{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:6VEW5QTMVZQAGSWCBVX74B6WHI","short_pith_number":"pith:6VEW5QTM","schema_version":"1.0","canonical_sha256":"f5496ec26cae60034ac20d6ffe07d63a1e37a3d48b94d3c21e8e29dd5f78e39c","source":{"kind":"arxiv","id":"1802.06359","version":1},"attestation_state":"computed","paper":{"title":"Geostatistical methods for disease mapping and visualization using data from spatio-temporally referenced prevalence surveys","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Abdisalan M. Noor, Emanuele Giorgi, Peter J. Diggle, Robert W. Snow","submitted_at":"2018-02-18T10:19:53Z","abstract_excerpt":"In this paper we set out general principles and develop geostatistical methods for the analysis of data from spatio-temporally referenced prevalence surveys. Our objective is to provide a tutorial guide that can be used in order to identify parsimonious geostatistical models for prevalence mapping. A general variogram-based Monte Carlo procedure is proposed to check the validity of the modelling assumptions. We describe and contrast likelihood-based and Bayesian methods of inference, showing how to account for parameter uncertainty under each of the two paradigms. We also describe extensions o"},"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":"1802.06359","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-02-18T10:19:53Z","cross_cats_sorted":[],"title_canon_sha256":"b0d9ec03e4298d6b40046a351d67b0f56282265a1cfd7321f8d9363d74c568c7","abstract_canon_sha256":"3ad49af90bb954e38dd2c3b0c75d8b980ff9d7b896e3accdc8ca830351efdafd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:03.501695Z","signature_b64":"7ie+nnhUcPMV4oK9d6OXM0khcScX66NfOnD+7p3MKSJTIkT4DIhrhZ9wrHKq+XJqo7KLhsObKhthrBttohogDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f5496ec26cae60034ac20d6ffe07d63a1e37a3d48b94d3c21e8e29dd5f78e39c","last_reissued_at":"2026-05-18T00:23:03.501072Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:03.501072Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Geostatistical methods for disease mapping and visualization using data from spatio-temporally referenced prevalence surveys","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Abdisalan M. Noor, Emanuele Giorgi, Peter J. Diggle, Robert W. Snow","submitted_at":"2018-02-18T10:19:53Z","abstract_excerpt":"In this paper we set out general principles and develop geostatistical methods for the analysis of data from spatio-temporally referenced prevalence surveys. Our objective is to provide a tutorial guide that can be used in order to identify parsimonious geostatistical models for prevalence mapping. A general variogram-based Monte Carlo procedure is proposed to check the validity of the modelling assumptions. We describe and contrast likelihood-based and Bayesian methods of inference, showing how to account for parameter uncertainty under each of the two paradigms. We also describe extensions o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.06359","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":""},"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":"1802.06359","created_at":"2026-05-18T00:23:03.501161+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.06359v1","created_at":"2026-05-18T00:23:03.501161+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.06359","created_at":"2026-05-18T00:23:03.501161+00:00"},{"alias_kind":"pith_short_12","alias_value":"6VEW5QTMVZQA","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_16","alias_value":"6VEW5QTMVZQAGSWC","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_8","alias_value":"6VEW5QTM","created_at":"2026-05-18T12:32:11.075285+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/6VEW5QTMVZQAGSWCBVX74B6WHI","json":"https://pith.science/pith/6VEW5QTMVZQAGSWCBVX74B6WHI.json","graph_json":"https://pith.science/api/pith-number/6VEW5QTMVZQAGSWCBVX74B6WHI/graph.json","events_json":"https://pith.science/api/pith-number/6VEW5QTMVZQAGSWCBVX74B6WHI/events.json","paper":"https://pith.science/paper/6VEW5QTM"},"agent_actions":{"view_html":"https://pith.science/pith/6VEW5QTMVZQAGSWCBVX74B6WHI","download_json":"https://pith.science/pith/6VEW5QTMVZQAGSWCBVX74B6WHI.json","view_paper":"https://pith.science/paper/6VEW5QTM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.06359&json=true","fetch_graph":"https://pith.science/api/pith-number/6VEW5QTMVZQAGSWCBVX74B6WHI/graph.json","fetch_events":"https://pith.science/api/pith-number/6VEW5QTMVZQAGSWCBVX74B6WHI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6VEW5QTMVZQAGSWCBVX74B6WHI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6VEW5QTMVZQAGSWCBVX74B6WHI/action/storage_attestation","attest_author":"https://pith.science/pith/6VEW5QTMVZQAGSWCBVX74B6WHI/action/author_attestation","sign_citation":"https://pith.science/pith/6VEW5QTMVZQAGSWCBVX74B6WHI/action/citation_signature","submit_replication":"https://pith.science/pith/6VEW5QTMVZQAGSWCBVX74B6WHI/action/replication_record"}},"created_at":"2026-05-18T00:23:03.501161+00:00","updated_at":"2026-05-18T00:23:03.501161+00:00"}