity to monitor and enforce mobility restrictions, households must voluntarily comply with social distancing rules for them to be effective. We study how social connectedness, measured by Facebook connections around the world, affects the efficacy of mobility restrictions.
Our empirical results suggest the flow of information through social connections is an economically significant driver of social distancing. A one-standard-deviation increase in social connections with China and Italy – the first countries with major outbreaks of the virus – increases the effectiveness of mobility restrictions by around 50%. For our analysis, we use Facebook’s county-level Social Connectedness Index and geolocation data from mobile devices from SafeGraph, which aggregates and provides anonymous user mobility data for the purpose of COVID-19 research. The effect of social connections exists regardless of political orientation but are stronger for Republican counties, which on average appear to comply less with mobility restrictions. It is also stronger for counties with older and less educated populations. Groups at higher risk from COVID-19 comply with restrictions better and are less affected by social connections. Our findings are consistent with social networks contributing to households’ information acquisition about the pandemic.
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