Corpus overview


Overview

MeSH Disease

Human Phenotype

Inertia (3)


Transmission

Seroprevalence

There are no seroprevalence terms in the subcorpus

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    The dispersion of spherical droplets in source-sink flows and their relevance to the COVID-19 pandemic

    Authors: Cathal Cummins; Olayinka Ajayi; Felicity Mehendale; Roman Gabl; Ignazio Maria Viola

    id:2007.05298v2 Date: 2020-07-10 Source: arXiv

    In this paper, we investigate the dynamics of spherical droplets in the presence of a source-sink pair flow field. The dynamics of the droplets is governed by the Maxey-Riley equation with Basset-Boussinesq history term neglected. We find that, in the absence of gravity, there are two distinct behaviours for the droplets: small droplets cannot go further than a specific distance, which we determine analytically, from the source before getting pulled into the sink. Larger droplets can travel TRANS further from the source before getting pulled into the sink by virtue of their larger inertia HP, and their maximum travelled TRANS distance is determined analytically. We investigate the effects of gravity, and we find that there are three distinct droplet behaviours categorised by their relative sizes: small, intermediate-sized, and large. Counterintuitively, we find that the droplets with minimum horizontal range are neither small nor large, but of intermediate size. Furthermore, we show that in conditions of regular human respiration, these intermediate-sized droplets range in size from a few $\mu$m to a few hundred $\mu$m. The result that such droplets have a very short range could have important implications for the interpretation of existing data on droplet dispersion.

    Impact of COVID-19 Behavioral Inertia HP on Reopening Strategies for New York City Transit

    Authors: Ding Wang; Brian Yueshuai He; Jingqin Gao; Joseph Y. J. Chow; Kaan Ozbay; Shri Iyer

    id:2006.13368v1 Date: 2020-06-23 Source: arXiv

    The COVID-19 pandemic has affected travel TRANS behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. A baseline model was previously developed and calibrated for pre-COVID conditions as MATSim-NYC. A new COVID model is calibrated that represents travel TRANS behavior during the COVID-19 pandemic by recalibrating the population agendas to include work-from-home and re-estimating the mode choice model for MATSim-NYC to fit observed traffic and transit ridership data. Assuming the change in behavior exhibits inertia HP during reopening, we analyze the increase in car traffic due to the phased reopen plan guided by the state government of New York. Four reopening phases and two reopening scenarios (with and without transit capacity restrictions) are analyzed. A Phase 4 reopening with 100% transit capacity may only see as much as 73% of pre-COVID ridership and an increase in the number of car trips by as much as 142% of pre-pandemic levels. Limiting transit capacity to 50% would decrease transit ridership further from 73% to 64% while increasing car trips to as much as 143% of pre-pandemic levels. While the increase appears small, the impact on consumer surplus is disproportionately large due to already increased traffic congestion. Many of the trips also get shifted to other modes like micromobility. The findings imply that a transit capacity restriction policy during reopening needs to be accompanied by (1) support for micromobility modes, particularly in non-Manhattan boroughs, and (2) congestion alleviation policies that focus on reducing traffic in Manhattan, such as cordon-based pricing.

    Observed mobility behavior data reveal social distancing inertia HP

    Authors: Sepehr Ghader; Jun Zhao; Minha Lee; Weiyi Zhou; Guangchen Zhao; Lei Zhang

    id:2004.14748v1 Date: 2020-04-30 Source: arXiv

    The research team has utilized an integrated dataset, consisting of anonymized location data, COVID-19 case data, and census population information, to study the impact of COVID-19 on human mobility. The study revealed that statistics related to social distancing, namely trip rate, miles traveled TRANS per person, and percentage of population staying at home have all showed an unexpected trend, which we named social distancing inertia HP. The trends showed that as soon as COVID-19 cases were observed, the statistics started improving, regardless of government actions. This suggests that a portion of population who could and were willing to practice social distancing voluntarily and naturally reacted to the emergence of COVID-19 cases. However, after about two weeks, the statistics saturated and stopped improving, despite the continuous rise in COVID-19 cases. The study suggests that there is a natural behavior inertia MESHD inertia HP toward social distancing, which puts a limit on the extent of improvement in the social-distancing-related statistics. The national data showed that the inertia HP phenomenon is universal, happening in all the U.S. states and for all the studied statistics. The U.S. states showed a synchronized trend, regardless of the timeline of their statewide COVID-19 case spreads or government orders.

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MeSH Disease
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Transmission
Seroprevalence


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