Corpus overview


MeSH Disease

Human Phenotype

Pneumonia (1389)

Fever (829)

Cough (689)

Hypertension (489)

Anxiety (486)


age categories (3897)

Transmission (3404)

gender (1792)

fomite (1391)

contact tracing (1267)

    displaying 41 - 50 records in total 18267
    records per page

    Modelling the dispersion of SARS-CoV-2 on a dynamic network graph

    Authors: Patrick Bryant; Arne Elofsson; Theresa Hippchen; Sylvia Olberg; Monique van Straaten; Hedda Wardemann; Erec Stebbins; Hans-Georg Kraeusslich; Ralf Bartenschlager; Hermann Brenner; Vibor Laketa; Ben Schoettker; Barbara Mueller; Uta Merle; Tim Waterboer; James Watmough; Jude Dzevela Kong; Iain Moyles; Huaiping Zhu

    doi:10.1101/2020.10.19.20215046 Date: 2020-10-21 Source: medRxiv

    Background When modelling the dispersion of an epidemic using R0 TRANS, one only considers the average number of individuals each infected individual will infect MESHD. However, we know from extensive studies of social networks that there is significant variation in the number of connections and thus social contacts each individual has. Individuals with more social contacts are more likely to attract and spread infection MESHD. These individuals are likely the drivers of the epidemic, so-called superspreaders. When many superspreaders are immune, it becomes more difficult for the disease to spread TRANS, as the connectedness of the social network dramatically decreases. If one assumes all individuals being equally connected and thus as likely to spread disease TRANS as in a SIR model, this is not true. Methods To account for the impact of social network structure on epidemic development, we model the dispersion of SARS-CoV-2 on a dynamic preferential attachment graph which changes appearance proportional to observed mobility changes. We sample a serial interval TRANS distribution that determines the probability of dispersion for all infected MESHD nodes each day. We model the dispersion in different age groups TRANS using age TRANS-specific infection MESHD fatality rates. We vary the infection probabilities in different age groups TRANS and analyse the outcome. Results The impact of movement on network dynamics plays a crucial role in the spread of infections. We find that higher movement results in higher spread due to an increased probability of new connections being made within a social network. We show that saturation in the dispersion can be reached much earlier on a preferential attachment graph compared to spread on a random graph, which is more similar to estimations using R0 TRANS. Conclusions We provide a novel method for modelling epidemics by using a dynamic network structure related to observed mobility changes. The social network structure plays a crucial role in epidemic development, something that is often overlooked.

    Effect of park use and landscape structure on COVID-19 transmission TRANS rates

    Authors: Thomas Frederick Johnson; Lisbeth A Hordley; Matthew P Greenwell; Luke C Evans; Monique van Straaten; Hedda Wardemann; Erec Stebbins; Hans-Georg Kraeusslich; Ralf Bartenschlager; Hermann Brenner; Vibor Laketa; Ben Schoettker; Barbara Mueller; Uta Merle; Tim Waterboer; James Watmough; Jude Dzevela Kong; Iain Moyles; Huaiping Zhu

    doi:10.1101/2020.10.20.20215731 Date: 2020-10-21 Source: medRxiv

    The COVID-19 pandemic has had severe impacts on global public health. In the UK, social distancing measures and a nationwide lockdown were introduced to reduce the spread of the virus. Green space accessibility may have been particularly important during this lockdown, as it could have provided benefits for physical and mental wellbeing, while also limiting the risk of transmission TRANS. However, the effects of public green space use on the rate of COVID-19 transmission TRANS are yet to be quantified, and as the size and accessibility of green spaces vary within local authorities, the risks and benefits to the public of using green space may well be context-dependent. To evaluate how green space affected COVID-19 transmission TRANS across 98 local authorities in England, we first split case rates into two periods, the pre-peak rise and the post-peak decline in cases, and assessed how baseline health and mobility variables influenced these rates. Next, looking at the residual case rates, we investigated how landscape structure (e.g. area and patchiness of green space) and park use influenced transmission TRANS. We first show that pre- and post-peak case rates were significantly reduced when overall mobility was low, especially in areas with high population clustering, and high population density during the post-peak period only. After accounting for known mechanisms behind transmission TRANS rates, we found that park use (showing a preference for park mobility) decreased residual pre-peak case rates, especially when green space was low and contiguous (not patchy). Whilst in the post-peak period, park use and green landscape structure had no effect on residual case rates. Our results show that utilising green spaces rather than other activities (e.g. visiting shops and workplaces) can reduce the transmission TRANS rate of COVID-19, especially during an exponential phase of transmission TRANS.

    Differential Impact of Mitigation Policies and Socioeconomic Status on COVID-19 Prevalence SERO and Social Distancing in the United States

    Authors: Hsien-Yen Chang; Wenze Tang; Elham Hatef; Christopher Kitchen; Jonathan P. Weiner; Hadi Kharrazi; Erec Stebbins; Hans-Georg Kraeusslich; Ralf Bartenschlager; Hermann Brenner; Vibor Laketa; Ben Schoettker; Barbara Mueller; Uta Merle; Tim Waterboer; James Watmough; Jude Dzevela Kong; Iain Moyles; Huaiping Zhu

    doi:10.1101/2020.10.20.20216119 Date: 2020-10-21 Source: medRxiv

    Background The spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission TRANS. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission TRANS and residents mobility across neighborhoods of different levels of socioeconomic disadvantage. Methods This was a comparative interrupted time-series analysis at the county level. We included 2,087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence SERO of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index, derived from the COVID-19 Impact Analysis Platform, to measure the social distancing practice. For the evaluation of implementation, the observation started from Mar 1 2020 to one day before lifting; and, for lifting, it ranged from one day after implementation to Jul 5 2020. We calculated a comparative change of daily trends in COVID-19 prevalence SERO and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately. Results On both stay-at-home implementation and lifting dates, COVID-19 prevalence SERO was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection MESHD after both stay-at-home implementation and relaxation. Conclusions Neighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.

    COVID-19 Pandemic and Tourism: The Impact of Health Risk Perception and Intolerance of Uncertainty on Travel TRANS Intentions

    Authors: Anastasiya Golets; Jéssica Farias; Ronaldo Pilati; Helena Costa

    id:10.20944/preprints202010.0432.v1 Date: 2020-10-21 Source:

    Understanding tourist behaviour during and after major tourism crises is essential to help destinations recover. The COVID-19 pandemic, a period of uncertainty and risk, makes it relevant to assess factors that influence travel TRANS intentions. There has been little research on tourist behaviour during health crises and, in particular, on perceived health risk and uncertainty effects on travel TRANS intentions. This study was carried out during the first months of the pandemic in Brazil and aims to investigate the role of health risk perception and intolerance of uncertainty on travel TRANS intentions for 2020 and 2021. We applied an online survey to 1,150 Brazilian participants from March to May of 2020. Our findings indicate that perceived COVID-19 severity, perceived probability of contracting it, and expected pandemic duration are significant predictors of travel TRANS intentions for both years. This paper sheds new light on tourist behaviour in the context of global health crises.

    Collect and Treat Urban Wastewater to Fight the Pandemic Disease of COVID-19 Effectively

    Authors: Abdol Aziz Shahraki

    id:10.20944/preprints202010.0441.v1 Date: 2020-10-21 Source:

    This paper presents multidisciplinary and innovative research concerning fighting against coronavirus through wastewater collection and treatment. Studies suggest that coronavirus exists in the wastewaters. Untreated wastewater is proved to spread the virus. Coronavirus is attacking people globally and shrinking the economy. This paper highlights the idea that the coronavirus shall be defeated with the help of wastewater collection and treatment as well. The question addressed by this paper is will communities defeat the coronavirus without well-collected and treated wastewaters? This research aims to display the role of wastewaters in the spread of coronavirus in cities and to require their collection. The methods to achieve the goals are theoretical surveys, case study strategy, mathematical modeling, statistical procedures, forecasting of future, and dialectical discussions. The findings of this research demonstrate the need for carefully collected and treated wastewaters to overcome the coronavirus. This paper gives suitable techniques to collect and treat wastewater such as wastewater stabilization ponds, bacterial reactors, and anaerobic ponds. The innovative idea of this paper, its suggested indicators to select a certain wastewater treatment technique in every city, and its outcome will assist the global community to fight the coronavirus more effectively.

    iMakerSpace Model: Shaping the 21st Century Workforce

    Authors: Ismail Fidan; Stephen Canfield; Vahid Motevalli; George Chitiyo; Mahdi Mohammadizadeh

    id:10.20944/preprints202010.0423.v1 Date: 2020-10-21 Source:

    Innovations in engineering education are undergoing a noticeable transformation. Higher education institutions are practicing distance education, remote laboratories, studio pedagogies and several other approaches in order to increase their students’ retention, success, and preparedness for the job market. In engineering education, maker spaces have become popular in the last ten years in universities as well as community colleges, high-schools and community innovation hubs. A large number of engineering colleges have allocated significant spaces, and at some universities entire buildings as maker spaces to be used for curricular and extracurricular activities. Success stories of these types of spaces are well documented. This paper describes the activities and programs held at Tennessee Tech University’s maker space called ‘iMakerSpace.’ These accomplishments include several workforce development activities. The impact and effectiveness of the iMakerSpace is evaluated through analysis of survey data.

    2.5 Million Person-Years of Life Have Been Lost Due to COVID-19 in the United States


    doi:10.1101/2020.10.18.20214783 Date: 2020-10-20 Source: medRxiv

    The COVID-19 pandemic, caused by tens of millions of SARS-CoV-2 infections MESHD world-wide, has resulted in considerable levels of mortality and morbidity. The United States has been hit particularly hard having 20 percent of the world's infections MESHD but only 4 percent of the world population. Unfortunately, significant levels of misunderstanding exist about the severity of the disease and its lethality. As COVID-19 disproportionally impacts elderly TRANS populations, the false impression that the impact on society of these deaths is minimal may be conveyed by some because elderly TRANS individuals are closer to a natural death MESHD. To assess the impact of COVID-19 in the US, I have performed calculations of person-years of life lost as a result of 194,000 premature deaths due to SARS-CoV-2 infection MESHD as of early October, 2020. By combining actuarial data on life expectancy and the distribution of COVID-19 associated deaths we estimate that over 2,500,000 person-years of life have been lost so far in the pandemic in the US alone, averaging over 13.25 years per person with differences noted between males TRANS and females TRANS. Importantly, nearly half of the potential years of life lost occur in non- elderly TRANS populations. Issues impacting refinement of these models and the additional morbidity caused by COVID-19 beyond lethality are discussed.

    A Sensitive, Rapid, and Portable CasRx-based Diagnostic Assay for SARS-CoV2

    Authors: Daniel J Brogan; Duverney Chaverra-Rodriguez; Calvin P Lin; Andrea L Smidler; Ting Yang; Lenissa M Alcantara; Igor Antoshechkin; Junru Liu; Robyn R Raban; Pedro Belda-ferre; Rob Knight; Elizabeth A Komives; Omar S. Akbari

    doi:10.1101/2020.10.14.20212795 Date: 2020-10-20 Source: medRxiv

    Since its first emergence from China in late 2019, the SARS-CoV-2 virus has spread globally despite unprecedented containment efforts, resulting in a catastrophic worldwide pandemic. Successful identification and isolation of infected individuals can drastically curtail virus spread and limit outbreaks. However, during the early stages of global transmission TRANS, point-of-care diagnostics were largely unavailable and continue to remain difficult to procure, greatly inhibiting public health efforts to mitigate spread. Furthermore, the most prevalent testing kits rely on reagent- and time-intensive protocols to detect viral RNA, preventing rapid and cost-effective diagnosis. Therefore the development of an extensive toolkit for point-of-care diagnostics that is expeditiously adaptable to new emerging pathogens is of critical public health importance. Recently, a number of novel CRISPR-based diagnostics have been developed to detect COVID-19. Herein, we outline the development of a CRISPR-based nucleic acid molecular diagnostic utilizing a Cas13d ribonuclease derived from Ruminococcus flavefaciens (CasRx) to detect SARS-CoV-2, an approach we term SENSR (Sensitive Enzymatic Nucleic-acid Sequence Reporter). We demonstrate SENSR robustly detects SARS-CoV-2 sequences in both synthetic and patient-derived samples by lateral flow and fluorescence, thus expanding the available point-of-care diagnostics to combat current and future pandemics.

    Antibody SERO Immunological Imprinting on COVID-19 Patients

    Authors: Teresa Aydillo; Alexander Rombauts; Daniel Stadlbauer; Sadaf Aslam; Gabriela Abelenda-Alonso; Alba Escalera; Fatima Amanat; Kaijun Jiang; Florian Krammer; Jordi Carratala; Adolfo Garcia-Sastre; Elizabeth A Komives; Omar S. Akbari

    doi:10.1101/2020.10.14.20212662 Date: 2020-10-20 Source: medRxiv

    While the current pandemic remains a thread to human health, the polyclonal nature of the antibody SERO response against SARS-CoV-2 is not fully understood. Other than SARS-CoV-2, humans are susceptible to six different coronaviruses, and previous exposure to antigenically related and divergent seasonal coronaviruses is frequent. We longitudinally profiled the early humoral immune response against SARS-CoV-2 on hospitalized COVID-19 patients, and quantify levels of pre-existing immunity to OC43, HKU1 and 223E seasonal coronaviruses. A strong back-boosting effect to conserved, but not variable regions of OC43 and HKU1 betacoronaviruses spike protein was observed. All patients developed antibodies SERO against SARS-CoV-2 spike MESHD and nucleoprotein, with peak induction at day 7 post hospitalization. However a negative correlation was found between antibody SERO memory boost to human coronaviruses and induction of IgG and IgM against SARS-CoV-2 spike. Our findings provide evidence of immunological imprinting that determine the antibody SERO profile to COVID-19 patients in an original antigenic sin fashion.

    Prediction of Covid-19 Infections Through December 2020 for 10 US States Incorporating Outdoor Temperature and School Re-Opening Effects-September Update

    Authors: Ty A Newell; Alexander Rombauts; Daniel Stadlbauer; Sadaf Aslam; Gabriela Abelenda-Alonso; Alba Escalera; Fatima Amanat; Kaijun Jiang; Florian Krammer; Jordi Carratala; Adolfo Garcia-Sastre; Elizabeth A Komives; Omar S. Akbari

    doi:10.1101/2020.10.15.20213223 Date: 2020-10-20 Source: medRxiv

    A two-parameter, human behavior Covid-19 infection growth MESHD model predicts total infections between -4.2% (overprediction) and 4.5% (underprediction) of actual infections from July 27, 2020 to September 30, 2020 for 10 US States (NY, WA, GA, IL, MN, FL, OH, MI MESHD, CA, NC). During that time, total Covid-19 infections for 9 of the 10 modeled US States grew by 60% ( MI MESHD) to 95% (MN). Only NY limited Covid-19 infection growth MESHD with an 11% increase from July 27 to September 30, 2020. September is a month with contraposing effects of increased social interaction HP social interaction TRANS (eg, physical school openings) and outdoor temperatures decreasing to the 50F (10C) to 70F (21C) range in which outdoor activities and building ventilation are beneficially increased. All State infection predictions except GA, FL and CA predictions through September 30 are bounded by four prediction scenarios (no school with outdoor temperature effect, no school with no outdoor temperature effect, school with temperature effect, school with no temperature effect). GA, FL and CA continued along a path slightly below the linear infection growth boundary separating infection growth MESHD and decay, resulting in overprediction of infection growth MESHD over the two month simulation period(-3.1% for GA, -1.9% for FL, and -4.5% for CA). Three eastern States (NY, NC, and GA) are most accurately represented by models that assume no significant change in social interactions HP social interactions TRANS coupled with minor outdoor temperature effects. Four midwestern States (IL, MI MESHD, MN, OH) are most accurately modeled with minor outdoor temperature effects due to a delayed decrease in average outdoor temperatures in the Midwest. The remaining three States (WA, FL, and CA) are also in good agreement with the model but with differing weather condition and social interaction HP social interaction TRANS impacts. Overall, model predictions continue to support the basic premise that human behavior in the US oscillates across a linear infection MESHD growth boundary that divides accelerated infection growth MESHD and decaying infection transmission TRANS.

The ZB MED preprint Viewer preVIEW includes all COVID-19 related preprints from medRxiv and bioRxiv, from ChemRxiv, from ResearchSquare, from arXiv and from and is updated on a daily basis (7am CET/CEST).
The web page can also be accessed via API.



MeSH Disease
Human Phenotype

Export subcorpus as...

This service is developed in the project nfdi4health task force covid-19 which is a part of nfdi4health.

nfdi4health is one of the funded consortia of the National Research Data Infrastructure programme of the DFG.