Evidence map›Paper›PMID 42747468›Full record

ArticleJournal of mathematical biology2026

A Fokker-Planck framework for control of epidemics.

Christian Parkinson, Souvik Roy

Abstract read
In one paragraph

Article in Journal of mathematical biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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.

Christian ParkinsonDepartment of Mathematics & Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA. chparkin@msu.edu.ORCID http://orcid.org/0000-0003-1532-5409
Souvik RoyDepartment of Mathematics, University of Texas at Arlington, Arlington, TX, USA.

Funding

Division of Mathematical Sciences 2230790Division of Mathematical Sciences 2309491
6 · The paper itself

Abstract

We present a control framework for stochastic compartmental models in epidemiology. In this framework, rather than directly controlling the stochastic system, we perform optimal control of an associated Fokker-Planck equation, with the goal of steering the distribution of possible solutions of the stochastic system to some desirable state. In particular, this allows for robust control mechanism with uncertainty not only in the dynamics, but also in the initial data. We formulate and fully analyze a partial differential equation constrained optimization problem, including a proof of existence of optimal controls via analysis of the control-to-state map, and a characterization of optimal controls via the Pontryagin minimum principle. We describe the application of the sequential quadratic Hamiltonian method to our problem, which provides numerical approximations of optimal control maps. We demonstrate our method using a minimal stochastic susceptible-infected-recovered model with different choices of cost functionals that represent different policy-maker concerns.

Indexed as

Communicable DiseasesEpidemicsEpidemiological ModelsModels, BiologicalComputer SimulationHumansMathematical ConceptsStochastic ProcessesEpidemiologyFokker-Planck equationOptimal controlPDE constrained optimizationSequential quadratic Hamiltonian method

Identifiers

PMID42747468
PMCPMC13582153

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.