{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZNXM4TJTRYWI667XSMPG62OKPP","short_pith_number":"pith:ZNXM4TJT","schema_version":"1.0","canonical_sha256":"cb6ece4d338e2c8f7bf7931e6f69ca7be45f6ae5bce57cf5529a44145e96a01d","source":{"kind":"arxiv","id":"2308.13262","version":1},"attestation_state":"computed","paper":{"title":"Generative Bayesian modeling to nowcast the effective reproduction number from line list data with missing symptom onset dates","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Adrian Lison, Jana Huisman, Sam Abbott, Tanja Stadler","submitted_at":"2023-08-25T09:27:50Z","abstract_excerpt":"The time-varying effective reproduction number $R_t$ is a widely used indicator of transmission dynamics during infectious disease outbreaks. Timely estimates of $R_t$ can be obtained from observations close to the original date of infection, such as the date of symptom onset. However, these data often have missing information and are subject to right truncation. Previous methods have addressed these problems independently by first imputing missing onset dates, then adjusting truncated case counts, and finally estimating the effective reproduction number. This stepwise approach makes it diffic"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2308.13262","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2023-08-25T09:27:50Z","cross_cats_sorted":[],"title_canon_sha256":"c35e4e086905fef51e17da9a339d040e6c5c09a445f18369b5dbbe4f4a395cf2","abstract_canon_sha256":"4e303bb474900cd7917fca94ba57ff58081674322223b8f7acacd727d010eb4a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:43:05.510219Z","signature_b64":"I82/I7QDPJqMM3LKeTli7H10F5D6/Yoq1nEBVZ9ygjiZZUt0y2cNdPV4L1QZhaMaZVxX5EPcZ8riqWkrqcZFBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb6ece4d338e2c8f7bf7931e6f69ca7be45f6ae5bce57cf5529a44145e96a01d","last_reissued_at":"2026-07-05T08:43:05.509750Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:43:05.509750Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generative Bayesian modeling to nowcast the effective reproduction number from line list data with missing symptom onset dates","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Adrian Lison, Jana Huisman, Sam Abbott, Tanja Stadler","submitted_at":"2023-08-25T09:27:50Z","abstract_excerpt":"The time-varying effective reproduction number $R_t$ is a widely used indicator of transmission dynamics during infectious disease outbreaks. Timely estimates of $R_t$ can be obtained from observations close to the original date of infection, such as the date of symptom onset. However, these data often have missing information and are subject to right truncation. Previous methods have addressed these problems independently by first imputing missing onset dates, then adjusting truncated case counts, and finally estimating the effective reproduction number. This stepwise approach makes it diffic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.13262","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2308.13262/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2308.13262","created_at":"2026-07-05T08:43:05.509803+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.13262v1","created_at":"2026-07-05T08:43:05.509803+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.13262","created_at":"2026-07-05T08:43:05.509803+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZNXM4TJTRYWI","created_at":"2026-07-05T08:43:05.509803+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZNXM4TJTRYWI667X","created_at":"2026-07-05T08:43:05.509803+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZNXM4TJT","created_at":"2026-07-05T08:43:05.509803+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.20520","citing_title":"AQUA: A Large Language Model for Aquaculture & Fisheries","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZNXM4TJTRYWI667XSMPG62OKPP","json":"https://pith.science/pith/ZNXM4TJTRYWI667XSMPG62OKPP.json","graph_json":"https://pith.science/api/pith-number/ZNXM4TJTRYWI667XSMPG62OKPP/graph.json","events_json":"https://pith.science/api/pith-number/ZNXM4TJTRYWI667XSMPG62OKPP/events.json","paper":"https://pith.science/paper/ZNXM4TJT"},"agent_actions":{"view_html":"https://pith.science/pith/ZNXM4TJTRYWI667XSMPG62OKPP","download_json":"https://pith.science/pith/ZNXM4TJTRYWI667XSMPG62OKPP.json","view_paper":"https://pith.science/paper/ZNXM4TJT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.13262&json=true","fetch_graph":"https://pith.science/api/pith-number/ZNXM4TJTRYWI667XSMPG62OKPP/graph.json","fetch_events":"https://pith.science/api/pith-number/ZNXM4TJTRYWI667XSMPG62OKPP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZNXM4TJTRYWI667XSMPG62OKPP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZNXM4TJTRYWI667XSMPG62OKPP/action/storage_attestation","attest_author":"https://pith.science/pith/ZNXM4TJTRYWI667XSMPG62OKPP/action/author_attestation","sign_citation":"https://pith.science/pith/ZNXM4TJTRYWI667XSMPG62OKPP/action/citation_signature","submit_replication":"https://pith.science/pith/ZNXM4TJTRYWI667XSMPG62OKPP/action/replication_record"}},"created_at":"2026-07-05T08:43:05.509803+00:00","updated_at":"2026-07-05T08:43:05.509803+00:00"}