Conditioning factors in the spreading of Covid-19 – Does geography matter?

Heliyon, Год журнала: 2024, Номер 10(3), С. e25810 - e25810

Опубликована: Фев. 1, 2024

There is evidence in literature that the spread of COVID-19 can be influenced by various geographic factors, including territorial features, climate, population density, socioeconomic conditions, and mobility. The objective paper to provide an updated review on geographical studies analysing factors which spreading. This took into account not only aspects but also COVID-19-related outcomes (infections deaths) allowing discern potential influencing role per type outcome. A total 112 scientific articles were selected, reviewed categorized according subject area, aim, country/region study, considered variables, spatial temporal units analysis, methodologies, main findings. Our showed features may have played a determining uneven geography COVID-19; for instance, certain agreement was found regarding direct relationship between urbanization degree infections. For what concerns climatic temperature variable correlated best with Together socio-demographic ones extensively taken account. Most analysed agreed density human mobility had significant infections deaths. analysis different approaches used investigate spreading pandemic revealed significance/representativeness outputs scale due great variability aspects. In fact, more robust association conducted at subnational or local rather than country scale.

Язык: Английский

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Effect of socioeconomic factors during the early COVID-19 pandemic: a spatial analysis

BMC Public Health, Год журнала: 2022, Номер 22(1)

Опубликована: Июнь 18, 2022

Abstract Background Spatial variability of COVID-19 cases may suggest geographic disparities social determinants health. analyses population-level data provide insight on factors that contribute to transmission, hospitalization, and death. Methods Generalized additive models were used map risk from March 2020 February 2021 in Orange County (OC), California. We geocoded analyzed 221,843 OC census tracts within a Poisson framework while smoothing over tract centroids. Location was randomly permuted 1000 times test for randomness. also separated the temporally observe if changed time. cases, hospitalizations, deaths mapped across adjusting demographic crude adjusted models. Results Risk statistically significant northern OC. Adjustment substantially decreased spatial risk, but areas remained significant. Inclusion location our considerably magnitude compared univariate However, percent minority (adjusted RR: 1.06, 95%CI: 1.07), average household size (aRR: 1.05, service industry 1.04, 1.06) significantly associated with In addition, did not change between surges ratios similar hospitalizations deaths. Conclusion Significant increased identified suggests environmental spread communities. Areas north despite adjustment, decreased. Additional investigation how protect vulnerable populations future infectious disease outbreaks.

Язык: Английский

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Spatiotemporal pattern of COVID-19 mortality and its relationship with socioeconomic and environmental factors in England

Spatial and Spatio-temporal Epidemiology, Год журнала: 2023, Номер 45, С. 100579 - 100579

Опубликована: Фев. 3, 2023

This paper investigated the spatiotemporal pattern of COVID-19 mortality and its socioeconomic environmental determinants in first second wave pandemic England. The rates for middle super output areas from March 2020 to April 2021 were used analysis. SaTScan was analysis geographically weighted Poisson regression (GWPR) investigate association with factors. results show that there significant variation hotspots deaths moving regions where outbreak initiated then spread other parts country. GWPR revealed age composition, ethnic deprivation, care home pollution all related mortality. Althoughthe relationship varied over space these factors fairly consistent wave.

Язык: Английский

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Spatio-temporal clustering analysis of COVID-19 cases in Johor

Infectious Disease Modelling, Год журнала: 2024, Номер 9(2), С. 387 - 396

Опубликована: Фев. 8, 2024

At the end of year 2019, a virus named SARS-CoV-2 induced coronavirus disease, which is very contagious and quickly spread around world. This new infectious disease called COVID-19. Numerous areas, such as economy, social services, education, healthcare system, have suffered grave consequences from invasion this deadly virus. Thus, thorough understanding COVID-19 required in order to deal with outbreak before it becomes an disaster. In research, daily reported cases 92 sub-districts Johor state, Malaysia, well population size associated each sub-district, are used study propagation across space time Johor. The frame research about 190 days, started August 5, 2021, until February 10, 2022. clustering technique known spatio-temporal clustering, considers metric was adapted determine hot-spot areas at sub-district level. results indicated that does spike dynamic populated state's economic centre (Bandar Bahru), during festive season. These findings empirically prove transmission rate directly proportional human mobility presence holidays. On other hand, result will help authority charge stopping preventing spreading become worsen national

Язык: Английский

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A novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States

Computational Statistics & Data Analysis, Год журнала: 2023, Номер 188, С. 107810 - 107810

Опубликована: Июнь 19, 2023

Язык: Английский

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Measurement of contagion spatial spread probability in public places: A case study on COVID-19

Applied Geography, Год журнала: 2022, Номер 143, С. 102700 - 102700

Опубликована: Апрель 7, 2022

Язык: Английский

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Exploration of the COVID-19 pandemic at the neighborhood level in an intra-urban setting

Frontiers in Public Health, Год журнала: 2023, Номер 11

Опубликована: Апрель 13, 2023

The COVID-19 pandemic represents a worldwide threat to health. Since its onset in 2019, the has proceeded different phases, which have been shaped by complex set of influencing factors, including public health and social measures, emergence new virus variants, seasonality. Understanding development incidence spatiotemporal patterns at neighborhood level is crucial for local authorities identify high-risk areas develop tailored mitigation strategies. However, analyses are scarce mostly limited specific phases pandemic. aim this study was explore scale an intra-urban setting over several (March 2020–December 2021). We used reported case data from department district Berlin-Neukölln, Germany, additional socio-demographic data, text documents materials on implemented measures. examined time context measures other with particular focus age groups. maps spatial scan statistics reveal changing patterns. Our results show that factors may influenced incidence. In particular, far-reaching contact reduction showed substantial impact Neukölln. observed group-specific effects: school closures had effect younger population (< 18 years), whereas start vaccination campaign primarily among elderly (> 65 years). analysis revealed were heterogeneously distributed across district. location also changed phases. study, existing studies supplemented our investigation course underlying processes small long period time. findings provide insights authorities, community planners, policymakers about level. These guiding decision-makers implementing

Язык: Английский

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Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022

PLoS ONE, Год журнала: 2024, Номер 19(2), С. e0297772 - e0297772

Опубликована: Фев. 1, 2024

During the SARS-CoV-2 pandemic, governments and public health authorities collected massive amounts of data on daily confirmed positive cases incidence rates. These sets provide relevant information to develop a scientific understanding pandemic’s spatiotemporal dynamics. At same time, there is lack comprehensive approaches describe classify patterns underlying dynamics COVID-19 across regions over time. This seriously constrains potential benefits for understand disease that would allow better risk communication strategies improved assessment mitigation policies efficacy. Within this context, we propose an exploratory statistical tool combines functional analysis with unsupervised learning algorithms extract meaningful about main mainland Portugal. We focus timeframe spanning from August 2020 March 2022, considering at municipality level. First, temporal evolution by as function outline variability using principal component analysis. Then, municipalities are classified according their similarities through hierarchical clustering adapted spatially correlated data. Our findings reveal disparities in between northern coastal versus those southern hinterland. also distinguish effects occurring during 2020–2021 period 2021–2022 autumn-winter seasons. The results proof-of-concept proposed approach can be used detect incidence. novel expands enhances existing tools

Язык: Английский

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Spatio-temporal Analysis of COVID-19 Hotspots in India Using Geographic Information Systems

International Journal of Geoinformatics, Год журнала: 2024, Номер unknown

Опубликована: Янв. 31, 2024

The aim of this study is to identify hotspot regions COVID-19 in India from March 2020 August 2023. Identifying hotspots essential for effective pandemic management, as it helps policymakers understand the dynamics virus spread and allows more precise public health campaigns. present a district level analysis at five different points time, where we calculate cumulative incidence rate (CIR), fatality (CFR) recovery (RR) COVID-19. Further, apply Global Moran's I, Getis-Ord Gi* Anselin local I index by using Geographic Information Systems (GIS) technology. results show that spatial temporal variation CIR very high across India. was recorded lower May affected people were immobilized due lockdown. However, CFR RR low inadequate medical facilities treatment. findings revealed mainly two existed until 2021, National Capital Region, Haryana, Punjab, Rajasthan, Uttar Pradesh Maharashtra south. scenario has entirely changed since January 2022, when northern into cold-spot southern coastal states have become hot-spot region. Combining with & offers method locating statistically significant case cluster areas identifying high-risk areas.

Язык: Английский

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Housing situations and local COVID-19 infection dynamics using small-area data

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Авг. 31, 2023

Abstract Low socio-economic status is associated with higher SARS-CoV-2 incidences. In this paper we study whether a result of differences in (1) the frequency, (2) intensity, and/or (3) duration local outbreaks depending on housing situations. So far, there not clear evidence which three factors dominates. Using small-scale data from neighborhoods German city Essen and flexible estimation approach does require prior knowledge about specific transmission characteristics SARS-CoV-2, behavioral responses or other potential model parameters, find for last hypotheses. Outbreaks do happen more often less well-off areas are severe (in terms number cases), but they longer. This indicates that gradient infection levels at least parts sustained spread infections worse conditions after suggests case an epidemic allocating scarce resources containment measures to poor might have greatest benefit.

Язык: Английский

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