scholarly journals Meta-analysis of the severe acute respiratory syndrome coronavirus 2 serial intervals and the impact of parameter uncertainty on the coronavirus disease 2019 reproduction number

2021 ◽  
pp. 096228022110651
Author(s):  
Robert Challen ◽  
Ellen Brooks-Pollock ◽  
Krasimira Tsaneva-Atanasova ◽  
Leon Danon
Keyword(s):  
Incubation Period ◽  
Time Delays ◽  
Hospital Admissions ◽  
Reproduction Number ◽  
Meta Analysis ◽  
Model Parameters ◽  
Small Data ◽  
Generation Interval ◽  
Serial Interval ◽  
The Impact

The serial interval of an infectious disease, commonly interpreted as the time between the onset of symptoms in sequentially infected individuals within a chain of transmission, is a key epidemiological quantity involved in estimating the reproduction number. The serial interval is closely related to other key quantities, including the incubation period, 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, hospital admissions, and deaths. Estimates of these quantities are often based on small data sets from early contact tracing and are subject to considerable uncertainty, which is especially true for early coronavirus disease 2019 data. In this paper, we estimate these key quantities in the context of coronavirus disease 2019 for the UK, including a meta-analysis of early estimates of the serial interval. We estimate distributions for the serial interval with a mean of 5.9 (95% CI 5.2; 6.7) and SD 4.1 (95% CI 3.8; 4.7) days (empirical distribution), the generation interval with a mean of 4.9 (95% CI 4.2; 5.5) and SD 2.0 (95% CI 0.5; 3.2) days (fitted gamma distribution), and the incubation period with a mean 5.2 (95% CI 4.9; 5.5) and SD 5.5 (95% CI 5.1; 5.9) days (fitted log-normal distribution). We quantify the impact of the uncertainty surrounding the serial interval, generation interval, incubation period, and time delays, on the subsequent estimation of the reproduction number, 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 modeling coronavirus disease 2019 transmission.

scholarly journals Meta-analysis of the SARS-CoV-2 serial interval and the impact of parameter uncertainty on the COVID-19 reproduction number

2020 ◽  
Author(s):  
Robert Challen ◽  
Ellen Brooks-Pollock ◽  
Krasimira Tsaneva-Atanasova ◽  
Leon Danon
Keyword(s):  
Incubation Period ◽  
Time Delays ◽  
Hospital Admissions ◽  
Reproduction Number ◽  
Meta Analysis ◽  
Model Parameters ◽  
Small Data ◽  
Generation Interval ◽  
Serial Interval ◽  
The Impact

AbstractThe serial interval of an infectious disease, commonly interpreted as the time between onset of symptoms in sequentially infected individuals within a chain of transmission, is a key epidemiological quantity involved in estimating the reproduction number. The serial interval is closely related to other key quantities, including the incubation period, 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, hospital admissions and deaths. Estimates of these quantities are often based on small data sets from early contact tracing and are subject to considerable uncertainty, which is especially true for early COVID-19 data. In this paper we estimate these key quantities in the context of COVID-19 for the UK, including a meta-analysis of early estimates of the serial interval. We estimate distributions for the serial interval 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 5.5 (95% CrI 5.1–5.8) and SD 4.9 (95% CrI 4.5–5.3) days (fitted log normal distribution). We quantify the impact of the uncertainty surrounding the serial interval, generation interval, incubation period and time delays, on the subsequent estimation of the reproduction number, 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 transmission.

scholarly journals Dynamics of the rumor-spreading model with hesitation mechanism in heterogenous networks and bilingual environment

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Shuai Yang ◽  
Haijun Jiang ◽  
Cheng Hu ◽  
Juan Yu ◽  
Jiarong Li
Keyword(s):  
Lyapunov Method ◽  
Reproduction Number ◽  
Model Parameters ◽  
Rumor Spreading ◽  
Spreading Model ◽  
Reductio Ad Absurdum ◽  
The Stability ◽  
The Impact ◽  
Theoretical Results ◽  
The Basic Reproduction Number

Abstract In this paper, a novel rumor-spreading model is proposed under bilingual environment and heterogenous networks, which considers that exposures may be converted to spreaders or stiflers at a set rate. Firstly, the nonnegativity and boundedness of the solution for rumor-spreading model are proved by reductio ad absurdum. Secondly, both the basic reproduction number and the stability of the rumor-free equilibrium are systematically discussed. Whereafter, the global stability of rumor-prevailing equilibrium is explored by utilizing Lyapunov method and LaSalle’s invariance principle. Finally, the sensitivity analysis and the numerical simulation are respectively presented to analyze the impact of model parameters and illustrate the validity of theoretical results.

scholarly journals A fractional order mathematical model for COVID-19 dynamics with quarantine, isolation, and environmental viral load

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Mohammed A. Aba Oud ◽  
Aatif Ali ◽  
Hussam Alrabaiah ◽  
Saif Ullah ◽  
Muhammad Altaf Khan ◽  
...  
Keyword(s):  
Epidemic Model ◽  
Reproduction Number ◽  
Model Parameters ◽  
Disease Dynamics ◽  
Threshold Parameter ◽  
Fractional Model ◽  
Infected People ◽  
Fractional Models ◽  
Disease Epidemics ◽  
The Impact

AbstractCOVID-19 or coronavirus is a newly emerged infectious disease that started in Wuhan, China, in December 2019 and spread worldwide very quickly. Although the recovery rate is greater than the death rate, the COVID-19 infection is becoming very harmful for the human community and causing financial loses to their economy. No proper vaccine for this infection has been introduced in the market in order to treat the infected people. Various approaches have been implemented recently to study the dynamics of this novel infection. Mathematical models are one of the effective tools in this regard to understand the transmission patterns of COVID-19. In the present paper, we formulate a fractional epidemic model in the Caputo sense with the consideration of quarantine, isolation, and environmental impacts to examine the dynamics of the COVID-19 outbreak. The fractional models are quite useful for understanding better the disease epidemics as well as capture the memory and nonlocality effects. First, we construct the model in ordinary differential equations and further consider the Caputo operator to formulate its fractional derivative. We present some of the necessary mathematical analysis for the fractional model. Furthermore, the model is fitted to the reported cases in Pakistan, one of the epicenters of COVID-19 in Asia. The estimated value of the important threshold parameter of the model, known as the basic reproduction number, is evaluated theoretically and numerically. Based on the real fitted parameters, we obtained $\mathcal{R}_{0} \approx 1.50$ R 0 ≈ 1.50 . Finally, an efficient numerical scheme of Adams–Moulton type is used in order to simulate the fractional model. The impact of some of the key model parameters on the disease dynamics and its elimination are shown graphically for various values of noninteger order of the Caputo derivative. We conclude that the use of fractional epidemic model provides a better understanding and biologically more insights about the disease dynamics.

Abstract 5133: Does Public Smoking Ban Reduce the Incidence of Myocardial Infarction in Michigan? A Systematic Review and Attributable Risk Analysis

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Mouaz Al-Mallah ◽  
Fadi Alqaisi ◽  
David Nerenz ◽  
Stephanie Boedeker ◽  
W. Douglas Weaver
Keyword(s):  
Hospital Admissions ◽  
Health Care Cost ◽  
Meta Analysis ◽  
Attributable Risk ◽  
Smoking Ban ◽  
Random Effects Model ◽  
Inclusion Criteria ◽  
Established Risk Factor ◽  
Potential Impact ◽  
The Impact

Background: Smoking is a well-established risk factor for cardiovascular disease. The Michigan legislature is currently considering a proposal for a comprehensive smoking ban (CSB) in Michigan. The potential impact of such a law on the incidence AMI is not known. We conducted a meta-analysis to study the impact of CSB on the incidence of AMI and calculated the impact of potential CSB on the incidence of AMI in Michigan. Methods: We searched MEDLINE, EMBASE, and Cochrane databases from inception till May 2008 for studies comparing the rates of AMI hospital admissions before and in the year after the implementation of CSB legislation. Of 135 potentially relevant articles screened initially, 5 studies met the inclusion criteria. A random-effects model meta-analysis was done and between-studies heterogeneity was compared with I2. The attributable risk (AR) of CSB on AMI incidence was calculated and multiplied with the number of AMI admissions in Michigan. Results: In the published studies, a CSB was associated with a decrease in the incidence of AMI (RR 96%, 95% CI 93%–100%, p=0.05). There was no heterogeneity between the included studies (I2<50%). The AR of CSB on the incidence of AMI is −4.2%. The average number of hospital admissions for AMI as first-listed diagnosis in Michigan between 1999 and 2006 was 27,007 per year. Thus, if a CSB legislation is implemented in Michigan in 2008, the calculated reduction of hospital admissions for AMI is 1130 admissions per year as of 2009.. Conclusion: CSB is associated with a significant reduction of annual hospital admissions for AMI. The financial impact of this reduction on health care cost is yet to be determined.

Abstract P84: The Impact of a Potential Nationwide Comprehensive Smoking Ban on Acute Myocardial Infarction Hospitalizations

2011 ◽  
Vol 4 (suppl_1) ◽  
Author(s):  
Mouaz H Al-Mallah ◽  
Owais Khawaja ◽  
Fadi Alqaisi ◽  
David Nerenz ◽  
W Douglas Weaver
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Hospital Admissions ◽  
Meta Analysis ◽  
Cost Savings ◽  
Smoking Ban ◽  
Disease States ◽  
Established Risk Factor ◽  
Department Of Health ◽  
The Impact

Introduction: Smoking is a well established risk factor for acute myocardial infarction (AMI). The potential impact of a nationwide comprehensive smoking ban (CSB) legislation on the incidence of AMI hospital admissions is not known. The aim of this analysis is to determine the impact of a nationwide CSB legislation on the incidence of AMI hospitalizations. Methods: We contacted the department of health at states with no CSB law for information on the total number of AMI discharges (ICD-9-CM 410), length of stay and charges in dollars for 2007. Expected decrease in the number of AMI in the year following a potential implementation of a nationwide CSB was calculated by multiplying the current number of AMI by the pooled relative risk reduction (RRR) obtained from a recent published meta analysis (RR 0.89). Results: In 2007, 37 States had CSB laws. There were 169,043 AMI hospitalizations in states without CSB. A nationwide smoking ban would result in 18,596 less AMI hospitalizations in the year following such a ban. This is associated with more than 92 million dollars in direct cost savings. Conclusion: A nationwide CSB legislation would result in significant reduction in the number of AMI hospitalizations. This is associated with significant cost saving. Further studies are needed to evaluate the impact of CSB on admission from other disease states.

A THEORETICAL ASSESSMENT OF THE EFFECTS OF DISTRIBUTED DELAY ON THE TRANSMISSION DYNAMICS OF HEPATITIS B

2015 ◽  
Vol 23 (03) ◽  
pp. 423-455
Author(s):  
P. MOUOFO TCHINDA ◽  
JEAN JULES TEWA ◽  
BOULECHARD MEWOLI ◽  
SAMUEL BOWONG
Keyword(s):  
Drug Therapy ◽  
Hepatitis B ◽  
Basic Reproduction Number ◽  
Disease Transmission ◽  
Time Delays ◽  
Reproduction Number ◽  
Distributed Delay ◽  
Global Dynamics ◽  
The Impact ◽  
The Basic Reproduction Number

In this paper, we investigate the global dynamics of a system of delay differential equations which describes the interaction of hepatitis B virus (HBV) with both liver and blood cells. The model has two distributed time delays describing the time needed for infection of cell and virus replication. We also include the efficiency of drug therapy in inhibiting viral production and the efficiency of drug therapy in blocking new infection. We compute the basic reproduction number and find that increasing delays will decrease the value of the basic reproduction number. We study the sensitivity analysis on the key parameters that drive the disease dynamics in order to determine their relative importance to disease transmission and prevalence. Our analysis reveals that the model exhibits the phenomenon of backward bifurcation (where a stable disease-free equilibrium (DFE) co-exists with a stable endemic equilibrium when the basic reproduction number is less than unity). Numerical simulations are presented to evaluate the impact of time-delays on the prevalence of the disease.

scholarly journals Mathematical analysis of an age structured epidemic model with a quarantine class

2021 ◽  
Author(s):  
Bedreddine AINSEBA ◽  
Tarik Touaoula ◽  
Zakia Sari
Keyword(s):  
Reproduction Number ◽  
Asymptotic Behavior Of Solutions ◽  
Model Parameters ◽  
Qualitative Behavior ◽  
Asymptotically Stable ◽  
Globally Asymptotically Stable ◽  
Age Structured ◽  
The Stability ◽  
The Impact

In this paper, an age structured epidemic Susceptible-Infected-Quarantined-Recovered-Infected (SIQRI) model is proposed, where we will focus on the role of individuals that leave their class of quarantine before being completely recovered and thus will participate again to the transmission of the disease. We investigate the asymptotic behavior of solutions by studying the stability of both trivial and positive equilibria. In order to see the impact of the different model parameters like the relapse rate on the qualitative behavior of our system, we firstly, give the explicit expression of the epidemic reproduction number $R_{0}.$ This number is a combination of the classical epidemic reproduction number for the SIQR model and a new epidemic reproduction number corresponding to the individuals infected by a relapsed person from the R-class. It is shown that, if $R_{0}\leq 1$, the disease free equilibrium is globally asymptotically stable and becomes unstable for $R_{0}>1$. Secondly, while $R_{0}>1$, a suitable Lyapunov functional is constructed to prove that the unique endemic equilibrium is globally asymptotically stable on some subset $\Omega_{0}.$

scholarly journals Effects of prolonged incubation period and centralized quarantine on the COVID-19 outbreak in Shijiazhuang, China: a modeling study

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Wenlong Zhu ◽  
Mengxi Zhang ◽  
Jinhua Pan ◽  
Ye Yao ◽  
Weibing Wang
Keyword(s):  
Nucleic Acid ◽  
Incubation Period ◽  
Likelihood Method ◽  
Cumulative Number ◽  
Nucleic Acid Testing ◽  
Prolonged Incubation ◽  
Serial Interval ◽  
Epidemic Period ◽  
Epidemiological Characteristics ◽  
The Impact

Abstract Background From 2 January to 14 February 2021, a local outbreak of COVID-19 occurred in Shijiazhuang, the capital city of Hebei Province, with a population of 10 million. We analyzed the characteristics of the local outbreak of COVID-19 in Shijiazhuang and evaluated the effects of serial interventions. Methods Publicly available data, which included age, sex, date of diagnosis, and other patient information, were used to analyze the epidemiological characteristics of the COVID-19 outbreak in Shijiazhuang. The maximum likelihood method and Hamiltonian Monte Carlo method were used to estimate the serial interval and incubation period, respectively. The impact of incubation period and different interventions were simulated using a well-fitted SEIR+q model. Results From 2 January to 14 February 2021, there were 869 patients with symptomatic COVID-19 in Shijiazhuang, and most cases (89.6%) were confirmed before 20 January. Overall, 40.2% of the cases were male, 16.3% were aged 0 to 19 years, and 21.9% were initially diagnosed as asymptomatic but then became symptomatic. The estimated incubation period was 11.6 days (95% CI 10.6, 12.7 days) and the estimated serial interval was 6.6 days (0.025th, 0.975th: 0.6, 20.0 days). The results of the SEIR+q model indicated that a longer incubation period led to a longer epidemic period. If the comprehensive quarantine measures were reduced by 10%, then the nucleic acid testing would need to increase by 20% or more to minimize the cumulative number of cases. Conclusions Incubation period was longer than serial interval suggested that more secondary transmission may occur before symptoms onset. The long incubation period made it necessary to extend the isolation period to control the outbreak. Timely contact tracing and implementation of a centralized quarantine quickly contained this epidemic in Shijiazhuang. Large-scale nucleic acid testing also helped to identify cases and reduce virus transmission.

scholarly journals Meta-analysis of several epidemic characteristics of COVID-19

2020 ◽  
Author(s):  
Panpan Zhang ◽  
Tiandong Wang ◽  
Sharon X. Xie
Keyword(s):  
Public Health ◽  
Doubling Time ◽  
Reproduction Number ◽  
Meta Analysis ◽  
Daily Work ◽  
Serial Interval ◽  
Key Characteristics ◽  
Public Health Strategies ◽  
The Basic Reproduction Number ◽  
Work And Life

AbstractAs the COVID-19 pandemic has strongly disrupted people’s daily work and life, a great amount of scientific research has been conducted to understand the key characteristics of this new epidemic. In this manuscript, we focus on four crucial epidemic metrics with regard to the COVID-19, namely the basic reproduction number, the incubation period, the serial interval and the epidemic doubling time. We collect relevant studies based on the COVID-19 data in China and conduct a meta-analysis to obtain pooled estimates on the four metrics. From the summary results, we conclude that the COVID-19 has stronger transmissibility than SARS, implying that stringent public health strategies are necessary.

scholarly journals Estimating the generation interval for COVID-19 based on symptom onset data

2020 ◽  
Author(s):  
Ganyani Tapiwa ◽  
Kremer Cécile ◽  
Chen Dongxuan ◽  
Torneri Andrea ◽  
Faes Christel ◽  
...  
Keyword(s):  
Incubation Period ◽  
Symptom Onset ◽  
Contact Tracing ◽  
Transmission Network ◽  
Generation Interval ◽  
Period Distribution ◽  
Reproduction Numbers ◽  
Serial Interval ◽  
Modelling Studies ◽  
Interval Distribution

AbstractBackgroundEstimating key infectious disease parameters from the COVID-19 outbreak is quintessential for modelling studies and guiding intervention strategies. Whereas different estimates for the incubation period distribution and the serial interval distribution have been reported, estimates of the generation interval for COVID-19 have not been provided.MethodsWe used outbreak data from clusters in Singapore and Tianjin, China to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network. From those estimates we obtained the proportions pre-symptomatic transmission and reproduction numbers.ResultsThe mean generation interval was 5.20 (95%CI 3.78-6.78) days for Singapore and 3.95 (95%CI 3.01-4.91) days for Tianjin, China when relying on a previously reported incubation period with mean 5.2 and SD 2.8 days. The proportion of pre-symptomatic transmission was 48% (95%CI 32-67%) for Singapore and 62% (95%CI 50-76%) for Tianjin, China. Estimates of the reproduction number based on the generation interval distribution were slightly higher than those based on the serial interval distribution.ConclusionsEstimating generation and serial interval distributions from outbreak data requires careful investigation of the underlying transmission network. Detailed contact tracing information is essential for correctly estimating these quantities.

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