Browsing by Author "Melesse, Sileshi Fanta."

Now showing 1 - 10 of 10

Analysis of discrete time competing risks data with missing failure causes and cured subjects.
(2023) Ndlovu, Bonginkosi Duncan.; Zewotir, Temesgen Tenaw.; Melesse, Sileshi Fanta.
This thesis is motivated by the limitations of the existing discrete time competing risks models vis-a-vis the treatment of data that comes with missing failure causes or a sizableproportions of cured subjects. The discrete time models that have been suggested to date (Davis and Lawrance, 1989; Tutz and Schmid, 2016; Ambrogi et al., 2009; Lee et al., 2018) are cause-specific-hazard denominated. Clearly, this fact summarily disqualifies these models from consideration if data comes with missing failure causes. It is also a well documented fact that naive application of the cause-specific-hazards to data that has a sizable proportion of cured subjects may produce downward biased estimates for these quantities. The existing models can be considered within the multiple imputation framework (Rubin, 1987) for handling missing failure causes, but the prospects of scaling them up for handling cured subjects are minimal, if not nil. In this thesis we address these issues concerning the treatment of missing failure causes and cured subjects in discrete time settings. Towards that end, we focus on the mixture model (Larson and Dinse, 1985) and the vertical model (Nicolaie et al., 2010) because these models possess certain properties which dovetail with the objectives of this thesis. The mixture model has been upgraded into a model that can handle cured subjects. Nicolaie et al. (2015) have demonstrated that the vertical model can also handle missing failure causes as is. Nicolaie et al. (2018) have also extended the vertical model to deal with cured subjects. Our strategy in this thesis is to exploit both the mixture model and the vertical model as a launching pad to advance discrete time models for handling data that comes with missing failure causes or cured subjects.
Covariates and latents in growth modelling.
(2014) Melesse, Sileshi Fanta.; Zewotir, Temesgen Tenaw.
The growth curve models are the natural models for the increment processes taking place gradually over time. When individuals are observed over time it is often apparent that they grow at different rates, even though they are clones and no differences in treatment or environment are present. Neverthless the classical growth curve model only deals with the average growth and does not account for individual differences, nor does it have room to accommodate covariates. Accordingly we strive to construct and investigate tractable models which incorporate both individual effects and covariates. The study was motivated by plantations of fast growing tree species, and the climatic and genetic factors that influence stem radial growth of juvenile Eucalyptus hybrids grown on the east coast of South Africa. Measurement of stem radius was conducted using dendrometres on eighteen sampled trees of two Eucalyptus hybrid clones (E. grandis χ E.urophylla, GU and E.grandis χ E. Camaldulensis, GC). Information on climatic data (temperature, rainfall, solar radiation, relative humidity and wind speed) was simultaneously collected from the study site. We explored various functional statistical models which are able to handle the growth, individual traits, and covariates. These models include partial least squares approaches, principal component regression, path models, fractional polynomial models, nonlinear mixed models and additive mixed models. Each one of these models has strengths and weaknesses. Application of these models is carried out by analysing the stem radial growth data. The partial least squares and principal component regression methods were used to identify the most important predictor for stem radial growth. Path models approach was then applied mainly to find some indirect effects of climatic factors. We further explored the tree specific effects that are unique to a particular tree under study by fitting a fractional polynomial model in the context of linear mixed effects model. The fitted fractional polynomial model showed that the relationship between stem radius and tree age is nonlinear. The performance of fractional polynomial models was compared with that of nonlinear mixed effects models. Using nonlinear mixed effects models some growth parameters like inflection points were estimated. Moreover, the fractional polynomial model fit was almost as good as the nonlinear growth curves. Consequently, the fractional polynomial model fit was extended to include the effects of all climatic variables. Furthermore, the parametric methods do not allow the data to decide the most suitable form of the functions. In order to capture the main features of the longitudinal profiles in a more flexible way, a semiparametric approach was adopted. Specifically, the additive mixed models were used to model the effect of tree age as well as the effect of each climatic factor.
Factors affecting child mortality in Lesotho using 2009 and 2014 LDHS data.
(2021) Mkhize, Nonduduzo Noxolo.; Melesse, Sileshi Fanta.; Mwambi, Henry Godwell.; Ramroop, Shaun.
Child mortality rate is known to be the important indicator of social development, quality of life, welfare as well as the overall health of the society. In most countries, especially the developing countries; the death of a child is usually caused by transferable, preventable diseases and poor health. Progress in improving under-five mortality since 1990 has been made globally. There has been a decline globally in under-five mortality from 12.7 million in 1990 to approximately 6 million in 2015. All regions except the developing countries in Sub-Saharan Africa, Central Asia, Southern Asia and Oceania had reduced the rate by 52% or more in 2013. Lesotho is a developing country with one of the highest rates of infant and child mortality. The study uncovers the factors influencing child mortality in Lesotho based on the Lesotho Demographic and Health Surveys for 2009 and 2014. The survey logistic regression, a model under the generalized linear model framework was used to find the factors related to under-five child mortality to account for the sampling designs complexity. The SLR model is not able to account for variability occurring from connection between subjects from the equal clusters and household. The generalized linear mixed model is then put into application. To ease the normality assumptions and the linearity assumption in the parametric models, the semi-parametric generalized additive model, was lastly used for the data. Finding the determining factors that result in child mortality will benefit the way intervention programs are planned and the formulation for policy makers to lead in the decreasing of child mortality; and accomplish MDGs. This study intends to improve the existing knowledge on child mortality in Lesotho by studying the determining factors in detail. Based on the previous studies this paper will recommend intervention designs and policy formulation. Overall, the findings of this research showed that birth order number, weight of child at birth, age of child, breastfeeding, wealth index, education attainment, mother’s age, type of place of residence, number of children living were the key determining factors of the under-five mortality in Lesotho. The study displays that policy makers should strengthen the interventions for child health in order to decrease child under-five mortality. The results achieved can help with the policy formulation to control and reduce child mortality. The government should continually assess current programs to review and develop programs that are more applicable.
Modelling tuberculosis risk factors among adult men in South Africa.
(2021) Mlondo, Muziwandile Nhlakanipho.; Melesse, Sileshi Fanta.; Mwambi, Henry Godwell.
Tuberculosis is among the major public health problems not only in South Africa but worldwide. Tuberculosis is an underlying cause of more than 1.5 million deaths each year worldwide, making it the world's top infectious killer. There are more cases for men than women. Such a heavy burden requires an understanding of the tuberculosis status of the people, especially among men, and associated risk factors. Therefore, this study uses some statistical methods that are suitable to estimate the effect of the risk factors associated with tuberculosis among adult men. The study used the 2016 South African Demographic and Health Survey data. The Generalized Linear Models, such as the binary logistic regression model that assumes a simple random sampling as a sampling method followed by survey logistics that incorporate the complex design by means of robust standard errors of estimates, were applied to the data. The findings revealed that models that account for complex design are more suitable than those that do not account for complexity. To account for variability between the primary sampling units generalized linear mixed model was then used. GLMMs accounts for correlation within clusters by means of random effects which also account for cluster to cluster heterogeneity. Further, a generalized additive mixed-effect model was used to fit nonlinear and non-normal data; the categorical variables were modeled parametrically and continuously by non-parametric models. The thesis also discussed limitations for each of these models. The findings from this study revealed that the risk factors of tuberculosis are: any chronic disease, current age, region, race, number of times away from home, marital status, weight, and interaction effect of chronic disease and age, the interaction effect of smoking status and number of household members.
Some statistical methods in analysis of single and multiple events with application to infant mortality data.
(2020) Gatabazi, Paul.; Melesse, Sileshi Fanta.; Ramroop, Shaun.
The time to event analysis or survival analysis aims at making inferences on the time elapsed between the recruitment of subjects or the onset of observations, until the occurrence of some event of interest. Methods used in general statistical analysis, in particular in regression analysis, are not directly applicable to time to event data due to covariate correlation, censoring and truncation. While analysing time to event data, medical statistics adopts mainly nonparametric methods due to difficulty in finding the adequate distribution of the phenomenon under study. This study reviews non-parametric classical methods of time to event analysis namely Aalen Additive Hazards Model (AAHM) trough counting and martingale processes, Cox Proportional Hazard Model (CPHM) and Cox-Aalen Hazards Model (CAHM) with application to the infant mortality at Kigali University Teaching Hospital (KUTH) in Rwanda. Proportional hazards assumption (PHA) was checked by assessing Kaplan-Meier estimates of survival functions per groups of covariates. Multiple events models were also reviewed and a model suitable to the dataset was selected. The dataset comprises 2117 newborns and socio-economic and clinical covariates for mothers and children. Two events per subject were modeled namely, the death and the occurrence of at least one of the conditions that may also cause long term death to infants. To overcome the instability of models (also known as checking consistence of models) and potential small sample size, re-sampling was applied to both CPHM and appropriate multiple events model. The popular non-parametric re-sampling methods namely bootstrap and jackknife for the available covariates were conducted and then re-sampled models were compared to the non-re-sampled ones. The results in different models reveal significant and non-significant covariates, the relative risk and related standard error and confidence intervals per covariate. Among the results, it was found that babies from under 20 years old mothers were at relatively higher risk and therefore, pregnancy of under 20 years old mothers should be avoided. It was also found that an infant’s abnormality in weight and head increases the risk of infant mortality, clinically recommended ways of keeping pregnancy against any cause of infant abnormality were then recommended.
Statistical Modeling of Acute HIV Infection from a Cohort of High-risk Individuals in South Africa = Ukufakwa kwamamodeli Ezibalomininingo Ekuthelelekeni Kwesikhashana nge-HIV Eqoqweni labantu Abasengcupheni Enkulu eNingizimu Afrika.
(2022) Yirga, Ashenafi Argaw.; Melesse, Sileshi Fanta.; Mwambi, Henry Godwell.; Ayele, Dawit Getnet.
In this dissertation, longitudinal data modeling approaches to analyze data on CD4 cell counts measured repeatedly in HIV-infected patients enrolled in the Centre for the AIDS Programme of Research in South Africa are investigated. Longitudinal data, or repeated measurement data, is a specific form of multilevel data. In longitudinal studies, repeated observations are made on an individual on one or more outcomes, including covariates information at a baseline and over time. Mixedeffects models have become popular for modeling longitudinal data. This statistical procedure also permits the estimation of variability in hierarchically structured data and examines the impacts of factors at different levels. Since longitudinal studies are often faced with the incompleteness of the data due to partially observed subjects, the mixed-effects model is by its very nature able to deal with unbalanced data of this nature. Therefore, the study adopts the mixed-effects model and identifies whether specific clinical and sociodemographic factors present in the data influenced CD4 count in a cohort of HIV-infected patients. Since it is of great interest for a biomedical analyst or an investigator to correctly model the CD4 cell count or disease biomarkers of a patient in the presence of covariates or factors determining the disease progression over time, the Poisson regression approach, which explain variability in counts, is considered. The Poisson generalized mixed-effects models can be an appropriate choice for repeated count data. However, this model is not realistic because of the restriction that the mean and variance are equal. Therefore, the Poisson mixed-effects model is replaced by the negative binomial mixed-effects model. The later model effectively managed over-dispersion of the longitudinal data. We evaluate and compare the proposed models and their application to model CD4 cell counts of HIV-infected patients recruited in the study data set. The results reveal that the negative binomial mixedeffects model has appropriate properties and outperforms the Poisson mixed-effects model in terms of handling the over-dispersion of the data. Multiple imputation techniques are also used to handle missing values in the dataset to validate parameter estimates in modeling the negative binomial mixed-effects model by assuming a missing at random missingness. To illustrate the full conditional distribution of the repeated outcome, a quantile mixed-effects model is employed. This gives greater inclusive statistical modeling than conventional ordinary mixed models. Quantile regression offers an invaluable tool to discern effects that would be missed by other conventional regression models, which are solely based on modeling conditional mean. The quantile regression model that assumes asymmetric Laplace distribution for the error term was applied to longitudinal CD4 count data. The exact maximum likelihood estimation of the covariate effects and variance-covariance elements in the quantile mixed-effects model was implemented using the Stochastic Approximation Expectation-Maximization algorithm. In the model, multiple random effects are also incorporated to consider the correlation among the observations. Thus, we obtain robust parameter estimates for various conditional distribution positions that communicate an inclusive and more complete picture of the effects. Furthermore, to get more insights into the functional relationship between the response variable and the covariates, the generalized additive mixed-effects models, such as the additive negative binomial mixed-effects model, a versatile model used to better understand and analyze complex nonlinear trajectories in an overdispersed longitudinal data, is applied. Following the additive negative binomial mixed-effects model, an attempt to fit additive quantile mixed-effects model, an efficient and flexible framework for nonparametric as well as parametric longitudinal forms of data analysis focused on features of the outcome beyond its central tendency, was made. The response variable at hand is a CD4 count of HIV-infected patients as a function of Highly Active Antiretroviral Therapy initiation and other relevant baseline characteristics of the patients. Thus, even though this is a biostatistics methodological dissertation research, some interesting clinical and sociodemographic findings are also discussed. Discussion and conclusion of the results from the proposed models with a suggestion of possible further research avenues completed the study. Iqoqa Kule dizetheshini izindlelasu zokulinganisela imininingo eziyilongitudinal modeling approaches ukuhlaziya imininingo yezibalo zamasosha e-CD4 count ezikalwa ngokuphindaphindeka ezigulini ezitheleleke nge-HIV ezibhalise eSikhungweni soHlelo Lokucwaninga nge-AIDS eNingizimu Afrika kuyaphenywa. Imininingo enqumile, noma imininingo ekalwa ngokuphindelela, iwuhlobo oluqondile lwemininingo emazingeni ahlukene. Ocwaningweni olunqumile, ukubheka okuphindwayo kwenziwa kumuntu oyedwa emphumeleni owodwa noma engaphezulu, okufaka ulwazi lwamakhovariyenti njengesisekelo nangokuhamba kwesikhathi. Amamodeli anemithelela exubile aseyathandeka ekuhambiselaneni nemininingo yemodeli enqumile. Inqubo yezibalomininingo iphinde ivumele ukuqagula ukuguquguquka emininingweni enomumo onokugibelana iphinde ihlole imithelela yezimo emazingeni ehlukene. Njengoba ucwaningo olunqumile luvame ukubhekana nokungaphothulwa kwemininingo ngenxa yabantu ababhekwe ingxenye, imodeli enomthelelangxube ngokomumo wayo iyakwazi ukubhekana nemininingo engabhalansile eyilolu hlobo bese luhlonza izimo zokokusebenza kwengqondo nomumoqoqobantu emphakathini emininingweni ethinta i-CD4 count eqoqweni leziguli ezitheleleke nge-HIV. Njengoba kunentshisekelo enkulu ukuba umhlaziyi wezempilokwelapha noma umphenyi enze imodeli ngendlela ukubalwa kwamasosha i-CD4 count noma amabhayomakha esifo esigulini ebukhoneni bamakhovariyenti noma izimo ezihlonza ukuqhubeka kwesifo ngokuhamba kwesikhathi, indlelasu yokunqandeka kwesifo ngokukaPoisson, okuchaza ukuguquguquka ngokubalwa, kuyabhekwa. Amamodeli kaPoisson abekwe eceleni anemithelela exubile angaba wukukhetha okuyikho kwemininingo yokubala kokuphindelela. Kodwa, le modeli ayivezi okuyikho ngenxa yokuvimbeleka ukuthi imini nevariyenti kuyalingana. Ngakho-ke, imodeli kaPoisson enemithelelangxube imelwe yimodeli enemithelelangxube yebhayinomiyali engeyinhle. Imodeli yakamuva ilawula ngempumelelo yokusabalalisa kakhulu imininingo enqumile. Sihlaziya siphinde siqhathanise namamodeli aphakanyisiwe nokusetshenziswa kwayo ekubaleni amasosha omzimba i-CD4 cell count yeziguli ezitheleleke nge-HIV abafakwe ekubambeni iqhaza emininingweni yocwaningo. Imiphumela iveza ukuthi imodeli yemithelelangxube yebhayinomiyali engeyinhle enezakhiwomumo eyiwo nesebenza yedlule ekaPoisson nemodeli enomthelelangxube ngokwemigomo yokubheka ukusabalalisa ngokweqile imininingo. Amasu amaningi emvezabubi asetshenziselwa ukubhekana nezimo ezingabonakali kwisethi yemininingo ehlaziya iziqagulo zamapharamitha ekufakweni kwemodeli enemithelelangxube yebhayinomiyali ngokuthatha ngokuthi kunokungatholakali okungahleliwe. Ukukhombisa ukusabalalisa okugcwele okunemibandela komphumela ophindaphindekile, imodeli enemithelelangxube iyasetshenziswa. Lokhu kuveza ukufaka imodeli yezibalomininingo ezifaka konke okunamamodeli ajwayelekile ayingxube. Ukuncipha kwekhwantayli kunika ithuluzi elingenamsebenzi ukuhlonza imithelela ebingetholwe amamodeli ejwayelekile okuncipha, agxile kuphela kwimini encike ekufakweni kwemodeli. Imodeli yokuncipha kwekhwantayli evuma ukusabalalisa i-Laplace etshekile yetemu elingene ngephutha kwasetshenziswa emininingweni yokubala i-CD4. Ukuqagula okuyikho okuphezulu kwemithelela yamakhovariyenti nezakhi zekhovariyensi-variyensi kwimodeli enemithelelangxube yekhwantayli eyaqaliswa ukusebenza kusetshenziswa i-algorithimu i- Stochastic Approximation Expectation-Maximization. Kwimodeli, imithelela engahlelekile emininingo iphinde yafakwa ukuze kubhekwane nokuxhumana kokuqashelwayo. Ngakho-ke, sithola ukuqagula amapharamitha okunzulu ngemumo yokusabalalisa okunemigomo eyehlukahlukene okunika isithombe esifaka konke nesiphelele semithelela. Ngaphezu kwalokho, ukuthola imibono eyongeziwe ngobudlelwane obusebenzayo phakathi kwevariyebhuli yempendulo namakhovariyethi, amamodeli anemithelelangxube eyongezwayo, njengemodeli yemithelela exubile engemihle yebhayinomiyali eyongezwayo, imodeli enguqunguqu isetshenziselwe ukuqonda kangcono ngemodeli yokuhlaziya izinkombakusasa ezingenamigoqo ezinkimbi emininingweni engumumokuqonda ohlakazwe kakhulu, iyasetshenziswa. Uma kulandelwa imodeli enemithelelangxube yebhayinomiyali engeyinhle eyongezwayo, ukuze kuhambelane nomzamo wokufaka imodeli enemithelelangxube ayikhwayintali eyongezwayo, uhlaka oluguqulekayo nolusebenza ngendlela yezindlela ezingahambelani nepharamethrikhi kanjalo nepharamethrikhi engumumokuqonda wokuhlaziya imininingo okugxile ezicini zomphumela owedlula injwayelosenzo ewumongo, nakho kwenziwa. Ivariyebhuli yempendulo esebenzayo yisibalo samasosha omzimba i-CD4 ezigulini ezitheleleke nge-HIV njengomzamokuziqamba Wengxubekwelapha Ethithibalisa igciwane leSandulela Ngculazi Esebenza Kakhulu kanye nezinye izici eziyisisekelo eziyiso zeziguli. Ngakho-ke nakuba lena kuyidizetheshini yocwaningo lwendlelakwenza yocwaningo kwezibalomininingokuphila, kuphinde kwadingidwa okutholakele kwezempilongqondo nakwisifundomumoqoqobantu emphakathini. Ukudingida nokuphothulwe yimiphumela yamamodeli aphakanyisiwe ngesiphakamiso sezindlela zokukwenza ucwaningo oluqhubekayo ukuphothula ucwaningo.
Statistical models to determine factors affecting under-five child mortality in South Africa.
(2020) Bovu, Andisiwe.; Melesse, Sileshi Fanta.
The level of under-five child mortality is an important indicator of economic, social and health development of the nation. In the last two decades, substantial progress has been made in improving under-five child mortality globally, with deaths dropping among children under the age of five years from approximately 12 million in 1990 to about 6.3 million in 2015. However, significant strides to address the key risk factors are still needed in the Sub-Saharan Africa region if they are to achieve the Sustainable Development Goals 2030. The key objective of the study is to identify key factors associated with mortality of children under the age of five years in South Africa. In order to identify these factors, the study used different statistical models that accommodate a binary response variable. Models used include Logistic Regression, Survey Logistic Regression, Generalized Linear Mixed Models and Generalized Additive Models. Although logistic regression is useful in modelling data with a dichotomous outcome, it is not suitable for modelling data obtained through a complex survey that incorporates weights, stratification and clustering. Survey logistic regression is used to model the relationship between binary dependent and the set of explanatory variables by making use of the sampling design information. In this case, the inclusion of random effects in the model results in generalized linear mixed models (GLMM). These models are an extension of linear mixed models that allow response variable from different distributions, such as binary responses. One can think of GLMM as an extension of generalized linear models (e.g. logistic regression) that combine both features of fixed and random effects. These statistical models assume linearity parametric form for the explanatory variable. However, this assumption of linear independence of response on covariates may not hold. Hence, we introduce generalized additive models (GAM). The GAM models show some non-linear relationship between the response variable and some covariates. The results showed that, the size of child at birth, breastfeeding, birth order number, ethnicity, number of children 5 under, total children ever born, source of drinking water and province were significantly associated with under-five child mortality. The study concludes that prolonged breastfeeding, improved health services and source of water are among the main factors to decline under-five child mortality further. Therefore, the study suggests that there is a need to strengthen child health interventions in South Africa to reduce the under-five mortality rate even more in order to achieve sustainable development goals (SDG) 2030.
Statistical models to study the BMI of under five children in Ethopia.
(2018) Yirga, Ashenafi Argaw.; Mwambi, Henry Godwell.; Ayele, Dawit Getnet.; Melesse, Sileshi Fanta.
Maternal and child malnutrition has long and short-term consequences on the health status of the people and on the country’s economy. It is among the major public health problems in Ethiopia. Worldwide, maternal and child malnutrition is an underlying cause for more than 3.5 million deaths each year. About 35% of the global disease burden is in under five children. Such a heavy burden requires an understanding of the nutritional status of the people, especially children under the age of five years and associated factors. Therefore, this study attempted to use possible statistical methods to estimate the effects of the risks related to the nutritional status of children. It also tried to identify the socio-economic and demographic factors that are associated with the BMI of under five children in Ethiopia. The study employed the 2016 Ethiopian Demographic and Health Survey data. A nationally representative sample of children under the age of five years was used to get information on weight and height measures of under five children. The BMI of children under five years of age was used as a response variable to fit weighted quantile regression. The covariates, age of a child, sex and other relevant socio-economic and demographic factors were used in the study. Following the quantile regression, the generalized linear models such as logistic regression model was applied after categorizing the response variable, BMI of under five children, into two categories namely normal and malnourished. Following binary logistic regression, an attempt to fit ordinal logistic regression was made. That means nutritional status was considered as ordinal outcome with four categories namely underweight, normal, overweight and obese. The findings and comparison of estimates using these different statistical methods with and without complex survey design were presented. The results revealed that methods that take into account the complex nature of the design, perform better than those that do not take this into account. It has also been found that age of a child, weight of child at birth, mother’s BMI, educational attainment of mother, region and wealth index were significantly associated with under five children’s nutritional status. Furthermore, the results are discussed and then a conclusion is made in the context of policy implication for Ethiopia.
Statistical models to understand factors associated with under-five child mortality in Tanzania.
(2016) Dlamini, Welcome Jabulani.; Melesse, Sileshi Fanta.; Mwambi, Henry Godwell.
The risk or probability of dying between birth and five years of age expressed per 1000 live births is known as Under-five mortality. The well-being of a child reflects household, community and national involvement on family health. This will have an immense future contribution towards the development of a country. Globally, a substantial progress in improving child survival since 1990 has been made. The decline globally in under-five mortality from approximately 12.7 million in 1990 to approximately 6.3 million in 2013 had been observed. Notably, all regions except Sub-Saharan Africa, Central Asia, Southern Asia and Oceania had reduced the rate by 52% or more in 2013. This study aims to identify factors that are associated with the under-five mortality in Tanzania. In order to robustly identify these factors, the study utilized different statistical models that accommodate a response which is dichotomous. Models studied include Logistic Regression (LR), Survey Logistic Regression (SLR), Generalized Linear Mixed Model (GLMM) and Generalized Additive Model (GAM). The result revealed that HIV status of the mother is associated with the under-five mortality. Furthermore, the results revealed that childbirth order number, breastfeeding and a total number of children alive affects the survival status of the child. The study shows that there is a need to intensify child health interventions to reduce the under-five mortality rate even more and to be in line with the millennium development goal 4(MDG4).
Zero-inflated regression models with application to water quality data from Umgeni Water.
(2017) Hlongwane, Zibusiso Sandile.; Mwambi, Henry Godwell.; Melesse, Sileshi Fanta.
Abstract available in PDF file.