Demographic Determinants of Testing Incidence and COVID-19 Infections in New York City Neighborhoods

Borjas, George J.

doi:10.3386/w26952

Cited by 147 publications

(170 citation statements)

References 2 publications

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“…We test this using four population characteristics of individual countries: Human Development Index (HDI), population aged over 65, mean household size and national population size. These variables have been selected based on recent studies that found them significant in explaining the COVID-19 outbreak at the early stages of its spread (8,29,46).…”

Section: Different Underlying Processes Characterise Types Of Spatialmentioning

confidence: 99%

“…Affluent, healthy and educated populations (HDI) are more likely to be highly mobile. Although larger household sizes and national populations have also been shown to increase COVID-19 cases, these are not clear-cut relationships (8). Older populations or populations with higher mortality rates are more likely to get tested than younger populations that may be asymptomatic (46,65).…”

Section: Datamentioning

confidence: 99%

“…This has played out on an international scale, with early outbreaks beyond China in highly globalised countries such as Japan and Singapore, and on a national scale with highly globalised subnational regions more impacted than others. This is evidenced by a large number of early cases in countries' densest, and often most affluent, regions-Lombardia (Italy) (7), New York (the United States) (8), Madrid (Spain) (9), and Tehran (Iran) (10), which all by far outnumbered cases in other regions within their respective countries. The geographical concentration of the previous outbreaks in particular cities (11) suggests that connectivity at the urban scale also plays an important role in COVID-19 diffusion.…”

Section: Introductionmentioning

confidence: 99%

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The Socio-Spatial Determinants of COVID-19 Diffusion: The Impact of Globalisation, Settlement Characteristics and Population

Sigler

Mahmuda

Kimpton

et al. 2020

Preprint

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Background: COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of the COVID-19 transmission, there remains a gap in knowledge regarding the drivers of geographical diffusion between places. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of COVID-19 diffusion over a six-week period in March and April 2020. Results: The quantile regression model suggest that globalisation and settlement population characteristics related to high human mobility predict disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total confirmed cases per million whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total confirmed cases per million. Conclusions: The analysis confirms that globalisation, settlement and population characteristics lead to greater disease diffusion, and primarily variables tied to high human mobility. These outcomes serve to inform policies around ‘flattening the curve’, particularly as they related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. Epidemiological strategies must be tailored to suit the range of human mobility patterns, as well as the variety of settlement and population characteristics.

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Section: Different Underlying Processes Characterise Types Of Spatialmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Socio-Spatial Determinants of COVID-19 Diffusion: The Impact of Globalisation, Settlement Characteristics and Population

Sigler

Mahmuda

Kimpton

et al. 2020

Preprint

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“…For example, incidence is directly affected by testing rate, so to assess whether increasing incidence is evidence of a surge in infections one must check the corresponding increase in testing. Test positivity rate seems to be a useful derived measure, tending to decrease as testing practices change to include more mild or moderate cases and tending to increase during a surge in infections [3]. Similarly, death rate per case, if higher, could indicate an overwhelmed hospital system or older demographic, or more likely could simply be an indication that local testing practices are only capturing severe, not moderate or mild cases.…”

Section: Introductionmentioning

confidence: 99%

COVID-Track: World and USA SARS-COV-2 Testing and COVID-19 Tracking

Zohner

Morris

2020

Preprint

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Background The COVID-19 pandemic has caused major health and socio-economic disruptions worldwide. Accurate investigation of emerging data is crucial to inform policy makers as they construct viral mitigation strategies. Complications such as variable testing rates and time lags in counting cases, hospitalizations and deaths make it challenging to accurately track and identify true infectious surges from available data, and requires a multi-modal approach that simultaneously considers testing, incidence, hospitalizations, and deaths. Although many websites and applications report a subset of these data, none of them provide graphical displays capable of comparing different states or countries on all these measures as well as various useful quantities derived from them. Here we introduce a freely available graphical application that allows the user to simultaneously assess time trends in these measures and compare various states or countries, equipping them with a tool to investigate the potential effects of the different mitigation strategies and timelines used by various jurisdictions. Findings COVID-TRACK is a Python based web-application that provides a platform for tracking testing, incidence, hospitalizations, and deaths related to COVID-19 along with various derived quantities. Our application makes the comparison across states or countries in the world easy to explore, with useful transformation options including per capita, log scale, and/or moving averages. We illustrate its use by assessing various viral trends in the USA and Europe. Conclusion The COVID-TRACK web-application is a user-friendly analytical tool to compare data and trends related to the COVID-19 pandemic across areas in the United States and worldwide. Our tracking tool provides a unique platform where trends can be monitored across geographical areas in the coming months to watch how the pandemic waxes and wanes throughout the summer and into a potential second wave in the fall.

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“…However, this study did not consider the other factors that are reported to in uence the spread of virus related to other weather parameters, epidemiology, and demography (Tian et a. 2020;Borjas 2020;Zhong et al 2020). There can be scores of unknown factors and processes that govern the pandemic's spread but are yet to be researched out due to the lack of credible data (Batista et al 2020;Peng et al 2020;Tang et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Coronavirus Pandemic vs. Temperature in the context of Indian Subcontinent – A preliminary statistical analysis

Meraj¹,

Farooq²,

Singh

et al. 2020

Preprint

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The novel coronavirus (COVID-19) has unleashed havoc across different countries and was declared a pandemic by the World Health Organization. Since certain evidences indicate a direct relationship of various viruses with the weather (temperature in particular), the same is being speculated about COVID-19; however, it is still under investigation as the pandemic is advancing the world over. In this study, we tried to analyze the spread of COVID-19 in the Indian sub-continent with respect to the local temperature regimes from 9 March, 2020, to 27 May, 2020. To establish the relation between COVID-19 and temperature in India, three different eco-geographical regions having significant temperature differences were taken into consideration for the analysis. We observed that except Maharashtra, Rajasthan and Kashmir showed a significantly positive correlation between the number of COVID-19 cases and the temperature during the period of study. The evidences based on the results presented in this research lead us to believe that the increasing temperature is beneficial to the COVID-19 spread, and the cases are going to rise further with the increasing temperature over India. We, therefore, conclude that the existing data, though limited, suggests that the spread of COVID-19 in India is not explained by the variation of temperature alone and is most likely driven by a host of other factors related to epidemiology, socio-economics and other climatic factors. Based on the results, it is suggested that temperature should not be considered as a yardstick for planning intervention strategies for controlling the COVID-19 pandemic.

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Demographic Determinants of Testing Incidence and COVID-19 Infections in New York City Neighborhoods

Cited by 147 publications

References 2 publications

The Socio-Spatial Determinants of COVID-19 Diffusion: The Impact of Globalisation, Settlement Characteristics and Population

The Socio-Spatial Determinants of COVID-19 Diffusion: The Impact of Globalisation, Settlement Characteristics and Population

COVID-Track: World and USA SARS-COV-2 Testing and COVID-19 Tracking

Coronavirus Pandemic vs. Temperature in the context of Indian Subcontinent – A preliminary statistical analysis

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