Bentham Science Publishers B.V.

 Journal

 Investigating The Resurgence Of Malaria Prevalence In South Africa Between 2015 And 2018: A Scoping Review

 Abiodun, G.J., Adebiyi, B., Abiodun, R.O., Oladimeji, O., Oladimeji, K., Adeola, A.M., Makinde, O.S., Okosun, K.O., Djidjou-Demasse, R., Semegni, V.J., Njabo, K.Y., Witbooi, P.J., Aceves, A.

 2020

Background: Malaria remains a serious concern in most African countries, causing nearly one million deaths globally every year. The review aims to examine the extent and nature of the resurgence of malaria prevalence in South Africa. Method: Using the Arksey and O'Malley framework, this scoping review included articles published between the year 2015 and 2018 on the resurgence of malaria prevalence in South Africa. Articles were searched from October 2018 to January 2019 using these electronic databases: CINAHL, Pubmed, Science Direct and SCOPUS. Grey literature from Google Scholar was also hand searched. Key search terms and subject headings such as climate variables; climate changes; climatic factors; malaria resurgence; malaria reoccurrence, and malaria increase over epidemic regions in South Africa were used to identify relevant articles. Articles for selection and characterization were performed by three independent reviewers. Data collected were synthesized qualitatively. Results: A total number of 748 studies were identified. Among these, 24 studies met the inclusion criteria. The results were grouped by factors (four main themes) that influenced the malaria resurgence: Climatic, Epidemiological, Socio-economic, and Environmental factors. Climatic factors were found to be the major factor responsible for the resurgence of malaria, as more than 55% of the selected articles were climate focused. This was followed by epidemiological, socio-economic and environmental factors, in that order. Grey literature from Google Scholar yielded no results. Conclusion: This study shows that malaria transmission in South Africa is more associated with climate. Climate-based malaria models could be used as early warning systems of malaria over the epidemic regions in South Africa. Since epidemiological factors also play significant roles in the transmission, regular and unrelaxed use of indoor residual spraying (IRS) should be encouraged in these regions. While some studies have indicated that vectors have developed resistance to insecticides, continuous research on developing new insecticides that could alter the resistance are ongoing. Individuals should also be educated on the importance and the usefulness of these deliveries. Furthermore, all efforts to eradicate malaria in South Africa must also target the epidemic neighbouring countries. 

 Taylor And Francis

 Journal

 The Impact Of Rainfall And Temperature On Malaria Dynamics In The KwaZula-Natal Province, South Africa

 Makinde, O.S. And Abiodun, G.J.

 2020

The impact of climate variability on malaria transmission cannot be overemphasized. The interconnection between them could be well established using both statistical and deterministic models. However, this is difficult due to limited long-term malaria data over the study areas. In this paper, analysis of simulated data from the previous study of (1) was carried out to draw important inference that offers in-depth understanding on climate-malaria incidence linkages over KwaZulu-Natal Province. In particular, linear models based on stepwise regression were formulated for the number of exposed, infected and recovered individuals from malaria based on some climate variables and number of susceptible individuals to malaria. In fitting linear model to malaria data, care must be taken in ensuring that residuals of the model are not serially correlated. Ljung-Box test shows that residuals of the models are serially correlated. As a remedial measure, autoregressive integrated moving average model was fitted to the correlated residuals. In addition, it was found that an increase in daily rain amount and mean temperature significantly raises the chance of exposure to malaria while number of susceptible and exposed individuals affects transmission of malaria infection. The proportion of recovered individuals depends much on the number of malaria infected individuals rather than climatic variables. 

 Taylor And Francis

 Journal

 Modelling The Gross Domestic Product Of Nigeria From 1985 To 2018

 Makinde, O.S., Adepetun, A.O. And Oseni, B.M.

 2020

Gross domestic products (GDP) can be viewed as an an internationally accepted measure of economy size and strength. Recent studies have described the relationship between economic growth in terms of gross domestic products and income, decline in mortality and improved health outcomes of some countries. In this paper, stochastic models based on autoregressive integrated moving average (ARIMA (p,d,q)) models of various orders are presented, with a view to identifying the optimal model for the gross domestic product of Nigeria. ARIMA (p,d,q) models are formulated for GDP from some economic components and Nigerian overall GDP on a yearly basis. The choice of ARIMA models of orders p and q is intended to retain any persistence in natural process. Observed data are compared with the fitted data using the optimal models. The result shows that the GDP from agriculture, industries, trade and services show statistically significant increasing trends (p-value < 2.22e-16) over the study period from 1985 to 2015 while the overall GDP decreases in 2016 due to a decrease in the GDP from industry and a slight decrease in GDP from each of construction, trade and services. Also, the Nigeria’s GDP data is linear, nonseasonal and has no structural break. 

 Taylor And Francis

 Journal

 On Rank Distribution Classifiers For High Dimensional Data

 Makinde, O.S.

 2020

Spatial sign and rank based methods have been studied in the recent literature, especially when the dimension is smaller than the sample size. In this paper, a classification method based on the distribution of rank functions for high dimensional data is considered with extension to functional data. The method is fully nonparametric in nature. The performance of the classification method is illustrated in comparison with some other classifiers using simulated and real data sets. Supporting code in R are provided for computational implementation of the classification method that will be of use to others. 

 MDPI

 Journal

 A Dynamical And Zero-inflated Negative Binomial Regression Modelling Of Malaria Incidence In Limpopo Province, South Africa

 Abiodun G.J., Makinde O.S., Adeola A.M., Njabo K.Y., Witbooi P.J., Djidjou-Demasse R., Botai J.O.

 2019

Recent studies have considered the connections between malaria incidence and climate variables using mathematical and statistical models. Some of the statistical models focused on time series approach based on Box–Jenkins methodology or on dynamic model. The latter approach allows for covariates different from its original lagged values, while the Box–Jenkins does not. In real situations, malaria incidence counts may turn up with many zero terms in the time series. Fitting time series model based on the Box–Jenkins approach and ARIMA may be spurious. In this study, a zero-inflated negative binomial regression model was formulated for fitting malaria incidence in Mopani and Vhembe?two of the epidemic district municipalities in Limpopo, South Africa. In particular, a zero-inflated negative binomial regression model was formulated for daily malaria counts as a function of some climate variables, with the aim of identifying the model that best predicts reported malaria cases. Results from this study show that daily rainfall amount and the average temperature at various lags have a significant influence on malaria incidence in the study areas. The significance of zero inflation on the malaria count was examined using the Vuong test and the result shows that zero-inflated negative binomial regression model fits the data better. A dynamical climate-based model was further used to investigate the population dynamics of mosquitoes over the two regions. Findings highlight the significant roles of Anopheles arabiensis on malaria transmission over the regions and suggest that vector control activities should be intense to eradicate malaria in Mopani and Vhembe districts. Although An. arabiensis has been identified as the major vector over these regions, our findings further suggest the presence of additional vectors transmitting malaria in the study regions. The findings from this study offer insight into climate-malaria incidence linkages over Limpopo province of South Africa. 

 Kuwait University

 Journal

 Classification Of Gene Expression Data: Distance Based Method

 Makinde, O.S.

 2019

Micro-array dataset is a classical example of high throughput data characterized with more features (genes) than sample points (gene expression levels). A number of classification techniques have been proposed in literature. Many of these methods are either computationally expensive or perform sub-optimally. In this paper, some distance functions are considered and classification rules based on the distance functions are formulated. The distance functions include average distance measure, distance to component-wise median, distance to mean. We also define a probabilistic approach to classification rules based on two of the distance measures. Gene selection technique based on shrunken centroids regularized discriminant analysis was employed on small round blue cell tissue, colon cancer, lymphoma, prostate cancer and leukaemia data before applying the classification rules. Three simulation studies were performed to mimic gene expression data. The performance of the classification methods mentioned above was compared with performance of some known classification methods in literature. The performance of the distance-based classification methods is competitive with some existing classification methods. Distance based methods implemented in this study are computationally simple and very cheap in terms of computational cost. 

 Taylor And Francis

 Journal

 On Maximum Depth Classifiers: Depth Distribution Approach

 Makinde O. S. And Fasoranbaku O. A.

 2018

In this paper, we consider the notions of data depth for ordering multivariate data and propose a classification rule based on the distribution of some depth functions in Rd. The equivalence of the proposed classification rule to optimal Bayes rule is discussed under suitable conditions. The performance of the proposed classification method is investigated in low- and high-dimensional setting using real datasets. Also, the performance of the proposed classification method is illustrated in comparison to some other depth-based classifiers using simulated data sets.

 

 Hindawi

 Journal

 Exploring The Influence Of Daily Climate Variables On Malaria Transmission And Abundance Of Anopheles Arabiensis Over Nkomazi Local Municipality, Mpumalanga Province, South Africa

 Abiodun G.J., Njabo K.Y., Witbooi P., Adeola A.M., Fuller T.L., Okosun K.O., Makinde O.S. And Botai J.

 2018

,e recent resurgence of malaria incidence across epidemic regions in South Africa has been linked to climatic and environmental factors. An in-depth investigation of the impact of climate variability and mosquito abundance on malaria parasite incidence may therefore offer useful insight towards the control of this life-threatening disease. In this study, we investigate the influence of climatic factors on malaria transmission over Nkomazi Municipality. ,e variability and interconnectedness between the variables were analyzed using wavelet coherence analysis. Time-series analyses revealed that malaria cases significantly declined after the outbreak in early 2000, but with a slight increase from 2015. Furthermore, the wavelet coherence and time-lagged correlation analyses identified rainfall and abundance of Anopheles arabiensis as the major variables responsible for malaria transmission over the study region. ,e analysis further highlights a high malaria intensity with the variables from 1998–2002, 2004–2006, and 2010–2013 and a noticeable periodicity value of 256–512 days. Also, malaria transmission shows a time lag between one month and three months with respect to mosquito abundance and the different climatic variables. ,e findings from this study offer a better understanding of the importance of climatic factors on the transmission of malaria. ,e study further highlights the significant roles of An. arabiensis on malaria occurrence over Nkomazi. Implementing the mosquito model to predict mosquito abundance could provide more insight into malaria elimination or control in Africa 

 Taylor And Francis

 Journal

 On Some Classifiers Based On Multivariate Ranks

 Olusola Makinde And Biman Chakraborty

 2017

Nonparametric approaches to classification has gained significant attention in the last two decades. In this paper, we propose a classification methodology based on the multivariate rank functions and show that it is a Bayes rule for spherically symmetric distributions with a location shift. We show that a rank based classifier is equivalent to optimal Bayes rule under suitable conditions. We also present an affine invariant version of the classifier. To accommodate different covariance structures, we construct a classifier based on the central rank region. Asymptotic properties of these classification methods are studied. We illustrate the performance of our propose methods in comparison to some other depth based classifiers using simulated and real data sets.

 

 Springer Verlag

 Journal

 Classification Rules Based On Distribution Functions Of Functional Depth

 Makinde O.S.

 2016

In ordering multivariate objects, the use of data depth provides a centreoutward ranking. The notion of data depth has been extended to functional data setting and applied in classifying functional data, for example maximal depth classification rules. In this paper, we explore notions of functional depth and propose a classification method based on distribution functions of data depth for functional data. Theperformance of this method is examined by using simulations and real data sets and the results are compared with the results from existing methods .