Presence and mobility of the population during the first wave of Covid-19 outbreak and lockdown in Italy

Beria, Paolo; Lunkar, Vardhman

doi:10.1016/j.scs.2020.102616

Cited by 119 publications

(83 citation statements)

References 21 publications

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“…To inform the policy-makers at both national and state levels, understanding the explanatory drivers and related confounding factors with spatial patterns and is of paramount importance. Timely studies have done much work of doing so (e.g., Beria & Lunkar, 2020 ; Hu, Roberts, Azevedo, & Milner, 2020 ; Rahman et al, 2020 ). However, this may not uncover the full picture since most of the factors change over time, namely being time-variant variables.…”

Section: Discussionmentioning

confidence: 99%

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

Maiti

Zhang

Sannigrahi

et al. 2021

Sustainable Cities and Society

107

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Since December 2019, the world has witnessed the stringent effect of an unprecedented global pandemic, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of January 29, 2021, there have been 100, 819, 363 confirmed cases and 2, 176, 159 deaths reported. Among the countries affected severely by COVID-19, the United States tops the list. Research has been conducted to discuss the causal associations between explanatory factors and COVID-19 transmission in contiguous United States. However, most of these studies focus more on spatial associations of the estimated parameters, yet exploring the time-varying dimension in spatial econometric modeling has appears to be utmost essential. This research adopts various relevant approaches to explore the potential effects of driving factors on COVID-19 counts in the contiguous United States. A total of three global spatial regression models and two local spatial regression models, the latter including geographically weighted regression (GWR) and multiscale GWR (MGWR), are performed at the county scale to take into account the scale effects. For COVID-19 cases, ethnicity, crime, and income factors are found to be the strongest covariates and explain most of the variance of modeling estimation. For COVID-19 deaths, migration (domestic and international) and income factors play a critical role in explaining spatial differences of COVID-19 deaths across counties. Such associations also exhibit temporal variations from March to July, as supported by better performance of MGWR than GWR. Both global and local associations among the parameters vary highly over space and change across time. Therefore, time dimension should be paid more attention to the spatial epidemiological analysis. Among the two local spatial regression models, MGWR performs more accurately, as it has slightly higher Adj. R 2 values (for cases, R 2 = 0.961; for deaths, R 2 = 0.962), compared to GWR’s Adj. R 2 values (for cases, R 2 = 0.954; for deaths, R 2 = 0.954). To inform policy-makers at the nation and state levels, understanding the place-based characteristics of the explanatory forces and related spatial patterns of the driving factors is of paramount importance. Since COVID-19 is not the first time we are facing public health emergency, the findings of the present research therefore could be used as a reference for policy designing and effective decision making.

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Section: Discussionmentioning

confidence: 99%

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

Maiti

Zhang

Sannigrahi

et al. 2021

Sustainable Cities and Society

107

View full text Add to dashboard Cite

show abstract

“…The network also shows that some researches explored the travel behavior and patterns of passengers to help understand the spread of the disease by traveling ( Chebli & Said, 2020 ; Fouquet & O’Garra, 2020 ; Li, Nguyen, & Andres Coca-Stefaniak, 2020 ; Neuburger & Egger, 2020 ; Shakibaei, de Jong, Alpkökin, & Rashidi, 2020 ). The network also presents the densest connection public – transport which explains the gravity of research work done on the disease spread through public transport ( Anan et al, 2020 ; Beria & Lunkar, 2020 ; Coppola & Fabiis, 2020 ; Zhen et al, 2020 ). Various aspects of sustainable designs and strategic responses for public transport that are linked to COVID-19 are discussed ( Cartenì, D’Acierno, & Gallo, 2020 ; Tirachini & Cats, 2020 ).…”

Section: Results Discussionmentioning

confidence: 99%

“…Further, lockdown and mobility also appear in the network, which suggests that a significant number of researchers focused on the impact of lockdown on the mobility of people ( Beria & Lunkar, 2020 ). The degree centrality measures of the heaviest nodes of the network also signify their in-depth coverage by the researchers by other countries with keyword travel (31) leading followed by disease (29), travelers (28), cases (23) and risk (23).…”

Section: Results Discussionmentioning

confidence: 99%

Exploring geographical distribution of transportation research themes related to COVID-19 using text network approach

Kutela¹,

Novat

Langa

2021

Sustainable Cities and Society

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“…Social or physical distancing is at the center of nonpharmacological responses to this pandemic, which aims to reduce person-to-person contacts that minimize public exposure to the virus ( Anderson, Heesterbeek, Klinkenberg, & Hollingsworth, 2020 ; Beria & Lunkar, 2021 ). Social distancing has been demonstrated to be strongly effective in reducing the viral transmission of COVID-19 in China ( Anderson et al, 2020 ), South Korea ( Park, Sun, Viboud, Grenfell, & Dushoff, 2020 ), the UK ( Jacob et al, 2020 ), and Brazil ( Coelho et al, 2020 ), which social vulnerability plays a critical role in impeding people’s compliance with social distancing in these countries.…”

Section: Introductionmentioning

confidence: 99%

Examining the spatial and temporal relationship between social vulnerability and stay-at-home behaviors in New York City during the COVID-19 pandemic

Zhai

2021

Sustainable Cities and Society

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Social distancing and particularly staying at home are effective public health responses to the COVID-19 pandemic. The sheer scale of behavior changes across a mass population scale is unprecedented and will undoubtedly cause disproportionate hardships for certain vulnerable groups of population and marginalized communities during different periods of the pandemic. However, at the community level, few studies have considered the spatial and temporal variations in such public health behavior changes during this pandemic. We applied a geographically and temporally weighted regression (GTWR) to analyze the spatiotemporal pattern of community stay-at-home behaviors against social vulnerability indicators at the census tract level in New York City from March to August 2020. Our findings are generally supporting the conventional wisdom of social vulnerability yet they also offer new insights. Despite the spatial variations in the effects of social vulnerability on stay-at-home behaviors, people from different vulnerable groups are also exhibiting varying reactions to the pandemic over the duration of this study, thereby highlighting the importance of understanding the spatiotemporal pattern of public health behaviors to develop an effective policy response to avoid the risk of deepening inequalities and to promote a just and sustainable urban future.

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Presence and mobility of the population during the first wave of Covid-19 outbreak and lockdown in Italy

Cited by 119 publications

References 21 publications

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

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

Exploring geographical distribution of transportation research themes related to COVID-19 using text network approach

Examining the spatial and temporal relationship between social vulnerability and stay-at-home behaviors in New York City during the COVID-19 pandemic

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