### Overview

MeSH Disease

Infections (270)

Death (97)

Human Phenotype

Falls (30)

Pneumonia (23)

Cough (11)

Fever (9)

Transmission

Seroprevalence
displaying 831 - 840 records in total 940
records per page

### Investigating the Impact of Asymptomatic TRANS Carriers TRANS on COVID-19 Transmission TRANS

Authors: Jacob B Aguilar; Jeremy Samuel Faust; Lauren M. Westafer; Juan B. Gutierrez

doi:10.1101/2020.03.18.20037994 Date: 2020-03-20 Source: medRxiv

Coronavirus disease 2019 (COVID-19) is a novel human respiratory disease MESHD caused by the SARS-CoV-2 virus. Asymptomatic TRANS carriers TRANS of the virus display no clinical symptoms but are known to be contagious. Recent evidence reveals that this sub-population, as well as persons with mild, represent a major contributor in the propagation of COVID-19. The asymptomatic TRANS sub-population frequently escapes detection by public health surveillance systems. Because of this, the currently accepted estimates of the basic reproduction number TRANS (Ro) of the virus are inaccurate. It is unlikely that a pathogen can blanket the planet in three months with an Ro in the vicinity of 3, as reported in the literature. In this manuscript, we present a mathematical model taking into account asymptomatic TRANS carriers TRANS. Our results indicate that an initial value of the effective reproduction number TRANS could range from 5.5 to 25.4, with a point estimate of 15.4, assuming mean parameters. The first three weeks of the model exhibit exponential growth, which is in agreement with average case data collected from thirteen countries with universal health care and robust communicable disease surveillance systems; the average rate of growth in the number of reported cases is 23.3% per day during this period.

### Fear, Access, and the Real-Time Estimation of Etiological Parameters for Outbreaks of Novel Pathogens

Authors: Nina H Fefferman; Eric Lofgren; Nianpeng Li; Pieter Blue; David Weber; Abdul-Aziz Yakubu

doi:10.1101/2020.03.19.20038729 Date: 2020-03-20 Source: medRxiv

Early analysis of outbreaks of novel pathogens to evaluate their likely public health impact depends on fitting predictive models to data gathered and updated in real-time. Both transmission TRANS rates and the critical threshold (i.e. the pathogen's ' reproductive number TRANS') are inferred by finding the values that provide the best model fit to reported case incidence. These models and inferred results are then the basic tools used for public health planning: how many people expected to be infected, at what scales of time and space, and whether potential intervention strategies impact disease MESHD transmission TRANS and spread. An underlying assumption, however, is that the ability to observe new cases is either constant, or at least constant relative to diagnostic test availability. We present a demonstration, discussion, and mathematical analysis of how this assumption of predictable observability in disease incidence can drastically impact model accuracy. We also demonstrate how to tailor estimations of these parameters to a few examples of different types of shifting influences acting on detection, depending on the likely sensitivity SERO of surveillance systems to errors from sources such as clinical testing rates and differences in healthcare-seeking behavior from the public over time. Finally, we discuss the implications of these corrections for both historical and current outbreaks.

### Modeling and Forecasting Trend of COVID-19 Epidemic in Iran

doi:10.1101/2020.03.17.20037671 Date: 2020-03-20 Source: medRxiv

Background: COVID-19 is an emerging disease MESHD and precise data are not available in the world and Iran. this study aimed to determine the epidemic trend and prediction of COVID-19 in Iran. Methods: This study is a secondary data analysis and modeling. We used the daily reports of definitive COVID-19 patients (sampling of severe cases and hospitalization) released by Iran Ministry of Health and Medical Education. Epidemic projection models of Gompertz, Von Bertalanffy and least squared error MESHD (LSE) were used to predict the number of cases at April 3, 2020 until May 13, 2020. Results: R0 TRANS in Iran was estimated to be 4.7 that has now fallen HP to below 2. Given the three different scenarios, the prediction of the patients on April 3, 2020 by Von Bertalanffy, Gompertz and LSE were estimated at 48200, 52500 and 58000, respectively. The number of deceased COVID-19 patients was also estimated to be 3600 individuals using the Von growth model, 4200 ones by Gompertz's model and 4850 ones according to the LSE method. To predict and estimate the number of patients and deaths MESHD in the end of epidemic based on Von and Gompertz models, we will have 87000 cases, 4900 and 11000 deaths until 13 May and 1 June, respectively. Conclusion: The process of controlling the epidemic is tangible. If enforcement and public behavior interventions continue with current trends, the control and reduction of the COVID-19 epidemic in Iran will be flat from April 28, until July, 2020 and new cases are expected to decline from the following Iranian new year.

### Spatial Visualization of Cluster-Specific COVID-19 Transmission TRANS Network in South Korea During the Early Epidemic Phase

Authors: James Yeongjun Park

doi:10.1101/2020.03.18.20038638 Date: 2020-03-20 Source: medRxiv

Background Coronavirus disease MESHD 2019 (COVID-19) has been rapidly spreading throughout China and other countries including South Korea. As of March 12, 2020, a total number of 7,869 cases and 66 deaths had been documented in South Korea. Although the first confirmed case TRANS in South Korea was identified on January 20, 2020, the number of confirmed cases TRANS showed a rapid growth on February 19, 2020 with a total number of 1,261 cases with 12 deaths based on the Korea Centers for Disease Control and Prevention (KCDC). Method Using the data of confirmed cases TRANS of COVID-19 in South Korea that are publicly available from the KCDC, this paper aims to create spatial visualizations of COVID-19 transmission TRANS between January 20, 2020 and February 19, 2020. Results Using spatial visualization, this paper identified two early transmission TRANS clusters in South Korea (Daegu cluster and capital area cluster). Using a degree-weighted centrality measure, this paper proposes potential super-spreaders of the virus in the visualized clusters. Conclusion Compared to various epidemiological measures such as the basic reproduction number TRANS, spatial visualizations of the cluster-specific transmission TRANS networks and the proposed centrality measure may be more useful to characterize super-spreaders and the spread of the virus especially in the early epidemic phase.

### On a quarantine model of coronavirus infection MESHD and data analysis

Authors: Vitaly Volpert; Malay Banerjee; Sergei Petrovskii

id:2003.09444v1 Date: 2020-03-20 Source: arXiv

Attempts to curb the spread of coronavirus by introducing strict quarantine measures apparently have different effect in different countries: while the number of new cases has reportedly decreased in China and South Korea, it still exhibit significant growth in Italy and other countries across Europe. In this brief note, we endeavour to assess the efficiency of quarantine measures by means of mathematical modelling. Instead of the classical SIR model, we introduce a new model of infection MESHD progression under the assumption that all infected individual are isolated after the incubation period TRANS in such a way that they cannot infect MESHD other people. Disease progression in this model is determined by the basic reproduction number TRANS $\mathcal{R}_0$ (the number of newly infected individuals during the incubation period TRANS), which is different compared to that for the standard SIR model. If $\mathcal{R}_0 >1$, then the number of latently infected individuals exponentially grows. However, if $\mathcal{R}_0 <1$ (e.g.~due to quarantine measures and contact restrictions imposed by public authorities), then the number of infected decays exponentially. We then consider the available data on the disease development in different countries to show that there are three possible patterns: growth dynamics, growth-decays dynamics, and patchy dynamics (growth-decay-growth). Analysis of the data in China and Korea shows that the peak of infection MESHD (maximum of daily cases) is reached about 10 days after the restricting measures are introduced. During this period of time, the growth rate of the total number of infected MESHD was gradually decreasing. However, the growth rate remains exponential in Italy. Arguably, it suggests that the introduced quarantine is not sufficient and stricter measures are needed.

### Transmissibility TRANS of 2019 Novel Coronavirus: zoonotic vs. human to human transmission TRANS, China, 2019-2020

Authors: Kenji Mizumoto; Katsushi Kagaya; Gerardo Chowell

doi:10.1101/2020.03.16.20037036 Date: 2020-03-20 Source: medRxiv

Objectives: The novel coronavirus (2019-nCoV) originating from Wuhan has rapidly spread throughout China. While the origin of the outbreak remains uncertain, accumulating evidence links a wet market in Wuhan for the early spread of 2019-nCoV. Similarly, the influence of the marketplace on the early transmission TRANS dynamics is yet to be investigated. Methods: Using the daily series of COVID-19 incidenceincluding contact history with the market, we have conducted quantitative modeling analyses to estimate the reproduction numbers TRANS (R) for the market-to-human and human-to-human transmission TRANS together with the reporting probability and the early effects of public health interventions. Results: Our mean R estimates for China in 2019-2020 are estimated at 0.37 (95%CrI: 0.02-1.78) for market-to-human transmission TRANS, and 3.87 (95%CrI: 3.18-4.78) for human-to-human transmission TRANS, respectively. Moreover we estimated that the reporting rate cases stemming from market-to-human transmission TRANS was 3-31 fold higher than that for cases stemming from human-to-human transmission TRANS, suggesting that contact history with the wet market played a key role in identifying COVID-19 cases. Conclusions: Our findings suggest that the proportions of asymptomatic TRANS and subclinical patients constitute a substantial component of the epidemic's magnitude. Findings suggest that the development of rapid diagnostic tests could help bring the epidemic more rapidly under control.

### A COVID-19 Epidemiological Model for Community and Policy Maker Use

Authors: Alex De Visscher

id:2003.08824v2 Date: 2020-03-19 Source: arXiv

An epidemiological model for COVID-19 was developed and implemented in MATLAB/GNU Octave for use by public health practitioners, policy makers and the general public. The model distinguishes four stages in the disease: infected MESHD, sick, seriously sick, and better. The model was preliminarily parameterized based on observations of the spread of the disease TRANS. The model is consistent with a mortality rate of 1.5 %. Preliminary simulations with the model indicate that concepts such as "herd immunity" and "flattening the curve" are highly misleading in the context of this virus. Public policies based on these concepts are inadequate to protect the population. Only reducing the R0 TRANS of the virus below 1 is an effective strategy for maintaining the death burden of COVID-19 within the normal range of seasonal flu. As R0 TRANS values estimated with the model range from 2.82 worldwide outside of China and 3.83 in the Western world in late February - early March 2020, this means social distancing with effectiveness greater than 65 % (worldwide) or 75 % (Western world) are needed to combat the virus successfully.

### Neural Network aided quarantine control model estimation of COVID spread in Wuhan, China

Authors: Raj Dandekar; George Barbastathis

id:2003.09403v1 Date: 2020-03-18 Source: arXiv

In a move described as unprecedented in public health history, starting 24 January 2020, China imposed quarantine and isolation restrictions in Wuhan, a city of more than 10 million people. This raised the question: is mass quarantine and isolation effective as a social tool in addition to its scientific use as a medical tool? In an effort to address this question, using a epidemiological model driven approach augmented by machine learning, we show that the quarantine and isolation measures implemented in Wuhan brought down the effective reproduction number TRANS R(t) of the CoVID-19 spread from R(t) > 1 to R(t) <1 within a month after the imposition of quarantine control measures in Wuhan, China. This ultimately resulted in a stagnation phase in the infected case count in Wuhan. Our results indicate that the strict public health policies implemented in Wuhan may have played a crucial role in halting down the spread of infection and such measures should potentially be implemented in other highly affected countries such as South Korea, Italy and Iran to curtail spread of the disease TRANS. Finally, our forecasting results predict a stagnation in the quarantine control measures implemented in Wuhan towards the end of March 2020; this would lead to a subsequent stagnation in the effective reproduction number TRANS at R(t) <1. We warn that immediate relaxation of the quarantine measures in Wuhan may lead to a relapse in the infection spread and a subsequent increase in the effective reproduction number TRANS to R(t) >1. Thus, it may be wise to relax quarantine measures after sufficient time has elapsed, during which maximum of the quarantined/isolated individuals are recovered.

### Short-term predictions and prevention strategies for COVID-19: A model based study

id:2003.08150v3 Date: 2020-03-18 Source: arXiv

An outbreak of respiratory disease MESHD caused by a novel coronavirus is ongoing from December 2019. As of July 22, 2020, it has caused an epidemic outbreak with more than 15 million confirmed infections TRANS infections MESHD and above 6 hundred thousand reported deaths worldwide. During this period of an epidemic when human-to-human transmission TRANS is established and reported cases of coronavirus disease MESHD 2019 (COVID-19) are rising worldwide, investigation of control strategies and forecasting are necessary for health care planning. In this study, we propose and analyze a compartmental epidemic model of COVID-19 to predict and control the outbreak. The basic reproduction number TRANS and control reproduction number TRANS are calculated analytically. A detailed stability analysis of the model is performed to observe the dynamics of the system. We calibrated the proposed model to fit daily data from the United Kingdom (UK) where the situation is still alarming. Our findings suggest that independent self-sustaining human-to-human spread ($R_0 TRANS>1$, $R_c>1$) is already present. Short-term predictions show that the decreasing trend of new COVID-19 cases is well captured by the model. Further, we found that effective management of quarantined individuals is more effective than management of isolated individuals to reduce the disease burden. Thus, if limited resources are available, then investing on the quarantined individuals will be more fruitful in terms of reduction of cases.

### Transmissibility TRANS of coronavirus disease MESHD 2019 (COVID-19) in Chinese cities with different transmission TRANS dynamics of imported cases

Authors: Ka Chun Chong; Wei Cheng; Shi Zhao; Feng Ling; Kirran Mohammad; Maggie Wang; Benny Zee; Lei Wei; Xi Xiong; Hengyan Liu; Jingxuan Wang; Enfu Chen

doi:10.1101/2020.03.15.20036541 Date: 2020-03-18 Source: medRxiv

Background: Monitoring the time-varying reproduction number TRANS (Rt) of the disease is useful in determining whether there is sustained transmission TRANS in a population. In this study, we examined Rt of COVID-19 and compared its transmissibility TRANS between different intervention periods in Hangzhou and Shenzhen. Methods: Daily aggregated counts of confirmed imported and local cases between January 1, 2020 and March 13, 2020 were analysed. A likelihood function was constructed to estimate Rt, accounting for imported cases. Results: Although Hangzhou had fewer number of cases than Shenzhen, Shenzhen had higher proportion of imported cases than Hangzhou (83% vs 29%). Since the epidemic of COVID-19 in Shenzhen was dominated by imported cases, Rt was kept below unity through time. On the contrary, Rt was greater than unity in Hangzhou from 16 January to 7 February due to the surge in local cases. Credits to the Wuhan lockdown and outbreak response measures following the local lockdown, Rt decreased steadily and dropped below unity in mid-February. Conclusion: The lockdown measures and local outbreak responses helped reduce the potential of local transmission TRANS in Hangzhou and Shenzhen. Meanwhile, cities with similar epidemic trend could have different transmission TRANS dynamics given the variation in imported cases.

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