Robot Dance: A mathematical optimization platform for intervention against COVID-19 in a complex network

Nonato, Luís Gustavo; Peixoto, Pedro S.; Pereira, Tiago; Sagastizábal, Claudia; Silva, Paulo J. S.

doi:10.1016/j.ejco.2022.100025

Cited by 7 publications

(9 citation statements)

References 40 publications

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“…We use the same notation as in [13]. Suppose there are k areas gathered in a set I, the time horizon being defined by initial and final times T 0 and T 1 .…”

Section: Methods An Seiqr Model With Mobilitymentioning

confidence: 99%

“…Moreover, r(t) ∈ [0, r 0 ] K ⊂ R K is the disease reproduction number, while T inc and T inf are the incubation and infection periods. For each area i, given an initial condition at time T 0 the disease evolution during the night is described by the classical SEIQR model [13].…”

Section: Methods An Seiqr Model With Mobilitymentioning

confidence: 99%

“…Our key proposal is to coordinate social-distancing measures with the geographical and temporal distribution of tests. The base case, taken as reference, considers the evolution of the viral transmission rate if no tests are performed and the sole mitigation measure is social distancing [13].…”

Section: Smart Testing In the State Of Sao Paulomentioning

confidence: 99%

“…Based on the structure of the considered uncertainty, the constraint can be cast into a deterministic equivalent reformulation. The procedure is explained in details in [13], we just mention a few key points here.…”

Section: Critical Care Beds Occupancymentioning

confidence: 99%

“…ensuring the local hospital capacity will not be exceeded, with 90% probability. The explicit deterministic equivalent formulation of this chance constraint is an affine constraint on I, involving the inverse cumulative function of the standard Gaussian distribution, we refer to [13] for the complete development.…”

Section: Critical Care Beds Occupancymentioning

confidence: 99%

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Smart testing and critical care bed sharing for COVID-19 control

Silva,

Pereira,

Sagastizabal

et al. 2021

Preprint

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During the early months of the current COVID-19 pandemic, social-distancing measures effectively slowed disease transmission in many countries in Europe and Asia, but the same benefits have not been observed in some developing countries such as Brazil. In part, this is due to a failure to organise systematic testing campaigns at nationwide or even regional levels. To gain effective control of the pandemic, decision-makers in developing countries, particularly those with large populations, must overcome difficulties posed by an unequal distribution of wealth combined with low daily testing capacities. The economic infrastructure of the country, often concentrated in a few cities, forces workers to travel from commuter cities and rural areas, which induces strong nonlinear effects on disease transmission. In the present study, we develop a smart testing strategy to identify geographic regions where COVID-19 testing could most effectively be deployed to limit further disease transmission. The strategy uses readily available anonymised mobility and demographic data integrated with intensive care unit (ICU) occupancy data and city-specific social-distancing measures. Taking into account the heterogeneity of ICU bed occupancy in differing regions and the stages of disease evolution, we use a datadriven study of the Brazilian state of Sao Paulo as an example to show that smart testing strategies can rapidly limit transmission while reducing the need for social-distancing measures, thus returning life to a so-called new normal, even when testing capacity is limited.

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“…We use the same notation as in [13]. Suppose there are k areas gathered in a set I, the time horizon being defined by initial and final times T 0 and T 1 .…”

Section: Methods An Seiqr Model With Mobilitymentioning

confidence: 99%

Section: Methods An Seiqr Model With Mobilitymentioning

confidence: 99%

Section: Smart Testing In the State Of Sao Paulomentioning

confidence: 99%

Section: Critical Care Beds Occupancymentioning

confidence: 99%

Section: Critical Care Beds Occupancymentioning

confidence: 99%

See 3 more Smart Citations

Smart testing and critical care bed sharing for COVID-19 control

Silva,

Pereira,

Sagastizabal

et al. 2021

Preprint

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Modelling pandemic behavior with a network-SIRD approach

Baude,

Kimms

2023

Cent Eur J Oper Res

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Due to the Corona pandemic, measures to control the spread of the virus are much debated in society. Different countries pursued different policy approaches to reduce the outbreak of the pandemic. This paper deals with the optimal selection of measures to control the outbreak of a pandemic. The focus is on the beginning of a pandemic, when no vaccines or medical supplies are available. To illustrate the different approaches taken by governments, we demonstrate some practical data. We formulate a mathematical program to minimize the economic damage caused by measures while simultaneously considering the humanitarian damage caused by infections. To consider the progress of the pandemic, our model is based on epidemiological multi-group-SIRD model. This model is looking at a bunch of districts and their contact rates with each other. In each district, the population is divided into subgroups. The measures restrict contacts between these subgroups and affect the pandemic process. In addition to the consideration of minimizing costs, a limited mortality rate is also considered. To illustrate the model, a small case study inspired by cities in Germany is presented.

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Optimal vaccination strategies on networks and in metropolitan areas

Aronna,

Moschen

2024

Infectious Disease Modelling

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Robot Dance: A mathematical optimization platform for intervention against COVID-19 in a complex network

Cited by 7 publications

References 40 publications

Smart testing and critical care bed sharing for COVID-19 control

Smart testing and critical care bed sharing for COVID-19 control

Modelling pandemic behavior with a network-SIRD approach

Optimal vaccination strategies on networks and in metropolitan areas

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