SARIMA Model for Malaria Admission Cases for Children Less Than Five Years in Kakamega County Referral Hospital

According to (MOH, 2016), malaria has become a killer disease to children in Kakamega County. Children less than five years are the most vulnerable to malaria. Lack of forecasting using available data on malaria indicators hinders the monitoring and control of the disease. This study therefore sought to formulate Seasonal Auto Regressive Integrated Moving Average (SARIMA) model for malaria admission cases for children less than five years in Kakamega County Referral Hospital. The objectives of the study were; to derive SARIMA model for forecasting malaria case admission for children less than five years and to use the derived SARIMA model to forecast malaria admission cases for children less than five years. Box Jenkins Methodology was used to derive SARIMA model. The appropriate model was SARIMA (0,2,2)(0,2,2)12. The study recommends this model to be used for planning and designing an effective control strategy for this category of children at the County level. Keywords: SARIMA model, Box Jenkins Methodology, Forecasting, Malaria admission cases. DOI: 10.7176/MTM/9-7-06 Publication date: July 31st 201

Climate Variability and Ross River Virus Transmission in Townsville Region, Australia 1985 to 1996

Background How climate variability affects the transmission of infectious diseases at a regional level remains unclear. In this paper, we assessed the impact of climate variation on the Ross River virus (RRv) transmission in the Townsville region, Queensland, north-east Australia. Methods Population-based information was obtained on monthly variations in RRv cases, climatic factors, sea level, and population growth between 1985 and 1996. Cross-correlations were computed for a series of associations between climate variables (rainfall, maximum temperature, minimum temperature, relative humidity and high tide) and the monthly incidence of RRv disease over a range of time lags. The impact of climate variability on RRv transmission was assessed using the seasonal auto-regressive integrated moving average (SARIMA) model. Results There were significant correlations of the monthly incidence of RRv to rainfall, maximum temperature, minimum temperature and relative humidity, all at a lag of 2 months, and high tide in the current month. The results of SARIMA models show that monthly average rainfall (β=0.0012, p=0.04) and high tide (β=0.0262, p=0.01) were significantly associated with RRv transmission, although temperature and relative humidity did not seem to have played an important role in the Townsville region. Conclusions Rainfall, and high tide were likely to be key determinants of RRv transmission in the Townsville region

Prediction of infectious disease epidemics via weighted density ensembles

Accurate and reliable predictions of infectious disease dynamics can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task, using different model structures, covariates, and targets for prediction. Experience has shown that the performance of these models varies; some tend to do better or worse in different seasons or at different points within a season. Ensemble methods combine multiple models to obtain a single prediction that leverages the strengths of each model. We considered a range of ensemble methods that each form a predictive density for a target of interest as a weighted sum of the predictive densities from component models. In the simplest case, equal weight is assigned to each component model; in the most complex case, the weights vary with the region, prediction target, week of the season when the predictions are made, a measure of component model uncertainty, and recent observations of disease incidence. We applied these methods to predict measures of influenza season timing and severity in the United States, both at the national and regional levels, using three component models. We trained the models on retrospective predictions from 14 seasons (1997/1998 - 2010/2011) and evaluated each model's prospective, out-of-sample performance in the five subsequent influenza seasons. In this test phase, the ensemble methods showed overall performance that was similar to the best of the component models, but offered more consistent performance across seasons than the component models. Ensemble methods offer the potential to deliver more reliable predictions to public health decision makers.Comment: 20 pages, 6 figure

A Functional Wavelet-Kernel Approach for Continuous-time Prediction

We consider the prediction problem of a continuous-time stochastic process on an entire time-interval in terms of its recent past. The approach we adopt is based on functional kernel nonparametric regression estimation techniques where observations are segments of the observed process considered as curves. These curves are assumed to lie within a space of possibly inhomogeneous functions, and the discretized times series dataset consists of a relatively small, compared to the number of segments, number of measurements made at regular times. We thus consider only the case where an asymptotically non-increasing number of measurements is available for each portion of the times series. We estimate conditional expectations using appropriate wavelet decompositions of the segmented sample paths. A notion of similarity, based on wavelet decompositions, is used in order to calibrate the prediction. Asymptotic properties when the number of segments grows to infinity are investigated under mild conditions, and a nonparametric resampling procedure is used to generate, in a flexible way, valid asymptotic pointwise confidence intervals for the predicted trajectories. We illustrate the usefulness of the proposed functional wavelet-kernel methodology in finite sample situations by means of three real-life datasets that were collected from different arenas