scholarly journals Re-estimation of basic reproduction number of COVID-19 based on the epidemic curve by symptom onset date

2021 ◽  
pp. 1-11
Author(s):  
K. Hong ◽  
S.J. Yum ◽  
J.H. Kim ◽  
B.C. Chun
Keyword(s):  
Basic Reproduction Number ◽  
Symptom Onset ◽  
Reproduction Number ◽  
Onset Date ◽  
Epidemic Curve

scholarly journals Sustainable social distancing through facemask use and testing during the Covid-19 pandemic

2020 ◽  
Author(s):  
Diego Chowell ◽  
Kimberlyn Roosa ◽  
Ranu Dhillon ◽  
Gerardo Chowell ◽  
Devabhaktuni Srikrishna
Keyword(s):  
Basic Reproduction Number ◽  
Symptom Onset ◽  
Social Protection ◽  
Reproduction Number ◽  
Type Model ◽  
Protective Behaviors ◽  
Social Distancing ◽  
And Control ◽  
Different Levels ◽  
The Basic Reproduction Number

We investigate how individual protective behaviors, different levels of testing, and isolation influence the transmission and control of the COVID-19 pandemic. Based on an SEIR-type model incorporating asymptomatic but infectious individuals (40%), we show that the pandemic may be readily controllable through a combination of testing, treatment if necessary, and self-isolation after testing positive (TTI) of symptomatic individuals together with social protection (e.g., facemask use, handwashing). When the basic reproduction number, R0, is 2.4, 65% effective social protection alone (35% of the unprotected transmission) brings the R below 1. Alternatively, 20% effective social protection brings the reproduction number below 1.0 so long as 75% of the symptomatic population is covered by TTI within 12 hours of symptom onset. Even with 20% effective social protection, TTI of 1 in 4 symptomatic individuals can substantially 'flatten the curve' cutting the peak daily incidence in half.

scholarly journals Time from Symptom Onset to Hospitalisation of Coronavirus Disease 2019 (COVID-19) Cases: Implications for the Proportion of Transmissions from Infectors with Few Symptoms

2020 ◽  
Vol 9 (5) ◽  
pp. 1297 ◽  
Author(s):  
Robin N. Thompson ◽  
Francesca A. Lovell-Read ◽  
Uri Obolski
Keyword(s):  
Basic Reproduction Number ◽  
Symptom Onset ◽  
Reproduction Number ◽  
Wide Spectrum ◽  
Control Measures ◽  
Current Evidence ◽  
The Novel ◽  
Novel Coronavirus ◽  
The Basic Reproduction Number

Interventions targeting symptomatic hosts and their contacts were successful in bringing the 2003 SARS pandemic under control. In contrast, the COVID-19 pandemic has been harder to contain, partly because of its wide spectrum of symptoms in infectious hosts. Current evidence suggests that individuals can transmit the novel coronavirus while displaying few symptoms. Here, we show that the proportion of infections arising from hosts with few symptoms at the start of an outbreak can, in combination with the basic reproduction number, indicate whether or not interventions targeting symptomatic hosts are likely to be effective. However, as an outbreak continues, the proportion of infections arising from hosts with few symptoms changes in response to control measures. A high proportion of infections from hosts with few symptoms after the initial stages of an outbreak is only problematic if the rate of new infections remains high. Otherwise, it can simply indicate that symptomatic transmissions are being prevented successfully. This should be considered when interpreting estimates of the extent of transmission from hosts with few COVID-19 symptoms.

scholarly journals The first wave of the SARS-CoV-2 epidemic in Tuscany (Italy): A SI2R2D compartmental model with uncertainty evaluation

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250029
Author(s):  
Michela Baccini ◽  
Giulia Cereda ◽  
Cecilia Viscardi
Keyword(s):  
Basic Reproduction Number ◽  
Compartmental Model ◽  
Reproduction Number ◽  
Case Fatality ◽  
Parametric Bootstrap ◽  
Model Parameters ◽  
Fatality Rate ◽  
Epidemic Curve ◽  
Plausible Values ◽  
The Basic Reproduction Number

With the aim of studying the spread of the SARS-CoV-2 infection in the Tuscany region of Italy during the first epidemic wave (February-June 2020), we define a compartmental model that accounts for both detected and undetected infections and assumes that only notified cases can die. We estimate the infection fatality rate, the case fatality rate, and the basic reproduction number, modeled as a time-varying function, by calibrating on the cumulative daily number of observed deaths and notified infected, after fixing to plausible values the other model parameters to assure identifiability. The confidence intervals are estimated by a parametric bootstrap procedure and a Global Sensitivity Analysis is performed to assess the sensitivity of the estimates to changes in the values of the fixed parameters. According to our results, the basic reproduction number drops from an initial value of 6.055 to 0 at the end of the national lockdown, then it grows again, but remaining under 1. At the beginning of the epidemic, the case and the infection fatality rates are estimated to be 13.1% and 2.3%, respectively. Among the parameters considered as fixed, the average time from infection to recovery for the not notified infected appears to be the most impacting one on the model estimates. The probability for an infected to be notified has a relevant impact on the infection fatality rate and on the shape of the epidemic curve. This stresses the need of collecting information on these parameters to better understand the phenomenon and get reliable predictions.

scholarly journals Simulating the progression of the COVID-19 disease in Cameroon using SIR models

2020 ◽  
Author(s):  
Ulrich KAMGUEM NGUEMDJO ◽  
Freeman MENO ◽  
Audric DONGFACK ◽  
Bruno VENTELOU
Keyword(s):  
Public Health ◽  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Sir Model ◽  
Health Policies ◽  
Public Health Policies ◽  
Epidemic Curve ◽  
Health Authorities ◽  
Public Health Authorities ◽  
The Basic Reproduction Number

This paper analyses the evolution of COVID 19 disease in Cameroon over the period March 6 April 2020 using SIR model. Specifically, 1) we evaluate the basic reproduction number of the virus. 2) Determine the peak of the infection and the spread-out period of the disease. 3) Simulate the interventions of public health authorities. Data used in this study is obtained from the Ministry of Health of Cameroon. The results suggest that over the period, the reproduction number of the COVID 19 in Cameroon is about 1.5 and the peak of the infection could occur at the end of May 2020 with about 7.7% of the population infected. Besides, implementation of efficient public health policies could help flattens the epidemic curve.

scholarly journals How should we present the epidemic curve for COVID-19?

2021 ◽  
Author(s):  
Jean-Paul R. Soucy ◽  
Sarah A. Buchan ◽  
Kevin A. Brown
Keyword(s):  
Public Health ◽  
Symptom Onset ◽  
Public Reporting ◽  
Downward Trend ◽  
Public Health Authority ◽  
Onset Date ◽  
Current Response ◽  
Sample Collection ◽  
The Public ◽  
Epidemic Curve

Epidemic curves are used by decision makers and the public to infer the trajectory of the COVID-19 pandemic and to understand the appropriateness of current response measures. Symptom onset date is commonly used to date cases on the epidemic curve in public health reports and dashboards. However, third-party trackers often plot cases on the epidemic curve by the date they were publicly reported by the public health authority. These two curves create very different impressions of epidemic progression. On April 1, the epidemic curve for Ontario, Canada based on public reporting date showed an accelerating epidemic, whereas the curve based on a proxy variable for symptom onset date showed a rapidly declining epidemic. This illusory downward trend (the "ghost trend") is a feature of epidemic curves anchored using date variables earlier in time than the date a case was publicly reported, such as symptom onset date or sample collection date. This is because newly discovered cases are backdated, creating a perpetual downward trend in incidence due to incomplete data in the most recent days. Public reporting date is not subject to backdating bias and can be used to visualize real-time epidemic curves meant to inform the public and policy makers.

scholarly journals A delayed SEIQR epidemic model of COVID-19 in Tokyo area

2020 ◽  
Author(s):  
Kazuo Maki
Keyword(s):  
Symptom Onset ◽  
Delay Time ◽  
Reproduction Number ◽  
Social Activity ◽  
Latency Period ◽  
Social Contact ◽  
Realistic Model ◽  
Time Profile ◽  
Onset Date ◽  
Pcr Test

The infection of COVID-19 has caused a global pandemic. In order to avoid excessive restriction to the social activity, a good strategy of quarantine based on a realistic model is expected. Several epidemic models with a quarantine compartment such as susceptible-exposed-infectious-quarantined-recovered (SEIQR) model have been applied. However, in the actual situation, the infection test and quarantine is often delayed from the beginning of the infectious stage.This article presents a delayed SEIQR model to analyze the effect of the delay of quarantine. The latency period (compartment E) and the incubation period were assumed to be 3 days and 5 days, respectively. Considering that the presymptomatic infection ratio is 0.4, the natural decay rate of the number of infectious patients was assumed to be 0.25 days-1. The recovery rate was assumed to be 0.07 days-1 from the typical PCR test positive period. The PCR test positive number in the period from March 10 to July 18 in 2020 in Tokyo area was analyzed. The delay time distribution of the quarantine was derived from the record of the symptom onset date, and was utilized to determine the delay time profile of quarantine in the model.It was found that the major contributor to the infection control was the restraint of social contact. However, the quarantine action also contributed to reducing the reproduction number by the ratio of 0.88 and 0.8 in the period from March 10 to June 3 and from June 4 to July 18, respectively. The delay of quarantine was found to be well correlated to the effectiveness of quarantine. Therefore, a record on a symptom onset date is very important to estimate the effect of quarantine measure. The basic reproduction number was estimated to be 2.56. In view of the presymptomatic infection ratio 0.4, it would be very hard to restrain the expansion of infection only by quarantining the symptomatic patients.

scholarly journals How should we present the epidemic curve for COVID-19?

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jean-Paul R. Soucy ◽  
Sarah A. Buchan ◽  
Kevin A. Brown
Keyword(s):  
Public Health ◽  
Symptom Onset ◽  
Health Authority ◽  
Public Reporting ◽  
Decision Makers ◽  
Public Health Authority ◽  
Onset Date ◽  
The Public ◽  
Epidemic Curve ◽  
Incident Cases

Epidemic curves are used by decision makers and the public to infer the trajectory of the COVID-19 pandemic and to understand the appropriateness of response measures. Symptom onset date is commonly used to date incident cases on the epidemic curve in public health reports and dashboards; however, third-party trackers date cases by the date they were publicly reported by the public health authority. These two curves create very different impressions of epidemic progression. On April 1, 2020, the epidemic curve based on public reporting date for Ontario, Canada showed an accelerating epidemic, whereas the curve based on a proxy variable for symptom onset date showed a rapidly declining epidemic. This illusory downward trend is a feature of epidemic curves anchored using date variables earlier in time than the date a case was publicly reported, such as the symptom onset date. Delays between the onset of symptoms and the detection of a case by the public health authority mean that recent days will always have incomplete case data, creating a downward bias. Public reporting date is not subject to this bias and can be used to visualize real-time epidemic curves meant to inform the public and decision makers.

scholarly journals Analysis and Prediction of the COVID-19 outbreak in Pakistan

2020 ◽  
Author(s):  
Mohsin Ali ◽  
Mudassar Imran ◽  
Adnan Khan
Keyword(s):  
Basic Reproduction Number ◽  
Epidemic Model ◽  
Reproduction Number ◽  
Case Scenario ◽  
Social Distancing ◽  
Epidemic Curve ◽  
The Basic Reproduction Number

AbstractIn this study we estimate the severity of the COVID-19 outbreak in Pakistan prior to and after lock down restrictions were eased. We also project the epidemic curve considering realistic quarantine, social distancing and possible medication scenarios. We use a deterministic epidemic model that includes asymptomatic, quarantined, isolated and medicated population compartments for our analysis. We calculate the basic reproduction number ℛ0 for the pre and post lock down periods, noting that during this time no medication was available.1 The pre-lock down value of ℛ0 is estimated to be 1.07 and the post lock down value is estimated to be 1.86. We use this analysis to project the epidemic curve for a variety of lock down, social distancing and medication scenarios. We note that if no substantial efforts are made to contain the epidemic, it will peak in mid of September, with the maximum projected active cases being close to 700,000. In a realistic, best case scenario, we project that the epidemic peaks in early to mid July with the maximum active cases being around 120000.We note that social distancing measures and medication if available will help flatten the curve, however without the reintroduction of further lock down it would be very difficult to bring ℛ0 below 1. Our study strongly supports the recent WHO recommendation of reintroducing lock downs to control the epidemic.

scholarly journals Imported and autochthonous cases in the dynamics of dengue epidemics in Brazil

2009 ◽  
Vol 43 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Nicolas Degallier ◽  
Charly Favier ◽  
Jean-Philippe Boulanger ◽  
Christophe Menkes
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Epidemiological Data ◽  
Epidemic Curve ◽  
Epidemic Dynamics ◽  
Reproduction Numbers ◽  
Imported Cases ◽  
And Control ◽  
Dengue Epidemic ◽  
The Basic Reproduction Number

OBJECTIVE: To estimate the basic reproduction number (R0) of dengue fever including both imported and autochthonous cases. METHODS: The study was conducted based on epidemiological data of the 2003 dengue epidemic in Brasília, Brazil. The basic reproduction number is estimated from the epidemic curve, fitting linearly the increase of initial cases. Aiming at simulating an epidemic with both autochthonous and imported cases, a "susceptible-infectious-resistant" compartmental model was designed, in which the imported cases were considered as an external forcing. The ratio between R0 of imported versus autochthonous cases was used as an estimator of real R0. RESULTS: The comparison of both reproduction numbers (only autochthonous versus all cases) showed that considering all cases as autochthonous yielded a R0 above one, although the real R0 was below one. The same results were seen when the method was applied on simulated epidemics with fixed R0. This method was also compared to some previous proposed methods by other authors and showed that the latter underestimated R0 values. CONCLUSIONS: It was shown that the inclusion of both imported and autochthonous cases is crucial for the modeling of the epidemic dynamics, and thus provides critical information for decision makers in charge of prevention and control of this disease.

scholarly journals Mathematical Perspective of Covid-19 Pandemic: Disease Extinction Criteria in Deterministic and Stochastic Models

2020 ◽  
Author(s):  
Debadatta Adak ◽  
Abhijit Majumder ◽  
Nandadulal Bairagi
Keyword(s):  
Basic Reproduction Number ◽  
Stochastic System ◽  
Deterministic Model ◽  
Reproduction Number ◽  
Case Fatality ◽  
Deterministic System ◽  
Globally Asymptotically Stable ◽  
Epidemic Curve ◽  
Interior Equilibrium ◽  
The Basic Reproduction Number

AbstractThe world has been facing the biggest virological invasion in the form of Covid-19 pandemic since the beginning of the year 2020. In this paper, we consider a deterministic epidemic model of four compartments classified based on the health status of the populations of a given country to capture the disease progression. A stochastic extension of the deterministic model is further considered to capture the uncertainty or variation observed in the disease transmissibility. In the case of a deterministic system, the disease-free equilibrium will be globally asymptotically stable if the basic reproduction number is less than unity, otherwise, the disease persists. Using Lyapunov functional methods, we prove that the infected population of the stochastic system tends to zero exponentially almost surely if the basic reproduction number is less than unity. The stochastic system has no interior equilibrium, however, its asymptotic solution is shown to fluctuate around the endemic equilibrium of the deterministic system under some parametric restrictions, implying that the infection persists. A case study with the Covid-19 epidemic data of Spain is presented and various analytical results have been demonstrated. The epidemic curve in Spain clearly shows two waves of infection. The first wave was observed during March-April and the second wave started in the middle of July and not completed yet. A real-time basic reproduction number has been given to illustrate the epidemiological status of Spain throughout the study period. Estimated cumulative numbers of confirmed and death cases are 1,613,626 and 42,899, respectively, with case fatality rate 2.66 per cent till the deadly virus is eliminated from Spain.

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