scholarly journals Using Supervised Machine Learning and Empirical Bayesian Kriging to Reveal Correlates and Patterns of COVID-19 Disease Outbreak in Sub-Saharan Africa: Exploratory Data Analysis

2020 ◽  
Author(s):  
Amobi Onovo ◽  
Akinyemi Atobatele ◽  
Abiye Kalaiwo ◽  
Christopher Obanubi ◽  
Ezekiel James ◽  
...  
Keyword(s):  
Machine Learning ◽  
Exploratory Data Analysis ◽  
Disease Outbreak ◽  
Sub Saharan Africa ◽  
Supervised Machine Learning ◽  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging ◽  
Exploratory Data ◽  
Sub Saharan

scholarly journals Using Supervised Machine Learning and Empirical Bayesian Kriging to reveal Correlates and Patterns of COVID-19 Disease outbreak in sub-Saharan Africa: Exploratory Data Analysis

2020 ◽  
Author(s):  
Amobi Andrew Onovo ◽  
Akinyemi Atobatele ◽  
Abiye Kalaiwo ◽  
Christopher Obanubi ◽  
Ezekiel James ◽  
...  
Keyword(s):  
Machine Learning ◽  
Positive Impact ◽  
Disease Outbreak ◽  
Rate Of Change ◽  
Sub Saharan Africa ◽  
Supervised Machine Learning ◽  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging ◽  
Sub Saharan

AbstractIntroductionCoronavirus disease 2019 (COVID-19) is an emerging infectious disease that was first reported in Wuhan1,2, China, and has subsequently spread worldwide. Knowledge of coronavirus-related risk factors can help countries build more systematic and successful responses to COVID-19 disease outbreak. Here we used Supervised Machine Learning and Empirical Bayesian Kriging (EBK) techniques to reveal correlates and patterns of COVID-19 Disease outbreak in sub-Saharan Africa (SSA).MethodsWe analyzed time series aggregate data compiled by Johns Hopkins University on the outbreak of COVID-19 disease across SSA. COVID-19 data was merged with additional data on socio-demographic and health indicator survey data for 39 of SSA’s 48 countries that reported confirmed cases and deaths from coronavirus between February 28, 2020 through March 26, 2020. We used supervised machine learning algorithm, Lasso for variable selection and statistical inference. EBK was used to also create a raster estimating the spatial distribution of COVID-19 disease outbreak.ResultsThe lasso Cross-fit partialing out predictive model ascertained seven variables significantly associated with the risk of coronavirus infection (i.e. new HIV infections among pediatric, adolescent, and middle-aged adult PLHIV, time (days), pneumococcal conjugate-based vaccine, incidence of malaria and diarrhea treatment). Our study indicates, the doubling time in new coronavirus cases was 3 days. The steady three-day decrease in coronavirus outbreak rate of change (ROC) from 37% on March 23, 2020 to 23% on March 26, 2020 indicates the positive impact of countries’ steps to stymie the outbreak. The interpolated maps show that coronavirus is rising every day and appears to be severely confined in South Africa. In the West African region (i.e. Burkina Faso, Ghana, Senegal, Cote d’Iviore, Cameroon, and Nigeria), we predict that new cases and deaths from the virus are most likely to increase.InterpretationIntegrated and efficiently delivered interventions to reduce HIV, pneumonia, malaria and diarrhea, are essential to accelerating global health efforts. Scaling up screening and increasing COVID-19 testing capacity across SSA countries can help provide better understanding on how the pandemic is progressing and possibly ensure a sustained decline in the ROC of coronavirus outbreak.FundingAuthors were wholly responsible for the costs of data collation and analysis.

scholarly journals Groundwater Quality Monitoring Using In-Situ Measurements and Hybrid Machine Learning with Empirical Bayesian Kriging Interpolation Method

2021 ◽  
Vol 12 (1) ◽  
pp. 132
Author(s):  
Delia B. Senoro ◽  
Kevin Lawrence M. de Jesus ◽  
Leonel C. Mendoza ◽  
Enya Marie D. Apostol ◽  
Katherine S. Escalona ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heavy Metal ◽  
Groundwater Quality ◽  
Quality Monitoring ◽  
Hybrid Technique ◽  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging ◽  
Groundwater Quality Monitoring ◽  
Hybrid Machine

This article discusses the assessment of groundwater quality using a hybrid technique that would aid in the convenience of groundwater (GW) quality monitoring. Twenty eight (28) GW samples representing 62 barangays in Calapan City, Oriental Mindoro, Philippines were analyzed for their physicochemical characteristics and heavy metal (HM) concentrations. The 28 GW samples were collected at suburban sites identified by the coordinates produced by Global Positioning System Montana 680. The analysis of heavy metal concentrations was conducted onsite using portable handheld X-Ray Fluorescence (pXRF) Spectrometry. Hybrid machine learning—geostatistical interpolation (MLGI) method, specific to neural network particle swarm optimization with Empirical Bayesian Kriging (NN-PSO+EBK), was employed for data integration, GW quality spatial assessment and monitoring. Spatial map of metals concentration was produced using the NN-PSO-EBK. Another, spot map was created for observed metals concentration and was compared to the spatial maps. Results showed that the created maps recorded significant results based on its MSEs with values such as 1.404 × 10−4, 5.42 × 10−5, 6.26 × 10−4, 3.7 × 10−6, 4.141 × 10−4 for Ba, Cu, Fe, Mn, Zn, respectively. Also, cross-validation of the observed and predicted values resulted to R values range within 0.934–0.994 which means almost accurate. Based on these results, it can be stated that the technique is efficient for groundwater quality monitoring. Utilization of this technique could be useful in regular and efficient GW quality monitoring.

Testing statistical methods to predict pesticide drift deposition

2020 ◽  
Author(s):  
Glenda Garcia-Santos ◽  
Michael Scheiber ◽  
Juergen Pilz
Keyword(s):  
Risk Assessment ◽  
Spatial Interpolation ◽  
Soil Surface ◽  
Farming Systems ◽  
Linear Interpolation ◽  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging ◽  
Spray Technique ◽  
Pesticide Drift

<p><span>We studied the case of the Andean </span><span>region in Colombia as example of non-mechanized small farming systems in which farmers </span><span>use handheld sprayers to spray pesticides. This is the most common </span>technique to spray <span>pesticide in developing countries. To better understand the spatial distribution of</span> airborne pesticide drift deposits<span> on the soil surface using that spray technique, nine different spatial interpolation </span><span>methods were tested using a surrogate tracer substance (Uranine) i.e. classical approaches </span><span>like the linear interpolation and kriging, and some advanced methods like spatial vine </span><span>copulas, the Karhunen-Loève expansion of the underlying random field, the integrated </span><span>nested Laplace approximation and the Empirical Bayesian Kriging used in ArcMap (GIS). </span><span>This study contributes to</span><span> future </span><span>studies on mass balance and risk assessment related to </span>environmental <span>drift pollution in developing </span><span>countries.</span></p>

scholarly journals Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 62
Author(s):  
Colby T. Ford ◽  
Daniel Janies
Keyword(s):  
Machine Learning ◽  
Computational Models ◽  
Artemisinin Resistance ◽  
Parasite Clearance ◽  
Machine Learning Algorithms ◽  
Sub Saharan Africa ◽  
Ensemble Machine Learning ◽  
Sub Saharan ◽  
Underlying Mechanisms

Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

scholarly journals Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 62
Author(s):  
Colby T. Ford ◽  
Daniel Janies
Keyword(s):  
Machine Learning ◽  
Computational Models ◽  
Artemisinin Resistance ◽  
Parasite Clearance ◽  
Machine Learning Algorithms ◽  
Sub Saharan Africa ◽  
Ensemble Machine Learning ◽  
Sub Saharan ◽  
Underlying Mechanisms

Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

scholarly journals Support for families of isolated or deceased COVID-19 patients in sub-Saharan Africa

2020 ◽  
Vol 7 (2) ◽  
pp. 205510292097529
Author(s):  
Francky Teddy Endomba ◽  
Guy Sadeu Wafeu ◽  
Arnauld Efon-Ekangouo ◽  
Linda Djune-Yemeli ◽  
Cyrille Donfo-Azafack ◽  
...  
Keyword(s):  
Mental Health ◽  
Infectious Disease ◽  
Disease Outbreak ◽  
Sub Saharan Africa ◽  
Psychological Consequences ◽  
Cultural Significance ◽  
Infectious Disease Outbreak ◽  
Cultural Considerations ◽  
Sub Saharan ◽  
Substantial Interest

Aside the direct effect of the COVID-19 on infected patients, this infectious disease outbreak has various psychological consequences. These mental health repercussions pertain to the general population of uninfected individuals, and particularly families of isolated or deceased COVID-19 patients. This aspect is of substantial interest amid sub-Saharan African communities, considering the key place and cultural significance of mourning and funerals in these settings. In this commentary, we discuss on the issue of psychological and social support of COVID-19 patients’ families, by taking into account some sub-Saharan African cultural considerations.

Sign in / Sign up

Export Citation Format

Share Document