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Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States

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TLDR
In this paper, the authors explored the potential effects of driving factors on COVID-19 counts in the contiguous United States and found that ethnicity, crime, and income factors are the strongest covariates and explain most of the variance of the modeling estimation.
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This article is published in Sustainable Cities and Society.The article was published on 2021-02-19 and is currently open access. It has received 85 citations till now.

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Are high-density districts more vulnerable to the COVID-19 pandemic?

TL;DR: In this article, the authors investigated the link between density and pandemic spread through a case study of Tehran that has been the epicenter of the pandemic in Iran Based on data obtained from an online platform and analyzed using structural equation modeling, they found that density alone cannot be considered a risk factor for the spread of COVID-19.
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What determines urban resilience against COVID-19: City size or governance capacity?

TL;DR: Zhang et al. as discussed by the authors analyzed the effects of urban governance and city size on COVID-19 prevention and control measures in 276 prefecture-level Chinese cities, using the ordinary least squares plus robust standard error strategy.
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Digital contact tracing, community uptake, and proximity awareness technology to fight COVID-19: a systematic review

TL;DR: A systematic review of digital contact tracing studies between January 1, 2020, and March 31, 2021 is presented in this article, where the authors evaluate whether digital contact trace can mitigate COVID-19 by reducing the effective reproductive number or the infected cases.
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Exploring the nexus between social vulnerability, built environment, and the prevalence of COVID-19: A case study of Chicago

TL;DR: This paper examined the effects of different social vulnerability and built environment factors on COVID-19 prevalence over two overlapping time periods (March to May and March to November of 2020) and found that zip codes with low educational attainment consistently experienced higher case rates over both periods.
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Spatio-temporal Patterns of the COVID-19 Pandemic, and Place-based Influential Factors at the Neighborhood scale in Tehran

TL;DR: In this paper, the authors analyzed 43,000 confirmed COVID-19 cases at the neighborhood level in Tehran, the capital of Iran, and found that demographic composition and major neighborhood-level physical attributes are important factors explaining high rates of infection and mortality.
References
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Random Forests

TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Under the hood Issues in the specification and interpretation of spatial regression models

TL;DR: A number of conceptual issues pertaining to the implementation of an explicit "spatial" perspective in applied econometrics are reviewed, both from a theory-driven as well as from a data-driven perspective.
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Permutation importance

TL;DR: Almann et al. as discussed by the authors introduced a heuristic for normalizing feature importance measures that can correct the feature importance bias, based on repeated permutations of the outcome vector for estimating the distribution of measured importance for each variable in a non-informative setting.
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Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in Northern Italy?

TL;DR: Evidence is provided that people living in an area with high levels of pollutant are more prone to develop chronic respiratory conditions and suitable to any infective agent, and the high level of pollution in Northern Italy should be considered an additional co-factor of the highlevel of lethality recorded in that area.
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