{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ISDPPAPEMEPRRXVFPU3D5E2H2L","short_pith_number":"pith:ISDPPAPE","schema_version":"1.0","canonical_sha256":"4486f781e4611f18dea57d363e9347d2db6b161e764dc569689c7616b8b2f4a8","source":{"kind":"arxiv","id":"2407.11349","version":1},"attestation_state":"computed","paper":{"title":"Scaling Hawkes processes to one million COVID-19 cases","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.CO","authors_text":"Andrew J. Holbrook, Marc A. Suchard, Seyoon Ko","submitted_at":"2024-07-16T03:33:29Z","abstract_excerpt":"Hawkes stochastic point process models have emerged as valuable statistical tools for analyzing viral contagion. The spatiotemporal Hawkes process characterizes the speeds at which viruses spread within human populations. Unfortunately, likelihood-based inference using these models requires $O(N^2)$ floating-point operations, for $N$ the number of observed cases. Recent work responds to the Hawkes likelihood's computational burden by developing efficient graphics processing unit (GPU)-based routines that enable Bayesian analysis of tens-of-thousands of observations. We build on this work and d"},"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":"2407.11349","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2024-07-16T03:33:29Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"fe76cf8e2c9afc551828c7773c3d3ea3af2469e05a4d215e59341f49fa75c2bc","abstract_canon_sha256":"3c367ad58ffeea81901b2293997b4bd012513b92385a18aecacc27d9dfac496e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:44:22.848905Z","signature_b64":"E7rzX5qPcUWJ1U41OiRSbTZn1Fp73afY7zdtwsz2P6qE7rALGQdRJyi9OTuEFTY+6JLQPEyHAkQO27L6ahLdAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4486f781e4611f18dea57d363e9347d2db6b161e764dc569689c7616b8b2f4a8","last_reissued_at":"2026-07-05T08:44:22.848515Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:44:22.848515Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scaling Hawkes processes to one million COVID-19 cases","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.CO","authors_text":"Andrew J. Holbrook, Marc A. Suchard, Seyoon Ko","submitted_at":"2024-07-16T03:33:29Z","abstract_excerpt":"Hawkes stochastic point process models have emerged as valuable statistical tools for analyzing viral contagion. The spatiotemporal Hawkes process characterizes the speeds at which viruses spread within human populations. Unfortunately, likelihood-based inference using these models requires $O(N^2)$ floating-point operations, for $N$ the number of observed cases. Recent work responds to the Hawkes likelihood's computational burden by developing efficient graphics processing unit (GPU)-based routines that enable Bayesian analysis of tens-of-thousands of observations. We build on this work and d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.11349","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/2407.11349/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":"2407.11349","created_at":"2026-07-05T08:44:22.848573+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.11349v1","created_at":"2026-07-05T08:44:22.848573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.11349","created_at":"2026-07-05T08:44:22.848573+00:00"},{"alias_kind":"pith_short_12","alias_value":"ISDPPAPEMEPR","created_at":"2026-07-05T08:44:22.848573+00:00"},{"alias_kind":"pith_short_16","alias_value":"ISDPPAPEMEPRRXVF","created_at":"2026-07-05T08:44:22.848573+00:00"},{"alias_kind":"pith_short_8","alias_value":"ISDPPAPE","created_at":"2026-07-05T08:44:22.848573+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ISDPPAPEMEPRRXVFPU3D5E2H2L","json":"https://pith.science/pith/ISDPPAPEMEPRRXVFPU3D5E2H2L.json","graph_json":"https://pith.science/api/pith-number/ISDPPAPEMEPRRXVFPU3D5E2H2L/graph.json","events_json":"https://pith.science/api/pith-number/ISDPPAPEMEPRRXVFPU3D5E2H2L/events.json","paper":"https://pith.science/paper/ISDPPAPE"},"agent_actions":{"view_html":"https://pith.science/pith/ISDPPAPEMEPRRXVFPU3D5E2H2L","download_json":"https://pith.science/pith/ISDPPAPEMEPRRXVFPU3D5E2H2L.json","view_paper":"https://pith.science/paper/ISDPPAPE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.11349&json=true","fetch_graph":"https://pith.science/api/pith-number/ISDPPAPEMEPRRXVFPU3D5E2H2L/graph.json","fetch_events":"https://pith.science/api/pith-number/ISDPPAPEMEPRRXVFPU3D5E2H2L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ISDPPAPEMEPRRXVFPU3D5E2H2L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ISDPPAPEMEPRRXVFPU3D5E2H2L/action/storage_attestation","attest_author":"https://pith.science/pith/ISDPPAPEMEPRRXVFPU3D5E2H2L/action/author_attestation","sign_citation":"https://pith.science/pith/ISDPPAPEMEPRRXVFPU3D5E2H2L/action/citation_signature","submit_replication":"https://pith.science/pith/ISDPPAPEMEPRRXVFPU3D5E2H2L/action/replication_record"}},"created_at":"2026-07-05T08:44:22.848573+00:00","updated_at":"2026-07-05T08:44:22.848573+00:00"}