scholarly journals Catching The Drivers of Inclusive Growth In Sub-Saharan Africa: An Application of Machine Learning

2021 ◽  
Author(s):  
Isaac Kwesi Ofori
Keyword(s):  
Machine Learning ◽  
Sub Saharan Africa ◽  
Inclusive Growth ◽  
Sub Saharan

scholarly journals Composite Index of Inclusive Growth in Sub-Saharan Africa: The Inclusion of the Informal Sector

2020 ◽  
pp. 1-16
Author(s):  
Omang Ombolo Messono ◽  
Nsoga Nsoga Mermoz Homère III
Keyword(s):  
Burkina Faso ◽  
Informal Sector ◽  
Composite Index ◽  
Principal Component ◽  
Sub Saharan Africa ◽  
Inclusive Growth ◽  
African Countries ◽  
Positive Growth ◽  
Sub Saharan ◽  
Growth Changes

This paper aims to provide a composite index of inclusive growth in 32 sub-Saharan African countries between 1995 and 2014 by taking into account the importance of the informal sector. Following the principal component analysis methods, we find specifically that except for countries such as Djibouti, Burkina Faso, Mauritius, Nigeria and Zimbabwe, inclusive growth has trended upward over the study period. This trend is non-linear and is characterized by two sub periods. From 1995 to 2005, the composite index of inclusive growth is essentially negative. On the other hand, positive growth in value is recorded over the second sub-period from 2005 to 2014. Overall and on average, these countries have experienced inclusive growth. Moreover, we also note that in countries such as Burkina Faso, Mauritius and Nigeria, on the side-lines of the informal sector inclusive growth has a negative trend. However, when we integrate the informal sector, the trend of inclusive growth changes sign and becomes positive.

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.

Infrastructure and inclusive growth in sub-Saharan Africa: An empirical analysis

2020 ◽  
Vol 20 (3) ◽  
pp. 187-207
Author(s):  
Onesmus Mbaabu Mutiiria ◽  
Qingjiang Ju ◽  
Koffi Dumor
Keyword(s):  
Energy Transport ◽  
Growth Index ◽  
Information And Communications Technology ◽  
Sub Saharan Africa ◽  
Inclusive Growth ◽  
Income Disparities ◽  
The Rich ◽  
Sub Saharan ◽  
Access And Affordability ◽  
Infrastructure Services

This study provides an empirical assessment of infrastructure and inclusive growth in sub-Saharan Africa (SSA). An inclusive growth index has been constructed and then used to test the infrastructure–inclusive growth nexus. The study has also examined whether infrastructure has a distributive impact on income groups. The overall analysis employed panel data collected from 31 SSA countries over the period 2003–17. The study found a positive link between infrastructure and inclusive growth. These results were significant for energy, transport and information and communications technology (ICT) infrastructures. It was also found that poorer people gain more benefits from the listed infrastructures than the rich, which shows that infrastructure plays an important role in the distribution of income. The overall results imply that infrastructure is vital in reducing income disparities and enhancing shared prosperity in SSA. Policies for increasing access and affordability of infrastructure services are highly recommended to promote inclusion.

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 Global Value Chain Participation and Inclusive Growth in Sub-Saharan Africa

2021 ◽  
Author(s):  
Kwasi Camara Obeng ◽  
Peter Yeltulme Mwinlaaru ◽  
Isaac Kwesi Ofori
Keyword(s):  
Value Chain ◽  
Sub Saharan Africa ◽  
Global Value Chain ◽  
Inclusive Growth ◽  
Sub Saharan
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