GIS-based spatio-temporal analysis and modeling of COVID-19 incidence rates in Europe

Kianfar, Nima; Mesgari, Mohammad Saadi

doi:10.1016/j.sste.2022.100498

Cited by 11 publications

(7 citation statements)

References 40 publications

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“…The combination of PCA and VIF allows for a more holistic evaluation of multicollinearity, validating the efficacy of the PCA dimensionality reduction while ensuring the dependability of regression model outcomes. This integrated approach fortifies the robustness of statistical analyses, contributing to a more comprehensive understanding of the interrelationships among variables, as evidenced in prior studies [ 25 , 26 ]. From Fig.…”

Section: Simulation Studymentioning

confidence: 82%

Optimizing spatio-temporal correlation structures for modeling food security in Africa: a simulation-based investigation

Bofa,

Zewotir

2024

BMC Bioinformatics

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This study investigates the impact of spatio- temporal correlation using four spatio-temporal models: Spatio-Temporal Poisson Linear Trend Model (SPLTM), Poisson Temporal Model (TMS), Spatio-Temporal Poisson Anova Model (SPAM), and Spatio-Temporal Poisson Separable Model (STSM) concerning food security and nutrition in Africa. Evaluating model goodness of fit using the Watanabe Akaike Information Criterion (WAIC) and assessing bias through root mean square error and mean absolute error values revealed a consistent monotonic pattern. SPLTM consistently demonstrates a propensity for overestimating food security, while TMS exhibits a diverse bias profile, shifting between overestimation and underestimation based on varying correlation settings. SPAM emerges as a beacon of reliability, showcasing minimal bias and WAIC across diverse scenarios, while STSM consistently underestimates food security, particularly in regions marked by low to moderate spatio-temporal correlation. SPAM consistently outperforms other models, making it a top choice for modeling food security and nutrition dynamics in Africa. This research highlights the impact of spatial and temporal correlations on food security and nutrition patterns and provides guidance for model selection and refinement. Researchers are encouraged to meticulously evaluate the biases and goodness of fit characteristics of models, ensuring their alignment with the specific attributes of their data and research goals. This knowledge empowers researchers to select models that offer reliability and consistency, enhancing the applicability of their findings.

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Section: Simulation Studymentioning

confidence: 82%

Optimizing spatio-temporal correlation structures for modeling food security in Africa: a simulation-based investigation

Bofa,

Zewotir

2024

BMC Bioinformatics

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“…The selected model has the least Akaike Information Criterion (AIC) from the stepwise forward approach that was conducted. In addition, Variance Inflation Factor (VIF)/generalized Variance Inflation Factor (GVIF) was used as regression diagnostic measure to detect the presence of collinear variables in order to avoid multicollinearity in our model and reduce standard error of model coefficients according to the works of 37 , 38 . Furthermore, we evaluated our designed model accuracy using cross-validation ( k -fold) technique.…”

Section: Methodsmentioning

confidence: 99%

Identifying childhood malaria hotspots and risk factors in a Nigerian city using geostatistical modelling approach

Bayode,

Siegmund

2024

Sci Rep

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Malaria ranks high among prevalent and ravaging infectious diseases in sub-Saharan Africa (SSA). The negative impacts, disease burden, and risk are higher among children and pregnant women as part of the most vulnerable groups to malaria in Nigeria. However, the burden of malaria is not even in space and time. This study explores the spatial variability of malaria prevalence among children under five years (U5) in medium-sized rapidly growing city of Akure, Nigeria using model-based geostatistical modeling (MBG) technique to predict U5 malaria burden at a 100 × 100 m grid, while the parameter estimation was done using Monte Carlo maximum likelihood method. The non-spatial logistic regression model shows that U5 malaria prevalence is significantly influenced by the usage of insecticide-treated nets—ITNs, window protection, and water source. Furthermore, the MBG model shows predicted U5 malaria prevalence in Akure is greater than 35% at certain locations while we were able to ascertain places with U5 prevalence > 10% (i.e. hotspots) using exceedance probability modelling which is a vital tool for policy development. The map provides place-based evidence on the spatial variation of U5 malaria in Akure, and direction on where intensified interventions are crucial for the reduction of U5 malaria burden and improvement of urban health in Akure, Nigeria.

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“…The global spatial autocorrelation (Global Moran’s I) was computed to assess whether the district level spatial distribution of HIV/AIDS self-testing uptake and the covariates was dispersed, clustered, or random. Moran’s I [ 36 ], was utilised in this study to assess the presence or absence of spatial dependence and clustering of residuals (the index ranges between −1 and +1). When Moran’s I is positive, the distribution has a propensity towards clustering of similar values (+1), and 0 is usually indicative of no spatial autocorrelation.…”

Section: Methodsmentioning

confidence: 99%

“…When Moran’s I is positive, the distribution has a propensity towards clustering of similar values (+1), and 0 is usually indicative of no spatial autocorrelation. However, for a negative Moran’s I (−1), the distribution tends towards a perfect dispersion, with clustering of dissimilar values [ 36 ]. A detailed explanation of spatial autocorrelation using Global Moran’s’ I is published in multiple studies [ 10 , 37 , 38 , 39 , 40 , 41 ].…”

Section: Methodsmentioning

confidence: 99%

Exploring Determinants of HIV/AIDS Self-Testing Uptake in South Africa Using Generalised Linear Poisson and Geographically Weighted Poisson Regression

et al. 2023

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Increased HIV/AIDS testing is of paramount importance in controlling the HIV/AIDS pandemic and subsequently saving lives. Despite progress in HIV/AIDS testing programmes, most people are still reluctant to test and thus are still unaware of their status. Understanding the factors associated with uptake levels of HIV/AIDS self-testing requires knowledge of people’s perceptions and attitudes, thus informing evidence-based decision making. Using the South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey of 2017 (SABSSM V), this study assessed the efficacy of Generalised Linear Poisson Regression (GLPR) and Geographically Weighted Poisson Regression (GWPR) in modelling the spatial dependence and non-stationary relationships of HIV/AIDS self-testing uptake and covariates. The models were calibrated at the district level across South Africa. Results showed a slightly better performance of GWPR (pseudo R2 = 0.91 and AICc = 390) compared to GLPR (pseudo R2 = 0.88 and AICc = 2552). Estimates of local intercepts derived from GWPR exhibited differences in HIV/AIDS self-testing uptake. Overall, the output of this study displays interesting findings on the levels of spatial heterogeneity of factors associated with HIV/AIDS self-testing uptake across South Africa, which calls for district-specific policies to increase awareness of the need for HIV/AIDS self-testing.

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GIS-based spatio-temporal analysis and modeling of COVID-19 incidence rates in Europe

Cited by 11 publications

References 40 publications

Optimizing spatio-temporal correlation structures for modeling food security in Africa: a simulation-based investigation

Optimizing spatio-temporal correlation structures for modeling food security in Africa: a simulation-based investigation

Identifying childhood malaria hotspots and risk factors in a Nigerian city using geostatistical modelling approach

Exploring Determinants of HIV/AIDS Self-Testing Uptake in South Africa Using Generalised Linear Poisson and Geographically Weighted Poisson Regression

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