Spatial and temporal heterogeneity of air pollution in East Africa

The Science of The Total Environment, Год журнала: 2023, Номер 886, С. 163734 - 163734

Опубликована: Апрель 28, 2023

Язык: Английский

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

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Март 5, 2024

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

Язык: Английский

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Factors predictive of epidemic waves of Covid-19 in Africa during the first two years of the pandemic.

IJID Regions, Год журнала: 2025, Номер unknown, С. 100574 - 100574

Опубликована: Янв. 1, 2025

Язык: Английский

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Geospatial modelling of COVID19 mortality in Oman using geographically weighted Poisson regression GWPR

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 8, 2025

The year 2020 witnessed the arrival of global COVID-19 pandemic, which became most devastating public health disaster in last decade. Understanding underlying spatial variations consequences particularly mortality, is crucial for plans and policies. Nevertheless, few studies have been conducted on key determinants mortality how these might vary geographically across developing nations. Therefore, this research aims to address gaps by adopting Geographically Weighted Poisson Regression (GWPR) model investigate heterogeneity Oman. findings indicated that local GWPR performed better than Ordinary Least Square (OLS) model, relationship between risk factors cases varied at a subnational scale. parameter estimates revealed elderly populations, respiratory diseases, population density were significant predicting cases. variable was influential regressor, followed diseases. formulated policy recommendations will provide decision-makers practitioners with related pandemic so future interventions preventive measures can mitigate high fatality risks.

Язык: Английский

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An investigation into the impact of temporality on COVID-19 infection and mortality predictions: new perspective based on Shapley Values

BMC Medical Research Methodology, Год журнала: 2025, Номер 25(1)

Опубликована: Апрель 24, 2025

Abstract Introduction Machine learning models have been employed to predict COVID-19 infections and mortality, but many were built on training testing sets from different periods. The purpose of this study is investigate the impact temporality, i.e., temporal gap between sets, model performances for predicting mortality. Furthermore, seeks understand causes temporality. Methods This used a surveillance dataset collected Brazil in year 2020, 2021 2022, prediction mortality using random forest logistic regression, with 20 features. Models trained tested based data years same as well, examine To further explain temporality its driving factors, Shapley values are quantify individual contributions predictions. Results For infection model, we found that had negative accuracy. On average, loss accuracy was 0.0256 regression 0.0436 when there sets. 0.0144 0.0098 forest, which means not strong model. uncovered reason behind such differences models. Conclusions Our confirmed performance infections, it did find value revealed fixed set four features made predominant across three (2020–2022), while no years.

Язык: Английский

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The future of spatial epidemiology in the AI era: enhancing machine learning approaches with explicit spatial structure

Geospatial health, Год журнала: 2025, Номер 20(1)

Опубликована: Июнь 4, 2025

Spatial epidemiology, defined as the study of spatial patterns in disease burdens or health outcomes, aims to estimate risk incidence by identifying geographical factors and populations at (Morrison et al., 2024). Research epidemiology relies on both conventional approaches Machine- Learning (ML) algorithms explore geographic diseases identify influential (Pfeiffer & Stevens, 2015). Traditional techniques, including autocorrelation using global Moran’s I, Geary’s C (Amgalan 2022), Ripley’s K Function (Kan Local Indicators Association (LISA) (Sansuk 2023), hotspot analysis Getis-Ord Gi* (Lun lag models (Rey Franklin, Geographically Weighted Regression (GWR) (Kiani 2024) are designed explicitly incorporate structure data into modelling, often referred spatially aware (Reich 2021). Beyond these models, several other that have been widely applied epidemiological studies include but not limited Bayesian account for uncertainty mapping, such Hierarchical Conditional Autoregressive (CAR), Besage, York, Mollie’ (BYM) (Louzada methods statistically rigorous techniques assume neighboring regions share similar values. Kulldorff’s Scan Statistic is another traditional technique uses a moving circular window extract significant clusters (Tango, Moreover, geostatistical Kriging Inverse Distance Weighting (IDW) allow continuous interpolation (Nayak [...]

Язык: Английский

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Living in a pandemic: A review of COVID-19 integrated risk management

International Journal of Disaster Risk Reduction, Год журнала: 2023, Номер 98, С. 104081 - 104081

Опубликована: Окт. 30, 2023

Managing new and complex risks has been one of the greatest societal challenges. At start COVID-19 pandemic, governments all over world had to make urgent decisions address a public health crisis. Such consider impacts alternative options on health, citizens' behaviours, countries' economies. However, most policies undertaken during first phase pandemic were based primarily data, with less emphasis given combining evidence e.g., social economic impacts. This resulted in serious consequences at individual level. In this paper, we conduct scoping review risk management literature focused integrating from behavioural domains. Using SPIDER method, selected sample papers using different approaches integrate diverse types knowledge evidence. Examples include multi-criteria model responses or geographical information systems supporting preparedness assessment. The results reveal that only two three domains considered majority these papers' approach is based. Also, than half domains, often providing frameworks are not tested empirically. Further, discuss emergent main themes research gaps including lack Global South perspective limited integration quantitative data. We conclude by recommendations future directions improve integrated management.

Язык: Английский

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Prevalence of anxiety, depression and post-traumatic stress disorder symptoms in children and adolescents during the COVID-19 pandemic: A systematic review and meta-analysis

Journal of Public Health, Год журнала: 2023, Номер unknown

Опубликована: Дек. 29, 2023

Язык: Английский

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

Healthcare, Год журнала: 2023, Номер 11(6), С. 881 - 881

Опубликована: Март 17, 2023

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

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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