Corpus overview


MeSH Disease

Death (1)

Infections (1)

Disease (1)

Human Phenotype

Inertia (5)



There are no seroprevalence terms in the subcorpus

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    Efficiency of Online Course of Medical Statistics in Nanjing Medical University During the COVID-19 Epidemic

    Authors: Senmiao Ni; Wensong Chen; Honggang Yi; Yang Zhao; Na Tong; Ran Chen; Hao Yu; Jiyong Liu; Jianling Bai; Feng Chen

    doi:10.21203/ Date: 2020-07-14 Source: ResearchSquare

    Background: To analyze the online course efficiency of a combined mode of Massive Open Online Course (MOOC) micro-video and E-learning platform in Nanjing Medical University during the COVID-19 epidemic. Methods: We developed a new questionnaire to assess the efficiency of online teaching of medical statistics in Nanjing Medical University. This investigation enrolled students participating in the online course of medical statistics from January 2020 to June 2020. The “Questionnaire Star” electronic questionnaire collection system was used to collect data. Results: In total, 1050 of the 1210 (86.78%) students completed the questionnaire, including 971 (92.48%) juniors. To be specific, 57.33% of the students majored in clinical medicine, 15.14% in pharmacy, 10.38% in pediatrics, 8.00% in medical imageology, and 6.29% in basic medicine. As to the question "Are you satisfied with the current online teaching method?", 354 (32.77%) students responded with "Agree" and "Strongly Agree", and 1012 (96.47%) thought they needed to consolidate what they had learned after returning to school. Most students reported their "Difficulties in the learning process" by "Learning motivation" and "Personal inertia HP" (59.90% and 58.29%, respectively).Conclusions: The online course of medical statistics was favored by most students, suggesting its efficiency an efficient alternative to classroom study during the COVID-19 pandemic. Yet there were still some problems, such as inconvenient communication between teachers and students, poor mastery of key knowledge, which should be resolved in classroom teaching at school. 

    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.

    Estimation of COVID-19 under-reporting in Brazilian States through SARI

    Authors: Balthazar Paixão; Lais Baroni; Rebecca Salles; Luciana Escobar; Carlos de Sousa; Marcel Pedroso; Raphael Saldanha; Rafaelli Coutinho; Fabio Porto; Eduardo Ogasawara

    id:2006.12759v1 Date: 2020-06-23 Source: arXiv

    Due to its impact, COVID-19 has been stressing the academy to search for curing, mitigating, or controlling it. However, when it comes to controlling, there are still few studies focused on under-reporting estimates. It is believed that under-reporting is a relevant factor in determining the actual mortality rate and, if not considered, can cause significant misinformation. Therefore, the objective of this work is to estimate the under-reporting of cases and deaths MESHD of COVID-19 in Brazilian states using data from the Infogripe on notification of Severe Acute Respiratory Infection MESHD (SARI). The methodology is based on the concepts of inertia HP and the use of event detection techniques to study the time series of hospitalized SARI cases. The estimate of real cases of the disease MESHD, called novelty, is calculated by comparing the difference in SARI cases in 2020 (after COVID-19) with the total expected cases in recent years (2016 to 2019) derived from a seasonal exponential moving average. The results show that under-reporting rates vary significantly between states and that there are no general patterns for states in the same region in Brazil.

    SIR-PID: A Proportional-Integral-Derivative Controller for COVID-19 Outbreak Containment

    Authors: Nicola Rossi; Aldo Ianni

    doi:10.1101/2020.05.30.20117556 Date: 2020-06-03 Source: medRxiv

    Ongoing social restrictions, as distancing and lockdown, adopted by many countries for contrasting the COVID-19 epidemic spread, try to find a trade-off between induced economic crisis, healthcare system collapse and costs in terms of human lives. Applying and removing restrictions on a system with uncontrollable inertia HP, as represented by an epidemic outbreak, may create critical instabilities, overshoots and strong oscillations of infected people around the desirable set-point, defined as the maximum number of hospitalizations acceptable by a given healthcare system. A good understanding of the system reaction to a change of the input control variable can be reasonably achieved using a proportional-integral-derivative controller, widely used in technological applications. In this paper we make use of this basic control theory for understanding the reaction of COVID-19 propagation to social restrictions and for exploiting a very known technology to reduce the epidemic damages through the correct tuning of the containment policy.

    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 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
Human Phenotype

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