{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:VJKQDQFP2MCAYH6HD3643YYOPV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"328d540ab37d21e3b7e506d4a8a948568320967e27d98751ceb3094e760a3396","cross_cats_sorted":["cs.LG","q-bio.PE"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.AP","submitted_at":"2020-10-01T11:01:49Z","title_canon_sha256":"9fc81e5e11dce8e152a97f3459816b16150475f240d010be3e899d2fefedca8c"},"schema_version":"1.0","source":{"id":"2010.00300","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.00300","created_at":"2026-07-05T03:31:54Z"},{"alias_kind":"arxiv_version","alias_value":"2010.00300v4","created_at":"2026-07-05T03:31:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.00300","created_at":"2026-07-05T03:31:54Z"},{"alias_kind":"pith_short_12","alias_value":"VJKQDQFP2MCA","created_at":"2026-07-05T03:31:54Z"},{"alias_kind":"pith_short_16","alias_value":"VJKQDQFP2MCAYH6H","created_at":"2026-07-05T03:31:54Z"},{"alias_kind":"pith_short_8","alias_value":"VJKQDQFP","created_at":"2026-07-05T03:31:54Z"}],"graph_snapshots":[{"event_id":"sha256:f6a142a0d3c6317c42a807aef3e2b96ac89940905af31413a7b409e8241e3023","target":"graph","created_at":"2026-07-05T03:31:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2010.00300/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Mathematical models in epidemiology are an indispensable tool to determine the dynamics and important characteristics of infectious diseases. Apart from their scientific merit, these models are often used to inform political decisions and intervention measures during an ongoing outbreak. However, reliably inferring the dynamics of ongoing outbreaks by connecting complex models to real data is still hard and requires either laborious manual parameter fitting or expensive optimization methods which have to be repeated from scratch for every application of a given model. In this work, we address ","authors_text":"Frederik Graw, Nico T. Mutters, Simiao Chen, Stefan T. Radev, Till B\\\"arnighausen, Ullrich K\\\"othe, Vanessa M. Eichel","cross_cats":["cs.LG","q-bio.PE"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.AP","submitted_at":"2020-10-01T11:01:49Z","title":"OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.00300","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1d8ebc81695432a7d137e76dbdfebec8092353d8fe2366b847c00f1d4d6ddfbf","target":"record","created_at":"2026-07-05T03:31:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"328d540ab37d21e3b7e506d4a8a948568320967e27d98751ceb3094e760a3396","cross_cats_sorted":["cs.LG","q-bio.PE"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.AP","submitted_at":"2020-10-01T11:01:49Z","title_canon_sha256":"9fc81e5e11dce8e152a97f3459816b16150475f240d010be3e899d2fefedca8c"},"schema_version":"1.0","source":{"id":"2010.00300","kind":"arxiv","version":4}},"canonical_sha256":"aa5501c0afd3040c1fc71efdcde30e7d65f5b1cd232a2e78b1280ea77f291fa8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aa5501c0afd3040c1fc71efdcde30e7d65f5b1cd232a2e78b1280ea77f291fa8","first_computed_at":"2026-07-05T03:31:54.253094Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:31:54.253094Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Gr1tm/RDS5l3Z+ia4SK9tGRybKR4ZXK12UL5vbrgCy4l4TOSCXG9MUNbwKgMp3OIHFUQvO8pBr2RJl0KUA6iDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:31:54.253652Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.00300","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1d8ebc81695432a7d137e76dbdfebec8092353d8fe2366b847c00f1d4d6ddfbf","sha256:f6a142a0d3c6317c42a807aef3e2b96ac89940905af31413a7b409e8241e3023"],"state_sha256":"6d89dcd4111a7c3e2592c04c098d7477313ab8c60041c30710f73e764b6818b5"}