A stochastic agent-based model of the SARS-CoV-2 epidemic in France

Hoertel, Nicolas; Blachier, Martin; Blanco, Carlos; Olfson, Mark; Massetti, Marc; Sánchez‐Rico, Marina; Limosin, Frédéric; Leleu, Henri

doi:10.1038/s41591-020-1001-6

Cited by 291 publications

(296 citation statements)

References 26 publications

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“…By multiplying Eqs. (10), (12), (14) and (16) by the coefficients c 1 , c 2 , c 3 and c 4 , respectively, and after a rearrangement we get…”

Section: Global Stability Of the Endemic Equilibriummentioning

confidence: 99%

“…In the case of apparition of the symptoms related to the disease, they are isolated, tested and hospitalized. Several mathematical models related to the COVID-19 epidemic have been studied (see [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]). In [18], Imai et al conducted computational modeling to establish the size of the disease outbreak in Wuhan.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the effects of contact tracing on COVID-19 transmission

Traoré

Konané

2020

Adv Differ Equ

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In this paper, a mathematical model for COVID-19 that involves contact tracing is studied. The contact tracing-induced reproduction number $\mathcal{R}_{q}$ R q and equilibrium for the model are determined and stabilities are examined. The global stabilities results are achieved by constructing Lyapunov functions. The contact tracing-induced reproduction number $\mathcal{R}_{q}$ R q is compared with the basic reproduction number $\mathcal{R}_{0}$ R 0 for the model in the absence of any intervention to assess the possible benefits of the contact tracing strategy.

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“…By multiplying Eqs. (10), (12), (14) and (16) by the coefficients c 1 , c 2 , c 3 and c 4 , respectively, and after a rearrangement we get…”

Section: Global Stability Of the Endemic Equilibriummentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling the effects of contact tracing on COVID-19 transmission

Traoré

Konané

2020

Adv Differ Equ

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“…However, previous literature does not contain a formal meta-analysis of RCTs in community setting that are currently available. The current evidence of the efficacy of face masks stems from prediction models showing that universal masking in public can have a substantial impact on spreading and does not require the use of medical masks or 100% compliance [2,3,10]. A review assessing masks in source control (in contrast to protection) recommended their use in the general population [11].…”

Section: Introductionmentioning

confidence: 99%

Face masks to prevent transmission of respiratory diseases: Systematic review and meta-analysis of randomized controlled trials^*

Ollila

Partinen

Koskela

et al. 2020

Preprint

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Background: Coronavirus Disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and spreads through droplet-mediated transmission on contaminated surfaces and in air. Mounting scientific evidence from observational studies suggests that face masks for the general public may reduce the spread of infections. However, results from randomized control trials (RCT) have been presented as inconclusive, and concerns related to the safety and efficacy of non-surgical face masks in non-clinical settings remain. This controversy calls for a meta-analysis which considers non-compliance in RCTs, the time-lag in benefits of universal masking, and possible adverse effects. Methods: We performed a meta-analysis of RCTs of non-surgical face masks in preventing viral respiratory infections in non-hospital and non-household settings at cumulative and maximum follow-up as primary endpoints. The search for RCTs yielded five studies published before May 29th, 2020. We pooled estimates from the studies and performed random-effects meta-analysis and mixed-effects meta-regression across studies, accounting for covariates in compliance vs. non-compliance in treatment. Results: Face masks decreased infections across all studies at maximum follow-up (p=0.0318$, RR=0.608 [0.387 - 0.956]), and particularly in studies without non-compliance bias. We found significant between-study heterogeneity in studies with bias (I^2=71.2%, p=0.0077). We also used adjusted meta-regression to account for heterogeneity. The results support a significant protective effect of masking (p=0.0006, beta=0.0214, SE= 0.0062). No severe adverse effects were detected. Interpretation: The meta-analysis of existing randomized control studies found support for the efficacy of face masks among the general public. Our results show that face masks protect populations from infections and do not pose a significant risk to users. Recommendations and clear communication concerning the benefits of face masks should be provided to limit the number of COVID-19 and other respiratory infections.

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“…However, while classical compartmentalized epidemiological models (15) or highly simplified individual-based models (16) seem to be relevant at the scale of an entire country, they are paradoxically not relevant at smaller scales, where it is of utmost importance to be able to accurately predict the impact of localized interventions. As a matter of fact, when an intervention is applied on a small population, the individual and social heterogeneities in terms of social or economic characteristics, medical profiles (17), spatial distribution (18), behaviors, opinion, or compliance to the public rules (19), are crucial factors to take into account in models. Moreover, among these features, some might remain constant (e.g., spatial distribution) but others can evolve during the intervention itself (e.g., compliance), making it difficult to approximate them with average values: models that only consider the evolution of the epidemic through the interactions between aggregated variables (representing compartments or other subsets of the population) are unable to represent these heterogeneities, let alone their evolution, and thus to use them for analysing, comparing, or even proposing possible interventions.…”

Section: Proposal: An Agent-based Spatially Explicit Modeling Kitmentioning

confidence: 99%

COMOKIT: A Modeling Kit to Understand, Analyze, and Compare the Impacts of Mitigation Policies Against the COVID-19 Epidemic at the Scale of a City

Gaudou

Huynh

Philippon

et al. 2020

Front. Public Health

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Since its emergence in China, the COVID-19 pandemic has spread rapidly around the world. Faced with this unknown disease, public health authorities were forced to experiment, in a short period of time, with various combinations of interventions at different scales. However, as the pandemic progresses, there is an urgent need for tools and methodologies to quickly analyze the effectiveness of responses against COVID-19 in different communities and contexts. In this perspective, computer modeling appears to be an invaluable lever as it allows for the in silico exploration of a range of intervention strategies prior to the potential field implementation phase. More specifically, we argue that, in order to take into account important dimensions of policy actions, such as the heterogeneity of the individual response or the spatial aspect of containment strategies, the branch of computer modeling known as agent-based modeling is of immense interest. We present in this paper an agent-based modeling framework called COVID-19 Modeling Kit (COMOKIT), designed to be generic, scalable and thus portable in a variety of social and geographical contexts. COMOKIT combines models of person-to-person and environmental transmission, a model of individual epidemiological status evolution, an agenda-based 1-h time step model of human mobility, and an intervention model. It is designed to be modular and flexible enough to allow modelers and users to represent different strategies and study their impacts in multiple social, epidemiological or economic scenarios. Several large-scale experiments are analyzed in this paper and allow us to show the potentialities of COMOKIT in terms of analysis and comparison of the impacts of public health policies in a realistic case study.

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A stochastic agent-based model of the SARS-CoV-2 epidemic in France

Cited by 291 publications

References 26 publications

Modeling the effects of contact tracing on COVID-19 transmission

Modeling the effects of contact tracing on COVID-19 transmission

Face masks to prevent transmission of respiratory diseases: Systematic review and meta-analysis of randomized controlled trials^*

COMOKIT: A Modeling Kit to Understand, Analyze, and Compare the Impacts of Mitigation Policies Against the COVID-19 Epidemic at the Scale of a City

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A stochastic agent-based model of the SARS-CoV-2 epidemic in France

Cited by 291 publications

References 26 publications

Modeling the effects of contact tracing on COVID-19 transmission

Modeling the effects of contact tracing on COVID-19 transmission

Face masks to prevent transmission of respiratory diseases: Systematic review and meta-analysis of randomized controlled trials*

COMOKIT: A Modeling Kit to Understand, Analyze, and Compare the Impacts of Mitigation Policies Against the COVID-19 Epidemic at the Scale of a City

Contact Info

Product

Resources

About

Face masks to prevent transmission of respiratory diseases: Systematic review and meta-analysis of randomized controlled trials^*