Corpus overview


Overview

MeSH Disease

Human Phenotype

Falls (10)

Pneumonia (6)

Hypertension (1)

Fever (1)


Transmission

Seroprevalence
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    The impact of digital contact tracing TRANS on the SARS-CoV-2 pandemic - a comprehensive modelling study

    Authors: Tina R Pollmann; Julia Pollmann; Christoph Wiesinger; Christian Haack; Lolian Shtembari; Andrea Turcati; Birgit Neumair; Stephan Meighen-Berger; Giovanni Zattera; Matthias Neumair; Uljana Apel; Augustine Okolie; Johannes Mueller; Stefan Schoenert; Elisa Resconi; Monica I Lupei; Christopher J Tignanelli

    doi:10.1101/2020.09.13.20192682 Date: 2020-09-14 Source: medRxiv

    Contact tracing TRANS is one of several strategies employed in many countries to curb the spread of SARS-CoV-2. Digital contact tracing TRANS (DCT) uses tools such as cell-phone applications to improve tracing TRANS speed and reach. We model the impact of DCT on the spread of the virus for a large epidemiological parameter space consistent with current literature on SARS-CoV-2. We also model DCT in combination with random testing (RT) and social distancing (SD). Modelling is done with two independently developed individual-based (stochastic) models that use the Monte Carlo technique, benchmarked against each other and against two types of deterministic models. For current best estimates of the number of asymptomatic TRANS SARS-CoV-2 carriers TRANS (approximately 40\%), their contagiousness (similar to that of symptomatic carriers TRANS), the reproductive number TRANS before interventions ( R0 TRANS at least 3) we find that DCT must be combined with other interventions such as SD and/or RT to push the reproductive number TRANS below one. At least 60\% of the population would have to use the DCT system for its effect to become significant. On its own, DCT cannot bring the reproductive number TRANS below 1 unless nearly the entire population uses the DCT system and follows quarantining and testing protocols strictly. For lower uptake of the DCT system, DCT still reduces the number of people that become infected. When DCT is deployed in a population with an ongoing outbreak where O(0.1\%) of the population have already been infected, the gains of the DCT intervention come at the cost of requiring up to 15% of the population to be quarantined (in response to being traced TRANS) on average each day for the duration of the epidemic, even when there is sufficient testing capability to test every traced TRANS person.

    COVID-19 outbreak and control in Kenya- Insights from a mathematical model

    Authors: Rachel Waema Mbogo; Titus Okellow Orwa

    doi:10.21203/rs.3.rs-77507/v1 Date: 2020-09-14 Source: ResearchSquare

    The coronavirus disease MESHD 2019 ( COVID -19) pandemic reached Kenya in March 2020 with the initial cases reported in the capital city Nairobi and in the coastal area Mombasa. As reported by the World Health Organization, the outbreak of COVID -19 has spread across the world, killed many, collapsed economies and changed the way people live since it was first reported in Wuhan, China, in the end of 2019. As of May 25,2020 It had led to over 100,000 confirmed cases TRANS in Africa with over 3000 deaths. The trend poses a huge threat to global public health. Understanding the early transmission TRANS dynamics of the infection MESHD and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission TRANS to occur in new areas. We employed a SEIHCRD mathematical transmission TRANS model with reported Kenyan data on cases of COVID -19 to estimate how transmission TRANS varies over time. The model is concise in structure, and successfully captures the course of the COVID -19 outbreak, and thus sheds light on understanding the trends of the outbreak. The next generation matrix approach was adopted to calculate the basic reproduction number TRANS ( $ R_0 TRANS$ ) from the model to assess the factors driving the infection . The results from the model analysis shows that non-pharmaceutical interventions over a relatively long period is needed to effectively get rid of the COVID -19 epidemic otherwise the rate of infection will continue to increase despite the increased rate of recovery.

    Modelling the first wave of the COVID-19 epidemic in the Czech Republic and the role of government interventions

    Authors: Ondrej Majek; Ondrej Ngo; Jiri Jarkovsky; Martin Komenda; Jarmila Razova; Ladislav Dusek; Tomas Pavlik; Noha Seoudi; Claire Morgan; Shakeel Shahdad; Padhraig S Fleming; Yunguang Li; Yinqin Zhu; Xiaoyu Zhang; Zhuang Liu; Rebiguli Aji; Xia Cai; Yutang Li; Di Qu; Yu Chen; Shibo Jiang; Qiao Wang; Hongbin Ji; Youhua Xie; Yihua Sun; Lu Lu; Yunjiao Zhou

    doi:10.1101/2020.09.10.20192070 Date: 2020-09-11 Source: medRxiv

    In the Czech Republic, the first COVID-19 cases were confirmed TRANS on 1 March 2020; early population interventions were adopted in the following weeks. A simple epidemiological model was developed to help decision-makers understand the course of the epidemic and perform short-term predictions. In this paper, we present the use of the model and estimated changes in the reproduction number TRANS (decrease from > 2.00 to < 1.00 over March and April) following adopted interventions.

    Long, thin transmission chains TRANS of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2 MESHD) may go undetected for several weeks at low to moderate reproductive numbers TRANS: Implications for containment and elimination strategy

    Authors: Gerry F Killeen; Deanna C Clemmer; Justin B Cox; Yetunde I Kayode; Victoria Zoccoli-Rodriguez; Harry E Taylor; Timothy P Endy; Joel R Wilmore; Gary Winslow; Sarah Tschudin-Sutter; Simon Fuchs; Julia Anna Bielicki; Hans Pargger; Martin Siegemund; Christian H. Nickel; Roland Bingisser; Michael Osthoff; Stefano Bassetti; Rita Schneider-Sliwa; Manuel Battegay; Hans H. Hirsch; Adrian Egli

    doi:10.1101/2020.09.04.20187948 Date: 2020-09-05 Source: medRxiv

    Especially at low to moderate reproductive numbers TRANS, the generally mild, non-specific symptomology of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) allows long MESHD, thin transmission chains TRANS to go undetected by passive surveillance over several weeks. This phenomenon has important implications: (1) Surveillance becomes less sensitive and reliable as an indicator of freedom from infection at the low reproductive numbers TRANS required to achieve elimination end points, passive surveillance systems may need to document an absence of new cases for at least a month to establish certainty of elimination. (2) Reproductive numbers TRANS should be kept as low as possible throughout such follow up periods without confirmed cases TRANS, to ensure such long, thin, undetected transmission chains TRANS all collapse before restrictions are eased and reproduction numbers TRANS are allowed to rebound. (3) While contact tracing TRANS systems may be highly effective when applied to large clusters in foci of elevated transmission TRANS where wide, rapidly expanding transmission chains TRANS are detected within two viral generations, large fractions of community transmission TRANS occurring through thinner, more extended transmission chains TRANS at lower reproductive numbers TRANS are often be too long to trace TRANS retrospectively and will be underrepresented in surveillance data. (4) Wherever surveillance systems are weak and/or younger age groups TRANS with lower rates of overt symptoms dominate transmission TRANS, containment effectiveness of contact tracing TRANS and isolation may be more severely limited, even at the higher reproduction numbers TRANS associated with larger outbreaks. While, contact tracing TRANS and isolation will remain vital for at least partially containing larger outbreaks, containment and elimination of SARS-CoV-2 will have to rely primarily upon the more burdensome and presumptive population-wide prevention measures that have proven so effective thus far against community transmission TRANS. Furthermore, these will have to be sustained at a much more stringent level and for longer periods after the last detected case than was necessary for SARS-CoV-1.

    Forecasting the outbreak of COVID-19 in Lebanon

    Authors: Omar El Deeb; Maya Jalloul; Lukas Schneider; Benedikt Gutsche; Dimitrije Markovic; Harry E Taylor; Timothy P Endy; Joel R Wilmore; Gary Winslow; Sarah Tschudin-Sutter; Simon Fuchs; Julia Anna Bielicki; Hans Pargger; Martin Siegemund; Christian H. Nickel; Roland Bingisser; Michael Osthoff; Stefano Bassetti; Rita Schneider-Sliwa; Manuel Battegay; Hans H. Hirsch; Adrian Egli

    doi:10.1101/2020.09.03.20187880 Date: 2020-09-05 Source: medRxiv

    in Lebanon using available data until August 25th, 2020 and forecasts the number of infections until the end of September using four different scenarios for mitigation measures reflected in the reproductive number TRANS Rt. Mitigation measures in Lebanon date back MESHD to early March soon after the first confirmed cases TRANS, and have been gradually lifted as of May. Thereafter, the country has witnessed a slow yet steady increase in the number of cases that has been significantly exacerbated after the explosion at Beirut harbor on August 4. Furthermore, we estimate the daily active cases in need of intensive care compared to the available number of beds and we assess accordingly that this capacity will be exhausted within a short span of time, unless severe measures are imposed.

    Transmission TRANS dynamics of COVID-19 in household and community settings in the United Kingdom

    Authors: Jamie Lopez Bernal; Nikolaos Panagiotopoulos; Chloe Byers; Tatiana Garcia Vilaplana; Nicola L Boddington; XuSheng Zhang; Andre Charlett; Suzanne Elgohari; Laura Coughlan; Rosie Whillock; Sophie Logan; Hikaru Bolt; Mary Sinnathamby; Louise Letley; Pauline MacDonald; Roberto Vivancos; Obaghe Edeghere; Charlotte Anderson; Karthik Paranthaman; Simon Cottrell; Jim McMenamin; Maria Zambon; Gavin Dabrera; Mary Ramsay; Vanessa Saliba

    doi:10.1101/2020.08.19.20177188 Date: 2020-08-22 Source: medRxiv

    Background: Households appear to be the highest risk setting for transmission TRANS of COVID-19. Large household transmission TRANS studies were reported in the early stages of the pandemic in Asia with secondary attack rates TRANS ranging from 5-30% but few large scale household transmission TRANS studies have been conducted outside of Asia. Methods: A prospective case ascertained study design based on the World Health Organization FFX protocol was undertaken in the UK following the detection of the first case in late January 2020. Household contacts TRANS of cases were followed using enhanced surveillance forms to establish whether they developed symptoms of COVID-19, became confirmed cases TRANS and their outcomes. Household secondary attack rates TRANS and serial intervals TRANS were estimated. Individual and household basic reproduction numbers TRANS were also estimated. The incubation period TRANS was estimated using known point source exposures that resulted in secondary cases TRANS. Results: A total of 233 households with two or more people were included with a total of 472 contacts. The overall household SAR TRANS was 37% (95% CI 31-43%) with a mean serial interval TRANS of 4.67 days, an R0 TRANS of 1.85 and a household reproduction number TRANS of 2.33. We find lower secondary attack rates TRANS in larger households. SARs were highest when the primary case TRANS was a child TRANS. We estimate a mean incubation period TRANS of around 4.5 days. Conclusions: High rates of household transmission TRANS of COVID-19 were found in the UK emphasising the need for preventative measures in this setting. Careful monitoring of schools reopening is needed to monitor transmission TRANS from children TRANS.

    Effects of (Un)lockdown on COVID-19 transmission TRANS: A mathematical study of different phases in India

    Authors: Rohit Kumar; Md. Zubbair Malik; Sapna Ratan Shah

    doi:10.1101/2020.08.19.20177840 Date: 2020-08-22 Source: medRxiv

    The novel coronavirus (SARS-CoV-2), identified in China at the end of the December 2019 is causing a potentially fatal respiratory syndrome MESHD (COVID-19), has meanwhile led to outbreak all over the globe. India has now become the third worst hit country globally with 16,38,870 confirmed cases TRANS and 35,747 confirmed deaths due to COVID-19 as of 31 July 2020. In this paper we have used mathematical modelling approach to study the effects of lockdowns and un-lockdowns on the pandemic evolution in India. This, study is based on SIDHARTHE model, which is an extension of classical SIR (Susceptible-Infected-Recovered) model. The SIDHARTHE model distinguish between the diagnosed and undiagnosed cases, which is very important because undiagnosed individuals are more likely to spread the virus than diagnosed individuals. We have stratified the lockdowns and un-lockdowns into seven phases and have computed the basic reproduction number TRANS R0 TRANS for each phase. We have calibrated our model results with real data from 20 March 2020 to 31 July 2020. Our results demonstrate that different strategies implemented by GoI, have delayed the peak of pandemic by approximately 100 days. But due to under-diagnosis of the infected asymptomatic TRANS subpopulation, a sudden outbreak of cases can be observed in India.

    Modeling the effects of prosocial awareness on COVID-19 dynamics: A case study on Colombia

    Authors: Indrajit Ghosh

    id:2008.09109v1 Date: 2020-08-20 Source: arXiv

    The ongoing COVID-19 pandemic caused by SARS-CoV-2 virus MESHD, a highly contagious virus, affected most of the countries of Earth. COVID-19 is causing obstacles for public health organizations and is affecting almost every aspect of human life. It has become an epidemic outbreak with more than 22 million confirmed infections TRANS and above 750 thousand deaths worldwide. Mathematical models may help to explore the transmission TRANS dynamics and control of COVID-19 in the absence of an effective medicine or ready-to-use vaccine. In this study, we consider a mathematical model on COVID-19 transmission TRANS with the prosocial awareness effect. The proposed model can have four equilibrium states based on different parametric conditions. The system has an awareness free, disease-free equilibrium which is locally asymptotically stable. The global stability conditions for this equilibrium is also studied. The basic reproduction number TRANS, $ R_0 TRANS$, is calculated using the next-generation matrix method. Using Lyapunov function theory and LaSalle Invariance Principle, the DFE is shown globally asymptotically stable under some parametric conditions. The existence of awareness free, endemic equilibrium and endemic equilibrium is presented. We have calibrated our proposed model parameters to fit daily cases and deaths from Colombia. Using the estimated parameters, we assess the impact of prosocial awareness during the outbreak and compare this strategy with popular control measures.

    A "Tail" of Two Cities: Fatality-based Modeling of COVID-19 Evolution in New York City and Cook County, IL

    Authors: Joshua Frieman

    doi:10.1101/2020.08.10.20170506 Date: 2020-08-12 Source: medRxiv

    I describe SIR modeling of the COVID-19 pandemic in two U.S. urban environments, New York City (NYC) and Cook County, IL, from onset through the month of June, 2020. Since testing was not widespread early in the pandemic in the U.S., I do not use data on confirmed cases TRANS and rely solely on public fatality data to estimate model parameters. Fits to the first 20 days of data determine a degenerate combination of the basic reproduction number TRANS, R0 TRANS, and the mean time to removal from the infectious population, 1/{gamma} with {gamma}( R0 TRANS - 1) = 0.25(0.21) inverse days for NYC (Cook County). Equivalently, the initial doubling time was td = 2.8(3.4) days for NYC (Cook). The early fatality data suggest that both locations had infections MESHD in early February. I model the mitigation measures implemented in mid-March in both locations (distancing, quarantine, isolation, etc) via a time-dependent reproduction number TRANS Rt that declines MESHD monotonically from R0 TRANS to a smaller asymptotic TRANS value, with a parameterized functional form. The timing (mid-March) and duration (several days) of the transitions in Rt appear well determined by the data. However, the fatality data determine only a degenerate combination of the parameters R0 TRANS, the percentage reduction in social contact due to mitigation measures, X, and the infection fatality rate (IFR), f . With flat priors, based on simulations the NYC model parameters have 95.45% credible intervals of R0 TRANS = 3.0 - 5.4, X = 80 - 99.9% and f = 2 - 6%, with 5 - 13% of the population asymptotically infected. A strong external prior indicating a lower value of f or of 1/{gamma} would imply lower values of R0 TRANS and X and higher percentage infection of the population. For Cook County, the evolution was qualitatively different: after mitigation measures were implemented, the daily fatality counts reached a plateau for about a month before tailing off. This is consistent with an SIR model that exhibits "critical slowing-down", in which Rt plateaus at a value just above unity. For Cook County, the 95.45% credible intervals for the model parameters are much broader and shifted downward, R0 TRANS = 1.4 - 4.7, X = 26 - 54%, and f = 0.1 - 0.6% with 15 - 88% of the population asymptotically infected. Despite the apparently lower efficacy of its social contact reduction measures, Cook County has had significantly fewer fatalities per population than NYC, D{infty}/N = 100 vs. 270 per 100,000. In the model, this is attributed to the lower inferred IFR for Cook; an external prior pointing to similar values of the IFR for the two locations would instead chalk up the difference in D/N to differences in the relative growth rate of the disease. I derive a model-dependent threshold, Xcrit, for "safe" re-opening, that is, for easing of contact reduction that would not trigger a second wave; for NYC, the models predict that increasing social contact by more than 20% from post-mitigation levels will lead to renewed spread, while for Cook County the threshold value is very uncertain, given the parameter degeneracies. The timing of 2nd-wave growth will depend on the amplitude of contact increase relative to Xcrit and on the asymptotic TRANS growth rate, and the impact in terms of fatalities will depend on the parameter f .

    Epidemiological Characteristics of COVID-19 under Government-mandated Control Measures in Inner Mongolia, China

    Authors: Sha Du; Haiwen Lu; Yuenan Su; Shufeng Bi; Jing Wu; Wenrui Wang; Xinhui Yu; Min Yang; Huiqiu Zheng; Xuemei Wang

    doi:10.21203/rs.3.rs-57472/v1 Date: 2020-08-11 Source: ResearchSquare

    BackgroundThere were 75 local confirmed cases TRANS during the COVID-19 epidemic followed by an outbreak of Wuhan in Inner Mongolia. The aims of our study were to provide reference to control measures of COVID-19 and scientific information for supporting government decision-making for serious infectious disease MESHD, in remote regions with relatively insufficient medical resources like Inner Mongolia.MethodsThe data published by Internet were summarized in order to describe the epidemiological and clinical characteristics of patients with COVID-19. The basic reproductive number (R TRANS 0 ), incubation period TRANS, time from illness onset to confirmed and the duration of hospitalization were analyzed. The composition of imported and local secondary cases TRANS and the mild/common and severe/critical cases among different ages TRANS, genders TRANS and major clinical symptoms were compared.ResultsIn 2020, from January 23 to February 19 (less than 1 month), 75 local cases of COVID-19 were confirmed in Inner Mongolia. Among them, the median age TRANS was 45 years old (34.0, 57.0), and 61.1% were male TRANS and 33 were imported (44.0%). 29 (38.7%) were detected through close contact TRANS tracking, more than 80.0% were mild/common cases. The fatality rate was 1.3% and the basic reproductive number (R TRANS 0 ) was estimated to be 2.3. The median incubation period TRANS was 8.5 days (6.0~12.0) and the maximum incubation period TRANS reached 28 days. There was a statistically difference in the incubation period TRANS between imported and local secondary cases TRANS ( P <0.001). The duration of hospitalization of patients with incubation period TRANS <8.5 days was higher than that of patients with incubation period TRANS ≥8.5 days (30.0 vs. 24.0 days).ConclusionIn Inner Mongolia, an early and mandatory control strategy by government associated with the rapidly reduced incidence of COVID-19, by which the epidemic growth was controlled completely. And the fatality rate of COVID-19 was relatively low.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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