Subway Ridership, Crowding, or Population Density: Determinants of COVID-19 Infection Rates in New York City

Hamidi, Shima; Hamidi, Iman

doi:10.1016/j.amepre.2020.11.016

Cited by 61 publications

(51 citation statements)

References 20 publications

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“…We select variables that, according to the literature, may affect the spread of the covid-19 virus. These regard demographics [11] , socio-economic factors [12] , [13] , [14] , [15] , [16] , commuting [17] , [18] , [19] , [20] and pollution/health [21] , [22] , [23] . All the variables are pre-determined with respect to the occurrence of the covid-19 pandemic.…”

Section: Methodsmentioning

confidence: 99%

Covid-19: Regional policies and local infection risk: Evidence from Italy with a modelling study

Guaitoli

Pancrazi

2021

The Lancet Regional Health - Europe

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Background : Policy-makers have attempted to mitigate the spread of covid-19 with national and local non-pharmaceutical interventions. Moreover, evidence suggests that some areas are more exposed than others to contagion risk due to heterogeneous local characteristics. We study whether Italy’s regional policies, introduced on 4th November 2020, have effectively tackled the local infection risk arising from such heterogeneity. Methods : Italy consists of 19 regions (and 2 autonomous provinces), further divided into 107 provinces. We collect 35 province-specific pre-covid variables related to demographics, geography, economic activity, and mobility. First, we test whether their within-region variation explains the covid-19 incidence during the Italian second wave. Using a LASSO algorithm, we isolate variables with high explanatory power. Then, we test if their explanatory power disappears after the introduction of the regional-level policies. Findings : The within-region variation of seven pre-covid characteristics is statistically significant (F-test p-value ) and explains 19% of the province-level variation of covid-19 incidence, on top of region-specific factors, before regional policies were introduced. Its explanatory power declines to 7% after the introduction of regional policies, but is still significant (p-value ), even in regions placed under stricter policies (p-value ). Interpretation : Even within the same region, Italy’s provinces differ in exposure to covid-19 infection risk due to local characteristics. Regional policies did not eliminate these differences, but may have dampened them. Our evidence can be relevant for policy-makers who need to design non-pharmaceutical interventions. It also provides a methodological suggestion for researchers who attempt to estimate their causal effects. Funding : None.

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Section: Methodsmentioning

confidence: 99%

Covid-19: Regional policies and local infection risk: Evidence from Italy with a modelling study

Guaitoli

Pancrazi

2021

The Lancet Regional Health - Europe

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“…The risk of infection was considered higher for trips with public transport and users were more negative towards public transport than towards other modes during COVID-19 ( Shamshiripour et al, 2020 ). Although, residents often considered public transport a high-risk travel mode in terms of virus transmission, there is research indicating that public transport ridership was not associated with COVID-19 infection ( Hamidi and Hamidi, 2021 ) and that car driving might have spread the disease more than public transport possibly because car drivers traveled more often, more freely, and longer distances during the pandemic ( Furth, 2020 , Levy, 2020 ). Residents’ perceptions towards public transport improved in areas where restrictions were lifted and life slowly returned to normal ( Beck and Hensher, 2020 ).…”

Section: Cities and Quality Of Life During Covid-19: A Reviewmentioning

confidence: 99%

How COVID-19 reshaped quality of life in cities: A synthesis and implications for urban planning

Mouratidis

2021

Land Use Policy

115

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This paper synthesizes knowledge on how the Coronavirus disease (COVID-19) pandemic reshaped the relationship between cities and quality of life. The possible impacts of cities on seven domains of quality of life – travel, leisure, work, social relationships, residential well-being, emotional responses, and health – during COVID-19 are reviewed. Findings indicate that the role of transport and land use, urban nature, public space, facilities and services, housing, and information and communications technology (ICT) in quality of life in cities was transformed during COVID-19. Access to healthcare facilities and services and local amenities; opportunities for walking and cycling; COVID-19-secure public transport; access to a car; urban blue or green space and access to nearby nature; easy access to open public space; living in a dwelling of sufficient size and quality; private or communal outdoor areas; and ICT infrastructure and systems possibly helped to mitigate the negative impacts of COVID-19 on quality of life in cities. Implications for urban planning and policy arise from the COVID-19 crisis, shedding light on ways to address inequities, support vulnerable groups, and improve quality of life in cities in times of pandemics but also under normal circumstances.

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“…In New York City, 24,841 COVID- 19 ). These findings are likely a reflection of a combination of factors including urban density, hospital bed and equipment shortages, racial/ethnic and socioeconomic disparities, local policymaker response, and New York City's status as a major center for global trade and tourism, and an early epicenter of the pandemic in the USA [11][12][13]. Further, state-and city-level analysis demonstrated that the economic burden of COVID-19 was predominantly concentrated in older age groups, consistent with prior literature showing the largest increase in mortality risk in patients over 50, and especially, over 60 years of age [14].…”

Section: Resultsmentioning

confidence: 99%

Economic burden of lives lost due to COVID-19 in New York State

et al. 2021

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Aim: To examine the economic impact of lives lost due to the COVID-19 pandemic across New York State. Materials & methods: Death counts by age range and period life expectancy were extracted from the NYS Department of Health, NYC Department of Health and Mental Hygiene, and Social Security Administration website. Years of potential life lost and value of statistical life (VSL) were calculated. Results: The average years of potential life lost per person was 12.72 and 15.13, and the VSL was US$119.62 and 90.45 billion, in NYS and NYC, respectively. VSL was greatest in Queens and Brooklyn, followed by the Bronx, Manhattan and Staten Island. Conclusion: New York City, specifically Queens and Brooklyn, bore the greatest economic burden of lives lost across the state.

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Subway Ridership, Crowding, or Population Density: Determinants of COVID-19 Infection Rates in New York City

Cited by 61 publications

References 20 publications

Covid-19: Regional policies and local infection risk: Evidence from Italy with a modelling study

Covid-19: Regional policies and local infection risk: Evidence from Italy with a modelling study

How COVID-19 reshaped quality of life in cities: A synthesis and implications for urban planning

Economic burden of lives lost due to COVID-19 in New York State

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