### Overview

MeSH Disease

COVID-19 (146)

Death (14)

Transmission

Seroprevalence
displaying 1 - 10 records in total 162
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### Meta-analysis of the SARS-CoV-2 serial interval TRANS and the impact of parameter uncertainty on the COVID-19 MESHD reproduction number TRANS

Authors: Robert Challen; Ellen Brooks-Pollock; Krasimira Tsaneva-Atanasova; Leon Danon; John Buresh; Mackenzie Edmondson; Peter A. Merkel; Ebbing Lautenbach; Rui Duan; Yong Chen; Liang Zhong; Angela SM Koh; Seow Yen Tan; Paul A Tambyah; Laurent Renia; Lisa F. P. Ng; David Chien Boon Lye; Christine Cheung; Sam T Douthwaite; Gaia Nebbia; Jonathan D Edgeworth; Ali R Awan; - The COVID-19 Genomics UK (COG-UK) consortium

doi:10.1101/2020.11.17.20231548 Date: 2020-11-18 Source: medRxiv

The serial interval TRANS of an infectious disease MESHD, commonly interpreted as the time between onset of symptoms TRANS in sequentially infected individuals within a chain of transmission TRANS, is a key epidemiological quantity involved in estimating the reproduction number TRANS. The serial interval TRANS is closely related to other key quantities, including the incubation period TRANS, the generation interval (the time between sequential infections) and time delays between infection and the observations associated with monitoring an outbreak such as confirmed cases TRANS, hospital admissions and deaths. Estimates of these quantities are often based on small data sets from early contact tracing TRANS and are subject to considerable uncertainty, which is especially true for early COVID-19 MESHD data. In this paper we estimate these key quantities in the context of COVID-19 MESHD for the UK, including a meta-analysis of early estimates of the serial interval TRANS. We estimate distributions for the serial interval TRANS with a mean 5.6 (95% CrI 5.1-6.2) and SD 4.2 (95% CrI 3.9-4.6) days (empirical distribution), the generation interval with a mean 4.8 (95% CrI 4.3-5.41) and SD 1.7 (95% CrI 1.0-2.6) days (fitted gamma distribution), and the incubation period with a mean TRANS 5.5 (95% CrI 5.1-5.8) and SD MESHD 4.9 (95% CrI 4.5-5.3) days (fitted log normal distribution). We quantify the impact of the uncertainty surrounding the serial interval TRANS, generation interval, incubation period TRANS and time delays, on the subsequent estimation of the reproduction number TRANS, when pragmatic and more formal approaches are taken. These estimates place empirical bounds on the estimates of most relevant model parameters and are expected to contribute to modelling COVID-19 MESHD transmission TRANS.

### Quantifying SARS-CoV-2 spread in Switzerland based on genomic sequencing data

Authors: Sarah Nadeau; Christiane Beckmann; Ivan Topolsky; Timothy Vaughan; Emma Hodcroft; Tobias Schaer; Ina Nissen; Natascha Santacroce; Elodie Burcklen; Pedro Ferreira; Kim Philipp Jablonski; Susana Posada-Cespedes; Vincenzo Capece; Sophie Seidel; Noemi Santamaria de Souza; Julia M. Martinez-Gomez; Phil Cheng; Philipp Bosshard; Mitchell P. Levesque; Verena Kufner; Stefan Schmutz; Maryam Zaheri; Michael Huber; Alexandra Trkola; Samuel Cordey; Florian Laubscher; Ana Rita Goncalves; Karoline Leuzinger; Madlen Stange; Alfredo Mari; Tim Roloff; Helena Seth-Smith; Hans Hirsch; Adrian Egli; Maurice Redondo; Olivier Kobel; Christoph Noppen; Niko Beerenwinkel; Richard A. Neher; Christian Beisel; Tanja Stadler

doi:10.1101/2020.10.14.20212621 Date: 2020-10-27 Source: medRxiv

Pathogen genomes provide insights into their evolution and epidemic spread. We sequenced 1,439 SARS-CoV-2 genomes from Switzerland, representing 3-7% of all confirmed cases TRANS per week. Using these data, we demonstrate that no one lineage became dominant, pointing against evolution towards general lower virulence. On an epidemiological level, we report no evidence of cryptic transmission TRANS before the first confirmed case TRANS. We find many early viral introductions from Germany, France, and Italy and many recent introductions from Germany and France. Over the summer, we quantify the number of non-traceable infections stemming from introductions, quantify the effective reproductive number TRANS, and estimate the degree of undersampling. Our framework can be applied to quantify evolution and epidemiology in other locations or for other pathogens based on genomic data.

### Efficacy of stay-at-home policy and transmission TRANS of COVID-19 MESHD in Toronto, Canada: a mathematical modeling study

Authors: Pei Yuan; Juan Li; Elena Aruffo; Qi Li; Tingting Zheng; Nicholas Ogden; Beate Sander; Jane Heffernan; Evgenia Gatov; Effie Gournis; Sarah Collier; Yi Tan; Jun Li; Julien Arino; Jacques Belair; James Watmough; Jude Dzevela Kong; Iain Moyles; Huaiping Zhu

doi:10.1101/2020.10.19.20181057 Date: 2020-10-21 Source: medRxiv

Background In many parts of the world, restrictive non-pharmaceutical interventions (NPI) that aim to reduce contact rates, including stay-at-home orders, limitations on gatherings, and closure of public places, are being lifted, with the possibility that the epidemic resurges if alternative measures are not strong enough. Here we aim to capture the combination of use of NPIs and reopening measures which will prevent an infection rebound. Methods We employ a SEAIR model with a household structure able to capture the stay-at-home policy (SAHP). To reflect the changes in the SAHP over the course of the epidemic, we vary the SAHP compliance rate, assuming that the time to compliance of all the people requested to stay-at-home follows a Gamma distribution. Using confirmed case TRANS data for the City of Toronto, we evaluate basic and instantaneous reproduction numbers TRANS and simulate how the average household size, the stay-at-home rate, the efficiency and duration of SAHP implementation, affect the outbreak trajectory. Findings The estimated basic reproduction number TRANS R_0 TRANS was 2.36 (95% CI: 2.28, 2.45) in Toronto. After the implementation of the SAHP, the contact rate outside the household fell HP by 39%. When people properly respect the SAHP, the outbreak can be quickly controlled, but extending its duration beyond two months (65 days) had little effect. Our findings also suggest that to avoid a large rebound of the epidemic, the average number of contacts per person per day should be kept below nine. This study suggests that fully reopening schools, offices, and other activities, is possible if the use of other NPIs is strictly adhered to. Interpretation Our model confirmed that the SAHP implemented in Toronto had a great impact in controlling the spread of COVID-19 MESHD. Given the lifting of restrictive NPIs, we estimated the thresholds values of the maximum number of contacts, probability of transmission TRANS and testing needed to ensure that the reopening will be safe, i.e. maintaining an R_t<1.

### Covid-19 MESHD epidemic under the K-quarantine model: Network approach

Authors: K. Choi; Hoyun Choi; B. Kahng

id:2010.07157v1 Date: 2020-10-14 Source: arXiv

The Covid-19 MESHD pandemic is ongoing worldwide, and the damage it has caused is unprecedented. For prevention, South Korea has adopted a local quarantine strategy rather than a global lockdown. This approach not only minimizes economic damage, but it also efficiently prevents the spread of the disease TRANS. In this work, the spread of COVID-19 MESHD under local quarantine measures is modeled using the Susceptible-Exposed-Infected-Recovered model on complex networks. In this network approach, the links connected to isolated people are disconnected and then reinstated when they are released. This link dynamics leads to time-dependent reproduction number TRANS. Numerical simulations are performed on networks with reaction rates estimated from empirical data. The temporal pattern of the cumulative number of confirmed cases TRANS is then reproduced. The results show that a large number of asymptomatic TRANS infected MESHD patients are detected as they are quarantined together with infected MESHD patients. Additionally, possible consequences of the breakdowns of local quarantine measures and social distancing are considered.

### Analysis and Prediction of COVID-19 MESHD Outbreak by the Numerical Modelling

doi:10.21203/rs.3.rs-92222/v1 Date: 2020-10-13 Source: ResearchSquare

Pandemic COVID-19 MESHD is a contagious disease affecting more than 200 countries, territories and regions. Recently, Iraq is one of the countries that has immensely suffered with this outbreak. The Kurdistan Region of Iraq (KRI) is also prone to the disease. Until now more than 23,000 confirmed cases TRANS have been recorded in the region. Since the onset of the COVID-19 MESHD in Wuhan, based on epidemiological modelling, researchers have used various models to predict the future of the epidemic and the time of peak, yielding a diverse number in different countries. This study aims to estimate the basic reproductive number TRANS ( R0 TRANS) for COVID-19 MESHD in KRI, using the standard SIR (Susceptible-Infected-Removed) epidemic model. A system of nonlinear differential equations is formulated and solved numerically by the 4th order Runge-Kutta method. Reproductive numbers R0 have been estimated by this method of fitting the curves between the actual daily data and numerical solution by applying the least square method. For the analysis, data were taken for the duration of 165 days from 1st of March to 12th August in a population of 5.2 million. It has been concluded that R0 is fluctuating during the outbreak with an average of 1.33, predicting that infected cases will reach their maximum value of around 540,000 on 5th of November 2020. Then the spread of the disease TRANS will die out since the number of susceptible will decrease to about 3.2 million. While the number of removed individuals will reach approximately to 1.5 million.

### Analysis and Prediction of COVID-19 MESHD Outbreak by the Numerical Modelling

doi:10.21203/rs.3.rs-92222/v2 Date: 2020-10-13 Source: ResearchSquare

Pandemic COVID-19 MESHD is a contagious disease affecting more than 200 countries, territories and regions. Recently, Iraq is one of the countries that has immensely suffered with this outbreak. The Kurdistan Region of Iraq (KRI) is also prone to the disease. Until now more than 23,000 confirmed cases TRANS have been recorded in the region. Since the onset of the COVID-19 MESHD in Wuhan, based on epidemiological modelling, researchers have used various models to predict the future of the epidemic and the time of peak, yielding a diverse number in different countries. This study aims to estimate the basic reproductive number TRANS ( R0 TRANS) for COVID-19 MESHD in KRI, using the standard SIR (Susceptible-Infected-Removed) epidemic model. A system of nonlinear differential equations is formulated and solved numerically by the 4th order Runge-Kutta method. Reproductive numbers R0 have been estimated by this method of fitting the curves between the actual daily data and numerical solution by applying the least square method. For the analysis, data were taken for the duration of 165 days from 1st of March to 12th August in a population of 5.2 million. It has been concluded that R0 is fluctuating during the outbreak with an average of 1.33, predicting that infected cases will reach their maximum value of around 540,000 on 5th of November 2020. Then the spread of the disease TRANS will die out since the number of susceptible will decrease to about 3.2 million. While the number of removed individuals will reach approximately to 1.5 million.

### Lessons from Pandemics: Computational agent-based model approach for estimation of downstream and upstream measures to achieve requisite societal behavioural changes

id:2010.04833v1 Date: 2020-10-09 Source: arXiv

Pandemics such as COVID-19 MESHD have lethal potential for inflicting long-lasting cyclic devastations if required preventive, curative and reformative steps are not taken up in time which puts forth mammoth multi-dimensional challenges for survival before mankind. Scientists and policymakers all around are striving to achieve R $\leq$ 1 alongside having less number of CoVID-19 MESHD patients. Lockdowns all across the globe have been implemented for the sake of social physical distancing. However, even if the desired R value TRANS status is achieved it becomes nowhere near safe. As normal social activity and inter-regional travel TRANS resumes, danger of contraction of the virus from undetected asymptomatic TRANS carriers TRANS and reactivation of the virus in previously affected patients looms over. The virus poses further threat due to its chances of resurgence, its mutative and adaptive nature thereby giving limited medical respite. The problems intensify with increasing population density whilst varying with several socio-economic-geo-cultural and human activity parameters. Such zoonotic pandemics unravel the primary challenges of all countries in securing the general wellbeing of the society. Ensuring a mechanism for policy designs envisaging crisis scenarios through continuous analysis of real-time region-specific data about societal activities and disease/health indicators can be the only solution. An approach perspective is discussed for addressing the tightly-coupled UN Sustainable goals (2, 3, 6, 12 and 13) for developing a general-scale computational agent-based model to estimate the downstream and upstream measures for achieving requisite societal behavioural changes with the prognostic knowledge concerning the conditions and options for future scenarios of stable sustainability.

### Estimation of the Basic Reproduction Number TRANS of SARS-CoV-2 in Bangladesh Using Exponential Growth Method

Authors: Riaz Mahmud; H. M. Abrar Fahim Patwari; AGUSTIN LUGO-RADILLO; FELIPE AGUILAR-SOLLANO; OLIVER MENDOZA-CANO; Pulak Agarwal; Bijaya Adhikari; B. Aditya Prakash; Max Tomlinson; Edwin Yoo; Daniel Howell; Elliot Eisenberg; Leonard Naymagon; Douglas Tremblay; Krishna Chokshi; Sakshi Dua; Andrew Dunn; Charles Powell; Sonali Bose; Tao Dong Dong; Timothy Donnison; David W Eyre; Amy Flaxman; Helen A Fletcher; Joshua Gardner; James T Grist; Carl-Philipp Hackstein; Kanoot Jaruthamsophon; Katie Jeffrey; Teresa Lambe; Lian Lee; Wenqin Li; Nicholas Lim; Philippa C Matthews; Alexander J Mentzer; Shona C Moore; Dean J Naisbitt; Monday Ogese; Graham Ogg; Peter Openshaw; Munir Pirmohamed; Andrew J Pollard; Narayan Ramamurthy; Patpong Rongkard; Sarah Rowland-Jones; Oliver L Sampson; Gavin Screaton; Alessandro Sette; Lizzie Stafford; Craig Thompson; Paul J Thomson; Ryan Thwaites; Vinicius Vieira; Daniela Weiskopf; Panagiota Zacharopoulou; - Oxford Immunology Network Covid-19 Response T cell Consortium; - Oxford Protective T cell Immunology for COVID-19 (OPTIC) Clinical team; Lance Turtle; Paul Klenerman; Philip Goulder; John Frater; Eleanor Barnes; Susanna Dunachie

doi:10.1101/2020.09.29.20203885 Date: 2020-09-29 Source: medRxiv

Objectives: In December 2019, a novel coronavirus (SARS-CoV-2) outbreak emerged in Wuhan, Hubei Province, China. Soon, it has spread out across the world and become an ongoing pandemic. In Bangladesh, the first case of novel coronavirus (SARS-CoV-2) was detected on March 8, 2020. Since then, not many significant studies have been conducted to understand the transmission TRANS dynamics of novel coronavirus (SARS-CoV-2) in Bangladesh. In this study, we estimated the basic reproduction number TRANS R 0 of novel coronavirus (SARS-CoV-2) in Bangladesh. Methods: The data of daily confirmed cases TRANS of novel coronavirus (SARS-CoV-2) in Bangladesh and the reported values of generation time of novel coronavirus (SARS-CoV-2) for Singapore and Tianjin, China, were collected. We calculated the basic reproduction number TRANS R0 TRANS by applying the exponential growth (EG) method. Epidemic data of the first 76 days and different values of generation time were used for the calculation. Results: The basic reproduction number TRANS R0 TRANS of novel coronavirus (SARS-CoV-2) in Bangladesh is estimated to be 2.66 [95% CI: 2.58-2.75], optimized R0 TRANS is 2.78 [95% CI: 2.69-2.88] using generation time 5.20 with a standard deviation of 1.72 for Singapore. Using generation time 3.95 with a standard deviation of 1.51 for Tianjin, China, R0 TRANS is estimated to be 2.15 [95% CI: 2.09-2.20], optimized R0 TRANS is 2.22 [95% CI: 2.16-2.29]. Conclusions: The calculated basic reproduction number TRANS R0 TRANS of novel coronavirus (SARS-CoV-2) in Bangladesh is significantly higher than 1, which indicates its high transmissibility TRANS and contagiousness.

### Modeling COVID-19 MESHD as a National Dynamics with a SARS-CoV-2 Prevalent Variant: Brazil - A Study Case

Authors: Sergio Celaschi; Rachel Louise Byrne; Alice Fraser; Sophie I Owen; Ana I Cubas Atienzar; Chris Williams; Grant A Kay; Luis E Cuevas; Joseph R A Fitchett; Tom Fletcher; Gala Garrod; Konstantina Kontogianni; Sanjeev Krishna; Stefanie Menzies; Tim Planche; Chris Sainter; Henry M Staines; Lance Turtle; Emily R Adams; Oliva Kuthuru; Eileen C. Goodwin; Madison E. Weirick; Marcus J. Bolton; Claudia P. Arevalo; Andre Ramos; Cristina Jasen; Heather M. Giannini; Kurt DAndrea; - The UPenn COVID Processing Unit; Nuala J. Meyer; Edward M. Behrens; Hamid Bassiri; Scott E. Hensley; Sarah E. Henrickson; David T. Teachey; Michael Michael R. Betts; E. John Wherry

doi:10.1101/2020.09.25.20201558 Date: 2020-09-27 Source: medRxiv

COVID-19 MESHD global dynamics is modeled by an adaptation of the deterministic SEIR Model, which takes into account two dominant lineages of the SARS-CoV-2, and a time-varying reproduction number TRANS to estimate the disease transmission TRANS behavior. Such a methodology can be applied worldwide to predict forecasts of the outbreak in any infected country. The pandemic in Brazil was selected as a first study case. Brazilian official published data from February 25th to August 30th, 2020 was used to adjust a few epidemiologic parameters. The estimated time-dependence mean value to the infected individuals ( confirmed cases TRANS) presents - in logarithmic scale - standard deviation SD = 0.08 for over six orders of magnitude. Data points for additional three weeks were added after the model was complete, granting confidence on the outcomes. By the end of 2020, the predicted numbers of confirmed cases TRANS in Brazil, within 95% credible intervals, may reach 6 Million (5 to 7), and fatalities would accounts for 180 (130 to 220) thousands. The total number of infected MESHD individuals is estimated to reach (13 +/- 1) Million, 6.2% of the Brazilian population. Regarding the original SARS-CoV-2 form and its variant, the only model assumption is their distinct incubation rates. The variant form reaches a maximum of 96% of exposed individuals as previously reported for South America.

### Early Indicators of COVID-19 MESHD Spread Risk Using Digital Trace TRANS Data of Population Activities

Authors: Xinyu Gao; Chao Fan; Yang Yang; Sanghyeon Lee; Qingchun Li; Mikel Maron; Ali Mostafavi

id:2009.09514v1 Date: 2020-09-20 Source: arXiv

The spread of pandemics such as COVID-19 MESHD is strongly linked to human activities. The objective of this paper is to specify and examine early indicators of disease spread TRANS risk in cities during the initial stages of outbreak based on patterns of human activities obtained from digital trace TRANS data. In this study, the Venables distance (D_v), and the activity density (D_a) are used to quantify and evaluate human activities for 193 US counties, whose cumulative number of confirmed cases TRANS was greater than 100 as of March 31, 2020. Venables distance provides a measure of the agglomeration of the level of human activities based on the average distance of human activities across a city or a county (less distance could lead to a greater contact risk). Activity density provides a measure of level of overall activity level in a county or a city (more activity could lead to a greater risk). Accordingly, Pearson correlation analysis is used to examine the relationship between the two human activity indicators and the basic reproduction number TRANS in the following weeks. The results show statistically significant correlations between the indicators of human activities and the basic reproduction number TRANS in all counties, as well as a significant leader-follower relationship (time lag) between them. The results also show one to two weeks' lag between the change in activity indicators and the decrease in the basic reproduction number TRANS. This result implies that the human activity indicators provide effective early indicators for the spread risk of the pandemic during the early stages of the outbreak. Hence, the results could be used by the authorities to proactively assess the risk of disease spread TRANS by monitoring the daily Venables distance and activity density in a proactive manner.

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