Do population density, socio-economic ranking and Gini Index of cities influence infection rates from coronavirus? Israel as a case study

Arbel, Yuval; Fialkoff, Chaim; Kerner, Amichai; Kerner, Miryam

doi:10.1007/s00168-021-01073-y

Cited by 20 publications

(8 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There was a positive association between the Covid-19 risk and the population density. The importance of population density agrees with other studies (Arbel, Fialkoff, Kerner, & Kerner, 2021;Moosa & Khatatbeh, 2021;Sy, White, & Nichols, 2021;Wong & Li, 2020). Despite differences in localised structure, the RR values were similar for both G=2 and G=3 when population density was included (Table 4).…”

Section: Bayesian Spatial Car Localised Modelssupporting

confidence: 89%

The Interplay Between Clusters, Covariates, and Spatial Priors in Spatial Modelling of Covid-19 in South Sulawesi Province, Indonesia

Aswi

Tiro

Sudarmin

et al. 2022

Medstat

View full text Add to dashboard Cite

A number of previous studies on Covid-19 have used Bayesian spatial Conditional Autoregressive (CAR) models. However, basic CAR models are at risk of over-smoothing if adjacent areas genuinely differ in risk. More complex forms, such as localised CAR models, allow for sudden disparities, but have rarely been applied to modelling Covid-19, and never with covariates. This study aims to evaluate the most suitable Bayesian spatial CAR localised models in modelling the number of Covid-19 cases with and without covariates, examine the impact of covariates and spatial priors on the identified clusters and which factors affect the Covid-19 risk in South Sulawesi Province. Data on the number of confirmed cases of Covid-19 (19 March 2020 -25 February 2022) were analyzed using the Bayesian spatial CAR localised model with a different number of clusters and priors. The results show that the Bayesian spatial CAR localised model with population density included fits the data better than a corresponding model without covariates. There was a positive correlation between the Covid-19 risk and population density. The interplay between covariates, spatial priors, and clustering structure influenced the performance of models. Makassar city and Bone have the highest and the lowest relative risk (RR) of Covid-19 respectively.

show abstract

Section: Bayesian Spatial Car Localised Modelssupporting

confidence: 89%

The Interplay Between Clusters, Covariates, and Spatial Priors in Spatial Modelling of Covid-19 in South Sulawesi Province, Indonesia

Aswi

Tiro

Sudarmin

et al. 2022

Medstat

View full text Add to dashboard Cite

show abstract

“…The minimum is 5,232 persons (Geva Binyamin) and the maximum is 865,721 persons (Jerusalem). Indeed, Israel is characterized by high urbanization levels and non-uniform distribution of population densities, which, in turn, might increase the spread of the pandemic [ 8 , 9 ].…”

Section: Methodsmentioning

confidence: 99%

“…Referring to the COVID19 pandemic, Israel provides an interesting case study. Three salient features of Israel are: (1) an accelerated urbanization process and non-uniform distribution of population densities, which, in turn, might increase the spread of the pandemic [ 8 , 9 ], (2) disparities in household income and socio-economic ranking of municipalities (cities and towns) [ 27 ]; and (3) the early initiation of a nationwide vaccination campaign leading to the full vaccination (i.e., receipt of two vaccine doses) of more than half the population by the end of March 2021 [ 10 , 11 ]. There are currently three vaccine types in Israel: (1) Pfizer (approved on December, 2020), (2) Moderna (approved on August, 2021) and (3) Astra-Zenika (approved on September, 2021).…”

Section: Introductionmentioning

confidence: 99%

The median age of a city’s residents and population density influence COVID 19 mortality growth rates: policy implications

Arbel

Kerner

et al. 2022

Isr J Health Policy Res

Self Cite

View full text Add to dashboard Cite

Background SARS-CoV-2 is an infectious virus, which has generated a global pandemic. Israel was one of the first countries to vaccinate its population, inaugurating the program on December 20, 2020. The objective of the current study is to investigate the projected daily COVID19 mortality growth rate with higher median age and population size of cities under two scenarios: with and without the BNT162b2 Pfizer vaccination against the SAR-COV2 virus. Methods This study employs a panel data-set. We follow the COVID19 mortality growth rate in each of the 173 Israeli cities and towns starting from March 21, 2020 (10 days after the first documentation of COVID19 cases in Israel) until September 21, 2021, where the BNT162b2 Pfizer vaccinations were available starting from December 20, 2020. Results Referring to the median age of municipal residents, findings suggest that the BNT162b2 Pfizer vaccinations attenuate the rise in anticipated daily mortality growth rate for cities and towns in which the median population age is 30 years old (the range in median age among the residents in the municipalities surveyed is 11–41 years). Moreover, referring to population size of cities, findings demonstrate that while under the scenario without vaccination, the daily mortality growth rate is anticipated to rise, under a comparable scenario with vaccination, daily mortality growth rate is anticipated to drop. Conclusions In crowded cities, where the median age is high, two perspectives of early and intensive public policy interventions are clearly required. The first perspective is extensive medical treatment, namely, extension of availability of medical physical and online services; dispensing designated medications; expansion of hospitalization facilities and information services particularly to susceptible populations. All measures will be taken with attention to age accessibility of these means. The second perspective is prevention via establishment of testing and vaccination complexes; elevation of dedicated health services, generating selective lockdowns; education for increasing awareness to social distancing, wearing masks and other preventive means.

show abstract

“…Spatial implications of the COVID-19 pandemic have been investigated from different perspectives. Some studies have analyzed geo-environmental factors (air pollution, weather conditions) (Coccia 2020 ), demographic characteristics (population density) (Mollato et al 2020 ; Arbel et al 2022 ), and climate conditions (temperature, humidity) (Oto-Peralías 2020 ; Ma et al 2020 ; Sajadi et al 2020 ; Wang et al 2020 ; Wu et al 2020 ; Rios and Gianmoena 2021 ) can affect the spread of COVID-19. Some studies have found a strong correlation between human mobility and the spread of disease (Chen et al 2020 ; Furceri et al 2020 ; Gross et al 2020 ; Zhang et al 2020 ; Hierro and Maza 2022 ).…”

Section: Spatial Implications Of Covid-19 Pandemic In Turkeymentioning

confidence: 99%

Special Feature on social, economic, and spatial impacts of COVID-19 pandemic in Turkey

Baycan

Tuysuz

2022

Asia-Pac J Reg Sci

View full text Add to dashboard Cite

This Special Feature investigates the social, economic, and spatial impacts of the COVID-19 pandemic in Turkey and highlights the factors differentiating Turkey from the other countries. The articles contributing to this Special Feature are classified into three main parts. The first group of articles addresses spatial implications of the COVID-19 pandemic in Turkey with a specific focus on the place-based factors affecting the spread of the pandemic, the determinants of pandemic-induced changes in intracity mobility, and the use of social media to forecast commercial real estate figures during COVID-19. The second group of articles investigates the social and economic implications of the COVID-19 pandemic and diversely affected economic sectors in Turkey. These articles analyze the vulnerability and resilience of regions and diversely affected economic sectors with a specific focus on the housing market that displays an opposite trend to international tendencies regarding transaction volumes and private rental housing prices. The third group of articles considers the economic impact of the COVID-19 pandemic from an international perspective. These articles analyze the impact of the COVID-19 pandemic on international trade with a specific focus on exports and the fragility of the global trade structure and network in the framework of global value chains. Analyzing the impacts of COVID-19 from different perspectives, the articles in this Special Feature reveal the factors differentiating Turkey from the other countries and highlight the challenges.

show abstract

Do population density, socio-economic ranking and Gini Index of cities influence infection rates from coronavirus? Israel as a case study

Cited by 20 publications

References 26 publications

The Interplay Between Clusters, Covariates, and Spatial Priors in Spatial Modelling of Covid-19 in South Sulawesi Province, Indonesia

The Interplay Between Clusters, Covariates, and Spatial Priors in Spatial Modelling of Covid-19 in South Sulawesi Province, Indonesia

The median age of a city’s residents and population density influence COVID 19 mortality growth rates: policy implications

Special Feature on social, economic, and spatial impacts of COVID-19 pandemic in Turkey

Contact Info

Product

Resources

About