A novel PRFB decomposition for non-stationary time-series and image analysis

This work presents a novel perfect reconstruction filterbank decomposition (PRFBD) method for nonlinear and non-stationary time-series and image data representation and analysis. The Fourier decomposition method (FDM), an adaptive approach based on Fourier representation (FR), is shown to be a special case of the proposed PRFBD. In addition, adaptive Fourier–Gauss decomposition (FGD) based on FR and Gaussian filters, and adaptive Fourier–Butterworth decomposition (FBD) based on Butterworth filters are developed as the other special cases of the proposed PRFBD method. The proposed theory of PRFBD can decompose any signal (time-series, image, or other data) into a set of desired number of Fourier intrinsic band functions (FIBFs) that follow the amplitude-modulation and frequency-modulation (AM-FM) representations. A generic filterbank representation, where perfect reconstruction can be ensured for any given set of lowpass or highpass filters, is also presented. We performed an extensive analysis on both simulated and real-life data (COVID-19 pandemic, Earthquake and Gravitational waves) to demonstrate the efficacy of the proposed method. The resolution results in the time-frequency representation demonstrate that the proposed method is more promising than the state-of-the-art approaches. © 2023 Elsevier B.V.

Ambient Fine Particulate Matter and COVID-19 in India

We examine the correlation between COVID-19 case activity and air pollution in two cities of Delhi and Mumbai in India. Data regarding air quality index (AQI) of PM2.5 and PM10 from Delhi and Mumbai were collected between July and November 2020. Within the same time period, confirmed cases and daily deaths due to COVID-19 in these two cities were also recorded. AQI levels in Delhi were worst in November (PM2.5: 446 ± 144.6 µg/m3;PM10: 318 ± 131.7 µg/m3) and were significantly higher as compared to Mumbai (PM2.5: 130 ± 41.2 µg/m3;PM10: 86 ± 21.2 µg/m3). This correlated with greater number of cases and higher mortality in Delhi (cases: 6243;deaths: 85) relative to Mumbai (cases: 1526;deaths: 35) during the same time period. This observational study shows that air pollution is associated with poor outcomes in patients with COVID-19. There is an urgent unmet need for appropriate public health measures to decrease air pollution along with strict policy change. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

AN IMPROVED DATA DRIVEN DYNAMIC SIRD MODEL FOR PREDICTIVE MONITORING OF COVID-19

COVID-19 pandemic spreaded across the world in early 2020. It forced many countries to impose lockdown to prevent surge in the number of infected cases. There has been a huge impact on social and economic activities worldwide. In this work, we carry out the functional modeling of COVID-19 infection trends using two models: the Gaussian mixture model (GMM) and the composite logistic growth model (CLGM). Unlike the traditional SIRD models that use numerical data fitting, we utilize the best data-fitted curves employing GMM and/or CLGM to construct the Susceptible-Infected-Recovered-Dead (SIRD) pandemic model. Further, we derive the explicit expressions of time-varying parameters of the SIRD model unlike most works that consider static parameters without any closed form solution. The proposed parameterized dynamic SIRD model is generically applicable to any pandemic, can capture the day-to-day dynamics of the pandemic and can assist the governing bodies in devising efficient action plans to deal with the prevailing pandemic.