Regression techniques were employed to model malaria cases as a function of environmental predictors. FK228 cell line The resulting model was used for predicting malaria risks in Afghanistan. The entire time series except the last 6 months is used for training, and the last 6-month data is used for prediction and validation.

Results: Vegetation index, in general, is the strongest predictor, reflecting the

fact that irrigation is the main factor that promotes malaria transmission in Afghanistan. Surface temperature is the second strongest predictor. Precipitation is not shown as a significant predictor, as it may not directly lead to higher larval population. Selleckchem PCI-34051 Autoregressiveness of the malaria epidemiological data is apparent from the analysis. The malaria time

series are modelled well, with provincial average R(2) of 0.845. Although the R2 for prediction has larger variation, the total 6-month cases prediction is only 8.9% higher than the actual cases.

Conclusions: The provincial monthly malaria cases can be modelled and predicted using satellite-measured environmental parameters with reasonable accuracy. The Third Strategic Approach of the WHO EMRO Malaria Control and Elimination Plan is aimed to develop a cost-effective surveillance system that includes forecasting, early warning and detection. The predictive and early warning capabilities shown in this paper support this strategy.”
“Dynamic mechanical analysis and dielectric relaxation spectra of conductive carbon black reinforced chlorosulfonated polyethylene (CSM) composites were used to study their relaxation behavior as a function of temperature and frequency, respectively. A marginal increase in glass transition temperature has been observed upto 30 phr carbon black filled polymer composite, beyond which it decreases, which has been explained on the basis of aggregation of filler particles in the polymer matrix. The strain dependent dynamical parameters were evaluated

at dynamic strain cancer metabolism inhibitor amplitudes of 0.1-200%. The nonlinearity in storage modulus increases with increase in filler loading. It can be explained on the basis of filler-polymer interaction and aggregation of the filler particulates. The frequency dependent dynamical mechanical analysis has also been studied at frequency range of 0.1-100 Hz. The variation in real and complex part of impedance with frequency has been studied as a function of filler loading. The effect of filler loading on ac conductivity has been observed as a function of frequency. An increase in conductivity value has been observed with increase in filler loading. This can be explained on the basis of formation of conducting paths between filler particulates. (C) 2010 Wiley Periodicals, Inc.