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10.1371/journal.pcbi.1008776

http://scihub22266oqcxt.onion/10.1371/journal.pcbi.1008776
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33661890!7963091!33661890
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suck abstract from ncbi


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pmid33661890      PLoS+Comput+Biol 2021 ; 17 (3): e1008776
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  • Age-structured non-pharmaceutical interventions for optimal control of COVID-19 epidemic #MMPMID33661890
  • Richard Q; Alizon S; Choisy M; Sofonea MT; Djidjou-Demasse R
  • PLoS Comput Biol 2021[Mar]; 17 (3): e1008776 PMID33661890show ga
  • 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.
  • |*Models, Biological[MESH]
  • |*SARS-CoV-2[MESH]
  • |Adolescent[MESH]
  • |Adult[MESH]
  • |Age Distribution[MESH]
  • |Aged[MESH]
  • |Aged, 80 and over[MESH]
  • |Basic Reproduction Number/statistics & numerical data[MESH]
  • |Burkina Faso/epidemiology[MESH]
  • |COVID-19/*epidemiology/*prevention & control/transmission[MESH]
  • |Child[MESH]
  • |Child, Preschool[MESH]
  • |Communicable Disease Control/methods/statistics & numerical data[MESH]
  • |Computational Biology[MESH]
  • |Contact Tracing/methods/statistics & numerical data[MESH]
  • |Female[MESH]
  • |France/epidemiology[MESH]
  • |Humans[MESH]
  • |Infant[MESH]
  • |Infant, Newborn[MESH]
  • |Male[MESH]
  • |Mathematical Concepts[MESH]
  • |Middle Aged[MESH]
  • |Models, Statistical[MESH]
  • |Pandemics/*prevention & control/statistics & numerical data[MESH]
  • |Physical Distancing[MESH]
  • |Vietnam/epidemiology[MESH]


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