Estimating the Level of Asymptomatic COVID-19 Infections in Northern Ireland in 2020

The identification and understanding of COVID-19 potential routes of transmission are fundamental to informing policies and strategies to successfully control the outbreak. Various studies highlighted asymptomatic infections as one of the silent drivers of the epidemic. An accurate estimation of the asymptomatic cases and the understanding of their contribution to the spread of the disease could enhance the effectiveness of current control strategies, mainly based on the symptom onset, to curb transmission. We investigate the dynamics of the COVID-19 epidemic in Northern Ireland during the period 1st March 25th to December 2020 to estimate the proportion of the asymptomatic infections in the country. We extended our previous model to include the stage of the asymptomatic infection, and we implement the corresponding deterministic model using a publicly available dataset. We partition the data into 11 sets over the period of study and fit the model parameters on the consecutive intervals using the cumulative number of confirmed positive cases for each interval. Moreover, we assess numerically the impacts of uncertainty in testing and we provide estimates of the reproduction numbers using the fitted parameters. We found that the proportion of asymptomatically infectious subpopulations, in Northern Ireland during the period of study, ranged between 5% and 25% of exposed individuals. Also, the estimate of the basic reproduction number, R0, is 3.3089. The lower and upper estimates for herd immunity are (0.6181, 0.7243) suggesting that around 70% of the population of Northern Ireland should acquire immunity via infection or vaccination, which is in line with estimates reported in other studies.

Inferring community transmission of SARS-CoV-2 in the United Kingdom using the ONS COVID-19 Infection Survey

Key epidemiological parameters,including the effective reproduction number,,and the instantaneous growth rate,,generated from an ensemble of models,have been informing public health policy throughout the COVID-19 pandemic in the four nations of the United Kingdom of Great Britain and Northern Ireland(UK).However,estimation of these quantities became challenging with the scaling down of surveillance systems as part of the transition from the“emergency”to“endemic”phase of the pandemic.The Office for National Statistics(ONS)COVID-19 Infection Survey(CIS)provided an opportunity to continue estimating these parameters in the absence of other data streams.We used a penalised spline model fitted to the publicly-available ONS CIS test positivity estimates to produce a smoothed estimate of the prevalence of SARS-CoV-2 positivity over time.The resulting fitted curve was used to estimate the“ONS-based”and across the four nations of the UK.Estimates produced under this model are compared to government-published estimates with particular consideration given to the contribution that this single data stream can offer in the estimation of these parameters.Depending on the nation and parameter,we found that up to 77%of the variance in the government-published estimates can be explained by the ONS-based estimates,demonstrating the value of this singular data stream to track the epidemic in each of the four nations.We additionally find that the ONS-based estimates uncover epidemic trends earlier than the corresponding government-published estimates.Our work shows that the ONS CIS can be used to generate key COVID-19 epidemiological parameters across the four UK nations,further underlining the enormous value of such population-level studies of infection.This is not intended as an alternative to ensemble modelling,rather it is intended as a potential solution to the aforementioned challenge faced by public health officials in the UK in early 2022.

Infection fatality rate and infection attack rate of COVID-19 in South American countries

Background:The ongoing COVID-19 pandemic hit South America badly with multiple waves.Different COVID-19 variants have been storming across the region,leading to more severe infections and deaths even in places with high vaccination coverage.This study aims to assess the spatiotemporal variability of the COVID-19 pandemic and estimate the infection fatality rate(IFR),infection attack rate(IAR)and reproduction number(R0)for twelve most affected South American countries.Methods:We fit a susceptible-exposed-infectious-recovered(SEIR)-based model with a time-varying transmission rate to the reported COVID-19 deaths for the twelve South American countries with the highest mortalities.Most of the epidemiological datasets analysed in this work are retrieved from the disease surveillance systems by the World Health Organization,Johns Hopkins Coronavirus Resource Center and Our World in Data.We investigate the COVID-19 mortalities in these countries,which could represent the situation for the overall South American region.We employ COVID-19 dynamic model with-and-without vaccination considering time-varying flexible transmission rate to estimate IFR,IAR and R0 of COVID-19 for the South American countries.Results:We simulate the model in each scenario under suitable parameter settings and yield biologically reasonable estimates for IFR(varies between 0.303% and 0.723%),IAR(varies between 0.03 and 0.784)and R0(varies between 0.7 and 2.5)for the 12 South American countries.We observe that the severity,dynamical patterns of deaths and time-varying transmission rates among the countries are highly heterogeneous.Further analysis of the model with the effect of vaccination highlights that increasing the vaccination rate could help suppress the pandemic in South America.Conclusions:This study reveals possible reasons for the two waves of COVID-19 outbreaks in South America.We observed reductions in the transmission rate corresponding to each wave plausibly due to improvement in nonpharmaceutical interventions measures and human protective behavioral reaction to recent deaths.Thus,strategies coupling social distancing and vaccination could substantially suppress the mortality rate of COVID-19 in South America.

An evaluation of mathematical models for the outbreak of COVID-19

Mathematical modelling performs a vital part in estimating and controlling the recent outbreak of coronavirus disease 2019(COVID-19).In this epidemic,most countries impose severe intervention measures to contain the spread of COVID-19.The policymakers are forced to make difficult decisions to leverage between health and economic development.How and when tomake clinical and public health decisions in an epidemic situation is a challenging question.The most appropriate solution is based on scientific evidence,which is mainly dependent on data and models.So one of the most critical problems during this crisis is whether we can develop reliable epidemiological models to forecast the evolution of the virus and estimate the effectiveness of various intervention measures and their impacts on the economy.There are numerous types of mathematical model for epidemiological diseases.In this paper,we present some critical reviews on mathematical models for the outbreak of COVID-19.Some elementary models are presented as an initial formulation for an epidemic.We give some basic concepts,notations,and foundation for epidemiological modelling.More related works are also introduced and evaluated by considering epidemiological features such as disease tendency,latent effects,susceptibility,basic reproduction numbers,asymptomatic infections,herd immunity,and impact of the interventions.

基于Beta-Negative Binomial的膨胀分布推广及应用——以新冠病毒肺炎感染人数为例

新型冠状病毒肺炎现存确诊人数是评估疫情发展形势的重要指标,精确刻画新冠病毒肺炎现存确诊人数的分布对于我国开展疫情防治工作具有重要意义.以2021年1月1日~6日我国34个地区新冠病毒肺炎现存确诊人数为研究对象,构建现存确诊人数Beta-Negative Binomial分布、0膨胀Beta-Negative Binomial分布和0-1膨胀Beta-Negative Binomial分布,通过EM算法给出参数的点估计,并基于Fisher信息矩阵计算参数的区间估计,同时基于LR检验给出3种分布中相对最优分布的选择方法.从数值模拟的效果来看,EM算法的收敛性很强、精度较高;从新冠病毒肺炎现存确诊人数分布的拟合效果来看,0-1膨胀Beta-Negative Binomial分布更适合描述我国34个地区现存确诊人数的分布状况.

Analysis of the outbreak of COVID-19 in Japan by SIQR model

The SIQR model is exploited to analyze the outbreak of COVID-19 in Japan where the number of the daily confirmed new cases is explicitly treated as an observable.It is assumed that the society consists of four compartments;susceptible individuals(S),infected individuals at large(I),quarantined patients(Q)and recovered individuals(R),and the time evolution of the pandemic is described by a set of ordinary differential equations.It is shown that the quarantine rate can be determined from the time dependence of the daily confirmed new cases,from which the number of infected individuals can be estimated.The infection rate and quarantine rate are determined for the period from mid-February to mid-April in Japan and transmission characteristics of the initial stages of the outbreak in Japan are analyzed in connection with the policies employed by the government.The effectiveness of different measures is discussed for controlling the outbreak and it is shown that identifying patients through PCR(Polymerase Chain Reaction)testing and isolating them in a quarantine is more effective than lockdown measures aimed at inhibiting social interactions of the general population.An effective reproduction number for infected individuals at large is introduced which is appropriate to epidemics controlled by quarantine measures.

Pay attention to situation of SARS-CoV-2 and TCM advantages in treatment of novel coronavirus infection

Since the outbreak of the new coronavirus epidemic,novel coronavirus has infected nearly 100,000 people in more than 110 countries.How to face this new coronavirus epidemic outbreak is an important issue.Basic reproduction number(R0)is an important parameter in epidemiology;The basic reproduction number of an infection can be thought of as the expected number of cases directly generated by one case in a population where all individuals are susceptible to infection.Epidemiology dynamics is a mathematical model based on a susceptibility-infection-recovery epidemic model.Researchers analyzed the epidemiological benefits of different transmission rates for the establishment of effective strategy in prevention and control strategies for epidemic infectious diseases.In this review,the early use of TCM for light and ordinary patients,can rapidly improve symptoms,shorten hospitalization days and reduce severe cases transformed from light and normal.Many TCM formulas and products have wide application in treating infectious and non-infectious diseases.The TCM theoretical system of treating epidemic diseases with TCM and the treatment scheme of integrated Chinese and Western medicine have proved their effectiveness in clinical practice.TCM can cure COVID-19 pneumonia,and also shows that the role of TCM in blocking the progress of COVID-19 pneumonia.