COVID-19 attack rate increases with city size

Stier, Andrew J.; Berman, Marc G.; Bettencourt, Luís M. A.

doi:10.1101/2020.03.22.20041004

Cited by 159 publications

(150 citation statements)

References 11 publications

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“…Moreover, note that the result in (A) appears to contrast with the results in Steir et al (5), who showed higher case growth rates with city size. The discrepancy can be explained by the different units employed in each study (case growth rate in (5) vs. fraction of total population infected at some point during a fixed time interval (this study)) and how ℛ was estimated in (5) (found to be city-size dependent) vs. assumed invariant in the present study. Case growth rate (number of new cases on day tnumber of new cases on day t-1 / number of new cases on day t-1) was found to increase with community size in the present study (not shown).…”

Section: Resultscontrasting

confidence: 83%

“…Although not explored here, numerical simulations for parameter values associated with points above the dashed line in (A) were influenced by 'herd immunity', i.e., when the proportion of the population in the susceptible class, S/N<(1.0-1.0/ℛ) (19). Moreover, note that the result in (A) appears to contrast with the results in Steir et al (5), who showed higher case growth rates with city size. The discrepancy can be explained by the different units employed in each study (case growth rate in (5) vs. fraction of total population infected at some point during a fixed time interval (this study)) and how ℛ was estimated in (5) (found to be city-size dependent) vs. assumed invariant in the present study.…”

Section: Resultsmentioning

confidence: 84%

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Importance of suppression and mitigation measures in managing COVID-19 outbreaks

Hochberg

2020

Preprint

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I employ a simple mathematical model of an epidemic process to evaluate how three basic quantities: the reproduction number (ℛ), the number of infectious individuals (I), and total community size (N) affect strategies to control COVID-19. Numerical simulations show that strict suppression measures at the beginning of an epidemic can create low infectious numbers, which thereafter can be managed by mitigation measures over longer periods to flatten the epidemic curve. The stronger the suppression measure, the faster it achieves the low numbers of infections that are conducive to subsequent management. Our results on short-term strategies point to either a two-step control strategy, following failed mitigation, that begins with suppression of the reproduction number, ℛC, below 1.0, followed by renewed mitigation measures that manage the epidemic by maintaining ℛC at approximately 1.0, or should suppression not be feasible, the progressive lowering of ℛC below 1.0. The full sequence of measures that we are observing in a number of countries, and likely to see in the longer term, can be symbolically represented as: ℛ0 à ℛC<ℛ0 à ℛC<<1.0 à ℛC≈1.0 à ℛC<1.0. We discuss the predictions of this analysis and how it fits into longer-term sequences of measures.

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Section: Resultscontrasting

confidence: 83%

Section: Resultsmentioning

confidence: 84%

Importance of suppression and mitigation measures in managing COVID-19 outbreaks

Hochberg

2020

Preprint

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“…To characterize the spread of COVID-19 in these areas, we assume an exponential growth of cases (valid for this novel virus during the time period we consider since no one yet had immunity) and characterize the growth exponent. In particular, following the method of [18], we subtract deaths from cases in each locality and find the slope of the log-log plot of cases as a function of time for each resulting time series of active COVID-19 cases. As noted there, estimating the growth rate of epidemics accurately is difficult, but we are only concerned with comparing growth rates among localities rather than obtaining precise growth exponent estimates.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 Growth Rate Decreases with Social Capital

Varshney

Socher

2020

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Background Social capital has been associated with many public health variables including mortality, obesity, diabetes, and sexually-transmitted disease rates. However, the relationship of social capital to the spread of infectious disease like COVID-19 is lacking. The COVID-19 pandemic presents an unprecedented threat to global health and economy, for which control strategies have relied on aggressive social distancing. However, an understanding of how social capital is related to changes in human mobility patterns for adherence to social distancing is lacking. Objective This study examines the association between state-and county-level social capital indices and community health indices in the United States, and the growth rate of COVID-19 cases. It also examines changes in human mobility. Methods Using publicly available state-and county-specific time series data for COVID-19 cases from March 13 to March 31, we used exponential fits to determine growth rate. We obtained publicly available mobility change data, originally measured from GPS-enabled mobile devices. The design was then stateand county-level correlation analysis with social capital and community health indices from the Social Capital Project (United States Senate). Results In bivariate linear correlation analyses, we find social capital and community health indices were negatively associated with COVID-19 growth rates at both the state and county levels. The correlation was strongest at the county level for the community health index: a one-unit increase in the county community health index was associated with a decrease in the COVID-19 growth rate exponent by 0.045. In further bivariate correlation analyses, we find that social capital indices were negatively associated with retail/recreation movement and positively associated with residential movement. That is, an increase in social capital is correlated with slower COVID-19 infection spread and more adherence to social distancing protocols. Conclusion Our results indicate the potential benefit of incorporating social capital concepts in planning policies to control the spread of COVID-19, e.g. different social distancing requirements in different communities. The results also indicate a need for further research into this potentially causal relationship, including examining interventions to increase social capital, community health, and institutional health.

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“…We selected this variable as cities are more prone to early disease diffusion than rural areas due to higher concentration of interaction and movement in urban areas (42). COVID-19 has been preliminary found to diffuse faster in more populous urban areas in the United States (64). Settlement variable 2 is population density, defined as the population per square kilometre across a national territory.…”

Section: Datamentioning

confidence: 99%

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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COVID-19 attack rate increases with city size

Cited by 159 publications

References 11 publications

Importance of suppression and mitigation measures in managing COVID-19 outbreaks

Importance of suppression and mitigation measures in managing COVID-19 outbreaks

COVID-19 Growth Rate Decreases with Social Capital

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

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