scholarly journals Predicted effects of summer holidays and seasonality on the SARS-Cov-2 epidemic in France

2020 ◽  
Author(s):  
Louis Duchemin ◽  
Philippe Veber ◽  
Mathilde Paris ◽  
Bastien Boussau
Keyword(s):  
Death Rate ◽  
Reproduction Number ◽  
Seir Model ◽  
Low Level ◽  
Death Toll ◽  
Reproduction Numbers ◽  
Second Wave ◽  
Epidemic Wave ◽  
Do So

1AbstractThe SARS-CoV-2 epidemic in France has had a large death toll. It has not affected all regions similarly, since the death rate can vary several folds between regions where the epidemic has remained at a low level and regions where it got an early burst. The epidemic has been slowed down by a lockdown that lasted for almost eight weeks, and individuals can now move between metropolitan French regions without restriction. In this report we investigate the effect on the epidemic of summer holidays, during which millions of individuals will move between French regions. Additionally, we evaluate the effect of strong or weak seasonality and of several values for the reproduction number on the epidemic, in particular on the timing, the height and the spread of a second wave. To do so, we extend a SEIR model to simulate the effect of summer migrations between regions on the number and distribution of new infections. We find that the model predicts little effect of summer migrations on the epidemic. However, all the reproduction numbers above 1.0 and the seasonality parameters we tried result in a second epidemic wave, with a peak date that can vary between October 2020 and April 2021. If the sanitary measures currently in place manage to keep the reproduction number below 1.0, the second wave will be avoided. If they keep the reproduction number at a low value, for instance at 1.1 as in one of our simulations, the second wave is flattened and could be similar to the first wave.

scholarly journals Modelling the Effectiveness of Epidemic Control Measures in Preventing the Transmission of COVID-19 in Malaysia

2020 ◽  
Vol 17 (15) ◽  
pp. 5509 ◽  
Author(s):  
Balvinder Singh Gill ◽  
Vivek Jason Jayaraj ◽  
Sarbhan Singh ◽  
Sumarni Mohd Ghazali ◽  
Yoon Ling Cheong ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Model Calibration ◽  
Reproduction Number ◽  
Close Contact ◽  
Movement Control ◽  
Control Measures ◽  
Seir Model ◽  
Epidemic Curve ◽  
Reproduction Numbers ◽  
Control Order

Malaysia is currently facing an outbreak of COVID-19. We aim to present the first study in Malaysia to report the reproduction numbers and develop a mathematical model forecasting COVID-19 transmission by including isolation, quarantine, and movement control measures. We utilized a susceptible, exposed, infectious, and recovered (SEIR) model by incorporating isolation, quarantine, and movement control order (MCO) taken in Malaysia. The simulations were fitted into the Malaysian COVID-19 active case numbers, allowing approximation of parameters consisting of probability of transmission per contact (β), average number of contacts per day per case (ζ), and proportion of close-contact traced per day (q). The effective reproduction number (Rt) was also determined through this model. Our model calibration estimated that (β), (ζ), and (q) were 0.052, 25 persons, and 0.23, respectively. The (Rt) was estimated to be 1.68. MCO measures reduce the peak number of active COVID-19 cases by 99.1% and reduce (ζ) from 25 (pre-MCO) to 7 (during MCO). The flattening of the epidemic curve was also observed with the implementation of these control measures. We conclude that isolation, quarantine, and MCO measures are essential to break the transmission of COVID-19 in Malaysia.

scholarly journals A Second Wave? What Do People Mean By COVID Waves? – A Working Definition of Epidemic Waves

2021 ◽  
Author(s):  
Stephen X. Zhang ◽  
Francisco Arroyo Marioli ◽  
Renfei Gao
Keyword(s):  
Common Ground ◽  
Reproduction Number ◽  
Resource Planning ◽  
Healthcare Organizations ◽  
The Public ◽  
Working Definition ◽  
Public Data ◽  
Definition Of ◽  
Second Wave ◽  
Epidemic Wave

AbstractPolicymakers and researchers describe the COVID-19 epidemics by waves without a common vocabulary on what constitutes an epidemic wave, either in terms of a working definition or operationalization, causing inconsistencies and confusions. A working definition and operationalization can be helpful to characterize and communicate about epidemics. We propose a working definition of epidemic waves in the ongoing COVID-19 pandemic and an operationalization based on the public data of the effective reproduction number R. Our operationalization characterizes the numbers and durations of waves (upward and downward) in 178 countries and reveals patterns that can enable healthcare organizations and policymakers to make better description and assessment of the COVID crisis to make more informed resource planning, mobilization, and allocation temporally in the continued COVID-19 pandemic.One Sentence SummaryA working definition and operationalization of waves to enable common ground to understand and communicate COVID-19 crisis.

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 An Estimation of Reproduction Number of SARS-CoV-2 by Age Class for Age Classes in Japan

2021 ◽  
Author(s):  
Junko Kurita ◽  
Takahide Hata ◽  
Tamie Sugawara ◽  
Yasushi Ohkusa ◽  
Atsuko Hata
Keyword(s):  
Elderly People ◽  
Reproduction Number ◽  
Nursing Schools ◽  
Age Classes ◽  
Infected People ◽  
Age Class ◽  
Source Of Infection ◽  
Reproduction Numbers ◽  
Infection Source ◽  
Do So

AbstractBackgroundInfectiousness of COVID-19 by age class inferred from the infection source and by infected people might be different. However, studies of such infectiousness have not been reported.ObjectThe object of this study was estimation of reproduction numbers by age class of the source of infection and the infected persons. To do so, after examining a new procedure for reproduction number estimation, we checked infected places by age class of the source of infection and the infected persons.MethodWe ignore patients who infected no one because their reliability might be lower than that of patients who infected more than one person. We estimated the reproduction number from the histogram of the number of the infected people by the same patient, assuming that the histogram follows an exponential distribution.Discussion and ConclusionThe obtained results demonstrated that the effective reproduction numbers for infection from children were very low. They were higher among adult and elderly people than among the same age class. Moreover, although the highest and second-highest infected places were ‘other’ and ‘at home’ with some exceptions, the data for infection at hospitals were remarkable among adults and elderly people. Among elderly people, infection at facilities for elderly was also high. Infections at nursing schools, schools, restaurants, and entertainment venues at night were negligible.

scholarly journals Estimating the effect of mobility on SARS-CoV-2 transmission during the first and second wave of the COVID-19 epidemic in Switzerland: a population-based study

2021 ◽  
Author(s):  
Adrian Lison ◽  
Joel Persson ◽  
Nicolas Banholzer ◽  
Stefan Feuerriegel
Keyword(s):  
Reproduction Number ◽  
Human Mobility ◽  
Population Based ◽  
Mobile Phone Data ◽  
Mobility Data ◽  
Daily Travel Distance ◽  
Mode Of Transport ◽  
Second Wave ◽  
Epidemic Wave ◽  
Daily Travel

The effect of mobility and its value for surveillance in different waves of the COVID–19 epidemic is still unclear. In this study, we compared the role of mobility during the first and second epidemic wave in Switzerland by analysing the link between daily travel distances and the effective reproduction number Rt of SARS–CoV–2. Here we used aggregated mobile phone data from a representative panel survey of the Swiss population to measure human mobility. We estimated the effects of reductions in daily travel distance on Rt via a regression model. We compared mobility effects between the first wave (March 2–April 7, 2020) and the second wave (October 1–December 10, 2020) across mode of transport, travel purpose, sociodemographic subgroup and movement radius. We found that human mobility was associated with the effective reproduction number of SARS–CoV–2 during both the first and second epidemic wave in Switzerland. The estimated relative effects of mobility were similar in both waves for all modes of transport, travel purposes, and sociodemographic subgroups but differed by movement radius. Moreover, smaller mobility reductions in the second wave translated into smaller overall reductions of Rt. Mobility data from mobile phones have a continued potential to support real–time surveillance of COVID–19 during epidemic waves.

scholarly journals Spatial variability in reproduction number and doubling time across two waves of the COVID-19 pandemic in South Korea, February to July 2020

2020 ◽  
Author(s):  
Eunha Shim ◽  
Amna Tariq ◽  
Gerardo Chowell
Keyword(s):  
South Korea ◽  
Doubling Time ◽  
Reproduction Number ◽  
Control Measures ◽  
List Type ◽  
Transmission Potential ◽  
Reproduction Numbers ◽  
Gyeonggi Province ◽  
The Government ◽  
Second Wave

AbstractObjectivesIn South Korea, 13,745 cases of coronavirus disease (COVID-19) have been reported as of 19 July, 2020. To examine the spatiotemporal changes in the transmission potential, we present regional estimates of the doubling time and reproduction number (Rt) of COVID-19 in the country.MethodsDaily series of confirmed COVID-19 cases in the most affected regions were extracted from publicly available sources. We employed established mathematical and statistical methods to investigate the time-varying reproduction numbers of the COVID-19 in Korea and its doubling time, respectively.ResultsAt the regional level, Seoul and Gyeonggi Province have experienced the first peak of COVID-19 in early March, followed by the second wave in early June, with Rt exceeding 3.0 and mean doubling time ranging from 3.6 to 10.1 days. As of 19 July, 2020, Gyeongbuk Province and Daegu are yet to experience a second wave of the disease, where the mean Rt reached 3.5-4.4 and doubling time ranging from 2.8 to 4.6 days during the first wave.ConclusionsOur findings support the effectiveness of control measures against COVID-19 in Korea. However, the easing of the restrictions imposed by the government in May 2020 facilitated a second wave in the greater Seoul area.HighlightsSouth Korea has experienced two spatially heterogenous waves of COVID-19.Seoul and Gyeonggi Province experienced two waves of COVID-19 in March and June.In the densely populated Seoul and nearby areas, reproduction numbers exceeded 3.0.The easing of the social distancing measures resulted in the second wave.

scholarly journals Projections and early-warning signals of a second wave of the COVID-19 epidemic in Illinois

2020 ◽  
Author(s):  
Zachary J. Weiner ◽  
George N. Wong ◽  
Ahmed Elbanna ◽  
Alexei V. Tkachenko ◽  
Sergei Maslov ◽  
...  
Keyword(s):  
Early Warning ◽  
Reproduction Number ◽  
Warning Signal ◽  
Infection Model ◽  
Effective Suppression ◽  
Reproduction Numbers ◽  
Age Of Infection ◽  
Second Wave ◽  
Derivatives Of ◽  
Doubling Times

We present two different scenarios for a second wave of the COVID-19 epidemic in Illinois and simulate them using our previously described age-of-infection model, calibrated to real-time hospital and deaths data. In the first scenario we assume that the parameters of the second wave in Illinois would be similar to those currently observed in other states such as Arizona, Florida, and Texas. We estimate doubling times of hospitalizations and test positivity in all states with relevant publicly available data and calculate the corresponding effective reproduction numbers for Illinois. These parameters are remarkably consistent in states with rapidly growing epidemics. We conjecture that the emergence of the second wave of the epidemic in these states can be attributed to superspreading events at large parties, crowded bars, and indoor dining. In our second, more optimistic scenario we assume changes in Illinois state policy would result in successful mitigation of superspreading events and thus would lower the effective reproduction number to the value observed in late June 2020. In this case our calculations show effective suppression of the second wave in Illinois. Our analysis also suggests that the logarithmic time derivatives of COVID-19 hospitalizations and case positivity can serve as a simple but strong early-warning signal of the onset of a second wave.

COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling (Preprint)

2020 ◽  
Author(s):  
Eduardo Atem De Carvalho ◽  
Rogerio Atem De Carvalho
Keyword(s):  
New York ◽  
Reproduction Number ◽  
Moving Average ◽  
Real Data ◽  
Time Dependent ◽  
Initial Value ◽  
Health Authorities ◽  
Reproduction Numbers ◽  
Effect Estimation ◽  
The Impact

BACKGROUND Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. OBJECTIVE This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. METHODS The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. RESULTS Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. CONCLUSIONS We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent, incubation period-independent Reproduction Numbers (Rt). We also demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

scholarly journals Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study

2021 ◽  
Vol 10 (6) ◽  
pp. 1256
Author(s):  
Ko Nakajo ◽  
Hiroshi Nishiura
Keyword(s):  
Model Comparison ◽  
Reproduction Number ◽  
State Of Emergency ◽  
Illness Onset ◽  
Epidemic Dynamics ◽  
Osaka Prefecture ◽  
Time Of Infection ◽  
Single Intervention ◽  
Second Wave ◽  
Combined Interventions

Estimation of the effective reproduction number, R(t), of coronavirus disease (COVID-19) in real-time is a continuing challenge. R(t) reflects the epidemic dynamics based on readily available illness onset data, and is useful for the planning and implementation of public health and social measures. In the present study, we proposed a method for computing the R(t) of COVID-19, and applied this method to the epidemic in Osaka prefecture from February to September 2020. We estimated R(t) as a function of the time of infection using the date of illness onset. The epidemic in Osaka came under control around 2 April during the first wave, and 26 July during the second wave. R(t) did not decline drastically following any single intervention. However, when multiple interventions were combined, the relative reductions in R(t) during the first and second waves were 70% and 51%, respectively. Although the second wave was brought under control without declaring a state of emergency, our model comparison indicated that relying on a single intervention would not be sufficient to reduce R(t) < 1. The outcome of the COVID-19 pandemic continues to rely on political leadership to swiftly design and implement combined interventions capable of broadly and appropriately reducing contacts.

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