Evidence map›Paper›PMID 34922976›Full record

ArticleMathematical biosciences2022

Optimal control of the SIR model with constrained policy, with an application to COVID-19.

Yujia Ding, Henry Schellhorn

Abstract read
In one paragraph

Article in Mathematical biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yujia DingInstitute of Mathematical Sciences, Claremont Graduate University, Claremont, CA 91711, United States of America. Electronic address: yujia.ding@cgu.edu.
Henry SchellhornInstitute of Mathematical Sciences, Claremont Graduate University, Claremont, CA 91711, United States of America. Electronic address: Henry.Schellhorn@cgu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article considers the optimal control of the SIR model with both transmission and treatment uncertainty. It follows the model presented in Gatto and Schellhorn (2021). We make four significant improvements on the latter paper. First, we prove the existence of a solution to the model. Second, our interpretation of the control is more realistic: while in Gatto and Schellhorn (2021) the control α is the proportion of the population that takes a basic dose of treatment, so that α>1 occurs only if some patients take more than a basic dose, in our paper, α is constrained between zero and one, and represents thus the proportion of the population undergoing treatment. Third, we provide a complete solution for the moderate infection regime (with constant treatment). Finally, we give a thorough interpretation of the control in the moderate infection regime, while Gatto and Schellhorn (2021) focused on the interpretation of the low infection regime. Finally, we compare the efficiency of our control to curb the COVID-19 epidemic to other types of control.

Indexed as

COVID-19EpidemicsEpidemiological ModelsHumansPolicySARS-CoV-2COVID-19EpidemiologyPopulation controlSIR modelStochastic optimal control

Identifiers

PMID34922976
PMCPMC8675184

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.