Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
    displaying 1 - 10 records in total 57
    records per page




    COVID-19 herd immunity in the Brazilian Amazon

    Authors: Lewis F Buss; Carlos Augusto Prete Jr.; Claudia MM Abrahim; Alfredo Mendrone Jr.; Tassila Salomon; Cesar de Almeida-Neto; Rafael FO França; Maria C Belotti; Maria PSS Carvalho; Allyson G Costa; Myuki AE Crispim; Suzete C Ferreira; Nelson A Fraiji; Susie Gurzenda; Charles Whittaker; Leonardo T Kamaura; Pedro L Takecian; Márcio K Moikawa; Anna S Nishiya; Vanderson Rocha; Nanci A Salles; Andreza A de Souza Santos; Martirene A da Silva; Brian Custer; Manoel Barral-Netto; Moritz Kraemer; Rafael HM Pererira; Oliver G Pybus; Michael P Busch; Márcia C Castro; Christopher Dye; Vitor H Nascimento; Nuno R Faria; Ester C Sabino

    doi:10.1101/2020.09.16.20194787 Date: 2020-09-21 Source: medRxiv

    The herd immunity threshold is the proportion of a population that must be immune to an infectious disease MESHD, either by natural infection MESHD or vaccination such that, in the absence of additional preventative measures, new cases decline and the effective reproduction number TRANS falls HP below unity. This fundamental epidemiological parameter is still unknown for the recently-emerged COVID-19, and mathematical models have predicted very divergent results. Population studies using antibody testing SERO to infer total cumulative infections can provide empirical evidence of the level of population immunity in severely affected areas. Here we show that the transmission TRANS of SARS-CoV-2 in Manaus, located in the Brazilian Amazon, increased quickly during March and April and declined more slowly from May to September. In June, one month following the epidemic peak, 44% of the population was seropositive for SARS-CoV-2, equating to a cumulative incidence of 52%, after correcting for the false-negative rate of the antibody test SERO. The seroprevalence SERO fell HP in July and August due to antibody SERO waning. After correcting for this, we estimate a final epidemic size of 66%. Although non-pharmaceutical interventions, plus a change in population behavior, may have helped to limit SARS-CoV-2 transmission TRANS in Manaus, the unusually high infection rate suggests that herd immunity played a significant role in determining the size of the epidemic.

    Suppression of COVID-19 infection by isolation time control based on the SIR model and an analogy from nuclear fusion research

    Authors: Osamu Mitarai; Nagato Yanagi; Gaurab Mukherjee; Monica S McAndrews; Elissa J Chesler; Judith A Blake; Sanduo Zheng; Jianping Wu; Devin J. Kenney; Douam Florian; Yimin Tong; Jin Zhong; Youhua Xie; Xinquan Wang; Zhenghong Yuan; Dongming Zhou; Rong Zhang; Qiang Ding; Kristen J Brennand; Katherine H Hullsiek; David R Boulware; SARAH M LOFGREN; Martirene A da Silva; Brian Custer; Manoel Barral-Netto; Moritz Kraemer; Rafael HM Pererira; Oliver G Pybus; Michael P Busch; Márcia C Castro; Christopher Dye; Vitor H Nascimento; Nuno R Faria; Ester C Sabino

    doi:10.1101/2020.09.18.20197723 Date: 2020-09-20 Source: medRxiv

    The coronavirus disease MESHD 2019 (COVID-19) has been damaging our daily life after declaration of pandemic. Therefore, we have started studying on the characteristics of Susceptible-Infectious-Recovered (SIR) model to know about the truth of infectious disease MESHD and our future. After detailed studies on the characteristics of the SIR model for the various parameter dependencies MESHD with respect to such as the outing restriction (lockdown) ratio and vaccination rate, we have finally noticed that the second term (isolation term) in the differential equation of the number of the infected is quite similar to the "helium ash particle loss term" in deuterium-tritium (D-T) nuclear fusion. Based on this analogy, we have found that isolation of the infected is not actively controlled in the SIR model. Then we introduce the isolation control time parameter q and have studied its effect on this pandemic. Required isolation time to terminate the COVID-19 can be estimated by this proposed method. To show this isolation control effect, we choose Tokyo for the model calculation because of high population density. We determine the reproduction number TRANS and the isolation ratio in the initial uncontrolled phase, and then the future number of the infected is estimated under various conditions. If the confirmed case TRANS can be isolated in 3~8 days by widely performed testing, this pandemic could be suppressed without awaiting vaccination. If the mild outing restriction and vaccination are taken together, the isolation control time can be longer. We consider this isolation time control might be the only solution to overcome the pandemic when vaccine is not available.

    Improved estimation of time-varying reproduction numbers TRANS at low case incidence and between epidemic waves

    Authors: Kris Varun Parag; Olivia Boyd; Lily Geidelberg; David Jorgensen; Fabricia F Nascimento; Igor Siveroni; Robert Johnson; Marc Baguelin; Zulma M Cucunuba; Elita Jauneikaite; Swapnil Mishra; Hayley A Thompson; Oliver J Watson; Neil Ferguson; Christl A Donnelly; Erik Volz; Philip Rosenstiel; Robert Markewitz; Klaus-Peter Wandinger; Jan Rybniker; Matthias Kochanek; Frank Leypoldt; Oliver A Cornely; Philipp Koehler; Andre Franke; Alexander Scheffold

    doi:10.1101/2020.09.14.20194589 Date: 2020-09-18 Source: medRxiv

    We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number TRANS, R, from the incidence of an infectious disease MESHD in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. This combination of maximising information and minimising assumptions, makes EpiFilter more statistically robust in periods of low incidence, where existing methods can struggle. As a result, we find EpiFilter to be particularly suited for assessing the risk of second waves of infection MESHD, in real time.

    Differential impact of non-pharmaceutical public health interventions on COVID-19 epidemics in the United States

    Authors: Xiaoshuang Liu; Xiao Xu; Guanqiao Li; Xian Xu; Yuyao Sun; Fei Wang; Xuanling Shi; Xiang Li; Guotong Xie; Linqi Zhang

    doi:10.21203/rs.3.rs-60056/v1 Date: 2020-08-15 Source: ResearchSquare

    The widespread pandemic of novel coronavirus disease MESHD 2019 (COVID-19) poses an unprecedented global health crisis. In the United States (US), different state governments have adopted various combinations of non-pharmaceutical public health interventions (NPIs) to mitigate the epidemic from February to April, 2020. Quantitative assessment on the effectiveness of NPIs is in great need to assist in guiding the individualized decision making for adjustment of interventions in the US and around the world. However, the impact of these approaches remain uncertain. Based on the reported cases, the effective reproduction number TRANS of COVID-19 epidemic for 50 states in the US was estimated. The measurement on the effectiveness of eight different NPIs was conducted by assessing risk ratios (RRs) between and NPIs through a generalized linear model (GLM). Different NPIs were found to have led to different levels of reduction in. Stay-at-home contributed approximately 51% (95% CI 46%-57%), gathering ban (more than 10 people) 19% (14%-24%), non-essential business closure 16% (10%-21%), declaration of emergency 13% (8%-17%), interstate travel TRANS restriction 11% (5%-16%), school closure 10% (7%-13%), initial business closure 10% (6%-14%), and gathering ban (more than 50 people) 6% (2%-11%). This retrospective assessment of NPIs on has shown that NPIs played critical roles on epidemic control in the US in the past several months. The quantitative results could guide individualized decision making for future adjustment of NPIs in the US and other countries for COVID-19 and other similar infectious diseases MESHD.

    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.

    Waves of COVID-19 pandemic. Detection and SIR simulations

    Authors: Igor Nesteruk

    doi:10.1101/2020.08.03.20167098 Date: 2020-08-04 Source: medRxiv

    Background. Unfortunately, the COVID-19 pandemic is still far from stabilizing. Of particular concern is the sharp increase in the number of diseases in June-July 2020. The causes and consequences of this sharp increase in the number of cases are still waiting for their researchers, but there is already an urgent need to assess the possible duration of the pandemic, the expected number of patients and deaths MESHD. The resumption of international passenger traffic needs the information for deciding which countries' citizens are welcome guests. Correct simulation of the infectious disease MESHD dynamics needs complicated mathematical models and many efforts for unknown parameters identification. Constant changes in the pandemic conditions (in particular, the peculiarities of quarantine and its violation, situations with testing and isolation of patients) cause various epidemic waves, lead to changes in the parameter values of the mathematical models. Objective. In this article, pandemic waves in Ukraine and in the world will be detected, calculated and discussed. The estimations for hidden periods, epidemic durations and final numbers of cases will be presented. The probabilities of meeting a person spreading the infection MESHD and reproduction numbers TRANS will be calculated for different countries and regions. Methods. We propose a simple method for the epidemic waves detection based on the differentiation of the smoothed number of cases. We use the known SIR (susceptible-infected-removed) model for the dynamics of the epidemic waves. The known exact solution of the SIR differential equations and statistical approach were modified and used. Results. The optimal values of the SIR model parameters were identified for four waves of pandemic dynamics in Ukraine and five waves in the world. The number of cases and the number of patients spreading the infection versus time were calculated. In particular, the pandemic probably began in August 2019. If current trends continue, the end of the pandemic should be expected no earlier than in March 2021 both in Ukraine and in the world, the global number of cases will exceed 20 million. The probabilities of meeting a person spreading the infection MESHD and reproduction numbers TRANS were calculated for different countries and regions. Conclusions. The SIR model and statistical approach to the parameter identification are helpful to make some reliable estimations of the epidemic waves. The number of persons spreading the infection versus time was calculated during all the epidemic waves. The obtained information will be useful to regulate the quarantine activities, to predict the medical and economic consequences of the pandemic and to decide which countries' citizens are welcome guests.

    Epidemiological characteristics of SARS-COV-2 in Myanmar MESHD

    Authors: Aung Min Thway; Htun Tayza; Tun Tun Win; Ye Minn Tun; Moe Myint Aung; Yan Naung Win; Kyaw M Tun

    doi:10.1101/2020.08.02.20166504 Date: 2020-08-04 Source: medRxiv

    Coronavirus disease (COVID-19) is an infectious disease MESHD caused by a newly discovered severe acute respiratory syndrome coronavirus 2 MESHD (SARS-CoV-2). In Myanmar, first COVID-19 reported cases were identified on 23rd March 2020. There were 336 reported confirmed cases TRANS, 261 recovered and 6 deaths through 13th July 2020. The study was a retrospective case series and all COVID-19 confirmed cases TRANS from 23rd March to 13th July 2020 were included. The data series of COVID-19 cases were extracted from the daily official reports of the Ministry of Health and Sports (MOHS), Myanmar and Centers for Disease Control and Prevention (CDC), Myanmar. Among 336 confirmed cases TRANS, there were 169 cases with reported transmission TRANS events. The median serial interval TRANS was 4 days (IQR 3, 2-5) with the range of 0 - 26 days. The mean of the reproduction number TRANS was 1.44 with (95% CI = 1.30-1.60) by exponential growth method and 1.32 with (95% CI = 0.98-1.73) confident interval by maximum likelihood method. This study outlined the epidemiological characteristics and epidemic parameters of COVID-19 in Myanmar. The estimation parameters in this study can be comparable with other studies and variability of these parameters can be considered when implementing disease control strategy in Myanmar.

    The basic reproduction number TRANS of SARS-CoV-2: a scoping review of available evidence

    Authors: Ann Barber; John M Griffin; Miriam Casey; Aine Collins; Elizabeth A Lane; Quirine Ten Bosch; Mart De Jong; David Mc Evoy; Andrew W Byrne; Conor G McAloon; Francis Butler; Kevin Hunt; Simon J More

    doi:10.1101/2020.07.28.20163535 Date: 2020-07-30 Source: medRxiv

    Background: The transmissibility TRANS of SARS-CoV-2 determines both the ability of the virus to invade a population and the strength of intervention that would be required to contain or eliminate the spread of infection MESHD. The basic reproduction number TRANS, R0 TRANS, provides a quantitative measure of the transmission TRANS potential of a pathogen. Objective: Conduct a scoping review of the available literature providing estimates of R0 TRANS for SARS-CoV-2, provide an overview of the drivers of variation in R0 TRANS estimates and the considerations taken in the calculation of the parameter. Design: Scoping review of available literature between the 01 December 2019 and 07 May 2020. Data sources: Both peer-reviewed and pre-print articles were searched for on PubMed, Google Scholar, MedRxiv and BioRxiv. Selection criteria: Studies were selected for review if (i) the estimation of R0 TRANS represented either the initial stages of the outbreak or the initial stages of the outbreak prior to the onset of widespread population restriction (lockdown), (ii) the exact dates of the study period were provided and (iii) the study provided primary estimates of R0 TRANS. Results: A total of 20 R0 TRANS estimates were extracted from 15 studies. There was substantial variation in the estimates reported. Estimates derived from mathematical models fell HP within a wider range of 1.94-6.94 than statistical models which fell HP between the range of 2.2 to 4.4. Several studies made assumptions about the length of the infectious period TRANS which ranged from 5.8-20 days and the serial interval TRANS which ranged from 4.41-14 days. For a given set of parameters a longer duration of infectiousness MESHD or a longer serial interval TRANS equates to a higher R0 TRANS. Several studies took measures to minimise bias in early case reporting, to account for the potential occurrence of super-spreading events, and to account for early sub-exponential epidemic growth. Conclusions: The variation in reported estimates of R0 TRANS reflects the complex nature of the parameter itself, including the context (i.e. social/spatial structure), the methodology used to estimate the parameter, and model assumptions. R0 TRANS is a fundamental parameter in the study of infectious disease MESHD dynamics however it provides limited practical applicability outside of the context in which it was estimated, and should be calculated and interpreted with this in mind.

    Epidemic Dynamics of COVID-19 Based on SEAIUHR Model Considering Asymptomatic TRANS Cases in Henan Province, China

    Authors: Chunyu Li; Yuchen Zhu; Chang Qi; Lili Liu; Dandan Zhang; Xu Wang; Kaili She; Yan Jia; Tingxuan Liu; Momiao Xiong; Xiujun Li

    doi:10.21203/rs.3.rs-50050/v1 Date: 2020-07-28 Source: ResearchSquare

    Background New coronavirus disease MESHD (COVID-19), an infectious disease MESHD caused by a type of novel coronavirus, has emerged in various countries since the end of 2019 and caused a global pandemic. Many infected people MESHD went undetected because their symptoms were mild or asymptomatic TRANS, but the proportion and infectivity of asymptomatic TRANS infections remained unknown. Therefore, in this paper, we analyzed the proportion and infectivity of asymptomatic TRANS cases, as we as the prevalence SERO of COVID-19 in Henan province.Methods We constructed SEAIUHR model based on COVID-19 cases reported from 21 January to 26 February 2020 in Henan province to estimate the proportion and infectivity of asymptomatic TRANS cases, as we as the change of effective reproductive number TRANS, \({R}_{t}\). At the same time, we simulated the changes of cases in different scenarios by changing the time and intensity of the implementation of prevention and control measures.Results The proportion of asymptomatic TRANS cases among COVID-19 infected individuals was 42% and infectivity of asymptomatic TRANS cases was 10% of that symptomatic ones. The basic reproductive number\({R TRANS}_{0}\)=2.73, and \({R}_{t}\) dropped below 1 on 1 February under a series of measures. If measures were taken five days earlier, the number of cases would be reduced by 2/3, and after 5 days the number would more than triple.Conclusions In Henan Province, the COVID-19 epidemic spread rapidly in the early stage, and there were a large number of asymptomatic TRANS infected individuals with relatively low infectivity. However, the epidemic was quickly brought under control with national measures, and the earlier measures were implemented, the better.

    The role of mathematical model in curbing COVID-19 in Nigeria

    Authors: Chinwendu Emilian Madubueze; Nkiru M. Akabuike; Dachollom Sambo

    doi:10.1101/2020.07.22.20159210 Date: 2020-07-25 Source: medRxiv

    The role of mathematical models in controlling infectious diseases MESHD cannot be overemphasized. COVID-19 is a viral disease that is caused by Severe Acute Respiratory Syndrome coronavirus 2 MESHD (SARS-CoV-2) which has no approved vaccine. The available control measures are non-pharmacological interventions like wearing face masks, social distancing, and lockdown which are being advocated for by the WHO. This work assesses the impact of non-pharmaceutical control measures (social distancing and use of face-masks) and mass testing on the spread of COVID-19 in Nigeria. A community-based transmission TRANS model for COVID-19 in Nigeria is formulated with observing social distancing, wearing face masks in public and mass testing. The model is parameterized using Nigeria data on COVID-19 in Nigeria. The basic reproduction number TRANS is found to be less than unity( R_0 TRANS<1) when the compliance with intervention measures is moderate (50%[≤]<70%) and the testing rate per day is moderate (0.5[≤]{sigma}_2<0.7) or when the compliance with intervention measures is strict ([≥]70%) and the testing rate per day is poor ({sigma}_2=0.3). This implies that Nigeria will be able to halt the spread of COVID-19 under these two conditions. However, it will be easier to enforce strict compliance with intervention measures in the presence of poor testing rate due to the limited availability of testing facilities and manpower in Nigeria. Hence, this study advocates that Nigerian governments (Federal and States) should aim at achieving a testing rate of at least 0.3 per day while ensuring that all the citizens strictly comply with wearing face masks and observing social distancing in public.

The ZB MED preprint Viewer preVIEW includes all COVID-19 related preprints from medRxiv and bioRxiv, from ChemRxiv, from ResearchSquare, from arXiv and from Preprints.org and is updated on a daily basis (7am CET/CEST).
The web page can also be accessed via API.

Sources


Annotations

All
None
MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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.