scholarly journals Modelling of the second (and subsequent) waves of the coronavirus epidemic. Spain and Germany as case studies

2020 ◽  
Author(s):  
Francisco de Castro
Keyword(s):  
Case Studies ◽  
Mortality Rates ◽  
Time Dependent ◽  
Seir Model ◽  
Considerable Uncertainty ◽  
Second Wave

AbstractThe first wave of the coronavirus pandemic is waning in many countries. Some of them are starting to lift the confinement measures adopted to control it, but there is considerable uncertainty about if it is too soon and it may cause a second wave of the epidemic. To explore this issue, I fitted a SEIR model with time-dependent transmission and mortality rates to data from Spain and Germany as contrasting case studies. The model reached an excellent fit to the data. I then simulated the post-confinement epidemic under several scenarios. The model shows that (in the absence of a vaccine) a second wave is likely inevitable and will arrive soon, and that a strategy of adaptive confinement may be effective to control it. The model also shows that just a few days delay in starting the confinement may have caused and excess of thousands of deaths in Spain.

scholarly journals Modelling of the second (and subsequent) waves of the coronavirus epidemic. Spain and Germany as case studies

2020 ◽  
Author(s):  
Francisco de Castro
Keyword(s):  
Case Studies ◽  
Mortality Rates ◽  
Time Dependent ◽  
Seir Model ◽  
Considerable Uncertainty ◽  
Second Wave

Abstract The first wave of the coronavirus pandemic is waning in many countries. Some of them are starting to lift the confinement measures adopted to control it, but there is considerable uncertainty about if it is too soon and it may cause a second wave of the epidemic. To explore this issue, I fitted a SEIR model with time-dependent transmission and mortality rates to data from Spain and Germany as contrasting case studies. The model reached an excellent fit to the data. I then simulated the post-confinement epidemic under several scenarios. The model shows that (in the absence of a vaccine) a second wave is likely inevitable and will arrive soon, and that a strategy of adaptive confinement may be effective to control it. The model also shows that just a few days delay in starting the confinement may have caused and excess of thousands of deaths in Spain.

scholarly journals Bayesian small-area modelling of COVID-19 cases and deaths in England and association with key risk factors: a combined space–time Susceptible-Exposed-Infected-Removed (SEIR) model

2020 ◽  
Author(s):  
Benn Sartorius ◽  
Andrew Lawson ◽  
Rachel L. Pullan
Keyword(s):  
Risk Factors ◽  
Small Area ◽  
Mortality Rates ◽  
Space Time ◽  
Spatiotemporal Variability ◽  
Population Mobility ◽  
Seir Model ◽  
Social Distancing ◽  
The Impact ◽  
Second Wave

Abstract Background: COVID-19 caseloads in England appear have passed through a first peak, with evidence of an emerging second wave. To ensure continued response to the epidemic is most effective, it is imperative to better understand both retrospectively and prospectively the geographical evolution of COVID-19 caseloads and deaths, identify localised areas in space-time at significantly higher risk, quantify the impact of changes in localised population mobility (or movement) on caseloads, identify localised risk factors for increased mortality and project the likely course of the epidemic at small-area resolution in coming weeks.Methods: We applied a Bayesian space–time SEIR model to assess the spatiotemporal variability of COVID-19 caseloads (transmission) and deaths at small-area scale in England (Middle Layer Super Output Area [MSOA], 6791 units) and by week (using observed data from week 5 to 34), including key determinants, the modelled transmission dynamics and spatial-temporal random effects. We also estimate the number of cases and deaths at small-area resolution with uncertainty projected forward in time by MSOA (up to week 51 of 2020), the impact mobility reductions (and subsequent easing) have had on COVID-19 caseloads and quantify the impact of key socio-demographic risk factors on COVID-19 related mortality risk by MSOA.Results: Reductions in population mobility due the course of the first lockdown had a significant impact on the reduction of COVID-19 caseloads across England, however local authorities have had a varied rate of reduction in population movement which our model suggest has substantially impacted the geographic heterogeneity in caseloads at small-area scale. The steady gain in population mobility, observed from late April, appears to have contributed to a slowdown in caseload reductions towards late June and subsequent steady increase signalling the start of the second wave. MSOA with higher proportions of elderly (70+ years of age) and elderly living in deprivation, both with very distinct geographic distributions, have a significantly elevated COVID-19 mortality rates.Conclusions: While non-pharmaceutical interventions (that is, reductions in population mobility and social distancing) had a profound impact on the trajectory of the first wave of the COVID-19 outbreak in England, increased population mobility appears to have contributed to the current increase signalling the start of the second wave. A number of contiguous small-areas appear to be at a significant elevated risk of high COVID-19 transmission, many of which are also at increased risk for higher mortality rates. A geographically staggered re-introduction of intensified social distancing measures is advised and limited cross MSOA movement if the magnitude and geographic extent of the second wave is to be reduced.

scholarly journals Estimating effective infection fatality rates during the course of the COVID-19 pandemic in Germany

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Christian Staerk ◽  
Tobias Wistuba ◽  
Andreas Mayr
Keyword(s):  
Age Distribution ◽  
Age Groups ◽  
Case Fatality ◽  
Time Dependent ◽  
Temporary Increase ◽  
International Studies ◽  
Age Group ◽  
Fatality Rate ◽  
Substantial Fraction ◽  
Second Wave

Abstract Background The infection fatality rate (IFR) of the Coronavirus Disease 2019 (COVID-19) is one of the most discussed figures in the context of this pandemic. In contrast to the case fatality rate (CFR), the IFR depends on the total number of infected individuals – not just on the number of confirmed cases. In order to estimate the IFR, several seroprevalence studies have been or are currently conducted. Methods Using German COVID-19 surveillance data and age-group specific IFR estimates from multiple international studies, this work investigates time-dependent variations in effective IFR over the course of the pandemic. Three different methods for estimating (effective) IFRs are presented: (a) population-averaged IFRs based on the assumption that the infection risk is independent of age and time, (b) effective IFRs based on the assumption that the age distribution of confirmed cases approximately reflects the age distribution of infected individuals, and (c) effective IFRs accounting for age- and time-dependent dark figures of infections. Results Effective IFRs in Germany are estimated to vary over time, as the age distributions of confirmed cases and estimated infections are changing during the course of the pandemic. In particular during the first and second waves of infections in spring and autumn/winter 2020, there has been a pronounced shift in the age distribution of confirmed cases towards older age groups, resulting in larger effective IFR estimates. The temporary increase in effective IFR during the first wave is estimated to be smaller but still remains when adjusting for age- and time-dependent dark figures. A comparison of effective IFRs with observed CFRs indicates that a substantial fraction of the time-dependent variability in observed mortality can be explained by changes in the age distribution of infections. Furthermore, a vanishing gap between effective IFRs and observed CFRs is apparent after the first infection wave, while an increasing gap can be observed during the second wave. Conclusions The development of estimated effective IFR and observed CFR reflects the changing age distribution of infections over the course of the COVID-19 pandemic in Germany. Further research is warranted to obtain timely age-stratified IFR estimates, particularly in light of new variants of the virus.

Case studies considering the influence of the time-dependent behaviour of concrete on the serviceability limit state design of composite steel-concrete buildings

2021 ◽  
pp. 137-156
Author(s):  
Alejandro Pérez Caldentey ◽  
John Hewitt ◽  
John van Rooyen ◽  
Graziano Leoni ◽  
Gianluca Ranzi ◽  
...  
Keyword(s):  
Case Studies ◽  
Limit State ◽  
Time Dependent ◽  
Commercial Building ◽  
Time Effects ◽  
Concrete Buildings ◽  
Time Dependent Behaviour ◽  
Dependent Behaviour ◽  
Composite Steel

<p>This chapter presents a number of case studies that deal with the service design of composite steel-concrete buildings associated with the time-dependent behaviour of the concrete. The particular focus of this chapter is to outline key design aspects that need to be accounted for in design and that are influenced by concrete time effects. The first case study provides an overview of the design considerations related to the time-dependent column shortening in typical multi-storey buildings by considering the layout of the Intesa Sanpaolo Headquarters in Turin as reference. The second case study focuses on a composite floor of a commercial building constructed in Australia and it provides an overview of the conceptual design used to select the steel beam framing arrangement to support the composite floor system while accounting for concrete cracking and time effects. The third case study deals with the Quay Quarter Tower that has been designed for the repurposing of an existing 50-year old building in Australia while accounting for the time-dependent interaction between the existing and the new concrete components of the building.</p>

scholarly journals Agent-Based Simulation for Evaluation of Contact-Tracing Policies Against the Spread of SARS-CoV-2

2020 ◽  
Author(s):  
Martin Bicher ◽  
Claire Rippinger ◽  
Christoph Urach ◽  
Dominik Brunmeir ◽  
Uwe Siebert ◽  
...  
Keyword(s):  
Case Studies ◽  
Exploratory Analysis ◽  
Contact Tracing ◽  
Agent Based Simulation ◽  
Effective Measure ◽  
Agent Based ◽  
The World ◽  
Different Characteristics ◽  
Second Wave ◽  
Restrictive Policy

AbstractThe decline of active COVID-19 cases in many countries in the world has proved that lockdown policies are indeed a very effective measure to stop the exponential spread of the virus. Still, the danger of a second wave of infections is omnipresent and it is clear, that every policy of the lockdown has to be carefully evaluated and possibly replaced by a different, less restrictive policy, before it can be lifted. Tracing of contacts and consequential tracing and breaking of infection-chains is a promising and comparably straightforward strategy to help containing the disease, although its precise impact on the epidemic is unknown. In order to quantify the benefits of tracing and similar policies we developed an agent-based model that not only validly depicts the spread of the disease, but allows for exploratory analysis of containment policies. We will describe our model and perform case studies in which we use the model to quantify impact of contact tracing in different characteristics and draw valuable conclusions about contact tracing policies in general.

scholarly journals COVID-19 hospital activity and in-hospital mortality during the first and second waves of the pandemic in England: an observational study

Thorax ◽  
2021 ◽  
pp. thoraxjnl-2021-218025
Author(s):  
William K Gray ◽  
Annakan V Navaratnam ◽  
Jamie Day ◽  
Julia Wendon ◽  
Tim W R Briggs
Keyword(s):  
Hospital Mortality ◽  
Hospital Stay ◽  
Ethnic Groups ◽  
Mortality Rates ◽  
Data Set ◽  
Black African ◽  
Substantial Decline ◽  
The Relationship ◽  
Second Wave

IntroductionWe aimed to examine the profile of, and outcomes for, all people hospitalised with COVID-19 across the first and second waves of the pandemic in England.MethodsThis was an exploratory retrospective analysis of observational data from the Hospital Episode Statistics data set for England. All patients aged ≥18 years in England with a diagnosis of COVID-19 who had a hospital stay that was completed between 1 March 2020 and 31 March 2021 were included. In-hospital mortality was the primary outcome of interest. The second wave was identified as starting on 1 September 2020. Multilevel logistic regression modelling was used to investigate the relationship between mortality and demographic, comorbidity and temporal covariates.ResultsOver the 13 months, 374 244 unique patients had a diagnosis of COVID-19 during a hospital stay, of whom 93 701 (25%) died in hospital. Adjusted mortality rates fell from 40%–50% in March 2020 to 11% in August 2020 before rising to 21% in January 2021 and declining steadily to March 2021. Improvements in mortality rates were less apparent in older and comorbid patients. Although mortality rates fell for all ethnic groups from the first to the second wave, declines were less pronounced for Bangladeshi, Indian, Pakistani, other Asian and black African ethnic groups.ConclusionsThere was a substantial decline in adjusted mortality rates during the early part of the first wave which was largely maintained during the second wave. The underlying reasons for consistently higher mortality risk in some ethnic groups merits further study.

scholarly journals Understanding the Spreading Patterns of COVID-19 in UK and Its Impact on Exit Strategies

2020 ◽  
Author(s):  
Maziar Nekovee
Keyword(s):  
Exponential Growth ◽  
Slow Growth ◽  
Contact Tracing ◽  
Mixing Models ◽  
Epidemic Spreading ◽  
Seir Model ◽  
Infected Population ◽  
Exit Strategies ◽  
Spatial Epidemic ◽  
Second Wave

Prior to lockdown the spread of COVID-19 in UK is found to be exponential, with an exponent &alpha;=0.207 In case of COVID-19 this spreading patterns is quantitatively better described with mobility-driven SIR-SEIR model [2] rather than the homogenous mixing models Lockdown has dramatically slowed down the spread of COVID-19 in UK, and even more significantly has changed the growth in the total number of infected from exponential to quadratic. This significant change is due a transition from a mobility-driven epidemic spreading to a spatial epidemic which is dominated by slow growth of spatially isolated clusters of infected population. Our results strongly indicated that, to avoid a return to exponential growth of COVID-19 (also known as &ldquo;second wave&rdquo;) mobility restrictions should not be prematurely lifted. Instead mobility should be kept restricted while new measures, such as wearing mask and contact tracing, get implemented in order to allow a safe exit from lockdown.

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