SOME STATISTICAL CONSIDERATIONS TO ON-FARM TRIALS IN KENYA

The issue of design and analysis of on-farm trials is not clearly understood by agricultural researcher in Kenya. Since on-farm trials require participation and co-operation of the farmers who often differ in education level, chances of collecting unreliable and quite variable data are high. This paper highlights the importance of collecting quality data from on-farm trials, and in particular from the researcher designed and farmer managed trial type. Some complexities associated with the implementation, application of basic statistical principles, and analysis of on-farm trials are discussed. Questions that need to be considered priori to the implementation of any trial implementation and certain intervention measures, which incorporate farmers\u27 views, are suggested. The importance of making certain technical adjustments to minimize large variation resulting in on-farm trials is also discussed. The dilemma Kenyan researchers face when conducting on-farm trials, where the farmers\u27 level of education differ greatly are highlighted through examples and some scenarios

ARIMA MODEL IN PREDICTING OF COVID-19 EPIDEMIC FOR THE SOUTHERN AFRICA REGION

Background: Coronavirus pandemic, a serious global public health threat, affects the Southern African countries more than any other country on the continent. The region has become the epicenter of the coronavirus with South Africa accounting for the most cases. To cap the deadly effect caused by the pandemic, we apply a statistical modelling approach to investigate and predict COVID-19 incidence. Methods: Using secondary data on the daily confirmed COVID-19 cases per million for Southern Africa Development Community (SADC) member states from March 5, 2020, to July 15, 2021, we model and forecast the spread of coronavirus in the region. We select the best ARIMA model based on the log-likelihood, AIC, and BIC of the fitted models. Results: The ARIMA (11,1,11) model for the complete data set was finally selected among ARIMA models based upon the parameter test and the Box–Ljung test. The ARIMA(11,1,9) was the best candidate for the training set. A 15-day forecast was also made from the model, which shows a perfect fit with the testing set.   Conclusion: The number of new COVID-19 cases per million for the SADC shows a downward trend, but the trend is characterized by peaks from time to time. Tightening up of the preventive measures continuously needs to be adapted in order to eradicate the coronavirus epidemic from the populatio

MIXED MODELS APPROACH TO ON-FARM TRIALS: AN ALTERNATIVE TO META-ANALYSIS FOR COMPARING ONE TREATMENT TO POSSIBLY DIFFERENT CONTROLS

The estimator of effect size, the sample mean difference divided by the sample standard error of the difference is studied in the context of mixed models and is related to the analysis of on-farm trials. A single treatment is compared against possibly different controls using a completely randomized design on each farm. A lower (1-α)100% confidence limit on mean difference of the treatment and the average control is obtained. The best linear unbiased predictors (BLUPs) of the mean difference of the treatment and the individual controls as well as the lower (1-α)100% prediction limits are provided. The effect of omitting or not omitting the farm-by-treatment interaction variance component in the weighting process is assessed using two numerical examples

Adverse Event Risk Assessment on Patients Receiving Combination Antiretroviral Therapy in South Africa

Purpose: To determine the risk factors for the development of serious adverse events (AEs) in black adult patients on combination antiretroviral therapy (cART). Methods: This prospective cohort study consisted of 368 adult black HIV positive patients receiving cART at the Grey's Hospital, KwaZulu-Natal, South Africa. Patients were intensively monitored for incidence of adverse events and the factors associated with their development, under the Antiretroviral Cohort Adverse Event Monitoring in KwaZulu-Natal (ACADEMIK). Multiple logistic regression models were used to identify the risk factors for AEs. Results: A total of 406 AEs were reported across the 13 patient hospital visits in the study. Peripheral neuropathy was the most prevalent adverse event (16%), followed by hypercholesterolaemia (14%), lipoatrophy/lipodystrophy (13%) and skin reaction (11%). Cluster differentiation (CD4) counts (p = 0.0280), age (p = 0.0227) and weight (p = 0.0017) were identified as the significant predictors for hypercholesterolaemia, while sex (p = 0.0309) was significant with respect to skin reaction. CD4 counts (p=0.0200) was also significant for lipoatrophy/lipodystrophy. Skin reaction (23%), diarrhea (18%), hypercholesterolaemia (15%), thrombocytopenia (15%) and peripheral neuropathy (13%) were the top five most incident AEs. Overall, about 46% of the regimens administered were tenofovir-based and 31% zidovudine-based. Conclusions: To enhance the prevention of hypercholesterolaemia, lipoatrophy/lipodystrophy and skin reaction among black adult HIV positive patients on cART, we recommend that CD4 counts and weight be closely monitored and documented during clinic visits

Short-term forecasting of confirmed daily COVID-19 cases in the Southern African Development Community region

Background: The coronavirus pandemic has resulted in complex challenges worldwide, and the Southern African Development Community (SADC) region has not been spared. The region has become the epicentre for coronavirus in the African continent. Combining forecasting techniques can help capture other attributes of the series, thus providing crucial information to address the problem. Objective: To formulate an effective model that timely predicts the spread of COVID-19 in the SADC region. Methods: Using the Quantile regression approaches; linear quantile regression averaging (LQRA), monotone composite quantile regression neural network (MCQRNN), partial additive quantile regression averaging (PAQRA), among others, we combine point forecasts from four candidate models namely, the ARIMA (p, d, q) model, TBATS, Generalized additive model (GAM) and a Gradient Boosting machine (GBM). Results: Among the single forecast models, the GAM provides the best model for predicting the spread of COVID-19 in the SADC region. However, it did not perform well in some periods. Combined forecasts models performed significantly better with the MCQRNN being the best (Theil’s U statistic=0.000000278). Conclusion: The findings present an insightful approach in monitoring the spread of COVID-19 in the SADC region. The spread of COVID-19 can best be predicted using combined forecasts models, particularly the MCQRNN approach. Keywords: Combined Forecasts; LQRA; PLAQR; OPERA; Quantile Regression Neural Networks; COVID-19

Know your HIV epidemic (KYE) report: review of the HIV epidemic in South Africa.

In order to update and consolidate South Africa’s evidence base for HIV-prevention interventions, it was decided by the Government of South Africa to commission a synthesis of the available data on the epidemiology of prevalent and incident HIV infections, and the wider epidemic context of these infections. This know your epidemic (KYE) approach has been successfully implemented in a number of sub-Saharan African countries.2 The process involves a desk review and secondary analysis of existing biological, behavioural and socio-demographic data in order to determine the epidemiology of new HIV infections. KYE reports present key findings and policy and programme recommendations which are grounded in local evidence and aim to support decision-making and improve HIV-prevention results. In 2010, South Africa also conducted a know your response (KYR) review, which critically assessed HIV-prevention policies, programmes and resource allocations. The overall results of this HIV epidemic review and the KYR review will be published in a separate, national KYE/KYR synthesis report

Modelling the length of time spent in an unemployment state in South Africa

The deteriorating global economic conditions have worsened the unemployment situation, especially among the youth in sub-Saharan Africa. Structural factors such as the length of time spent in unemployment and job sustainability have a considerable effect on the persistence of unemployment for an individual. Non-parametric models were fitted to data consisting of 4.9 million unemployed South Africans to determine the duration dependence and probabilities associated with unemployment. The prospect of finding employment depends on unemployment duration where the rate of finding employment decreases as the length of time in unemployment increases. On average, nemployment exit is observed at lower rates, which translates to people remaining unemployed for longer durations. The human capital of the unemployed deteriorates when more time is spent in an unemployment state, thus making one less employable. Based on the Markov chain processes results, the created jobs are less sustainable because the employed transition back to an unemployment state over time. These findings suggest that the problem of unemployment in South Africa is multidimensional. Significance: • The structural factors associated with unemployment should be modelled to address the unemployment situation in South Africa. • The probability of remaining unemployed increases as the length of stay in unemployment increases. • The lengthy unemployment duration results from a low rate of exiting unemployment

Strengthening research skills and creativity among PhD students in RUFORUM regional PhD programmes : report

Meeting: Regional Scientific Data Management and Learning Workshop for PhD Students and Staff, Bunda College of Agriculture, 14-18 November 2011, Lilongwe, MalawiThe training course (2011) was supported by Regional Universities Forum for Capacity Building in Agriculture (RUFORUM). The course aimed to provide PhD students in Malawi who are involved in agricultural research systems, with additional skills to design, collect, manage, analyse and present results from their data and to improve the efficiency of agricultural information and research. The report documents activities, outputs and outcomes of the training

The use of appropriate statistical techniques in addressing food production challenges of smallholder farmers

Statistic

Repeated-Measures Analysis in the Context of Heteroscedastic Error Terms with Factors Having Both Fixed and Random Levels

The design and analysis of experiments which involve factors each consisting of both fixed and random levels fit into linear mixed models. The assumed linear mixed-model design matrix takes either a full-rank or less-than-full-rank form. The complexity of the data structures of such experiments falls in the model-selection and parameter-estimation process. The fundamental consideration in the estimation process of linear models is the special case in which elements of the error vector are assumed equal and uncorrelated. However, different assumptions on the structure of the variance–covariance matrix of error vector in the estimation of parameters of a linear mixed model may be considered. We conceptualise a repeated-measures design with multiple between-subjects factors, in which each of these factors has both fixed and random levels. We focus on the construction of linear mixed-effects models, the estimation of variance components, and hypothesis testing in which the default covariance structure of homoscedastic error terms is not appropriate. We illustrate the proposed approach using longitudinal data fitted to a three-factor linear mixed-effects model. The novelty of this approach lies in the exploration of the fixed and random levels of the same factor and in the subsequent interaction effects of the fixed levels. In addition, we assess the differences between levels of the same factor and determine the proportion of the total variation accounted for by the random levels of the same factor