EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization DOI Creative Commons
Lee Mason, Blánaid Hicks, Jonas S. Almeida

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Dec. 1, 2023

The analysis of data over space and time is a core part descriptive epidemiology, but the complexity spatiotemporal makes this challenging. There need for methods that simplify exploration such tasks as surveillance hypothesis generation. In paper, we use combined clustering dimensionality reduction (hereafter referred to 'cluster embedding' methods) spatially visualize patterns in epidemiological time-series data. We compare several cluster embedding techniques see which performs best along variety internal validation metrics. find based on k-means generally perform better than self-organizing maps real world data, with some minor exceptions. also introduce EpiVECS, tool allows user explore results using interactive visualization. EpiVECS available privacy preserving, in-browser open source web application at https://episphere.github.io/epivecs .

Language: Английский

Neighborhood-level inequalities and influencing factors of COVID-19 incidence in Berlin based on Bayesian spatial modelling DOI Creative Commons
Sida Zhuang, Kathrin Wolf,

Tillman Schmitz

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 104, P. 105301 - 105301

Published: Feb. 23, 2024

Numerous studies have explored influencing factors in COVID-19, yet empirical evidence on spatiotemporal dynamics of COVID-19 inequalities concerning both socioeconomic and environmental at an intra-urban scale is lacking. This study, therefore, focuses neighborhood-level spatial the incidences relation to for Berlin-Neukölln, Germany, covering six pandemic periods (March 2020 December 2021). Spatial Bayesian negative binomial mixed-effect models were employed identify risk patterns different periods. We identified that (1) relative risks varied across time space, with sociodemographic exerting a stronger influence over features; (2) as most predictors, population migrant backgrounds was positively associated, 65 negatively associated incidence; (3) certain neighborhoods consistently faced elevated incidence. study highlights potential structural health within communities, lower status higher incidence diverse Our findings indicate locally tailored interventions citizens are essential address foster more sustainable urban environment.

Language: Английский

Citations

7
The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis DOI Creative Commons
Alireza Mohammadi, Elahe Pishgar, Munazza Fatima

et al.

Tropical Medicine and Infectious Disease, Journal Year: 2023, Volume and Issue: 8(2), P. 85 - 85

Published: Jan. 26, 2023

There are different area-based factors affecting the COVID-19 mortality rate in urban areas. This research aims to examine rates and their geographical association with various socioeconomic ecological determinants 350 of Tehran’s neighborhoods as a big city. All deaths related included from December 2019 July 2021. Spatial techniques, such Kulldorff’s SatScan, geographically weighted regression (GWR), multi-scale GWR (MGWR), were used investigate spatially varying correlations between predictors, including air pollutant factors, status, built environment public transportation infrastructure. The city’s downtown northern areas found be significantly clustered terms spatial temporal high-risk for mortality. MGWR model outperformed OLS models an adjusted R2 0.67. Furthermore, was associated quality (e.g., NO2, PM10, O3); pollution increased, so did Additionally, aging illiteracy positively rates. Our approach this study could implemented potential associations other emerging infectious diseases worldwide.

Language: Английский

Citations

14
Bayesian spatio-temporal analysis of the COVID-19 pandemic in Catalonia DOI Creative Commons
Pau Satorra, Cristian Tebé

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

Published: Feb. 20, 2024

Abstract In this study, we modelled the incidence of COVID-19 cases and hospitalisations by basic health areas (ABS) in Catalonia. Spatial, temporal spatio-temporal trends were described using estimation methods that allow to borrow strength from neighbouring time points. Specifically, used Bayesian hierarchical models estimated with Integrated Nested Laplace Approximation (INLA). An exploratory analysis was conducted identify potential ABS factors associated hospitalisations. High heterogeneity hospitalisation found between along waves pandemic. Urban have a higher than rural areas, while socio-economic deprivation area addition, full vaccination coverage each showed protective effect on risk

Language: Английский

Citations

5
A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology DOI Creative Commons
Yufeng Wang, Xue Chen, Feng Xue

et al.

ISPRS International Journal of Geo-Information, Journal Year: 2024, Volume and Issue: 13(3), P. 97 - 97

Published: March 18, 2024

Spatial epidemiology investigates the patterns and determinants of health outcomes over both space time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity incorporate spatial temporal dependencies, uncertainties, intricate interactions. However, complexity modelling computations associated with vary across different diseases. Presently, there is a limited comprehensive overview applications in epidemiology. This article aims address gap through thorough review. The review commences by delving into historical development concerning disease mapping, prediction, regression analysis. Subsequently, compares these terms data distribution, general models, environmental covariates, parameter estimation methods, model fitting standards. Following this, essential preparatory processes are outlined, encompassing acquisition, preprocessing, available statistical software. further categorizes summarizes application Lastly, critical examination advantages disadvantages along considerations for application, provided. enhance comprehension dynamic distribution prediction epidemics. By facilitating effective scrutiny, especially context global COVID-19 pandemic, holds significant academic merit practical value. It also contribute improved ecological epidemiological prevention control strategies.

Language: Английский

Citations

5
Data-Driven Mathematical Modeling Approaches for COVID-19: a survey DOI Creative Commons
Jacques Demongeot, Pierre Magal

Physics of Life Reviews, Journal Year: 2024, Volume and Issue: 50, P. 166 - 208

Published: Aug. 8, 2024

In this review, we successively present the methods for phenomenological modeling of evolution reported and unreported cases COVID-19, both in exponential phase growth then a complete epidemic wave. After case an isolated wave, several successive waves separated by endemic stationary periods. Then, treat multi-compartmental models without or with age structure. Eventually, review literature, based on 260 articles selected 11 sections, ranging from medical survey hospital to forecasting dynamics new general population. This favors approach over mechanistic choice references provides simulations number observed COVID-19 10 states (California, China, France, India, Israel, Japan, New York, Peru, Spain United Kingdom).

Language: Английский

Citations

5
An ecological study of COVID-19 outcomes among Florida counties DOI Creative Commons
Sobur Ali, Taj Azarian

BMC Public Health, Journal Year: 2025, Volume and Issue: 25(1)

Published: Feb. 12, 2025

During the COVID-19 pandemic, Florida reported some of highest numbers cases and deaths in US; however, county-level variation outcomes has yet to be comprehensively investigated. The present ecological study aimed assess correlates among counties that explain case rate, mortality fatality rate (CFR) across pandemic waves. We obtained administrative data reports from public repositories. tested spatial autocorrelation geographic clustering CFR. Stepwise linear regression was employed investigate association between 17 demographic, socioeconomic, health-related predictors. found CFR were significantly higher rural compared urban counties, which significant differences vaccination coverage also observed. Multivariate analysis percentage population aged over 65 years, obese people, predictors rate. Median age, coverage, people who smoke, with diabetes influencing factors for Importantly, associated a reduction (R = -0.26, p 0.03) -0.51, < 0.001). Last, we dependencies play role explaining variations counties. Our findings emphasize need targeted, equitable health strategies reduce disparities enhance resilience during crises.

Language: Английский

Citations

0
Determinants of viral haemorrhagic fever risk in Africa’s tropical moist forests: A scoping review of spatial, socio-economic, and environmental factors DOI Creative Commons

Ines Kamguem Sopbue,

Nathalie Kirschvink,

Abel Wade

et al.

PLoS neglected tropical diseases, Journal Year: 2025, Volume and Issue: 19(1), P. e0012817 - e0012817

Published: Jan. 16, 2025

Background Viral haemorrhagic fevers (VHFs) are identified by international health authorities as priorities for research and development, they pose a threat to global economy. VHFs zoonotic diseases whose acute forms in humans present syndrome shock, with mortality rates of up 90%. This work aims at synthetizing existing knowledge on spatial spatially aggregable determinants that support the emergence maintenance African countries covered tropical moist forest, better identify map areas risk. Methodology/principal findings Using Preferred Reporting Items Systematic reviews Meta-Analyses (PRISMA-ScR) guidelines, extension scoping reviews, we searched PubMed, Embase, CAB Abstracts, Scopus databases. English French peer-reviewed documents were retrieved using Boolean logic keyword search terms. The analysis 79 articles published between 1993 2023 offers comprehensive overview complex interactions among abiotic, biotic, demographic, socio-economic, cultural, political risk factors driving forests. Human-to-human transmission is mainly driven political, demographic factors, whereas spillover determined almost all groups especially those an anthropogenic nature. Conclusions/significance Many questions remain unanswered regarding epidemiology By elucidating relevant which have already been studied, this review seeks advance hotspot predictions, mapping disease surveillance control systems improvement.

Language: Английский

Citations

0
SpaCE: a spatial counterfactual explainable deep learning model for predicting out-of-hospital cardiac arrest survival outcome DOI
Jielu Zhang, Lan Mu, Donglan Zhang

et al.

International Journal of Geographical Information Science, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 32

Published: Jan. 28, 2025

Language: Английский

Citations

0
Spatial and Spatiotemporal Machine Learning Models for COVID-19 Dynamics: A Review of Methodology and Reporting Practices DOI

Hassan Ajulo,

Faith Alele, Theophilus I. Emeto

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
SIMPLINET: a transmission network simplification method for spatiotemporal epidemic modelling DOI Creative Commons
Kemin Zhu, Ling Yin, Shang Wang

et al.

International Journal of Geographical Information Science, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 27

Published: March 12, 2025

Language: Английский

Citations

0