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An integrated deterministic-stochastic approach for forecasting the long-term trajectories of COVID-19

International Journal of Modeling, Simulation, and Scientific Computing 2021年第3期12卷 63-75页

作者： indrajit ghosh Tanujit Chakraborty Indian Statistical Institute 203 B.T.Road BaranagarKolkata 700108India

The ongoing coronavirus disease 2019(COVID-19)pandemic is one of the major health emergencies in decades that affected almost every country in the *** of June 30,2020,it has caused an outbreak with more than 10 million confirmed infections,and more than 500,000 reported deaths *** to the unavailability of an effective treatment(or vaccine)and insufficient evidence regarding the transmission mechanism of the epidemic,the world population is currently in a vulnerable *** daily cases data sets of COVID-19 for profoundly affected countries represent a stochastic process comprised of deterministic and stochastic *** study proposes an integrated deterministic–stochastic approach to forecast the long-term trajectories of the COVID-19 cases for Italy and *** deterministic component of the daily-cases univariate time series is assessed by an extended version of the SIR[Susceptible–Infected–Recovered–Protected–Isolated(SIRCX)]model,whereas its stochastic component is modeled using an autoregressive(AR)time series *** proposed integrated SIRCX-AR(ISA)approach based on two operationally distinct modeling paradigms utilizes the superiority of both the deterministic SIRCX and stochastic AR models to find the long-term trajectories of the epidemic *** analysis based on the proposed ISA model shows significant improvement in the long-term forecasting of COVID-19 cases for Italy and Spain in comparison to the ODE-based SIRCX *** estimated Basic reproduction numbers for Italy and Spain based on SIRCX model are found to be 4.78 and 3.25,*** model-based results reveal that the number of cases in Italy and Spain between 11 May,2020–9 June,2020 will be 10,982(6383–15,582)and 13,731(3395–29,013),***,the expected number of daily cases on 9 July,2020 for Italy and Spain is estimated to be 30(0–183)and 92(0–602),respectively.

关键词： COVID-19 Integrated approach Prediction Extended SIR model AR model

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高速公路事故清理时间影响因素分析(英文)

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交通运输系统工程与信息 2012年第3期12卷 75-89页

作者： ghosh indrajit 国家技术学院

交通事故是导致高速公路延误的一个主要因素,同时也降低了道路通行能力、车辆以及人员的安全性,甚至有可能导致二次事故发生.对此,相关部门已开始制定一些事故管理方案以降低或者消除事故影响并最大限度地增加道路通行能力和车辆行驶安... 详细信息

交通事故是导致高速公路延误的一个主要因素,同时也降低了道路通行能力、车辆以及人员的安全性,甚至有可能导致二次事故发生.对此,相关部门已开始制定一些事故管理方案以降低或者消除事故影响并最大限度地增加道路通行能力和车辆行驶安全性.应密歇根交通部要求对底特律市高速公路网事故清理时间影响因素进行分析,基于交通流数据、道路几何特征及大量的事故数据,建立了参数化危险持续时间模型.所建立的四个模型分别基于不同的概率分布.结果表明,四个模型的性能大体相似,但是相对于其他分布,log-logistic分布更适合于事故清理时间分析.对事故清理时间影响显著的因素有:事故发生的时间、路段交通特性和几何特性,以及事故特征.

关键词：高速公路事故参数化危险持续时间模型

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COVID-19 strict lockdown impact on urban air quality and atmospheric temperature in four megacities of India

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Geoscience Frontiers 2022年第6期13卷 279-290页

作者： Subodh Chandra Pal indrajit Chowdhuri Asish Saha Manoranjan ghosh Paramita Roy Biswajit Das Rabin Chakrabortty Manisa Shit Department of Geography The University of BurdwanBardhaman713104West BengalIndia Center for Rural Development and Innovative Sustainable Technology Indian Institute of Technology KharagpurWest Bengal721302India India Smart Cities Fellow National Institute of Urban AffairsNew Delhi110003India Department of Geography Raiganj UniversityRaiganjUttar Dinajpur733134West BengalIndia

COVID-19 pandemic has forced to lockdown entire India starting from 24th March 2020 to 14th April 2020(first phase), extended up to 3rd May 2020(second phase), and further extended up to 17th May 2020(third phase) with limited relaxation in non-hotspot areas. This strict lockdown has severely curtailed human activity across India. Here, aerosol concentrations of particular matters(PM) i.e., PM,PM, carbon monoxide(CO), nitrogen dioxide(NO), sulphur dioxide(SO), ammonia(NH) and ozone(O), and associated temperature fluctuation in four megacities(Delhi, Mumbai, Kolkata, and Chennai)from different regions of India were investigated. In this pandemic period, air temperature of Delhi,Kolkata, Mumbai and Chennai has decreased about 3 ℃, 2.5 ℃, 2 ℃ and 2 ℃ respectively. Compared to previous years and pre-lockdown period, air pollutants level and aerosol concentration(-41.91%,-37.13%,-54.94% and-46.79% respectively for Delhi, Mumbai, Kolkata and Chennai) in these four megacities has improved drastically during this lockdown period. Emission of PMhas experienced the highest decrease in these megacities, which directly shows the positive impact of restricted vehicular movement. Restricted emissions produce encouraging results in terms of urban air quality and temperature, which may encourage policymakers to consider it in terms of environmental sustainability.

关键词： COVID-19 Aerosol Air pollutant Climate Megacity

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