Epidemiological monitoring and control perspectives: application of a parsimonious modelling framework to the COVID-19 dynamics in France

Sofonea, Mircea T.; Reyné, Bastien; Elie, Baptiste; Djidjou‐Demasse, Ramsès; Selinger, Christian; Michalakis, Yannis; Alizon, Samuel

doi:10.1101/2020.05.22.20110593

Cited by 34 publications

(85 citation statements)

References 33 publications

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“…where S 0 is the initial susceptible population, K is the contact matrix and ω(a, i) is the infectiousness of individuals of age a infected since time i (Appendix A). We set R 0 = 3.3 [49,50] and it follows that…”

Section: The Basic Reproduction Number Rmentioning

confidence: 99%

Age-structured non-pharmaceutical interventions for optimal control of COVID-19 epidemic

Richard

Alizon

Choisy

et al. 2020

Preprint

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In an epidemic, individuals can widely differ in the way they spread the infection, for instance depending on their age or on the number of days they have been infected for. The latter allows to take into account the variation of infectiousness as a function of time since infection. In the absence of pharmaceutical interventions such as a vaccine or treatment, non-pharmaceutical interventions (e.g.} social distancing) are of great importance to mitigate the pandemic. We propose a model with a double continuous structure by host age and time since infection. By applying optimal control theory to our age-structured model, we identify a solution minimizing deaths and costs associated with the implementation of the control strategy itself. This strategy depends on the age heterogeneity between individuals and consists in a relatively high isolation intensity over the older populations during a hundred days, followed by a steady decrease in a way that depends on the cost associated to a such control. The isolation of the younger population is weaker and occurs only if the cost associated with the control is relatively low. We show that the optimal control strategy strongly outperforms other strategies such as uniform constant control over the whole populations or over its younger fraction. These results bring new facts the debate about age-based control interventions and open promising avenues of research, for instance of age-based contact tracing.

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Section: The Basic Reproduction Number Rmentioning

confidence: 99%

Age-structured non-pharmaceutical interventions for optimal control of COVID-19 epidemic

Richard

Alizon

Choisy

et al. 2020

Preprint

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“…(Salje et al, 2020) used a Bayesian model similar to ours, except that they used both hospitalization and deaths data, but did not model the saturation of the population as the epidemic progresses and the proportion of susceptible individuals decreases in the population, and did not use a mixture model to account for heterogeneities in the lockdown efficacy between regions. A source of difference between our model, the model of (Sofonea et al, 2020), and theirs is the values of the Infection Fatality Ratios that were used. They based their IFR on the data from the Diamond Princess cruise ship, while (Sofonea et al, 2020) and we based ours on data from Wuhan, in China.…”

Section: Discussionmentioning

confidence: 99%

“…Most are compartmental models, which include Susceptible Infected Removed/Recovered (SIR) or Susceptible Exposed Infected Removed/Recovered (SEIR) models. Such models can be used in a deterministic framework, as in (Magal and Webb, 2020;Massonnaud et al, 2020;Roux et al, 2020;Sofonea et al, 2020), can be used for performing simulations by including stochasticity through resampling steps in an otherwise deterministic framework (Neher et al, 2020), or can be used in a completely stochastic framework, as in (Flaxman et al, 2020;Salje et al, 2020). Deterministic models have small computational requirements, but probabilistic approaches lend themselves to statistical inference, e.g.…”

Section: Introductionmentioning

confidence: 99%

Bayesian investigation of SARS-CoV-2-related mortality in France

Duchemin

Veber

Boussau

2020

Preprint

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The SARS-CoV-2 epidemic in France has focused a lot of attention as it has had one of the largest death tolls in Europe. It provides an opportunity to examine the effect of the lockdown and of other events on the dynamics of the epidemic. In particular, it has been suggested that municipal elections held just before lockdown was ordered may have helped spread the virus. In this manuscript we use Bayesian models of the number of deaths through time to study the epidemic in 13 regions of France. We found that the models accurately predict the number of deaths 2 to 3 weeks in advance, and recover estimates that are in agreement with recent models that rely on a different structure and different input data. In particular, the lockdown reduced the viral reproduction number by ≈ 80%. However, using a mixture model, we found that the lockdown had had different effectiveness depending on the region, and that it had been slightly more effective in decreasing the reproduction number in denser regions. The mixture model predicts that 2.08 (95% CI : 1.85-2.47) million people had been infected by May 11, and that there were 2567 (95% CI : 1781-5182) new infections on May 10. We found no evidence that the reproduction numbers differ between week-ends and week days, and no evidence that the reproduction numbers increased on the election day. Finally, we evaluated counterfactual scenarios showing that ordering the lockdown 1 to 7 days sooner would have resulted in 19% to 76% fewer deaths, but that ordering it 1 to 7 days later would have resulted in 21% to 266% more deaths. Overall, the predictions of the model indicate that holding the elections on March 15 did not have a detectable impact on the total number of deaths, unless it motivated a delay in imposing the lockdown.

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“…In our numerical approach, we set R 0 = 3.3 [63,64] for all three countries and corresponding values for S 0 and K for each country. We then successively determine rð � U Þ and α by (15) and (16) respectively.…”

Section: The Basic Reproduction Number Rmentioning

confidence: 99%

Age-structured non-pharmaceutical interventions for optimal control of COVID-19 epidemic

et al. 2021

Self Cite

View full text Add to dashboard Cite

In an epidemic, individuals can widely differ in the way they spread the infection depending on their age or on the number of days they have been infected for. In the absence of pharmaceutical interventions such as a vaccine or treatment, non-pharmaceutical interventions (e.g. physical or social distancing) are essential to mitigate the pandemic. We develop an original approach to identify the optimal age-stratified control strategy to implement as a function of the time since the onset of the epidemic. This is based on a model with a double continuous structure in terms of host age and time since infection. By applying optimal control theory to this model, we identify a solution that minimizes deaths and costs associated with the implementation of the control strategy itself. We also implement this strategy for three countries with contrasted age distributions (Burkina-Faso, France, and Vietnam). Overall, the optimal strategy varies throughout the epidemic, with a more intense control early on, and depending on host age, with a stronger control for the older population, except in the scenario where the cost associated with the control is low. In the latter scenario, we find strong differences across countries because the control extends to the younger population for France and Vietnam 2 to 3 months after the onset of the epidemic, but not for Burkina Faso. Finally, we show that the optimal control strategy strongly outperforms a constant uniform control exerted over the whole population or over its younger fraction. This improved understanding of the effect of age-based control interventions opens new perspectives for the field, especially for age-based contact tracing.

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Epidemiological monitoring and control perspectives: application of a parsimonious modelling framework to the COVID-19 dynamics in France

Cited by 34 publications

References 33 publications

Age-structured non-pharmaceutical interventions for optimal control of COVID-19 epidemic

Age-structured non-pharmaceutical interventions for optimal control of COVID-19 epidemic

Bayesian investigation of SARS-CoV-2-related mortality in France

Age-structured non-pharmaceutical interventions for optimal control of COVID-19 epidemic

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