Population density and basic reproductive number of COVID-19 across United States counties
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Abstract
The basic reproductive number (R 0 ) is a function of contact rates among individuals, transmission probability, and duration of infectiousness. We sought to determine the association between population density and R 0 of SARS-CoV-2 across U.S. counties. We conducted a cross-sectional analysis using linear mixed models with random intercept and fixed slopes to assess the association of population density and R 0 , and controlled for state-level effects using random intercepts. We also assessed whether the association was differential across county-level main mode of transportation percentage as a proxy for transportation accessibility, and adjusted for median household income. The median R 0 among the United States counties was 1.66 (IQR: 1.35–2.11). A population density threshold of 22 people/km 2 was needed to sustain an outbreak. Counties with greater population density have greater rates of transmission of SARS-CoV-2, likely due to increased contact rates in areas with greater density. An increase in one unit of log population density increased R 0 by 0.16 (95% CI: 0.13 to 0.19). This association remained when adjusted for main mode of transportation and household income. The effect of population density on R 0 was not modified by transportation mode. Our findings suggest that dense areas increase contact rates necessary for disease transmission. SARS-CoV-2 R 0 estimates need to consider this geographic variability for proper planning and resource allocation, particularly as epidemics newly emerge and old outbreaks resurge.
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SciScore for 10.1101/2020.06.12.20130021: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources All analyses was conducted in R version 4.0.0.24 The figure and removal of adjacent counties in the sensitivity analyses were done with ArcGIS. ArcGISsuggested: (ArcGIS for Desktop Basic, RRID:SCR_011081)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has a number of limitations. While we demonstrate that population density is associated with R0, we estimated R0 based on the number of reported cases; therefore, the incidence of COVID-19 across US counties may …
SciScore for 10.1101/2020.06.12.20130021: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources All analyses was conducted in R version 4.0.0.24 The figure and removal of adjacent counties in the sensitivity analyses were done with ArcGIS. ArcGISsuggested: (ArcGIS for Desktop Basic, RRID:SCR_011081)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has a number of limitations. While we demonstrate that population density is associated with R0, we estimated R0 based on the number of reported cases; therefore, the incidence of COVID-19 across US counties may be underestimated at varying rates due to differential testing. Testing data at the county-level currently do not exist, and we were unable to adjust for the number of tests performed. To mitigate this limitation, we included a random intercept term to adjust for state-level effects, and thus differential testing across states were accounted by our model. Differential testing by local governments within states are less likely to strongly impact our findings, as most funding and budgets for COVID-19 is distributed at the state-level.31,32 We also conducted a sensitivity analysis using death data which demonstrates the robustness of our findings. Additionally, we had to limit our analysis to counties that had sufficient case data in order to accurately estimate R0. Given our findings that the counties excluded in the analysis had a significantly lower density and presumably very low R0 due to lack of cases, the true association between population density and R0 would likely be greater than what we report in our analysis. Another limitation is that our model also assumes homogenous mixing, which may can be an oversimplification of the heterogeneity in contact patterns within populations.4,33 However, previous research has shown that population structure only ch...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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