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    Rough-Fuzzy CPD: A Gradual Change Point Detection Algorithm

    Authors: Ritwik Bhaduri; Subhrajyoty Roy; Sankar K. Pal

    id:2010.06370v1 Date: 2020-10-10 Source: arXiv

    Changepoint detection is the problem of finding abrupt or gradual changes in time series data when the distribution of the time series changes significantly. There are many sophisticated statistical algorithms for solving changepoint detection problem, although there is not much work devoted towards gradual changepoints as compared to abrupt ones. Here we present a new approach to solve changepoint detection problem using fuzzy rough set theory which is able to detect such gradual changepoints. An expression for the rough-fuzzy estimate of changepoints is derived along with its mathematical properties concerning fast computation. In a statistical hypothesis testing framework, asymptotic distribution of the proposed statistic on both single and multiple changepoints is derived under null hypothesis enabling multiple changepoint detection. Extensive simulation studies have been performed to investigate how simple crude statistical measures of disparity can be subjected to improve their efficiency in estimation of gradual changepoints. Also, the said rough-fuzzy estimate is robust to signal-to-noise ratio, high degree of fuzziness in true changepoints and also to hyper parameter values. Simulation studies reveal that the proposed method beats other fuzzy methods and also popular crisp methods like WBS MESHD, PELT and BOCD in detecting gradual changepoints. The applicability of the estimate is demonstrated using multiple real-life datasets including Covid-19 MESHD. We have developed the python package "roufcp" for broader dissemination of the methods.

    Significance between Air pollutants, Meteorological Factors and COVID-19 MESHD Infections: Probable Evidences in India

    Authors: Mrunmayee M Sahoo

    doi:10.21203/rs.3.rs-73771/v1 Date: 2020-09-07 Source: ResearchSquare

    SARS-CoV-2 (Coronavirus) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection MESHD, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors and the daily reported infection cases caused by novel coronavirus in India. The daily positive infected cases, air pollution and meteorological factors in 288 districts were collected from January 30, 2020 to April 23, 2020 in India. Speraman’s correlation and generalised additive model were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2 and SO2) and eight meteorological factors (Temp, DTR, RH, AH MESHD, AP, RF, WS MESHD and WD MESHD) with COVID-19 MESHD infected cases. The study indicated that a 10 µg/m3 increase during (Lag0-14) in PM2.5, PM10 and NO2 was resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01) and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of COVID 19 infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19 MESHD infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19 MESHD infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there is significant relationship between air pollutants and meteorological factors with COVID-19 MESHD infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease MESHD

    CovidDeep: SARS-CoV-2/ COVID-19 MESHD Test Based on Wearable Medical Sensors and Efficient Neural Networks

    Authors: Shayan Hassantabar; Novati Stefano; Vishweshwar Ghanakota; Alessandra Ferrari; Gregory N. Nicola; Raffaele Bruno; Ignazio R. Marino; Kenza Hamidouche; Niraj K. Jha

    id:2007.10497v3 Date: 2020-07-20 Source: arXiv

    The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands, and also suffers from a relatively low positive detection rate in the early stages of the resultant COVID-19 MESHD disease. Hence, there is a need for an alternative approach for repeated large-scale testing of SARS-CoV-2/ COVID-19 MESHD. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus. We collected data from 87 individuals, spanning three cohorts including healthy, asymptomatic, and symptomatic patients. We trained DNNs on various subsets of the features automatically extracted from six WMS MESHD and questionnaire categories to perform ablation studies to determine which subsets are most efficacious in terms of test accuracy for a three-way classification. The highest test accuracy obtained was 98.1%. We also augmented the real training dataset with a synthetic training dataset drawn from the same probability distribution to impose a prior on DNN weights and leveraged a grow-and-prune synthesis paradigm to learn both DNN architecture and weights. This boosted the accuracy of the various DNNs further and simultaneously reduced their size and floating-point operations.

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MeSH Disease
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