Metastable states in plateaus and multi-wave epidemic dynamics of Covid-19 spreading in Italy

AbstractThe control of Covid 19 epidemics by public health policy in Italy during the first and the second epidemic waves has been driven by using reproductive number Rt(t) to identify the supercritical (percolative), the subcritical (arrested), separated by the critical regime. Here we show that to quantify the Covid-19 spreading rate with containment measures there is a need of a 3D expanded parameter space phase diagram built by the combination of Rt(t) and doubling time Td(t). In this space we identify the Covid-19 dynamics in Italy and its administrative Regions. The supercritical regime is mathematically characterized by (i) the power law of Td vs. [Rt(t) − 1] and (ii) the exponential behaviour of Td vs. time, either in the first and in the second wave. The novel 3D phase diagram shows clearly metastable states appearing before and after the second wave critical regime. for loosening quarantine and tracing of actives cases. The metastable states are precursors of the abrupt onset of a next nascent wave supercritical regime. This dynamic description allows epidemics predictions needed by policymakers interested to point to the target "zero infections" with the elimination of SARS-CoV-2, using the Finding mobile Tracing policy joint with vaccination-campaign, in order to avoid the emergence of recurrent new variants of SARS-CoV-2 virus, accompined by recurrent long lockdowns, with large economical losses, and large number of fatalities.

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Dynamics of Contagion and Submerged Infections During the Second Wave of The SARS-CoV-2 Epidemic in the Italian Regions

10.21203/rs.3.rs-130717/v1 ◽

2021 ◽

Author(s):

Giulia Cereda ◽

Cecilia Viscardi ◽

Luca Gherardini ◽

Fabrizia Mealli ◽

Michela Baccini

Keyword(s):

Local Level ◽

Healthcare Services ◽

Reproductive Number ◽

Epidemic Dynamics ◽

The North ◽

Italian Regions ◽

Short Time ◽

Regional Elections ◽

Second Wave ◽

Epidemic Wave

Abstract After the SARS-CoV-2 outbreak in spring 2020, Italy faced a second epidemic wave in autumn. Using a SIRD model calibrated on COVID19-related deaths, we describe the regional epidemic dynamics from August to November 2020. We explore the time-varying reproductive number, R0(t), and quantify the number of infections, included their submerged portion, under different infection fatality rate scenarios. Results indicate that during the second epidemic wave, R0(t) changed over time heterogeneously across regions, with some important common elements including a mid-October peak and a decline during November, which suggest the possible role in inflating or deflating the contagion rate of specific events (e.g. schools reopening, regional elections) and of the restrictions imposed at the national and local level to reduce the infection spread. Despite the decline of R0(t) in most regions, the prevalence of circulating infections estimated at the end of the study period was not negligible, in particular in the North of the country. This suggests that even small increases of R0(t) in December may lead in a short time to unsustainable levels of contagion spread, depending on the regional supply of hospital and ICU beds and healthcare services throughout the territory.

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

Journal of Clinical Medicine ◽

10.3390/jcm10061256 ◽

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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A time-domain phase diagram of metastable states in a charge ordered quantum material

Nature Communications ◽

10.1038/s41467-021-22646-7 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Jan Ravnik ◽

Michele Diego ◽

Yaroslav Gerasimenko ◽

Yevhenii Vaskivskyi ◽

Igor Vaskivskyi ◽

...

Keyword(s):

Phase Diagram ◽

Time Domain ◽

Metastable States ◽

Scanning Tunneling ◽

Photon Density ◽

Tunneling Microscopy ◽

Equilibrium Conditions ◽

Time Resolved ◽

A Charge ◽

Non Equilibrium

AbstractMetastable self-organized electronic states in quantum materials are of fundamental importance, displaying emergent dynamical properties that may be used in new generations of sensors and memory devices. Such states are typically formed through phase transitions under non-equilibrium conditions and the final state is reached through processes that span a large range of timescales. Conventionally, phase diagrams of materials are thought of as static, without temporal evolution. However, many functional properties of materials arise as a result of complex temporal changes in the material occurring on different timescales. Hitherto, such properties were not considered within the context of a temporally-evolving phase diagram, even though, under non-equilibrium conditions, different phases typically evolve on different timescales. Here, by using time-resolved optical techniques and femtosecond-pulse-excited scanning tunneling microscopy (STM), we track the evolution of the metastable states in a material that has been of wide recent interest, the quasi-two-dimensional dichalcogenide 1T-TaS2. We map out its temporal phase diagram using the photon density and temperature as control parameters on timescales ranging from 10−12 to 103 s. The introduction of a time-domain axis in the phase diagram enables us to follow the evolution of metastable emergent states created by different phase transition mechanisms on different timescales, thus enabling comparison with theoretical predictions of the phase diagram, and opening the way to understanding of the complex ordering processes in metastable materials.

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Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait Based on Daily Observation and Basic Reproduction Number Dynamics

Kuwait Journal of Science ◽

10.48129/kjs.splcov.14501 ◽

2021 ◽

Author(s):

Kayode Oshinubi ◽

◽

Fahimah Al-Awadhi ◽

Mustapha Rachdi ◽

Jacques Demongeot ◽

...

Keyword(s):

Basic Reproduction Number ◽

Reproduction Number ◽

Moving Average ◽

Arima Model ◽

Exponential Model ◽

Spreading Rate ◽

Support Vector ◽

Global Threat ◽

Log Linear ◽

Second Wave

Coronavirus (COVID-19) has continued to be a global threat to public health. When the coronavirus pandemic began early in 2020, experts wondered if there would be waves of cases, a pattern seen in other virus pandemics. The overall pattern so far has been one of increasing cases of COVID-19 followed by a decline, and we observed a second wave of increased cases and yet we are still exploring this pandemic. Hence, updating the prediction model for the new cases of COVID-19 for different waves is essential to monitor the spreading of the virus and control the disease. Time series models have extensively been considered as the convenient methods to predict the prevalence or spreading rate of the disease. This study, therefore, aimed to apply the Autoregressive Integrated Moving Average (ARIMA) modelling approach for predicting new cases of coronavirus (COVID-19). We propose a deterministic method to predict the basic reproduction number Ro of first and second wave transition of COVID-19 cases in Kuwait and also to forecast the daily new cases and deaths of the pandemic in the country. Forecasting has been done using ARIMA model, Exponential smoothing model, Holt’s method, Prophet forecasting model and machine learning models like log-linear, polynomial and support vector regressions. The results presented aligned with other methods used to predict Ro in first and second waves and the forecasting clearly shows the trend of the pandemic in Kuwait. The deterministic prediction of Ro is a good forecasting tool available during the exponential phase of the contagion, which shows an increasing trend during the beginning of the first and second waves of the pandemic in Kuwait. The results show that support vector regression has achieved the best performance for prediction while a simple exponential model without trend gives good optimal results for forecasting of Kuwait COVID-19 data.

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Estimating the burden of SARS-CoV-2 in France

Science ◽

10.1126/science.abc3517 ◽

2020 ◽

Vol 369 (6500) ◽

pp. 208-211 ◽

Cited By ~ 208

Author(s):

Henrik Salje ◽

Cécile Tran Kiem ◽

Noémie Lefrancq ◽

Noémie Courtejoie ◽

Paolo Bosetti ◽

...

Keyword(s):

Intensive Care ◽

Severe Acute Respiratory Syndrome ◽

Credible Interval ◽

Control Measures ◽

Reproductive Number ◽

Infected Population ◽

Population Immunity ◽

The Impact ◽

Second Wave ◽

Death Data

France has been heavily affected by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and went into lockdown on 17 March 2020. Using models applied to hospital and death data, we estimate the impact of the lockdown and current population immunity. We find that 2.9% of infected individuals are hospitalized and 0.5% of those infected die (95% credible interval: 0.3 to 0.9%), ranging from 0.001% in those under 20 years of age to 8.3% in those 80 years of age or older. Across all ages, men are more likely to be hospitalized, enter intensive care, and die than women. The lockdown reduced the reproductive number from 2.90 to 0.67 (77% reduction). By 11 May 2020, when interventions are scheduled to be eased, we project that 3.5 million people (range: 2.1 million to 6.0 million), or 5.3% of the population (range: 3.3 to 9.3%), will have been infected. Population immunity appears to be insufficient to avoid a second wave if all control measures are released at the end of the lockdown.

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Strong spatial embedding of social networks generates nonstandard epidemic dynamics independent of degree distribution and clustering

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1910181117 ◽

2020 ◽

Vol 117 (38) ◽

pp. 23636-23642

Author(s):

David J. Haw ◽

Rachael Pung ◽

Jonathan M. Read ◽

Steven Riley

Keyword(s):

Spatial Correlation ◽

Dynamic Models ◽

Peak Height ◽

Single Parameter ◽

Clustering Coefficient ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Human Pathogens ◽

Final Size ◽

Epidemic Dynamics

Some directly transmitted human pathogens, such as influenza and measles, generate sustained exponential growth in incidence and have a high peak incidence consistent with the rapid depletion of susceptible individuals. Many do not. While a prolonged exponential phase typically arises in traditional disease-dynamic models, current quantitative descriptions of nonstandard epidemic profiles are either abstract, phenomenological, or rely on highly skewed offspring distributions in network models. Here, we create large socio-spatial networks to represent contact behavior using human population-density data, a previously developed fitting algorithm, and gravity-like mobility kernels. We define a basic reproductive numberR0for this system, analogous to that used for compartmental models. Controlling forR0, we then explore networks with a household–workplace structure in which between-household contacts can be formed with varying degrees of spatial correlation, determined by a single parameter from the gravity-like kernel. By varying this single parameter and simulating epidemic spread, we are able to identify how more frequent local movement can lead to strong spatial correlation and, thus, induce subexponential outbreak dynamics with lower, later epidemic peaks. Also, the ratio of peak height to final size was much smaller when movement was highly spatially correlated. We investigate the topological properties of our networks via a generalized clustering coefficient that extends beyond immediate neighborhoods, identifying very strong correlations between fourth-order clustering and nonstandard epidemic dynamics. Our results motivate the observation of both incidence and socio-spatial human behavior during epidemics that exhibit nonstandard incidence patterns.

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Phase diagram with a region of liquid carbon-diamond metastable states

Technical Physics Letters ◽

10.1134/s1063785010060210 ◽

2010 ◽

Vol 36 (6) ◽

pp. 559-562 ◽

Cited By ~ 9

Author(s):

A. Yu. Basharin ◽

V. S. Dozhdikov ◽

A. V. Kirillin ◽

M. A. Turchaninov ◽

L. R. Fokin

Keyword(s):

Phase Diagram ◽

Metastable States ◽

Liquid Carbon

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The phase diagram and spinodal decomposition of metastable states of Lennard-Jones system

High Temperature ◽

10.1134/s0018151x07010063 ◽

2007 ◽

Vol 45 (1) ◽

pp. 37-48 ◽

Cited By ~ 21

Author(s):

A. Yu. Kuksin ◽

G. E. Norman ◽

V. V. Stegailov

Keyword(s):

Phase Diagram ◽

Spinodal Decomposition ◽

Metastable States ◽

Lennard Jones

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A time-domain phase diagram of metastable states in a charge ordered quantum material

10.21203/rs.3.rs-102013/v1 ◽

2020 ◽

Author(s):

Jan Ravnik ◽

Michele Diego ◽

Yaroslav Gerasimenko ◽

Yevhenii Vaskivskyi ◽

Igor Vaskivskyi ◽

...

Keyword(s):

Phase Diagram ◽

Time Domain ◽

Metastable States ◽

Scanning Tunneling ◽

Photon Density ◽

Tunneling Microscopy ◽

Final State ◽

Time Resolved ◽

Domain Phase ◽

A Charge

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Digital Proximity Tracing in the COVID-19 Pandemic on Empirical Contact Networks

10.21203/rs.3.rs-41017/v1 ◽

2020 ◽

Author(s):

Giulia Cencetti ◽

Gabriele Santin ◽

Antonio Longa ◽

Emanuele Pigani ◽

Alain Barrat ◽

...

Keyword(s):

Disease Outbreaks ◽

Contact Tracing ◽

Reproductive Number ◽

Modeling Framework ◽

Effective Measure ◽

Epidemic Dynamics ◽

Contact Patterns ◽

Contact Data ◽

High Level ◽

The Impact

Abstract Digital contact tracing is increasingly considered as a tool to control infectious disease outbreaks. As part of a broader test, trace, isolate, and quarantine strategy, digital contract tracing apps have been proposed to alleviate lock-downs, and to return societies to a more normal situation in the ongoing COVID-19 crisis. Early work evaluating digital contact tracing did not consider important features and heterogeneities present in real-world contact patterns which impact epidemic dynamics. Here, we fill this gap by considering a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing apps in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread of COVID-19 in realistic scenarios such as a university campus, a workplace, or a high school. We find that restrictive policies are more effective in confining the epidemics but come at the cost of quarantining a large part of the population. It is possible to avoid this effect by considering less strict policies, which only consider contacts with longer exposure and at shorter distance to be at risk. Our results also show that isolation and tracing can help keep re-emerging outbreaks under control provided that hygiene and social distancing measures limit the reproductive number to 1.5. Moreover, we confirm that a high level of app adoption is crucial to make digital contact tracing an effective measure. Our results may inform app-based contact tracing efforts currently being implemented across several countries worldwide.

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