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

2021 ◽  
Vol 17 (9) ◽  
pp. e1009347
Author(s):  
Kris V. Parag
Keyword(s):  
Real Time ◽  
Reproduction Number ◽  
Spatial Scales ◽  
Window Size ◽  
Time Varying ◽  
Markov State ◽  
State Process ◽  
Reproduction Numbers ◽  
Low Incidence ◽  
Estimation Of Time

We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease 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. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where several existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are advantageous for assessing the risk of upcoming waves of infection or the influence of interventions, in real time and at various spatial scales.

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

2020 ◽  
Author(s):  
Kris Varun Parag
Keyword(s):  
Real Time ◽  
Change Point ◽  
Reproduction Number ◽  
Window Size ◽  
Time Varying ◽  
Markov State ◽  
State Process ◽  
Reproduction Numbers ◽  
Low Incidence ◽  
Estimation Of Time

We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease 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, in real time.

scholarly journals COVID-19 Time-varying Reproduction Numbers Worldwide: An Empirical Analysis of Mandatory and Voluntary Social Distancing

2021 ◽  
Author(s):  
Alexander Chudik ◽  
M. Hashem Pesaran ◽  
Alessandro Rebucci
Keyword(s):  
Reproduction Number ◽  
Transmission Rate ◽  
Economic Incentives ◽  
Moment Condition ◽  
Time Varying ◽  
Linear Interaction ◽  
Susceptible Population ◽  
Social Distancing ◽  
Transmission Rates ◽  
Reproduction Numbers

AbstractThis paper estimates time-varying COVID-19 reproduction numbers worldwide solely based on the number of reported infected cases, allowing for under-reporting. Estimation is based on a moment condition that can be derived from an agent-based stochastic network model of COVID-19 transmission. The outcomes in terms of the reproduction number and the trajectory of per-capita cases through the end of 2020 are very diverse. The reproduction number depends on the transmission rate and the proportion of susceptible population, or the herd immunity effect. Changes in the transmission rate depend on changes in the behavior of the virus, re-flecting mutations and vaccinations, and changes in people’s behavior, reflecting voluntary or government mandated isolation. Over our sample period, neither mutation nor vaccination are major factors, so one can attribute variation in the transmission rate to variations in behavior. Evidence based on panel data models explaining transmission rates for nine European countries indicates that the diversity of outcomes resulted from the non-linear interaction of mandatory containment measures, voluntary precautionary isolation, and the economic incentives that gov-ernments provided to support isolation. These effects are precisely estimated and robust to various assumptions. As a result, countries with seemingly different social distancing policies achieved quite similar outcomes in terms of the reproduction number. These results imply that ignoring the voluntary component of social distancing could introduce an upward bias in the estimates of the effects of lock-downs and support policies on the transmission rates.JEL ClassificationD0, F6, C4, I120, E7

scholarly journals Estimation of the Time-Varying Reproduction Number of COVID-19 Outbreak in China

2020 ◽  
Author(s):  
Chong You ◽  
Yuhao Deng ◽  
Wenjie Hu ◽  
Jiarui Sun ◽  
Qiushi Lin ◽  
...  
Keyword(s):  
Standard Deviation ◽  
World Wide ◽  
Reproduction Number ◽  
Infectious Period ◽  
Time Varying ◽  
Exponential Growth Rate ◽  
Reproduction Numbers ◽  
Serial Interval ◽  
Controlled Reproduction ◽  
Novel Coronavirus

BackgroundThe 2019-nCoV outbreak in Wuhan, China has attracted world-wide attention. As of February 11, 2020, a total of 44730 cases of novel coronavirus-infected pneumonia associated with COVID-19 were confirmed by the National Health Commission of China.MethodsThree approaches, namely Poisson likelihood-based method (ML), exponential growth rate-based method (EGR) and stochastic Susceptible-Infected-Removed dynamic model-based method (SIR), were implemented to estimate the basic and controlled reproduction numbers.ResultsA total of 71 chains of transmission together with dates of symptoms onset and 67 dates of infections were identified among 5405 confirmed cases outside Hubei as reported by February 2, 2020. Based on this information, we find the serial interval having an average of 4.41 days with a standard deviation of 3.17 days and the infectious period having an average of 10.91 days with a standard deviation of 3.95 days.ConclusionsThe controlled reproduction number is declining. It is lower than one in most regions of China, but is still larger than one in Hubei Province. Sustained efforts are needed to further reduce the Rc to below one in order to end the current epidemic.

scholarly journals Real-time Nowcasting and Forecasting of COVID-19 Dynamics in England: the first wave?

2020 ◽  
Author(s):  
Paul J Birrell ◽  
Joshua Blake ◽  
Edwin van Leeuwen ◽  
Nick Gent ◽  
Daniela De Angelis ◽  
...  
Keyword(s):  
Real Time ◽  
Reproduction Number ◽  
Mitigation Measures ◽  
Contact Tracing ◽  
The Real ◽  
South West ◽  
Effective Contact ◽  
Ongoing Work ◽  
Reproduction Numbers ◽  
Second Wave

England has been heavily affected by the SARS-CoV-2 pandemic, with severe 'lock-down' mitigation measures now gradually being lifted. The real-time pandemic monitoring presented here has contributed to the evidence informing this pandemic management. Estimates on the 10th May showed lock-down had reduced transmission by 75%, the reproduction number falling from 2.6 to 0.61. This regionally-varying impact was largest in London of 81% (95% CrI: 77%-84%). Reproduction numbers have since slowly increased, and on 19th June the probability that the epidemic is growing was greater than 50% in two regions, South West and London. An estimated 8% of the population had been infected, with a higher proportion in London (17%). The infection-to-fatality ratio is 1.1% (0.9%-1.4%) overall but 17% (14%-22%) among the over-75s. This ongoing work will be key to quantifying any widespread resurgence should accrued immunity and effective contact tracing be insufficient to preclude a second wave.

scholarly journals Nine-month Trend of Time-Varying Reproduction Numbers of COVID-19 in West of Iran

2021 ◽  
Vol 21 (2) ◽  
pp. e00517-e00517
Author(s):  
Ebrahim Rahimi ◽  
Seyed Saeed Hashemi Nazari ◽  
Yaser Mokhayeri ◽  
Asaad Sharhani ◽  
Rasool Mohammadi
Keyword(s):  
Reproduction Number ◽  
Infectious Period ◽  
Time Varying ◽  
Cross Sectional ◽  
Disease Epidemiology ◽  
Infectious Disease Epidemiology ◽  
Contact Rates ◽  
Reproduction Numbers ◽  
Serial Interval ◽  
Log Normal

Background: The basic reproduction number (R0) is an important concept in infectious disease epidemiology and the most important parameter to determine the transmissibility of a pathogen. This study aimed to estimate the nine-month trend of time-varying R of COVID-19 epidemic using the serial interval (SI) and Markov Chain Monte Carlo in Lorestan, west of Iran. Study design: Descriptive study. Methods: This study was conducted based on a cross-sectional method. The SI distribution was extracted from data and log-normal, Weibull, and Gamma models were fitted. The estimation of time-varying R0, a likelihood-based model was applied, which uses pairs of cases to estimate relative likelihood. Results: In this study, Rt was estimated for SI 7-day and 14-day time-lapses from 27 February-14 November 2020. To check the robustness of the R0 estimations, sensitivity analysis was performed using different SI distributions to estimate the reproduction number in 7-day and 14-day time-lapses. The R0 ranged from 0.56 to 4.97 and 0.76 to 2.47 for 7-day and 14-day time-lapses. The doubling time was estimated to be 75.51 days (95% CI: 70.41, 81.41). Conclusions: Low R0 of COVID-19 in some periods in Lorestan, west of Iran, could be an indication of preventive interventions, namely quarantine and isolation. To control the spread of the disease, the reproduction number should be reduced by decreasing the transmission and contact rates and shortening the infectious period.

Real-Time Estimation of Time-Varying Bending Modes Using Fiber Bragg Grating Sensor Arrays

AIAA Journal ◽  
2013 ◽  
Vol 51 (1) ◽  
pp. 178-185 ◽  
Author(s):  
Hao Jiang ◽  
Bartel van der Veek ◽  
Daniel Kirk ◽  
Hector Gutierrez
Keyword(s):  
Fiber Bragg Grating ◽  
Real Time ◽  
Sensor Arrays ◽  
Time Estimation ◽  
Bragg Grating ◽  
Time Varying ◽  
Real Time Estimation ◽  
Bending Modes ◽  
Bragg Grating Sensor ◽  
Estimation Of Time

scholarly journals Estimation of local time-varying reproduction numbers in noisy surveillance data

2021 ◽  
Author(s):  
Wenrui Li ◽  
Katia Bulekova ◽  
Brian Gregor ◽  
Laura F. White ◽  
Eric D. Kolaczyk
Keyword(s):  
Local Time ◽  
Reproduction Number ◽  
Infected Individual ◽  
Accurate Estimation ◽  
Time Varying ◽  
Simulation Studies ◽  
Expected Number ◽  
Reproduction Numbers ◽  
Practical Performance ◽  
Local Transmission

AbstractA valuable metric in understanding infectious disease local dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia.

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