A new distribution, the New Weibull-Pareto, is defined and studied. Various properties of the distributions are obtained and the method of maximum likelihood used to estimate the parameters of the distribution. The usefulness of the distribution has also been demonstrated by applying it to real life data.

Abstract:Ectoparasites are an important factor in bat health due to emergent diseases and their associated threats to global public health. The diverse foraging habits of bats expose them to different surfaces which may influence ectoparasite infestations. In spite of these, most studies often overlook dietary specialisations when observing ectoparasite loads. The present paper quantitatively investigates whether foraging strategies as well as other host characteristics (sex, age, trunk and patagial area) influence ectoparasite (nycteribiids and mites) loads of bats. Ectoparasite counts and morphometric data were taken from mist net captures of bats. We then developed and compared models for modeling bat ectoparasite abundance under various distributions using generalised linear models. The negative binomial distribution consistently proved to be adequate for modeling mite, nycteribiid and total ectoparasite abundance based on information-theoretic approaches. Generally, females and frugivores had higher ectoparasite loads conditional on bat sex and diet, respectively. Contrary to nycteribiid abundance, mite abundance was positively related to patagial area. Thus, our findings suggest that dietary guild, sex and patagia of hosts (as well as age-nycteribiid abundance) are significant determinants of ectoparasite abundance.

River discharge data offer a rich source of information for reservoir management and flood control, if modelling can separate out the effects of rainfall, land use, soil type, relief, and weather conditions. In this paper, we model river discharge data from the Black Volta River, using Generalised Additive Mixed Models (GAMMs) with a space-time interaction represented via a tensor product of continuous time and discrete space. River discharge data from January 2000 to December 2009 for the four gauge stations along the Black Volta River namely, Lawra, Chache, Bui and Bamboi were obtained from the hydrological services department of Ghana and used for model fitting. Four GAMMs were explored, two with space-time interactions and two without space-time interactions. The comparison of the performance of the models with space-time interactions and those without space-time interactions based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) suggests that in this application, the former is better overall and in particular for modelling local variations. Further, a model with space and time main effects performed better compared with one without space and time main effects. After model selection, checking and validation, there is evidence for increasing river discharge from the most upstream gauge station to the most downstream gauge station for the study period.

In this study, a new continuous distribution called the Kumaraswamy Erlang-truncated exponential distribution is introduced and studied. The mathematical properties of the new model such as the quantile function, moments and moment generating function and order statistics are derived. The estimation of the parameters of the model is approached by the method of maximum likelihood. The importance of the model is illustrated by means of application to real data set.

The monitoring of CD4 counts are a basis for assessing the effectiveness of most HIV treatments. Understanding the way CD4 cells change over time among patients on ART could provide insight into the way Patients respond to treatment and how effective treatment is with time. This study examine the changes in CD4 countover time and the effect of some plausible factors on this change for Patients who were on Antiretroviral Therapy(ART) in The Builsa District Hospital in Ghana. Retrospective data from the HIV/AIDS Monitoring Program at the Builsa District hospital, in which patients had enrolled and their CD4 cell count were regularly being monitored every six months, forming repeated measures of CD4 counts, was used for our study: Profile analysis was used to study the pattern of change in the CD4 count. While treatment remained effective, the results showed that the trend of CD4 count over time was logarithmic indicating that the effectiveness of treatment, decreased with time. While the gender and marital status of the patientsdo not show any statistically significant differentials to this, the educational and religious status of the patients, as well as the drug used in the treatment do.