Identification of Thresholds on Population Density for Understanding Transmission of COVID‐19
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Abstract
Pathways of transmission of coronavirus (COVID‐19) disease in the human population are still emerging. However, empirical observations suggest that dense human settlements are the most adversely impacted, corroborating a broad consensus that human‐to‐human transmission is a key mechanism for the rapid spread of this disease. Here, using logistic regression techniques, estimates of threshold levels of population density were computed corresponding to the incidence (case counts) in the human population. Regions with population densities greater than 3,000 person per square mile in the United States have about 95% likelihood to report 43,380 number of average cumulative cases of COVID‐19. Since case numbers of COVID‐19 dynamically changed each day until 30 November 2020, ca. 4% of US counties were at 50% or higher probability to 38,232 number of COVID‐19 cases. While threshold on population density is not the sole indicator for predictability of coronavirus in human population, yet it is one of the key variables on understanding and rethinking human settlement in urban landscapes.
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SciScore for 10.1101/2022.01.27.22269840: (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
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This anomaly is a potential limitation of the logistical regression methods, in dealing with locations with low population densities and high cases. However, it is understood that any regressive model considering population density as the single explanatory variable likely would fail to explain a high number of cases in less densely populated regions. Lastly, an important observation with reference to population density and case …
SciScore for 10.1101/2022.01.27.22269840: (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
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This anomaly is a potential limitation of the logistical regression methods, in dealing with locations with low population densities and high cases. However, it is understood that any regressive model considering population density as the single explanatory variable likely would fail to explain a high number of cases in less densely populated regions. Lastly, an important observation with reference to population density and case analysis can be discerned from Table 4, namely the percentage difference between the maximum and average probabilities of high number of cases, being very high for many states, notably those with high maximum average probabilities. This signifies that only a few counties of the state account for a large number of cases, and the state as a whole would not be an epicenter of COVID-19. The analysis in our study assumes a uniform population distribution. In reality, the population is generally not distributed evenly across the county, as most of the population clusters in and around cities. The lack of a standard sub-county level case count, which rules out the possibility of conducting a more realistic city-level threshold analysis, forms a limitation of our study.Furthermore, though county population density is generally considered a reliable predictor to explain COVID-19 cases due to its high explanatory power (Riley, 2007; Wong & Li, 2020), controlling it for other variables such as population size could bring valuable insights. to determine running t...
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.
Results from scite Reference Check: We found no unreliable references.
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