{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/planning-as-inference-in-epidemiological","title":"Planning as Inference in Epidemiological Models","arxiv_id":"2003.13221","date":"2020-03-30","proceeding":null,"authors":["Frank Wood","Andrew Warrington","Saeid Naderiparizi","Christian Weilbach","Vaden Masrani","William Harvey","Adam Scibior","Boyan Beronov","John Grefenstette","Duncan Campbell","Ali Nasseri"],"abstract":"In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models. The kind of inference tasks undertaken include computing the posterior distribution over controllable, via direct policy-making choices, simulation model parameters that give rise to acceptable disease progression outcomes. Among other things, we illustrate the use of a probabilistic programming language that automates inference in existing simulators. Neither the full capabilities of this tool for automating inference nor its utility for planning is widely disseminated at the current time. Timely gains in understanding about how such simulation-based models and inference automation tools applied in support of policymaking could lead to less economically damaging policy prescriptions, particularly during the current COVID-19 pandemic.","url_abs":"https://arxiv.org/abs/2003.13221v3","url_pdf":"https://arxiv.org/pdf/2003.13221v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"planning-as-inference-in-epidemiological","repo_url":"https://github.com/plai-group/covid","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"probabilistic-programming","task_name":"Probabilistic Programming"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2003.13221","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.13221"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/plai-group/covid","reach":null}],"summary":{"ran_draft_wrong":2,"ran_honours":1},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"f8c7d168a43b7eae","entry":"get_diff","repo":"plai-group/covid","repo_kind":"official","path":"SEIR/seir.py","file_url":"https://github.com/plai-group/covid/blob/HEAD/SEIR/seir.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"f8c7d168a43b7eae"}},{"code_sha256_prefix":"4ef5fb3988bb54d5","entry":"sample_x0","repo":"plai-group/covid","repo_kind":"official","path":"SEIR/seir.py","file_url":"https://github.com/plai-group/covid/blob/HEAD/SEIR/seir.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"4ef5fb3988bb54d5"}},{"code_sha256_prefix":"35de7b3dee57574a","entry":"simulate_seir","repo":"plai-group/covid","repo_kind":"official","path":"SEIR/seir.py","file_url":"https://github.com/plai-group/covid/blob/HEAD/SEIR/seir.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"35de7b3dee57574a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}