Sarita Azad

Multiresolution analysis for separating closely spaced frequencies with an application to Indian monsoon rainfall data

In this paper we make use of the multiresolution properties of discrete wavelets, including their ability to remove interference, to reveal closely spaced spectral peaks. We propose a procedure which we first verify on two test signals, and then apply it to the time series of homogeneous Indian monsoon rainfall annual data. We show that, compared to empirical mode decomposition, discrete wavelet analysis is more effective in identifying closely spaced frequencies if used in combination with classical power spectral analysis of wavelet-based partially reconstructed time series. An effective criterion based on better localization of specific frequency components and accurate estimation of their amplitudes is used to select an appropriate wavelet. It is shown here that the discrete Meyer wavelet has the best frequency properties among the wavelet families considered (Haar, Daubechies, Coiflet and Symlet). In rainfall data, the present analysis reveals two additional spectral peaks besides the fifteen found by classical spectral analysis. Moreover, these two new peaks have been found to be statistically significant, although a detailed discussion of testing for significance is being presented elsewhere.

Analyzing seasonality of tuberculosis across Indian states and union territories

A significant seasonal variation in tuberculosis (TB) is observed in north India during 2006–2011, particularly in states like Himachal Pradesh, Haryana and Rajasthan. To quantify the seasonal variation, we measure average amplitude (peak to trough distance) across seasons in smear positive cases of TB and observe that it is maximum for Himachal Pradesh (40.01%) and minimum for Maharashtra (3.87%). In north India, smear positive cases peak in second quarter (April–June) and reach a trough in fourth quarter (October–December), however low seasonal variation is observed in southern region of the country. The significant correlations as 0.64 (p-value < 0.001), 0.54 (p-value < 0.01) and 0.42 (p-value < 0.05) are observed between minimum temperature and seasonality of TB at lag-1 in north, central and northeast India respectively. However, in south India, this correlation is not significant.

Bayesian Melding Approach to Estimate the Reproduction Number for Tuberculosis Transmission in Indian States and Union Territories

Tuberculosis (TB) is one of the most common infectious diseases and a leading cause of death in the world. Despite the full implementation of Revised National Tuberculosis Control Programme, the disease continues to be a leading cause of morality and economic burden in India. The basic reproduction is a fundamental key parameter that quantifies the spread of a disease. In this article, we present a Bayesian melding approach to estimate the basic reproduction number using a deterministic model of TB. We present a point estimate of the basic reproduction number of 35 states and union territories of India during 2006 to 2011. The basic reproduction number of TB for India is computed to be 0.92, which indicates the slow elimination of TB in India during 2006 to 2011.

Malaria Incidence Forecasting and Its Implication to Intervention Strategies in South East Asia Region

Forecasting an epidemic is a complex task because of its dependence on multiple parameters. The challenges pose by sparse and error-prone data is addressed by stochastic data assimilation model. A two-step algorithm based on ensemble Kalman filter is applied to forecast malaria incidence. The temporal dependence of the data is modelled using simple Markov process and the time series is cast into a state space model.