scholarly journals Effective immunity and second waves: a dynamic causal modelling study

2020 ◽  
Vol 5 ◽  
pp. 204
Author(s):  
Karl J. Friston ◽  
Thomas Parr ◽  
Peter Zeidman ◽  
Adeel Razi ◽  
Guillaume Flandin ◽  
...  
Keyword(s):  
Causal Model ◽  
Therapeutic Interventions ◽  
Window Of Opportunity ◽  
Effective Population ◽  
Causal Modelling ◽  
Population Immunity ◽  
Technical Report ◽  
Dynamic Causal Model ◽  
Second Wave ◽  
Modelling Study

This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.

scholarly journals Effective immunity and second waves: a dynamic causal modelling study

2020 ◽  
Vol 5 ◽  
pp. 204
Author(s):  
Karl J. Friston ◽  
Thomas Parr ◽  
Peter Zeidman ◽  
Adeel Razi ◽  
Guillaume Flandin ◽  
...  
Keyword(s):  
Causal Model ◽  
Therapeutic Interventions ◽  
Window Of Opportunity ◽  
Effective Population ◽  
Causal Modelling ◽  
Population Immunity ◽  
Technical Report ◽  
Dynamic Causal Model ◽  
Second Wave ◽  
Modelling Study

This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.

scholarly journals Testing and tracking in the UK: A dynamic causal modelling study

2021 ◽  
Vol 5 ◽  
pp. 144
Author(s):  
Karl J. Friston ◽  
Thomas Parr ◽  
Peter Zeidman ◽  
Adeel Razi ◽  
Guillaume Flandin ◽  
...  
Keyword(s):  
Model Parameters ◽  
Clear Cut ◽  
Causal Modelling ◽  
The United Kingdom ◽  
Serological Studies ◽  
The Uk ◽  
Dynamic Causal Model ◽  
Second Wave ◽  
Face Validation ◽  
Dynamic Causal Modelling

By equipping a previously reported dynamic causal modelling of COVID-19 with an isolation state, we were able to model the effects of self-isolation consequent on testing and tracking. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic—and could only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For example, under plausible (graded) relaxations of social distancing, a rebound of infections is highly unlikely. The emergence of a second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A testing and tracking policy—implemented at the present time—will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with 50,000 tests per day). These conclusions are based upon a dynamic causal model for which we provide some construct and face validation—using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies.

scholarly journals Dynamic causal modelling of COVID-19

2020 ◽  
Vol 5 ◽  
pp. 89 ◽  
Author(s):  
Karl J. Friston ◽  
Thomas Parr ◽  
Peter Zeidman ◽  
Adeel Razi ◽  
Guillaume Flandin ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Causal Model ◽  
State Of The Art ◽  
Herd Immunity ◽  
Nonlinear Effects ◽  
Model Inversion ◽  
Neuronal Ensembles ◽  
Technical Report ◽  
Per Se ◽  
Dynamic Causal Model

This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations—to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.

scholarly journals Testing and tracking in the UK: A dynamic causal modelling study

2020 ◽  
Vol 5 ◽  
pp. 144 ◽  
Author(s):  
Karl J. Friston ◽  
Thomas Parr ◽  
Peter Zeidman ◽  
Adeel Razi ◽  
Guillaume Flandin ◽  
...  
Keyword(s):  
Model Parameters ◽  
Clear Cut ◽  
Causal Modelling ◽  
The United Kingdom ◽  
Serological Studies ◽  
The Uk ◽  
Dynamic Causal Model ◽  
Second Wave ◽  
Face Validation ◽  
Dynamic Causal Modelling

By equipping a previously reported dynamic causal modelling of COVID-19 with an isolation state, we were able to model the effects of self-isolation consequent on testing and tracking. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic—and could only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For example, under plausible (graded) relaxations of social distancing, a rebound of infections is highly unlikely. The emergence of a second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A testing and tracking policy—implemented at the present time—will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with 50,000 tests per day). These conclusions are based upon a dynamic causal model for which we provide some construct and face validation—using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies.

scholarly journals Dynamic causal modelling of COVID-19

2020 ◽  
Vol 5 ◽  
pp. 89 ◽  
Author(s):  
Karl J. Friston ◽  
Thomas Parr ◽  
Peter Zeidman ◽  
Adeel Razi ◽  
Guillaume Flandin ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Causal Model ◽  
State Of The Art ◽  
Herd Immunity ◽  
Nonlinear Effects ◽  
Model Inversion ◽  
Neuronal Ensembles ◽  
Technical Report ◽  
Per Se ◽  
Dynamic Causal Model

This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations—to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.

scholarly journals Lockdown to contain COVID-19 is a window of opportunity to prevent the second wave

2020 ◽  
Vol 27 (5) ◽  
Author(s):  
Annelies Wilder-Smith ◽  
Yaneer Bar-Yam ◽  
Dale Fisher
Keyword(s):  
Window Of Opportunity ◽  
Second Wave

scholarly journals Second wave of COVID-19 and hesitation of vaccination in India

2021 ◽  
Vol 9 (6) ◽  
pp. 1853
Author(s):  
Amandeep Singh ◽  
Harleen Kaur
Keyword(s):  
Vaccination Program ◽  
Window Of Opportunity ◽  
Slow Pace ◽  
The Government ◽  
Second Wave

Amid the surge of COVID-19 cases in India have already risked its "window of opportunity" period to have ramped up its progressive and determined vaccination program. Today, as the government has the escalating threat of coronavirus variants, it has entered into the second wave, facing an uptick in COVID-19 cases. The vaccination drive-through vital is again running at a slow pace.

scholarly journals Estimating the burden of SARS-CoV-2 in France

Science ◽  
2020 ◽  
Vol 369 (6500) ◽  
pp. 208-211 ◽  
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.

201. A Dynamic Causal Model of the Depressed Self

2018 ◽  
Vol 83 (9) ◽  
pp. S81
Author(s):  
Christopher Davey ◽  
Michael Breakspear ◽  
Jesus Pujol ◽  
Ben Harrison
Keyword(s):  
Causal Model ◽  
Dynamic Causal Model
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