Corpus overview


MeSH Disease

Human Phenotype

Falls (10)

Pneumonia (6)

Hypertension (1)

Fever (1)


    displaying 41 - 50 records in total 149
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    Transmission TRANS Potential and Forecasting The Number of Coronavirus Disease MESHD 2019 in Hubei Province, China

    Authors: Ke-wei Wang; Jie Gao; Hua Wang; Xiao-long Wu; Qin-fang Yuan; Yang Cheng

    doi:10.21203/ Date: 2020-06-19 Source: ResearchSquare

    Background: Coronavirus disease 2019 (COVID-19) was first reported in Wuhan, Hubei province, China. We aimed to describe the temporal and spatial distribution and the transmission TRANS dynamics of COVID-19 and to assess whether a hybrid model can forecast the trend of COVID-19 in Hubei Province, China. Method: The data of COVID-19 cases were obtained from the websites of Chinese Center for Disease Control and Prevention, whereas the data on the resident population were obtained from the websites of Hubei Provincial Bureau of Statistics. The temporal and spatial distribution and the transmission TRANS dynamics of COVID-19 were described. A combination of autoregressive integrated moving average (ARIMA) and support vector machine was constructed to forecast the trend of COVID-19. Results: A total of 56,062 confirmed COVID-19 cases, which were mainly concentrated in Wuhan, were reported from January 16 to March 16, 2020 in Hubei Province, China. The daily number of confirmed cases TRANS exponentially increased to 3,156 before February 4, 2020, fluctuated to 4,823 before February 13, 2020, and then markedly decreased to 1 after March 16, 2020. The highest mean reproduction number TRANS R(t) of 9.48 was recorded on January 16, 2020, after which it decreased to 2.15 on February 2, 2020 and further decreased to less than 1 on February 13, 2020. In the modeling stage, the mean square error, mean absolute error, and mean absolute percentage error of the hybrid ARIMA–SVM model decreased by 98.59%, 89.19% and 89.68%, and those of SVM decreased by 98.58%, 87.71%, and 88.94%, respectively, compared with the ARIMA model. Similar results were obtained in the forecasting stage.Conclusion: Public health interventions resulted in the terminal phase of COVID-19 in Hubei province. The hybrid ARIMA–SVM model may be a reliable tool for forecasting the trend of the COVID-19 epidemic.

    SARS-Cov-2 trajectory predictions and scenario simulations from a global perspective: a modelling study

    Authors: Tianan Yang; Yexin Liu; Wenhao Deng; Weigang Zhao; Jianwei Deng

    doi:10.21203/ Date: 2020-06-17 Source: ResearchSquare

    The coronavirus SARS-CoV-2 emerging from Wuhan, China has developed into a global epidemic. Here, we combine both human mobility and non-pharmaceutical interventions (social-distancing and suspected-cases isolation) into SEIR transmission TRANS model to understand how coronavirus transmits in a global environment. Dynamic trends of region-specific time-variant reproduction number TRANS, social-distancing rate, work-resumption rate, and suspected-cases isolation rate have been estimated and plotted for each region by fitting random walk transmission TRANS processes to the real total confirmed cases TRANS of each region. We find after shutdown in Wuhan, the reproduction number TRANS in Wuhan greatly declined from 6·982 (95% CI, 2·558-14·668) on January 23, 2020 to 1·130 (95% CI, 0·289-3·279) on February 7, 2020, and there was a higher intervention level in terms of social distancing and suspected-case isolation in Wuhan than the Chinese average and Western average, for the period from the shutdown in Wuhan to mid-March. Future epidemic trajectories of Western countries up to October 10, 2020, have been predicted with 95% confidence intervals. Through the scenario simulation, we discover the benefits of earlier international travel TRANS ban and rigorous intervention strategies, and the significance of non-pharmaceutical interventions. From a global perspective, it is vital for each country to control the risks of imported cases, and execute rigorous non-pharmaceutical interventions before successful vaccination development. 

    High temperature has no impact on the reproduction number TRANS and new cases of COVID-19 in Bushehr, Iran

    Authors: Ebrahim Sahafizadeh; Samaneh Sartoli

    doi:10.1101/2020.06.14.20130906 Date: 2020-06-16 Source: medRxiv

    Background: COVID-19 was first reported in Iran on February 19, 2020. Bushehr, one of the warmest provinces of Iran, was the last province confirmed to be infected on March 5, 2020. At the beginning of April, Bushehr was announced as a white, coronavirus-free, province. However, increasing the temperature in the next months did not affect the spread of coronavirus and the number of confirmed cases TRANS increased during the next months, so that Bushehr was announced as a Red province on June 13, 2020. Methods: This paper aims 1) to estimate the reproduction number TRANS of COVID-19 in Bushehr considering COVID-19 reported cases of Bushehr from April to June 12, 2020, using exponential function and SIR epidemic model, and 2) to investigate the impact of temperature on reproduction number TRANS and the spread of coronavirus in Bushehr the temperature data. Result: The reproduction number TRANS is estimated to be between 2.564 to 2.641. Regarding the increase of the temperature during April to June, the results show that not only was the spread of COVID-19 not reduced but it also increased. Conclusions: Data analysis on this study shows that high temperature has no impact on the reproduction number TRANS and does not slow down the spread of coronavirus in Bushehr.

    Assessment of Early Mitigation Measures Against COVID-19 in Puerto Rico: March 15-May 15, 2020

    Authors: Miguel Valencia; Jose E Becerra; Juan C Reyes; Kenneth G Castro

    doi:10.1101/2020.06.11.20127019 Date: 2020-06-16 Source: medRxiv

    Background: On March 15, 2020 Puerto Rico implemented non-pharmaceutical interventions (NPIs), including a mandatory curfew, as part of a state of emergency declaration to mitigate the community transmission TRANS of the SARS-CoV-2 virus. The strict enforcement of this curfew was extended through May 25, with a gradual relaxation beginning on May 1. This report summarizes an assessment of these early mitigation measures on the progression of COVID-19 in the island. Methods and Findings: From March 15 to May 15, 2020, 41,748 results of molecular (RT-PCR) tests were reported to the Puerto Rico Department of Health. Of these, 1,866 (4.5%) were positive, corresponding to 1,219 individuals with COVID-19 included in the study. We derived the epidemic growth rates (r) and the corresponding reproductive numbers (R TRANS) from the epidemic curve of these 1,219 individuals with laboratory-confirmed diagnosis of COVID-19 using their date of test collection as a proxy for symptoms onset TRANS. We then compared the observed cases with the R-based epidemic model projections had the mitigation measures not been implemented. Computations were conducted in the R packages forecast, incidence and projections. The number of daily RT-PCR- confirmed cases TRANS peaked on March 30 (84 cases), showing a weekly cyclical trend, with lower counts on weekends and a decreasing secular trend since March 30. The initial exponential growth rate (r) was 17.0% (95% CI: 8.4%, 25.6%), corresponding to a doubling of cases every 4.1 days, and to a reproduction number TRANS (Ro) of 1.89 (95% CI: 1.41, 2.39). After March 30, the r value TRANS reverted to an exponential decay rate (negative) of -3.6% (95% CI: -5.7%, -1.4%), corresponding to a halving of cases every 19.4 days and to an Ro of 0.90 (95% CI: 0.84, 0.97). Had the initial growth rate been maintained, a total of 18,699 (96%CI: 4,113, 87,438) COVID-19 cases would have occurred by April 30 compared with 1,119 observed. Conclusions: Our findings are consistent with very effective implementation of early non-pharmaceutical interventions (NPIs) as mitigation measures in Puerto Rico. These results serve as a baseline to assess the impact of the transition from mitigation to containment stages in Puerto Rico.

    Optimizing the COVID-19 Intervention Policy in Scotland and the Case for Testing and Tracing TRANS

    Authors: Andreas Grothey; Kenneth I.M. McKinnon

    doi:10.1101/2020.06.11.20128173 Date: 2020-06-12 Source: medRxiv

    Unlike other European countries the UK has abandoned widespread testing and tracing TRANS of known SARS-CoV-2 carriers TRANS in mid-March. The reason given was that the pandemic was out of control and with wide community based spread it would not be possible to contain it by tracing TRANS any longer. Like other countries the UK has since relied on a lockdown as the main measure to contain the virus (or more precisely the reproduction number TRANS R at significant economic and social cost. It is clear that this level of lockdown cannot be sustained until a vaccine is available, yet it is not clear what an exit strategy would look like that avoids the danger of a second (or subsequent waves). In this paper we argue that, when used within a portfolio of intervention strategies, widespread testing and tracing TRANS leads to significant cost savings compared to using lockdown measures alone. While the effect is most pronounced if a large proportion of the infectious population can be identified and their contacts traced TRANS, under reasonable assumptions there are still significant savings even if the fraction of infectious people found by tracing TRANS is small. We also present a policy optimization model that finds, for given assumptions on the disease parameters, the best intervention strategy to contain the virus by varying the degree of tracing TRANS and lockdown measure (and vaccination once that option is available) over time. We run the model on data fitted to the published COVID-19 outbreak figures for Scotland. The model suggests an intervention strategy that keeps the number of COVID-19 deaths low using a combination of tracing TRANS and lockdown. This strategy would only require lockdown measures equivalent to a reduction of R to about 1.8--2.0 if lockdown was used alone, at acceptable economic cost, while the model finds no such strategy without tracing TRANS enabled.

    Impact of public health measures to control SARS-CoV-2Outbreak: a data-driven analysis

    Authors: Hugues Turbe; Mina Bjelogrlic; Arnaud Robert; Christophe Gaudet-Blavignac; Christian Lovis; Jean-Philippe Goldman

    doi:10.1101/2020.06.10.20126870 Date: 2020-06-11 Source: medRxiv

    With the rapid spread of the SARS-CoV-2 virus since Fall HP 2019, public health confinement measures to contain the propagation of the pandemic are taken. Our method to estimate the reproductive number TRANS using Bayesian inference with time-dependent priors enhances previous approaches by considering a dynamic prior continuously updated as restrictive measures and comportments within the society evolve. In addition, to allow direct comparison between reproductive number TRANS and introduction of public health measures in a specific country, the infection dates are inferred from daily confirmed cases TRANS and death MESHD with the mean time between a case being declared as positive and its death estimated on 1430 cases at 10.7 days. The evolution of the reproductive rate in combination with the stringency index is analyzed on 31 European countries. We show that most countries required tough state interventions with a stringency index equal to 83.6 out of 100 to reduce the reproductive number TRANS below one and control the progression of the epidemic. In addition, we show a direct correlation between the time taken to introduce restrictive measures and the time required to contain the spread of the epidemic with a median time of 8 days. Our analysis reinforces the importance of having a fast response with a coherent and comprehensive set of confinement measures to control the epidemic. Only combinations of non-pharmaceutical interventions (NPIs) have shown to be effective.

    Scrutinizing the heterogeneous spreading of COVID-19 outbreak in Brazilian territory

    Authors: Rafael Marques Da Silva; Carlos Fabio de Oliveira Mendes; Cesar Manchein

    doi:10.1101/2020.06.05.20123604 Date: 2020-06-09 Source: medRxiv

    After the spread of COVID-19 out of China, the evolution of the pandemic shows remarkable similarities and differences among countries across the world. Eventually, such characteristics are also observed between different regions of the same country. Herewith, we study the heterogeneous spreading of the confirmed infected cases and deaths by the COVID-19 until May 30th, 2020, in the Brazilian territory, which has been seen as the current epicenter of the pandemic in South America. Our first set of results is related to the similarities and it shows that: (i) a power-law growth of the cumulative number of infected people MESHD is observed for federative units of the five regions of Brazil; and (ii) the Distance Correlation (DC) calculated between the time series of the most affected federative units and the curve that describes the evolution of the pandemic in Brazil remains about 1 in most of the time, while such quantity calculated for the federative units with a low incidence of newly infected people remains about 0.95. In the second set of results, we focus on the heterogeneous distribution of the confirmed cases TRANS and deaths MESHD, which is demonstrated by the fact that only three regions concentrate 92% of the cases. By applying the epidemiological SIRD model we estimated the effective reproduction number TRANS Re during the pandemic evolution and found that: (i) the mean value of Re for the eight most affected federative units in Brazil is about 2; (ii) the current value of Re for Brazil is greater than 1, which indicates that the epidemic peak is far; and (iii) Ceara was the only federative unit for which the current Re < 1. Based on these findings, we projected the effects of increase or decrease the effective reproduction number TRANS and concluded that if the value of Re increases 20%, not only the peak might grow at least 40% but also its occurrence might be anticipated, which hastens the collapse of the public health care system. In all cases, to keep the effective reproduction number TRANS 20% below the current one can save thousands of people in the long term.

    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.

    Dynamics and Prediction of the COVID-19 Epidemics in the US:a Compartmental Model with Deep Learning MESHD Enhancement

    Authors: QI DENG

    doi:10.1101/2020.05.31.20118414 Date: 2020-06-03 Source: medRxiv

    Background: Compartmental models dominate epidemic modeling. Estimations of transmission TRANS parameters between compartments are typically done through stochastic parameterization processes that depend upon detailed statistics on transmission TRANS characteristics, which are economically and resource-wide expensive to collect. We apply deep learning techniques as a lower data dependency alternative to estimate transmission TRANS parameters of a customized compartmental model, for the purposes of simulating the dynamics of the US COVID-19 epidemics and projecting its further development. Methods: We construct a compartmental model. We develop a multistep deep learning methodology to estimate the models transmission TRANS parameters. We then feed the estimated transmission TRANS parameters to the model to predict the development of the US COVID-19 epidemics for 35 and 42 days. Epidemics are considered suppressed when the basic reproduction number TRANS ( R_0 TRANS) becomes less than one. Results: The deep learning-enhanced compartmental model predicts that R_0 TRANS will become less than one around June 19 to July 3, 2020, at which point the epidemics will effectively start to die out, and that the US Infected population will peak round June 18 to July 2, 2020 between 1.34 million and 1.41 million individual cases. The models also predict that the number of accumulative confirmed cases TRANS will cross the 2 million mark around June 10 to 11, 2020. Conclusions: Current compartmental models require stochastic parameterization to estimate the transmission TRANS parameters. These models effectiveness depends upon detailed statistics on transmission TRANS characteristics. As an alternative, deep learning techniques are effective in estimating these stochastic parameters with greatly reduced dependency on data particularity.

    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.

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MeSH Disease
Human Phenotype

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