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. 2021;11(1):2.
doi: 10.1186/s13362-020-00098-w. Epub 2021 Jan 6.

Flattening the curves: on-off lock-down strategies for COVID-19 with an application to Brazil

Luís Tarrataca  1 , Claudia Mazza Dias  2 , Diego Barreto Haddad  1 , Edilson Fernandes De Arruda  3   4
Affiliations

Flattening the curves: on-off lock-down strategies for COVID-19 with an application to Brazil

Luís Tarrataca et al. J Math Ind. 2021.

Abstract

The current COVID-19 pandemic is affecting different countries in different ways. The assortment of reporting techniques alongside other issues, such as underreporting and budgetary constraints, makes predicting the spread and lethality of the virus a challenging task. This work attempts to gain a better understanding of how COVID-19 will affect one of the least studied countries, namely Brazil. Currently, several Brazilian states are in a state of lock-down. However, there is political pressure for this type of measures to be lifted. This work considers the impact that such a termination would have on how the virus evolves locally. This was done by extending the SEIR model with an on / off strategy. Given the simplicity of SEIR we also attempted to gain more insight by developing a neural regressor. We chose to employ features that current clinical studies have pinpointed has having a connection to the lethality of COVID-19. We discuss how this data can be processed in order to obtain a robust assessment.

Supplementary information: The online version contains supplementary material available at 10.1186/s13362-020-00098-w.

Keywords: COVID-19; Coronavirus; Lockdown; Neural network; Quarantine; Seir models.

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Conflict of interest statement

Competing interestsThe authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
SEIR dynamics for Brazil
Figure 2
Figure 2
SEIR dynamics for 25% and 75% thresholds
Figure 3
Figure 3
SEIR dynamics for 25% and 75% thresholds, zoom
Figure 4
Figure 4
SEIR dynamics for 50% and 100% thresholds
Figure 5
Figure 5
SEIR dynamics for 50% and 100% thresholds, zoom
Figure 6
Figure 6
25%–75% strategy with linear release
Figure 7
Figure 7
25%–75% strategy with linear release-zoom
Figure 8
Figure 8
60%–90% strategy with linear release
Figure 9
Figure 9
60%–90% strategy with linear release, zoom
Figure 10
Figure 10
Block diagram of the architecture of a single neural predictor (i.e., a single model)
Figure 11
Figure 11
Block diagram of the proposed committee machine. Note that the combination step is the median operator, and that the confidence interval can be computed using variability statistics derived from the “Model Selection” procedure
Figure 12
Figure 12
Ilustration of the k-fold procedure. In this paper, the performance is evaluated from the computation of the mean absolute error between the estimated CFR and the one computed from WOI data
Algorithm 1
Algorithm 1
Evaluating MAE
See this image and copyright information in PMC

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