Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.

We investigate thin film sensing capabilities of a terahertz (THz) metamaterial, which comprises of an array of single split gap ring resonators (SRRs). The top surface of the proposed metamaterial is covered with a thin layer of analyte in order to examine various sensing parameters. The sensitivity and corresponding figure of merit (FoM) of the odd and even resonant modes are analyzed with respect to different thicknesses of the coated analyte film. The sensing parameters of different resonance modes are elaborated and explained with appropriate physical explanations. We have also employed a semi-analytical transmission line model in order to validate our numerically simulated observations. Such study should be very useful for the development of metamaterials based sensing devices, bio-sensors etc in near future.

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We experimentally demonstrate a three-dimensional plasmonic terahertz waveguide by lithographically patterning an array of sub-wavelength pillars on a silicon substrate. Doped silicon can exhibit conductive properties at terahertz frequencies, making it a convenient substitute for conventional metals in plasmonic devices. However, the surface wave solution at a doped silicon surface is usually poorly confined and lossy. Here we demonstrate that by patterning the silicon surface with an array of sub-wavelength pillars, the resulting structure can support a terahertz surface mode that is tightly confined in both transverse directions. Further, we observe that the resonant behavior associated with the surface modes depends on the dimensions of the pillars, and can be tailored through control of the structural parameters. We experimentally fabricated devices with different geometries, and characterized the performance using terahertz time-domain spectroscopy. The resulting waveguide characteristics are confirmed using finite element numerical simulations, and we further show that a simple one-dimensional analytical theory adequately predicts the observed dispersion relation.