scholarly journals Spatial distribution and sociodemographic risk factors of malaria in Nigerian children less than 5 years old

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Chigozie Louisa J. Ugwu ◽  
Temesgen Zewotir
Keyword(s):  
Spatial Variability ◽  
Spatial Correlation ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Malaria Prevalence ◽  
High Rate ◽  
Bed Nets ◽  
Targeted Interventions ◽  
North East ◽  
Socioeconomic And Demographic Factors

Malaria remains a leading cause of morbidity and mortality among children in Nigeria less than 5 years old (under-5). This study utilized nationally representative secondary data extracted from the 2015 Nigeria Malaria Indicator Survey (NMIS) to investigate the spatial variability in malaria distribution in those under- 5 and to explore the influence of socioeconomic and demographic factors on malaria prevalence in this population group. To account for spatial correlation, a Spatially Generalized Linear Mixed Model (SGMM) was employed and predictive risk maps was developed using Kriging. Highly significant spatial variability in under-5 malaria distribution was observed (p<0.0001) with a higher likelihood of malaria prevalence in this group in the North-west and North-east of the country. The number of malaria infections increased with age, children aged between 49-59 months were found to be at a higher risk (Odds Ratio=4.680, 95% CI=3.674 to 5.961 at p<0.0001). After accounting for spatial correlation, we observed a strong significant association between the non-availability or non-use of mosquito bed-nets, low household socioeconomic status, low level of mother’s educational attainment, family size, anaemia prevalence, rural type of residence and under-5 malaria prevalence. Faced with a high rate of under-5 mortality due to malaria in Nigeria, targeted interventions (which requires the identification of the child’s location) may reduce malaria prevalence, and we conclude that socioeconomic impediments need to be confronted to reduce the burden of childhood malaria infection.

scholarly journals LuxUS: DNA Methylation Analysis Using Generalized Linear Mixed Model with Spatial Correlation

2019 ◽  
Author(s):  
Viivi Halla-aho ◽  
Harri Lähdesmäki
Keyword(s):  
Dna Methylation ◽  
Spatial Correlation ◽  
Mixed Model ◽  
Bisulfite Sequencing ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Statistical Testing ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Bisulfite Sequencing Data

AbstractMotivationDNA methylation is an important epigenetic modification, which has multiple functions. DNA methylation and its connections to diseases have been extensively studied in recent years. It is known that DNA methylation levels of neighboring cytosines are correlated and that differential DNA methylation typically occurs rather as regions instead of individual cytosine level.ResultsWe have developed a generalized linear mixed model, LuxUS, that makes use of the correlation between neighboring cytosines to facilitate analysis of differential methylation. LuxUS implements a likelihood model for bisulfite sequencing data that accounts for experimental variation in underlying biochemistry. LuxUS can model both binary and continuous covariates, and mixed model formulation enables including replicate and cytosine random effects. Spatial correlation is included to the model through a cytosine random effect correlation structure. We show with simulation experiments that by utilizing the spatial correlation we gain more power to the statistical testing of differential DNA methylation. Results with real bisulfite sequencing data set show that LuxUS is able to detect biologically significant differentially methylated cytosines.AvailabilityThe tool is available at https://github.com/hallav/LuxUS.Supplementary informationSupplementary data are available at bioRxiv.

MC Simulation of SEBLUP with Spatial Linear Mixed Model for SAE

2014 ◽  
Vol 685 ◽  
pp. 618-622
Author(s):  
Yan Yu Liu ◽  
Ming Zhong Jin ◽  
De You Xie ◽  
Min Qing Gong
Keyword(s):  
Spatial Correlation ◽  
Small Area ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Likelihood Estimation ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Mutual Independence ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

For small area estimation (SAE) Spatial Empirical Best Linear Unbiased Prediction, SEBLUP, is involved in linear mixed model with spatial correlation while Empirical Best Linear Unbiased Prediction, EBLUP, often with mutual independence. In this paper, we discussed maximum likelihood estimation (MLE) and compared the efficiency. Simulation shows that SEBLUP with spatial correlation data of spatial small area is more effective than EBLUP.

scholarly journals High Prevalence and Factors Associated With the Distribution of the Integron intI1 and intI2 Genes in Scottish Cattle Herds

2021 ◽  
Vol 8 ◽  
Author(s):  
Cristina Fernández Rivas ◽  
Thibaud Porphyre ◽  
Margo E. Chase-Topping ◽  
Charles W. Knapp ◽  
Helen Williamson ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Multiple Drug Resistance ◽  
Bacterial Species ◽  
Multiple Drug ◽  
Regional Effect ◽  
Factors Associated ◽  
North East ◽  
Antimicrobial Resistance Genes ◽  
Cattle Herds

Integrons are genetic elements that capture and express antimicrobial resistance genes within arrays, facilitating horizontal spread of multiple drug resistance in a range of bacterial species. The aim of this study was to estimate prevalence for class 1, 2, and 3 integrons in Scottish cattle and examine whether spatial, seasonal or herd management factors influenced integron herd status. We used fecal samples collected from 108 Scottish cattle herds in a national, cross-sectional survey between 2014 and 2015, and screened fecal DNA extracts by multiplex PCR for the integrase genes intI1, intI2, and intI3. Herd-level prevalence was estimated [95% confidence interval (CI)] for intI1 as 76.9% (67.8–84.0%) and intI2 as 82.4% (73.9–88.6%). We did not detect intI3 in any of the herd samples tested. A regional effect was observed for intI1, highest in the North East (OR 11.5, 95% CI: 1.0–130.9, P = 0.05) and South East (OR 8.7, 95% CI: 1.1–20.9, P = 0.04), lowest in the Highlands. A generalized linear mixed model was used to test for potential associations between herd status and cattle management, soil type and regional livestock density variables. Within the final multivariable model, factors associated with herd positivity for intI1 included spring season of the year (OR 6.3, 95% CI: 1.1–36.4, P = 0.04) and watering cattle from a natural spring source (OR 4.4, 95% CI: 1.3–14.8, P = 0.017), and cattle being housed at the time of sampling for intI2 (OR 75.0, 95% CI: 10.4–540.5, P &lt; 0.001). This study provides baseline estimates for integron prevalence in Scottish cattle and identifies factors that may be associated with carriage that warrant future investigation.

scholarly journals Spatial Modelling of Malaria Prevalence in Kenya

2021 ◽  
pp. 8-21
Author(s):  
Morris Mwenda John ◽  
Elphas Luchemo ◽  
Ayubu Anapapa
Keyword(s):  
Urban Areas ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Cultural Practices ◽  
Social Economic ◽  
Malaria Prevalence ◽  
Sub Saharan Africa ◽  
P Value ◽  
Economic Factors ◽  
Malaria Burden

Malaria is one of the leading causes of deaths in Kenya. Malaria is a vector-borne disease caused by a parasite of the genus plasmodium. Complete eradication of malaria in the country has remained a problem. A lot of effort and resources has been put in the fight against malaria in developing countries which has led to underdevelopment and low human development index. Malaria burden affects the world’s poorest countries. About 90% of the malaria burden is reported in sub-Saharan Africa. The disease has led to high mortality cases in children and pregnant women. Despite the massive government eradication campaign, new and resurgent cases have been recorded. The specific objective was to determine the malaria risk factors and spatial distribution in Kenya. The 2015 malaria indicator survey data was used for the study. Demographic and social-economic factors were used as predictor variables. A generalized linear mixed model was used to determine the spatial variation and prevalence of malaria in Kenya. Demographic and social-economic factors were found to have significant impact on Prevalence of malaria in kenya. Most cases of malaria were reported in lake, western and coastal regions. The most prone areas were Kisumu, Homabay, Kakamega and Mombasa. There were less cases in central Kenya counties like Nyeri, Tharaka-Nithi with a significant number reported in arid and semi-arid regions of Northern-Kenya counties of Garissa, Mandera, Baringo. Rural population was more susceptible to malaria compared to those in urban areas. The odds of getting (verse not getting malaria) in places of residence increases by 1.32, which is estimated to .28, CIs 95% (1.01, 1.72), and a p-value .04. Malaria prevalence varied significantly from one region to another. The study established that Spatial autocorrelation exists among regions mostly due to weather patterns, geography, cultural practices and socio-economic factors.

scholarly journals LuxUS: DNA methylation analysis using generalized linear mixed model with spatial correlation

2020 ◽  
Vol 36 (17) ◽  
pp. 4535-4543
Author(s):  
Viivi Halla-aho ◽  
Harri Lähdesmäki
Keyword(s):  
Dna Methylation ◽  
Spatial Correlation ◽  
Mixed Model ◽  
Bisulfite Sequencing ◽  
Linear Mixed Model ◽  
Epigenetic Modification ◽  
Random Effect ◽  
Generalized Linear Mixed Model ◽  
Statistical Testing ◽  
Supplementary Information

Abstract Motivation DNA methylation is an important epigenetic modification, which has multiple functions. DNA methylation and its connections to diseases have been extensively studied in recent years. It is known that DNA methylation levels of neighboring cytosines are correlated and that differential DNA methylation typically occurs rather as regions instead of individual cytosine level. Results We have developed a generalized linear mixed model, LuxUS, that makes use of the correlation between neighboring cytosines to facilitate analysis of differential methylation. LuxUS implements a likelihood model for bisulfite sequencing data that accounts for experimental variation in underlying biochemistry. LuxUS can model both binary and continuous covariates, and mixed model formulation enables including replicate and cytosine random effects. Spatial correlation is included to the model through a cytosine random effect correlation structure. We show with simulation experiments that using the spatial correlation, we gain more power to the statistical testing of differential DNA methylation. Results with real bisulfite sequencing dataset show that LuxUS is able to detect biologically significant differentially methylated cytosines. Availability and implementation The tool is available at https://github.com/hallav/LuxUS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals The use of linear mixed effects models to investigate local adaptation in marine fish subpopulations: case study, changing spawning times of common sole (Solea solea)

2019 ◽  
Vol 76 (7) ◽  
pp. 2297-2304
Author(s):  
Jennifer I Fincham ◽  
Jon Barry
Keyword(s):  
Local Adaptation ◽  
Fisheries Management ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Environmental Gradients ◽  
Management Strategies ◽  
Marine Species ◽  
Window Analysis ◽  
North East ◽  
The North

Abstract Populations along environmental gradients have the potential to adapt to their own local environments. It is important to understand these adaptations in fisheries stocks to fully inform fisheries management strategies. With this is mind, sea temperatures are an important cue in timing for many marine species, including sole in the North-East Atlantic Ocean. We used spawning data and modelled sea surface temperature (SST) data from sole subpopulations to examine the possibility of local adaptation of their spawning times to rising temperature. Climate window analysis was used, in a linear mixed model using mean spawning week and SST, to investigate statistically significant differences between subpopulations of sole. There was no evidence of local adaptation to changing temperatures for these subpopulations. This suggests that their spawning-time reaction to changing temperatures is currently due to their subpopulation’s mean plasticity. Using climate window analysis and modelled temperature data we have demonstrated a method of examining spawning changes in marine populations along a temperature gradient. Recruitment and spawning success are key elements of fisheries population models which contribute to fisheries management. Further understanding of the influence of temperature on recruitment will help inform future modelling.

scholarly journals LuxHS: DNA methylation analysis with spatially varying correlation structure

2020 ◽  
Author(s):  
Viivi Halla-aho ◽  
Harri Lähdesmäki
Keyword(s):  
Dna Methylation ◽  
Spatial Correlation ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Methylation Analysis ◽  
Correlation Pattern ◽  
Analysis Tools ◽  
Spatially Varying ◽  
Dna Methylation Analysis ◽  
Differential Methylation Analysis

ABSTRACTBisulfite sequencing (BS-seq) is a popular method for measuring DNA methylation in basepair-resolution. Many BS-seq data analysis tools utilize the assumption of spatial correlation among the neighboring cytosines’ methylation states. While being a fair assumption, most existing methods leave out the possibility of deviation from the spatial correlation pattern. Our approach builds on a method which combines a generalized linear mixed model (GLMM) with a likelihood that is specific for BS-seq data and that incorporates a spatial correlation for methylation levels. We propose a novel technique using a sparsity promoting prior to enable cytosines deviating from the spatial correlation pattern. The method is tested with both simulated and real BS-seq data and compared to other differential methylation analysis tools.

Sign in / Sign up

Export Citation Format

Share Document