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.

scholarly journals Dynamics Analysis of a Viral Infection Model with a General Standard Incidence Rate

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Yu Ji ◽  
Muxuan Zheng
Keyword(s):  
Viral Infection ◽  
Incidence Rate ◽  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Infection Model ◽  
Asymptotically Stable ◽  
Globally Asymptotically Stable ◽  
Reproduction Numbers ◽  
The Basic Reproduction Number ◽  
General Standard

The basic viral infection models, proposed by Nowak et al. and Perelson et al., respectively, have been widely used to describe viral infection such as HBV and HIV infection. However, the basic reproduction numbers of the two models are proportional to the number of total cells of the host's organ prior to the infection, which seems not to be reasonable. In this paper, we formulate an amended model with a general standard incidence rate. The basic reproduction number of the amended model is independent of total cells of the host’s organ. When the basic reproduction numberR0<1, the infection-free equilibrium is globally asymptotically stable and the virus is cleared. Moreover, ifR0>1, then the endemic equilibrium is globally asymptotically stable and the virus persists in the host.

GLOBAL ANALYSIS OF A VIRAL INFECTION MODEL WITH APPLICATION TO HBV INFECTION

2010 ◽  
Vol 18 (02) ◽  
pp. 325-337 ◽  
Author(s):  
YU JI ◽  
LEQUAN MIN ◽  
YONGAN YE
Keyword(s):  
Viral Infection ◽  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Global Analysis ◽  
Hiv Infections ◽  
Hbv Infection ◽  
Infection Model ◽  
Globally Asymptotically Stable ◽  
Reproduction Numbers ◽  
The Basic Reproduction Number

The basic models of within-host viral infection, proposed by Nowak and May2 and Perelson and Nelson,5 have been widely used in the studies of HBV and HIV infections. The basic reproduction numbers of the two models are proportional to the number of total cells of the host's organ prior to the infection. In this paper, we formulate an amended Perelson and Nelson's model with standard incidence. The basic reproduction number of the amended model is independent of total cells of the host's organ. If the basic reproduction number R0 < 1, then the infection-free equilibrium is globally asymptotically stable and the virus is cleared; if R0 > 1, then the virus persists in the host, and solutions approach either an endemic equilibrium or a periodic orbit. Numerical simulations of this model agree well with the clinical HBV infection data. This can provide a possible interpretation for the viral oscillation behaviors, which were observed in chronic HBV infection patients.

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 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 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.

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.

Data-driven tools for assessing and combating COVID-19 outbreaks in Brazil based on analytics and statistical methods

2021 ◽  
Keyword(s):  
Data Analytics ◽  
Exploratory Data Analysis ◽  
Reproduction Number ◽  
Healthcare Systems ◽  
Regional Level ◽  
Data Driven ◽  
Effective Response ◽  
Reproduction Numbers ◽  
Exploratory Data ◽  
Using Data

The COVID-19 pandemic is one of the worst public health crises in Brazil and the world that has ever been faced. One of the main challenges that the healthcare systems have when decision-making is that the protocols tested in other epidemics do not guarantee success in controlling the spread of COVID-19, given its complexity. In this context, an effective response to guide the competent authorities in adopting public policies to fight COVID-19 depends on thoughtful analysis and effective data visualization, ideally based on different data sources. In this paper, we discuss and provide tools that can be helpful using data analytics to respond to the COVID-19 outbreak in Recife, Brazil. We use exploratory data analysis and inferential study to determine the trend changes in COVID-19 cases and their effective or instantaneous reproduction numbers. According to the data obtained of confirmed COVID-19 cases disaggregated at a regional level in this zone, we note a heterogeneous spread in most megaregions in Recife, Brazil. When incorporating quarantines decreed, effectiveness is detected in the regions. Our results indicate that the measures have effectively curbed the spread of the disease in Recife, Brazil. However, other factors can cause the effective reproduction number to not be within the expected ranges, which must be further studied.

scholarly journals Impression Offsetting as an Early Warning Signal of Low CEO Confidence in Acquisitions

2019 ◽  
Vol 62 (5) ◽  
pp. 1307-1332 ◽  
Author(s):  
Daniel L. Gamache ◽  
Gerry McNamara ◽  
Scott D. Graffin ◽  
Jason Kiley ◽  
Jerayr Haleblian ◽  
...  
Keyword(s):  
Early Warning ◽  
Warning Signal ◽  
Early Warning Signal

scholarly journals Quantifying the relationship between lockdowns, mobility, and effective reproduction number (Rt) during the COVID-19 pandemic in the Greater Toronto Area

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Christopher Dainton ◽  
Alexander Hay
Keyword(s):  
Public Health ◽  
Secular Trend ◽  
Reproduction Number ◽  
Time Lag ◽  
Correlation Coefficients ◽  
Greater Toronto Area ◽  
Global Mobility ◽  
The Relationship ◽  
Second Wave ◽  
Over Time

Abstract Background The effectiveness of lockdowns in mitigating the spread of COVID-19 has been the subject of intense debate. Data on the relationship between public health restrictions, mobility, and pandemic growth has so far been conflicting. Objective We assessed the relationship between public health restriction tiers, mobility, and COVID-19 spread in five contiguous public health units (PHUs) in the Greater Toronto Area (GTA) in Ontario, Canada. Methods Weekly effective reproduction number (Rt) was calculated based on daily cases in each of the five GTA public health units between March 1, 2020, and March 19, 2021. A global mobility index (GMI) for each PHU was calculated using Google Mobility data. Segmented regressions were used to assess changes in the behaviour of Rt over time. We calculated Pearson correlation coefficients between GMI and Rt for each PHU and mobility regression coefficients for each mobility variable, accounting for time lag of 0, 7, and 14 days. Results In all PHUs except Toronto, the most rapid decline in Rt occurred in the first 2 weeks of the first province-wide lockdown, and this was followed by a slight trend to increased Rt as restrictions decreased. This trend reversed in all PHUs between September 6th and October 10th after which Rt decreased slightly over time without respect to public health restriction tier. GMI began to increase in the first wave even before restrictions were decreased. This secular trend to increased mobility continued into the summer, driven by increased mobility to recreational spaces. The decline in GMI as restrictions were reintroduced coincides with decreasing mobility to parks after September. During the first wave, the correlation coefficients between global mobility and Rt were significant (p < 0.01) in all PHUs 14 days after lockdown, indicating moderate to high correlation between decreased mobility and decreased viral reproduction rates, and reflecting that the incubation period brings in a time-lag effect of human mobility on Rt. In the second wave, this relationship was attenuated, and was only significant in Toronto and Durham at 14 days after lockdown. Conclusions The association between mobility and COVID-19 spread was stronger in the first wave than the second wave. Public health restriction tiers did not alter the existing secular trend toward decreasing Rt over time.

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