{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:DVJWZV6BOITH6TMKVZBV5HHJKL","short_pith_number":"pith:DVJWZV6B","schema_version":"1.0","canonical_sha256":"1d536cd7c172267f4d8aae435e9ce952d3705bb0f577f33ddfe84f3035d9e3d7","source":{"kind":"arxiv","id":"2105.00620","version":2},"attestation_state":"computed","paper":{"title":"COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"cs.LG","authors_text":"Shihao Yang, Siawpeng Er, Tuo Zhao","submitted_at":"2021-05-03T04:00:59Z","abstract_excerpt":"The global spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has cast a significant threat to mankind. As the COVID-19 situation continues to evolve, predicting localized disease severity is crucial for advanced resource allocation. This paper proposes a method named COURAGE (COUnty aggRegation mixup AuGmEntation) to generate a short-term prediction of 2-week-ahead COVID-19 related deaths for each county in the United States, leveraging modern deep learning techniques. Specifically, our method adopts a self-attention model from Natural Language Processing, known as th"},"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":"2105.00620","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-05-03T04:00:59Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"ffc4e53a520a6143ec32a1a7977defd76379912e11b9a62954980ed2b475320c","abstract_canon_sha256":"b7c3270b41e611f4d2be725fa0d11d91c6d58dca26ab7cf2324bc104f838383f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:47:59.823545Z","signature_b64":"4oOSUOF9fAg0DZONd59Tkq+L3e3L0io7mtHBPgJ4BHWjDSsugHkEMh+KzPI0Jxn4Vod9HemUBuWJGdfHfSkUDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d536cd7c172267f4d8aae435e9ce952d3705bb0f577f33ddfe84f3035d9e3d7","last_reissued_at":"2026-07-05T02:47:59.823031Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:47:59.823031Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"cs.LG","authors_text":"Shihao Yang, Siawpeng Er, Tuo Zhao","submitted_at":"2021-05-03T04:00:59Z","abstract_excerpt":"The global spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has cast a significant threat to mankind. As the COVID-19 situation continues to evolve, predicting localized disease severity is crucial for advanced resource allocation. This paper proposes a method named COURAGE (COUnty aggRegation mixup AuGmEntation) to generate a short-term prediction of 2-week-ahead COVID-19 related deaths for each county in the United States, leveraging modern deep learning techniques. Specifically, our method adopts a self-attention model from Natural Language Processing, known as th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.00620","kind":"arxiv","version":2},"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/2105.00620/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":"2105.00620","created_at":"2026-07-05T02:47:59.823097+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.00620v2","created_at":"2026-07-05T02:47:59.823097+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.00620","created_at":"2026-07-05T02:47:59.823097+00:00"},{"alias_kind":"pith_short_12","alias_value":"DVJWZV6BOITH","created_at":"2026-07-05T02:47:59.823097+00:00"},{"alias_kind":"pith_short_16","alias_value":"DVJWZV6BOITH6TMK","created_at":"2026-07-05T02:47:59.823097+00:00"},{"alias_kind":"pith_short_8","alias_value":"DVJWZV6B","created_at":"2026-07-05T02:47:59.823097+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/DVJWZV6BOITH6TMKVZBV5HHJKL","json":"https://pith.science/pith/DVJWZV6BOITH6TMKVZBV5HHJKL.json","graph_json":"https://pith.science/api/pith-number/DVJWZV6BOITH6TMKVZBV5HHJKL/graph.json","events_json":"https://pith.science/api/pith-number/DVJWZV6BOITH6TMKVZBV5HHJKL/events.json","paper":"https://pith.science/paper/DVJWZV6B"},"agent_actions":{"view_html":"https://pith.science/pith/DVJWZV6BOITH6TMKVZBV5HHJKL","download_json":"https://pith.science/pith/DVJWZV6BOITH6TMKVZBV5HHJKL.json","view_paper":"https://pith.science/paper/DVJWZV6B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.00620&json=true","fetch_graph":"https://pith.science/api/pith-number/DVJWZV6BOITH6TMKVZBV5HHJKL/graph.json","fetch_events":"https://pith.science/api/pith-number/DVJWZV6BOITH6TMKVZBV5HHJKL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DVJWZV6BOITH6TMKVZBV5HHJKL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DVJWZV6BOITH6TMKVZBV5HHJKL/action/storage_attestation","attest_author":"https://pith.science/pith/DVJWZV6BOITH6TMKVZBV5HHJKL/action/author_attestation","sign_citation":"https://pith.science/pith/DVJWZV6BOITH6TMKVZBV5HHJKL/action/citation_signature","submit_replication":"https://pith.science/pith/DVJWZV6BOITH6TMKVZBV5HHJKL/action/replication_record"}},"created_at":"2026-07-05T02:47:59.823097+00:00","updated_at":"2026-07-05T02:47:59.823097+00:00"}