Corpus overview


MeSH Disease

Human Phenotype

Fever (1)


    displaying 1 - 10 records in total 36
    records per page

    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.

    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.

    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 .

    Dynamic Public Health Interventions Consistent With the Development of COVID-19 Epidemic: The Targeted Prevention and Control Guidelines in Mainland, China

    Authors: Xinlei Miao; Zhiyuan Wu; Chen Qiao; Mengmeng Liu; Zhiwei Li; Yijie Wang; Zongkai Xu; Xiuhua Guo; Qun Meng

    doi:10.21203/ Date: 2020-08-08 Source: ResearchSquare

    Background: This study aims to describe the dynamic characteristics of COVID-19 transmission TRANS and the public health interventions in three phases in mainland, China.Methods: The number of daily reported new confirmed cases TRANS, severe cases and asymptomatic TRANS infected MESHD cases from Jan 10 to Jul 10 was analyzed. We calculated the effective reproduction number TRANS (Rt) to reflect the dynamic characteristics of epidemic transmission TRANS and intervention effect. According to the overall guidelines for prevention and control, we divided the past six months into three phases and summarized the features of main public health interventions in each phase.Results: The daily confirmed cases TRANS and severe cases of COVID-19 mainly concentrated in the first phase and the maximum Rt reached 10.75 (95%CI: 10.26-11.24). With the society-wide efforts and joint prevention and control strategy, Rt began to decline below 1.0 from Feb 19. In the second phase, the occurrence of imported infected cases caused small fluctuations. The preventive strategy, preventing both imported cases and local spread of epidemic, was mainly taken. In the third phase, the government adopted policies to prevent imported cases and domestic re-infections, responding to the regular epidemic prevention demands. Conclusion: Social isolation, wearing masks, digital management based on community and area hierarchical control were effective public health interventions in consistent with the development of COVID-19 epidemic. The targeted dynamic interventions in different phases could provide reference for other countries and regions to deal with COVID-19.

    COVID-19 pandemic in Djibouti: epidemiology and the response strategy followed to contain the virus during the first two months, 17 March to 16 May 2020

    Authors: Mohamed Elhakim; Saleh Banoita Tourab; Ahmed Zouiten

    doi:10.1101/2020.08.03.20167692 Date: 2020-08-04 Source: medRxiv

    Background: First cases of COVID-19 were reported from Wuhan, China, in December 2019, and it progressed rapidly. On 30 January, WHO declared the new disease as a PHEIC, then as a Pandemic on 11 March. By mid-March, the virus spread widely; Djibouti was not spared and was hit by the pandemic with the first case detected on 17 March. Djibouti worked with WHO and other partners to develop a preparedness and response plan, and implemented a series of intervention measures. MoH together with its civilian and military partners, closely followed WHO recommended strategy based on four pillars: testing, isolating, early case management, and contact tracing TRANS. From 17 March to 16 May, Djibouti performed the highest per capita tests in Africa and isolated, treated and traced the contacts TRANS of each positive case, which allowed for a rapid control of the epidemic. Methods: COVID-19 data included in this study was collected through MoH Djibouti during the period from 17 March to 16 May 2020. Results: A total of 1,401 confirmed cases TRANS of COVID-19 were included in the study with 4 related deaths (CFR: 0.3%) and an attack rate TRANS of 0.15%. Males TRANS represented (68.4%) of the cases, with the age group TRANS 31-45 years old (34.2%) as the most affected. Djibouti conducted 17,532 tests, and was considered as a champion for COVID-19 testing in Africa with 18.2 tests per 1000 habitant. All positive cases were isolated, treated and had their contacts traced TRANS, which led to early and proactive diagnosis of cases and in turn yielded up to 95-98% asymptomatic TRANS cases. Recoveries reached 69% of the infected cases with R0 TRANS (0.91). The virus was detected in 4 regions in the country, with the highest percentage in the capital (83%). Conclusion: Djibouti responded to COVID-19 pandemic following an efficient and effective strategy, using a strong collaboration between civilian and military health assets that increased the response capacities of the country. Partnership, coordination, solidarity, proactivity and commitment were the pillars to confront COVID-19 pandemic.

    COVID-19 Transmission TRANS Dynamics and Final Epidemic Size

    Authors: Daifeng Duan; Cuiping Wang; Yuan Yuan

    doi:10.21203/ Date: 2020-07-08 Source: ResearchSquare

    We propose two kinds of compartment models to study the transmission TRANS dynamics of COVID-19 virus and to explore the potential impact of the interventions, to disentangle how transmission TRANS is affected in different age group TRANS. Starting with an SEAIQR model by combining the effect from exposure, asymptomatic TRANS and quarantine, then extending the model to an two groups with ages TRANS below and above 65 years old, and classify the infectious individuals according to their severity, we focus our analysis on each model with and without vital dynamics. In the models with vital dynamics, we study the dynamical properties including the global stability of the disease free equilibrium and the existence of endemic equilibrium, with respect to the basic reproduction number TRANS. Whereas in the models without vital dynamics, we address the final epidemic size rigorously, which is one of the common but difficult questions regarding an epidemic. Finally, using the data of COVID-19 confirmed cases TRANS in Canada and Newfoundland & Labrador province, we can parameterize the models to estimate the basic reproduction number TRANS and the final epidemic size of disease transmission TRANS.

    COVID-19 in China: Risk Factors and R0 TRANS Revisited

    Authors: Irtesam Mahmud Khan; Wenyi Zhang; Sumaira Zafar; Yong Wang; Junyu He; Hailong Sun; Jailos Lubinda; Ubydul Haque; M Sohel Rahman

    doi:10.21203/ Date: 2020-06-30 Source: ResearchSquare

    Background: The COVID-19 epidemic had spread rapidly through China and subsequently has proliferated globally leading to a pandemic situation around the globe. Human-to-human transmissions TRANS, as well as asymptomatic TRANS transmissions TRANS of the infection, have been confirmed TRANS infection, have been confirmed MESHD. As of April 3rd, public health crisis in China due to COVID-19 is potentially under control. Methods: We compiled a daily dataset of case counts, mortality, recovery, temperature, population density, and demographic information for each prefecture during the period of January 11 to April 07, 2020 (excluding Wuhan from our analysis due to missing data). Understanding the characteristics of spatiotemporal clustering of the COVID-19 epidemic and R0 TRANS is critical in effectively preventing and controlling the ongoing global pandemic. The prefectures were grouped based on several relevant features using unsupervised machine learning techniques. We performed a computational analysis utilizing the reported cases in China to estimate the revised R0 TRANS among different regions for prevention planning in an ongoing global pandemic. Results: Finally, our results indicate that the impact of temperature and demographic (different age group TRANS percentage compared to the total population) factors on virus transmission TRANS may be characterized using a stochastic transmission TRANS model. Conclusions: Such predictions will help prioritize segments of a given community/ region for action and provide a visual aid in designing prevention strategies for a specific geographic region. Furthermore, revised estimation and our methodology will aid in improving the human health consequences of COVID-19 elsewhere.

    Covid-19: analysis of a modified SEIR model, a comparison of different intervention strategies and projections for India

    Authors: Arghya Das; Abhishek Dhar; Anupam Kundu; Srashti Goyal

    doi:10.1101/2020.06.04.20122580 Date: 2020-06-05 Source: medRxiv

    Modeling accurately the evolution and intervention strategies for the Covid-19 pandemic is a challenging problem. We present here an analysis of an extended Susceptible-Exposed-Infected-Recovered (SEIR) model that accounts for asymptomatic TRANS carriers TRANS, and explore the effect of different intervention strategies such as social distancing (SD) and testing-quarantining (TQ). The two intervention strategies (SD and TQ) try to reduce the disease reproductive number TRANS R0 TRANS to a target value R0target < 1, but in distinct ways, which we implement in our model equations. We find that for the same target R0target < 1, TQ is more efficient in controlling the pandemic than lockdowns that only implement SD. However, for TQ to be effective, it has to be based on contact TRANS tracing TRANS and the ratio of tests/day to the number of new cases/day has to be scaled with the mean number of contacts of an infectious person, which would be high in densely populated regions with low levels of SD. We point out that, apart from R0 TRANS, an important quantity is the largest eigenvalue of the linearised dynamics which provides a more complete understanding of the disease progression, both pre- and post- intervention, and explains observed data for many countries. Weak intervention strategies (that reduce R0 TRANS but not to a value less than 1) can reduce the peak values of infections and the asymptotic TRANS infections and the asymptotic MESHD affected population. We provide simple analytic expressions for these in terms of the disease parameters and apply them in the Indian context to obtain heuristic projections for the course of the pandemic. We find that the predictions strongly depend on the assumed fraction of asymptomatic TRANS carriers TRANS.

    The effectiveness and perceived burden of nonpharmaceutical interventions against COVID-19 transmission TRANS: a modelling study with 41 countries

    Authors: Jan Markus Brauner; Sören Mindermann; Mrinank Sharma; Anna B Stephenson; Tomáš Gavenčiak; David Johnston; Gavin Leech; John Salvatier; George Altman; Alexander John Norman; Joshua Teperowski Monrad; Tamay Besiroglu; Hong Ge; Vladimir Mikulik; Meghan A. Hartwick; Yee Whye Teh; Leonid Chindelevitch; Yarin Gal; Jan Kulveit

    doi:10.1101/2020.05.28.20116129 Date: 2020-05-30 Source: medRxiv

    Background: Governments are attempting to control the COVID-19 pandemic with nonpharmaceutical interventions (NPIs). However, it is still largely unknown how effective different NPIs are at reducing transmission TRANS. Data-driven studies can estimate the effectiveness of NPIs while minimizing assumptions, but existing analyses lack sufficient data and validation to robustly distinguish the effects of individual NPIs. Methods: We collect chronological data on NPIs in 41 countries between January and May 2020, using independent double entry by researchers to ensure high data quality. We estimate NPI effectiveness with a Bayesian hierarchical model, by linking NPI implementation dates to national case and death counts. To our knowledge, this is the largest and most thoroughly validated data-driven study of NPI effectiveness to date. Results: We model each NPI's effect as a multiplicative (percentage) reduction in the reproduction number TRANS R. We estimate the mean reduction in R across the countries in our data for eight NPIs: mandating mask-wearing in (some) public spaces (2%; 95% CI: -14%-16%), limiting gatherings to 1000 people or less (2%; -20%-22%), to 100 people or less (21%; 1%-39%), to 10 people or less (36%; 16%-53%), closing some high-risk businesses (31%; 13%-46%), closing most nonessential businesses (40%; 22%-55%), closing schools and universities (39%; 21%-55%), and issuing stay-at-home orders (18%; 4%-31%). These results are supported by extensive empirical validation, including 15 sensitivity SERO analyses. Conclusions: Our results suggest that, by implementing effective NPIs, many countries can reduce R below 1 without issuing a stay-at-home order. We find a surprisingly large role for school and university closures in reducing COVID-19 transmission TRANS, a contribution to the ongoing debate about the relevance of asymptomatic TRANS carriers TRANS in disease spread TRANS. Banning gatherings and closing high-risk businesses can be highly effective in reducing transmission TRANS, but closing most businesses only has limited additional benefit.

    A model for COVID-19 with isolation, quarantine and testing as control measures

    Authors: Maria Soledad Aronna; Roberto Guglielmi; Lucas Machado Moschen

    doi:10.1101/2020.05.29.20116897 Date: 2020-05-29 Source: medRxiv

    In this article we propose a compartmental model for the dynamics of Coronavirus Disease MESHD 2019 (COVID-19). We take into account the presence of asymptomatic TRANS infections MESHD and the main policies that have been adopted so far to contain the epidemic: isolation (or social distancing) of a portion of the population, quarantine for confirmed cases TRANS and testing. We model isolation by separating the population in two groups: one composed by key-workers that keep working during the pandemic and have a usual contact rate, and a second group consisting of people that are enforced/recommended to stay at home. We refer to quarantine as strict isolation, and it is applied to confirmed infected cases. In the proposed model, the proportion of people in isolation, the level of contact reduction and the testing rate are control parameters that can vary in time, representing policies that evolve in different stages. We obtain an explicit expression for the basic reproduction number TRANS R0 TRANS in terms of the parameters of the disease and of the control policies. In this way we can quantify the effect that isolation and testing have in the evolution of the epidemic. We present a series of simulations to illustrate different realistic scenarios. From the expression of R0 TRANS and the simulations we conclude that isolation (social distancing) and testing among asymptomatic TRANS cases are fundamental actions to control the epidemic, and the stricter these measures are and the sooner they are implemented, the more lives can be saved. Additionally, we show that people that remain in isolation significantly reduce their probability of contagion, so risk groups should be recommended to maintain a low contact rate during the course of the epidemic.

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.