Hijacking Malaria Simulators with Probabilistic Programming

Gram-Hansen, Bradley; Witt, Christian Schroeder de; Rainforth, Tom; Torr, Philip H. S.; Teh, Yee Whye; Baydin, Atılım Güneş

doi:10.48550/arxiv.1905.12432

Cited by 1 publication

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“…There are at least two existing papers that we have identified that explore using probabilistic programming coupled to epidemiological simulators; [29] which used Libbi [83] and [49] which used PyProb. The latter is an example of work that "hijacks" a malaria simulator in the same way we "hijacked" the FRED simulator in this paper.…”

Section: Methodsmentioning

confidence: 99%

“…Recent examples of its use include inference in the standard model of physics conditioning on observed detector outputs [46,47], inference about internal composite material cure processes through a composite material cure simulator conditioned on observed surface temperatures [48], and inference about malaria spread in a malaria simulator [49].…”

Section: Probabilistic Programmingmentioning

confidence: 99%

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Planning as Inference in Epidemiological Models

Wood¹,

Warrington²,

Naderiparizi³

et al. 2020

Preprint

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In this work we demonstrate how existing software tools can be used to automate parts of infectious disease-control policy-making via performing inference in existing epidemiological dynamics models. The kind of inference tasks undertaken include computing, for planning purposes, the posterior distribution over putatively controllable, via direct policy-making choices, simulation model parameters that give rise to acceptable disease progression outcomes. Neither the full capabilities of such inference automation software tools nor their utility for planning is widely disseminated at the current time. Timely gains in understanding about these tools and how they can be used may lead to more fine-grained and less economically damaging policy prescriptions, particularly during the current COVID-19 pandemic.

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Section: Methodsmentioning

confidence: 99%

Section: Probabilistic Programmingmentioning

confidence: 99%