A spatial-temporal analysis at the early stages of the COVID-19 pandemic and its determinants: The case of Recife neighborhoods, Brazil

PLoS ONE, Journal Year: 2022, Volume and Issue: 17(5), P. e0268538 - e0268538

Published: May 17, 2022

The outbreak of COVID-19 has led to there being a worldwide socio-economic crisis, with major impacts on developing countries. Understanding the dynamics disease and its driving factors, small spatial scale, might support strategies control infections. This paper explores impact neighborhoods Recife, Brazil, for which we examine set drivers that combines factors presence non-stop services. A three-stage methodology was conducted by conducting statistical analysis, including clusters regression models. data were investigated concerning ten dates between April July 2020. Hotspots most affected regions their determinant effects highlighted. We have identified confirmed cases carried from well-developed neighborhood socially deprived areas, along emergence hotspots case-fatality rate. influence age-groups, income, level education, access essential services spread also verified. recognition variables becomes vital pinpointing vulnerable areas. Consequently, specific prevention actions can be developed these places, especially in heterogeneous cities.

Language: Английский

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Spatiotemporal Modeling of the Association between Neighborhood Factors and COVID-19 Incidence Rates in Scotland

The Professional Geographer, Journal Year: 2023, Volume and Issue: 75(5), P. 803 - 815

Published: May 30, 2023

AbstractThis study aims to investigate the association between neighborhood-level factors and COVID-19 incidence in Scotland from a spatiotemporal perspective. The outcome variable is Scotland. Based on identification of wave peaks for cases 2020 2021, confirmed can be divided into four phases. To model incidence, sixteen neighborhood are chosen as predictors. Geographical random forest models used examine variation major determinants incidence. spatial analysis indicates that proportion religious people most strongly associated with southern Scotland, whereas particulate matter northern Also, crowded households, prepandemic emergency admission rates, health social workers eastern central respectively. A possible explanation predictors might influenced by local context (e.g., people's lifestyles), which spatially variant across temporal dominant also vary different phases, suggesting pandemic-related policy should take variations account.本研究旨在从时空角度研究苏格兰的社区因素与新冠肺炎发病率之间的关系。输出变量是苏格兰的新冠肺炎发病率。通过确定2020年至2021年期间的新冠肺炎病例峰值, 苏格兰新冠肺炎确诊病例可分为四个阶段。我们选择16个社区因素作为预测因子, 对新冠肺炎发病率进行建模。采用地理随机森林模型, 研究了新冠肺炎发病率主要决定因素的时空变化。空间分析表明, 在苏格兰南部, 宗教人士的比例与新冠肺炎发病率的关系最密切。在苏格兰北部, 颗粒物与新冠肺炎发病率的关系最紧密。此外, 在苏格兰东部和中部, 拥挤的家庭、流行病之前的紧急住院率、卫生和社会工作者分别与新冠肺炎发病率密切相关。一种可能的解释是, 预测因子和新冠肺炎发病率之间的关联可能受到当地环境(例如, 人们的生活方式)的影响, 而这种影响在苏格兰各地具有空间差异性。时间分析表明, 新冠肺炎发病率的主导因素在不同阶段有所不同, 这表明流行病政策应当考虑时空变化。Desde una perspectiva espaciotemporal, este estudio pretende investigar la asociación entre los factores nivel de vecindario y incidencia en Escocia. La resultante es del partir identificación picos oleada casos COVID-19, el confirmados Escocia pueden dividirse cuatro fases. Para modelar dieciséis vecinales se escogieron como predictores. Se usaron modelos geográficos bosque aleatorio para examinar variación espaciotemporal principales determinantes COVID-19. El análisis espacial indica que proporción gente religiosa lo más fuertemente asocia con sur Escocia, mientras materiales particulados son asociados norte Igualmente, hacinamiento hogares, las tasas ingreso urgencias prepandémicas trabajadores salubridad sociales, asocian partes oriental respectivamente. Una posible explicación esto ente predictores podría verse influida por contexto estilos vida gente), variables espacialmente través dominantes también varían diferentes fases, sugiriendo políticas relacionadas pandemia deberían tener cuenta variaciones espaciotemporales.Key Words: COVID-19geographical modelneighborhood factorsScotlandspatial-temporal pattern关键词:: 新冠肺炎地理随机森林模型社区因素苏格兰时空模式。Palabras clave:: COVID-19Escociafactores vecindadmodelo geográfico aleatoriopatrón AcknowledgmentsWe gratefully acknowledge support Scottish Funding Council DDI Data Platforms Innovation ProgrammeSupplemental MaterialSupplemental data this article accessed publisher's Web site at https://doi.org/10.1080/00330124.2023.2194363.Additional informationNotes contributorsRuoyu WangRUOYU WANG Research Fellow Centre Public Health, Queen's University Belfast, BT12 6BA, UK. E-mail: [email protected]. His research interests include healthy geography public health.Tom ClemensTOM CLEMENS geographer how physical environment impacts well-being. [email protected] DouglasMARGARET DOUGLAS Consultant Health Honorary Clinical Senior Lecturer, Glasgow, G12 8QQ, [email protected]. Her all policies, impact assessment, links place economic health.Markéta KellerMARKÉTA KELLER Healthcare Scientist Epidemiology, EH8 9AG, [email protected]. primary interest an epidemiology interplay medical psychological health.Dan van der HorstDAN VAN DER HORST Professor Energy, Environment Society, Edinburgh, EH9 3JW, [email protected]. He studies why unsustainable development persists institutions learn use scarce resources less wasteful, harmful, unequal ways.

Language: Английский

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A Bayesian spatio-temporal model of COVID-19 spread in England

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: May 6, 2024

Abstract Exploring the spatio-temporal variations of COVID-19 transmission and its potential determinants could provide a deeper understanding dynamics disease spread. This study aimed to investigate spread infections in England, examine associations with socioeconomic, demographic environmental risk factors. We obtained weekly reported cases from 7 March 2020 26 2022 at Middle Layer Super Output Area (MSOA) level mainland England publicly available datasets. With these data, we conducted an ecological predict infection identify factors using Bayesian hierarchical model. The model outperformed ordinary least squares geographically weighted regression terms prediction accuracy. over space time was heterogeneous. Hotspots exhibited inconsistent clustering patterns time. Risk found be positively associated were: annual household income [relative (RR) = 1.0008, 95% Credible Interval (CI) 1.0005–1.0012], unemployment rate [RR 1.0027, CI 1.0024–1.0030], population density on log scale 1.0146, 1.0129–1.0164], percentage Caribbean 1.0022, 1.0009–1.0036], adults aged 45–64 years old 1.0031, 1.0024–1.0039], particulate matter ( $$\text {PM}_{2.5}$$ PM 2.5 ) concentrations 1.0126, 1.0083–1.0167]. highlights importance considering demographic, analysing England. findings assist policymakers developing tailored public health interventions localised level.

Language: Английский

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A geographical information system model to define COVID-19 problem areas with an analysis in the socio-economic context at the regional scale in the North of Spain

Geospatial health, Journal Year: 2022, Volume and Issue: 17(s1)

Published: March 18, 2022

The work presented concerns the spatial behaviour of coronavirus disease 2019 (COVID-19) at regional scale and socio-economic context problem areas over 2020-2021 period. We propose a replicable geographical information systems (GIS) methodology based on geocodification analysis COVID-19 microdata registered by health authorities Government Cantabria, Spain from beginning pandemic register (29th February 2020) to 2nd December 2021. virus was studied using ArcGIS Pro 1x1 km vector grid as homogeneous reference layer. GIS 45,392 geocoded cases revealed clear process contraction after spread in 2020 with 432 km2 reduced 126.72 framework showed complex relationships between explanatory variables related household characteristics, conditions demographic structure. Local bivariate fuzzier results persistent hotspots urban peri-urban areas. Questions about ‘where, when how’ contribute learning experience we must draw inspiration from, explore connections to, those confronting issues current pandemic.

Language: Английский

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Does Fear of COVID-19 Prolongation Lead to Future Career Anxiety Among Adolescents? The Mediating Role of Depressive Symptoms

Journal of Child & Adolescent Trauma, Journal Year: 2022, Volume and Issue: 16(3), P. 527 - 536

Published: Dec. 7, 2022

Language: Английский

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Spatial mapping and socio-demographic determinants of COVID-19 mortality in India

Journal of Family Medicine and Primary Care, Journal Year: 2021, Volume and Issue: 10(11), P. 4200 - 4204

Published: Nov. 1, 2021

COVID-19 is caused by SARS-CoV-2. The first case of was detected in Wuhan city China December 2019. Geographic information system (GIS) mapping important for the surveillance infectious diseases.The objectives study are to map spatially total cases and fatality rate build a linear regression model mortality based on socio-demographic variables.We plotted epidemiological data Indian states as 11th May 2021 using Q-GIS software. We used variables predictors developed model.Adjusted R-squared deaths 0.82.There spatial variations deaths.

Language: Английский

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Incidencia de factores socioeconómicos y culturales en la propagación de la infección por SARS-CoV-2 en las regiones peruanas

Acta Universitaria, Journal Year: 2024, Volume and Issue: 34, P. 1 - 13

Published: May 30, 2024

El objetivo de este artículo es identificar los factores riesgo del entorno socioeconómico y cultural que incidieron en la propagación diferenciada infecciones por SARS-CoV-2 a nivel las 24 regiones políticas peruanas. Con información regional correspondiente años 2020 2021, se efectuaron regresiones lineales multivariables con el método mínimos cuadrados ordinarios. Los resultados indican niveles contagios covid-19 están asociados positivamente tasa empleo formal, número establecimientos salud cada 100 mil habitantes, acceso al consumo agua red pública infracción normas sanitarias, pero negativamente médicos 1000 habitantes. En conclusión, infectados entre peruanas guarda relación socioeconómicos culturales.

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The Relationship Between COVID-19 and Urban Features in the Light of Recent Quantitative Studies

Advances in civil and industrial engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 329 - 380

Published: Oct. 25, 2024

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has profoundly impacted urban environments globally. virus's dependence on physical proximity for transmission and necessity individuals to congregate in spaces meet various needs accelerated its spread. Therefore, as with many other epidemic diseases, investigating factors behind contact spread become an important issue pandemic well. This study investigates relationship between features synthesizing recent quantitative research. review revealed that it is possible conceptualise which are influencing sociodemographic characteristics, healthcare services, infrastructure, tourism activities, economic conditions. Understanding these determinants essential developing targeted strategies control of enhance resilience against future pandemics.

Language: Английский

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Decongesting Global Cities as Part of Health Reform in the Era of COVID-19: Impacts and Implications for Zimbabwe

Global perspectives on health geography, Journal Year: 2023, Volume and Issue: unknown, P. 189 - 208

Published: Jan. 1, 2023

Language: Английский

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Türkiye Kentleri için Koronavirüs Hastalığına Dayanıklılık: Bir İndeks Önerisi

Resilience, Journal Year: 2023, Volume and Issue: 7(2), P. 429 - 453

Published: Dec. 19, 2023

20. yy.’dan itibaren kentlerin şoklara karşı daha dayanıklı hale getirilmesi maksadıyla yapılan araştırmalar Covid-19 salgınının yaşanması sonrasında yeniden gündeme gelmiş ve bu defa salgınlar kent sağlığı bağlamında tartışılmaya başlanmıştır. Küresel ölçekte insan yaşamını tehdit eden acil durumu iyi anlayabilmek kentsel anlamda çözüm arayışları ortaya koyabilmek adına çalışmalar, ağırlıklı olarak mekânların çeşitli nitelikteki özelliklerinin salgını ile şekillerde ilişkisini koymayı amaçlamaktadır. Çünkü salgın hastalıkların meydana gelmesi, yayılımı kontrolü gibi birçok önemli husus sunduğu koşullara bağlı değişmektedir. Bu bağlamda; toplum sağlığının sürdürülebilirliğini sağlayabilmek için öncelikle kentleri getirmek oldukça bir durum haline gelmiştir. çalışma karşısında Türkiye kentlerinin dayanıklılığını koyan indeks geliştirilmiştir. Elde edilen bulgulara göre döneminde ülkemizin güney kesimlerinin sahip oldukları doğal, sosyal, ekonomik mekânsal koşullar sebebiyle kuzey kesimlerine az etkilendikleri görülmüş dağılımın ilişkilerini modelleyen coğrafi regresyon modeli tekniği uygulanarak indeksin geçerliliği değerlendirilmiştir. Çalışmada geliştirilen indeks, sırasındaki vaka sayılarının dağılımlarından bağımsız şekilde, göstergelerde değişen özelliklerine karşısındaki dayanıklılıklarını tespit edebilmeye olanak sağlaması literatüre katkı sağlamaktadır.

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