SciBeh-Topic-Visualization

mobility, crime, gdp, employment, restriction

Topic 32

mobility crime gdp employment restriction economic queensland park effectiveness travel confinement county environmental area australia

Impact of urban structure on COVID-19 spread
July 30, 2020 · · Original resource · preprint

The ongoing COVID-19 pandemic has created a global crisis of massive scale. Prior research indicates that human mobility is one of the key factors involved in viral spreading. Indeed, in a connected planet, rapid world-wide spread is enabled by long-distance air-, land- and sea-transportation among countries and continents, and subsequently fostered by commuting trips within densely populated cities. While early travel restrictions contribute to delayed disease spread, their utility is much reduced if the disease has a long incubation period or if there is asymptomatic transmission. Given the lack of vaccines, public health officials have mainly relied on non-pharmaceutical interventions, including social distancing measures, curfews, and stay-at-home orders. Here we study the impact of city organization on its susceptibility to disease spread, and amenability to interventions. Cities can be classified according to their mobility in a spectrum between compact-hierarchical and decentralized-sprawled. Our results show that even though hierarchical cities are more susceptible to the rapid spread of epidemics, their organization makes mobility restrictions quite effective. Conversely, sprawled cities are characterized by a much slower initial spread, but are less responsive to mobility restrictions. These findings hold globally across cities in diverse geographical locations and a broad range of sizes. Our empirical measurements are confirmed by a simulation of COVID-19 spread in urban areas through a compartmental model. These results suggest that investing resources on early monitoring and prompt ad-hoc interventions in more vulnerable cities may prove most helpful in containing and reducing the impact of present and future pandemics.
covid-19
policy
transmission
non-pharmaceutical intervention
modeling
strategy
mobility
geography
transport
travel restriction
transmission, cov-2, secondary, china, household
mobility, crime, gdp, employment, restriction
Global supply-chain effects of COVID-19 control measures

Countries have sought to stop the spread of coronavirus disease 2019 (COVID-19) by severely restricting travel and in-person commercial activities. Here, we analyse the supply-chain effects of a set of idealized lockdown scenarios, using the latest global trade modelling framework. We find that supply-chain losses that are related to initial COVID-19 lockdowns are largely dependent on the number of countries imposing restrictions and that losses are more sensitive to the duration of a lockdown than its strictness. However, a longer containment that can eradicate the disease imposes a smaller loss than shorter ones. Earlier, stricter and shorter lockdowns can minimize overall losses. A ‘go-slow’ approach to lifting restrictions may reduce overall damages if it avoids the need for further lockdowns. Regardless of the strategy, the complexity of global supply chains will magnify losses beyond the direct effects of COVID-19. Thus, pandemic control is a public good that requires collective efforts and support to lower-capacity countries.
covid-19
lockdown
modeling
economic impact
duration
restriction
market
supply chain
death, england, estimate, excess, wale
mobility, crime, gdp, employment, restriction
Crowding and the epidemic intensity of COVID-19 transmission

The COVID-19 pandemic is straining public health systems worldwide and major non-pharmaceutical interventions have been implemented to slow its spread. During the initial phase of the outbreak the spread was primarily determined by human mobility. Yet empirical evidence on the effect of key geographic factors on local epidemic spread is lacking. We analyse highly-resolved spatial variables for cities in China together with case count data in order to investigate the role of climate, urbanization, and variation in interventions across China. Here we show that the epidemic intensity of COVID-19 is strongly shaped by crowding, such that epidemics in dense cities are more spread out through time, and denser cities have larger total incidence. Observed differences in epidemic intensity are well captured by a metapopulation model of COVID-19 that explicitly accounts for spatial hierarchies. Densely-populated cities worldwide may experience more prolonged epidemics. Whilst stringent interventions can shorten the time length of these local epidemics, although these may be difficult to implement in many affected settings.
covid-19
china
transmission
epidemiology
infection rate
transmission, cov-2, secondary, china, household
mobility, crime, gdp, employment, restriction
Economic and social consequences of human mobility restrictions under COVID-19

In response to the coronavirus disease 2019 (COVID-19) pandemic, several national governments have applied lockdown restrictions to reduce the infection rate. Here we perform a massive analysis on near–real-time Italian mobility data provided by Facebook to investigate how lockdown strategies affect economic conditions of individuals and local governments. We model the change in mobility as an exogenous shock similar to a natural disaster. We identify two ways through which mobility restrictions affect Italian citizens. First, we find that the impact of lockdown is stronger in municipalities with higher fiscal capacity. Second, we find evidence of a segregation effect, since mobility contraction is stronger in municipalities in which inequality is higher and for those where individuals have lower income per capita. Our results highlight both the social costs of lockdown and a challenge of unprecedented intensity: On the one hand, the crisis is inducing a sharp reduction of fiscal revenues for both national and local governments; on the other hand, a significant fiscal effort is needed to sustain the most fragile individuals and to mitigate the increase in poverty and inequality induced by the lockdown.
covid-19
italy
lockdown
social
inequality
consequences
economic
mobility, crime, gdp, employment, restriction
political, attitude, partisan, democracy, ideology
Linking excess mortality to Google mobility data during the COVID-19 pandemic in England and Wales

Following the outbreak of COVID-19, a number of non-pharmaceutical interventions have been implemented to contain the spread of the pandemic. Despite the recent reduction in the number of infections and deaths in Europe, it is still unclear to which extent these governmental actions have contained the spread of the disease and reduced mortality. In this article, we estimate the effects of reduced human mobility on excess mortality using digital mobility data at the regional level in England and Wales. Specifically, we employ the Google COVID-19 Community Mobility Reports, which offer an approximation to the changes in mobility due to different social distancing measures. Considering that changes in mobility would require some time before having an effect on mortality, we analyse the relationship between excess mortality and lagged indicators of human mobility. We find a negative association between excess mortality and time spent at home, as well as a positive association with changes in outdoor mobility, after controlling for the time trend of the pandemic and regional differences. We estimate that almost 130,000 excess deaths have been averted as a result of the increased time spent at home. In addition to addressing a key scientific question, our results have important policy implications for future pandemics and a potential second wave of COVID-19.
covid-19
policy
non-pharmaceutical intervention
mobility
excess mortality
prevention
government response
england
transmission reduction
case decrease
google
wales
death, england, estimate, excess, wale
mobility, crime, gdp, employment, restriction
The Causal Effect of Social Distancing on the Spread of SARS-CoV-2

To what degree does social distancing have a causal effect on the spread of SARS-CoV-2? To generate causal evidence, we show that week to week changes in weather conditions provided a natural experiment that altered daily travel and movement outside the home, and thus affected social distancing in the first several weeks when Covid-19 began to spread in many U.S. counties. Using aggregated mobile phone location data and leveraging changes in social distancing driven by weekly weather conditions, we provide the first causal evidence on the effect of social distancing on the spread of SARS-CoV-2. Results show that a 1 percent increase in distance traveled leads to an 8.1 percent increase in new cases per capita in the following week, and a 1 percent increase in non-essential visits leads to a 6.9 percent increase in new cases per capita in the following week. Results are stronger in densely populated counties and close to zero in less densely populated counties.
covid-19
usa
social distancing
transmission
density
travel
cellphone data
smartphone
causality
adherence
movement
natural experiment
mobility, crime, gdp, employment, restriction
political, attitude, partisan, democracy, ideology
Do socio-economic indicators associate with COVID-2019 cases? Findings from a Philippine study

Background: A wide spectrum of indicators has been postulated to associate with Coronavirus Disease 2019 (Covid-2019) cases. Among which were demographic profile, latitude, humidity, temperature, and ozone concentration. Despite obtaining significant results, there is still a dearth of research exploring other substantial determinants of Covid-2019 cases. The Philippine government is currently challenged to address issues pertaining to poverty and substinence. Empirical evidence of these studies suggests how identification of potential indicators could aid in the formulation of targeted strategies to mitigate future health problems. In this study, seven socio-economic indicators were associated with Covid-2019 cases across 17 regions in the Philippines. This is a retrospective study utilizing readily accessible public data in the analysis. Socio-economic indicators used were poverty incidence, magnitude of poor families, substinence incidence, and magnitude of substinence poor population. In addition, the income, expenditure, and savings recorded per Philippine region were taken for the analysis. A single Philippine region was the sampling unit; hence, a total of 17 regions were assessed. Covid-2019 cases as of April 7, 2020 were considered for the analysis. Descriptive statistics, Kendall rank correlation, and stepwise regression were used to determine if the seven socio-economic indicators were associated with Covid-2019 cases. Substinence incidence and income were retained for the regression model, which explained 87.2 percent of the variance in the Covid-2019 cases (R2 = .872). The results indicated that for every 1,000 PhP increase in income, there was a decrease of 3.99 Covid-2019 cases in each Philippine region. Meanwhile, for every 1.0 percent increase in substinence incidence, there was an increase of 3.34 Covid-2019 cases in each Philippine region. High income and low substinence incidence are associated with significant reductions in Covid-2019 cases across the 17 regions of the Philippines. This provides additional knowledge to policy makers and health officials in formulating targeted strategies to regions that could potentially record high number of Covid-2019 cases in the future. Early identification of these high-risk regions would warrant prompt preventive measures. Given the seasonal and recurring nature of Covid-2019 with respect to previous outbreaks, it is essential for the Philippine government to formulate directed policies and innovate programs that would decrease substinence and increase income. Concerted multi-region efforts should be made to prepare for possible infection outbreaks in the future. Additional studies could be explored in the future to capture significant changes in the socio-economic indicators.
covid-19
disproportionate impact
modeling
strategy
geography
inequality
demographics
association
regression
death, england, estimate, excess, wale
mobility, crime, gdp, employment, restriction
The Effect of Natural Disasters on Economic Activity in US Counties: A Century of Data

More than 100 natural disasters strike the United States every year, causing extensive fatalities and damages. We construct the universe of US federally designated natural disasters from 1920 to 2010. We find that severe disasters increase out-migration rates at the county level by 1.5 percentage points and lower housing prices/rents by 2.5–5.0 percent. The migration response to milder disasters is smaller but has been increasing over time. The economic response to disasters is most consistent with falling local productivity and labor demand. Disasters that convey more information about future disaster risk increase the pace of out-migration.
big data
usa
risk
economy
impact
market
natural disaster
mobility, crime, gdp, employment, restriction
political, attitude, partisan, democracy, ideology
The effect of large-scale anti-contagion policies on the COVID-19 pandemic

Governments around the world are responding to the novel coronavirus (COVID-19) pandemic1 with unprecedented policies designed to slow the growth rate of infections. Many actions, such as closing schools and restricting populations to their homes, impose large and visible costs on society, but their benefits cannot be directly observed and are currently understood only through process-based simulations2–4. Here, we compile new data on 1,717 local, regional, and national non-pharmaceutical interventions deployed in the ongoing pandemic across localities in China, South Korea, Italy, Iran, France, and the United States (US). We then apply reduced-form econometric methods, commonly used to measure the effect of policies on economic growth5,6, to empirically evaluate the effect that these anti-contagion policies have had on the growth rate of infections. In the absence of policy actions, we estimate that early infections of COVID-19 exhibit exponential growth rates of roughly 38% per day. We find that anti-contagion policies have significantly and substantially slowed this growth. Some policies have different impacts on different populations, but we obtain consistent evidence that the policy packages now deployed are achieving large, beneficial, and measurable health outcomes. We estimate that across these six countries, interventions prevented or delayed on the order of 62 million confirmed cases, corresponding to averting roughly 530 million total infections. These findings may help inform whether or when these policies should be deployed, intensified, or lifted, and they can support decision-making in the other 180+ countries where COVID-19 has been reported
covid-19
big data
efficacy
social distancing
policy
transmission
epidemiology
case
mobility, crime, gdp, employment, restriction
political, attitude, partisan, democracy, ideology
How mobility patterns drive disease spread: A case study using public transit passenger card travel data

Outbreaks of infectious diseases present a global threat to human health and are considered a major health-care challenge. One major driver for the rapid spatial spread of diseases is human mobility. In particular, the travel patterns of individuals determine their spreading potential to a great extent. These travel behaviors can be captured and modelled using novel location-based data sources, e.g., smart travel cards, social media, etc. Previous studies have shown that individuals who cannot be characterized by their most frequently visited locations spread diseases farther and faster; however, these studies are based on GPS data and mobile call records which have position uncertainty and do not capture explicit contacts. It is unclear if the same conclusions hold for large scale real-world transport networks. In this paper, we investigate how mobility patterns impact disease spread in a large-scale public transit network of empirical data traces. In contrast to previous findings, our results reveal that individuals with mobility patterns characterized by their most frequently visited locations and who typically travel large distances pose the highest spreading risk.
data
public health
transmission
modeling
mobility
social media
infectious disease
travel
public transport
citation
gps
network, complex, graph, multiplex, structure
mobility, crime, gdp, employment, restriction