Essec\Faculty\Model\Contribution {#2205
#_index: "academ_contributions"
#_id: "14268"
#_source: array:26 [
"id" => "14268"
"slug" => "designing-an-optimal-sequence-of-non%e2%80%90pharmaceutical-interventions-for-controlling-covid-19"
"yearMonth" => "2022-12"
"year" => "2022"
"title" => "Designing an optimal sequence of non‐pharmaceutical interventions for controlling COVID-19"
"description" => "BISWAS, D. et ALFANDARI, L. (2022). Designing an optimal sequence of non‐pharmaceutical interventions for controlling COVID-19. <i>European Journal of Operational Research</i>, 303(3), pp. 1372-1391."
"authors" => array:2 [
0 => array:2 [
"name" => "BISWAS Debajyoti"
"bid" => "B00763884"
]
1 => array:3 [
"name" => "ALFANDARI Laurent"
"bid" => "B00000901"
"slug" => "alfandari-laurent"
]
]
"ouvrage" => ""
"keywords" => array:5 [
0 => "OR in healthcare"
1 => "COVID-19"
2 => "Non-Pharmaceutical interventions"
3 => "Scheduling"
4 => "Integer programming"
]
"updatedAt" => "2023-08-30 10:46:26"
"publicationUrl" => "https://doi.org/10.1016/j.ejor.2022.03.052"
"publicationInfo" => array:3 [
"pages" => "1372-1391"
"volume" => "303"
"number" => "3"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "The COVID-19 pandemic has had an unprecedented impact on global health and the economy since its inception in December, 2019 in Wuhan, China. Non-pharmaceutical interventions (NPI) like lockdowns and curfews have been deployed by affected countries for controlling the spread of infections. In this paper, we develop a Mixed Integer Non-Linear Programming (MINLP) epidemic model for computing the optimal sequence of NPIs over a planning horizon, considering shortages in doctors and hospital beds, under three different lockdown scenarios. We analyse two strategies - centralised (homogeneous decisions at the national level) and decentralised (decisions differentiated across regions), for two objectives separately - minimization of infections and deaths, using actual pandemic data of France. We linearize the quadratic constraints and objective functions in the MINLP model and convert it to a Mixed Integer Linear Programming (MILP) model. A major result that we show analytically is that under the epidemic model used, the optimal sequence of NPIs always follows a decreasing severity pattern. Using this property, we further simplify the MILP model into an Integer Linear Programming (ILP) model, reducing computational time up to 99%. Our numerical results show that a decentralised strategy is more effective in controlling infections for a given severity budget, yielding up to 20% lesser infections, 15% lesser deaths and 60% lesser shortages in healthcare resources. These results hold without considering logistics aspects and for a given level of compliance of the population."
"en" => "The COVID-19 pandemic has had an unprecedented impact on global health and the economy since its inception in December, 2019 in Wuhan, China. Non-pharmaceutical interventions (NPI) like lockdowns and curfews have been deployed by affected countries for controlling the spread of infections. In this paper, we develop a Mixed Integer Non-Linear Programming (MINLP) epidemic model for computing the optimal sequence of NPIs over a planning horizon, considering shortages in doctors and hospital beds, under three different lockdown scenarios. We analyse two strategies - centralised (homogeneous decisions at the national level) and decentralised (decisions differentiated across regions), for two objectives separately - minimization of infections and deaths, using actual pandemic data of France. We linearize the quadratic constraints and objective functions in the MINLP model and convert it to a Mixed Integer Linear Programming (MILP) model. A major result that we show analytically is that under the epidemic model used, the optimal sequence of NPIs always follows a decreasing severity pattern. Using this property, we further simplify the MILP model into an Integer Linear Programming (ILP) model, reducing computational time up to 99%. Our numerical results show that a decentralised strategy is more effective in controlling infections for a given severity budget, yielding up to 20% lesser infections, 15% lesser deaths and 60% lesser shortages in healthcare resources. These results hold without considering logistics aspects and for a given level of compliance of the population."
]
"authors_fields" => array:2 [
"fr" => "Autre discipline"
"en" => "Other Discipline"
]
"indexedAt" => "2024-05-08T03:22:01.000Z"
"docTitle" => "Designing an optimal sequence of non‐pharmaceutical interventions for controlling COVID-19"
"docSurtitle" => "Articles"
"authorNames" => "BISWAS Debajyoti, <a href="/cv/alfandari-laurent">ALFANDARI Laurent</a>"
"docDescription" => "<span class="document-property-authors">BISWAS Debajyoti, ALFANDARI Laurent</span><br><span class="document-property-authors_fields">Autre discipline</span> | <span class="document-property-year">2022</span>"
"keywordList" => "<a href="#">OR in healthcare</a>, <a href="#">COVID-19</a>, <a href="#">Non-Pharmaceutical interventions</a>, <a href="#">Scheduling</a>, <a href="#">Integer programming</a>"
"docPreview" => "<b>Designing an optimal sequence of non‐pharmaceutical interventions for controlling COVID-19</b><br><span>2022-12 | Articles </span>"
"docType" => "research"
"publicationLink" => "<a href="https://doi.org/10.1016/j.ejor.2022.03.052" target="_blank">Designing an optimal sequence of non‐pharmaceutical interventions for controlling COVID-19</a>"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 8.203265
+"parent": null
}