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TRUVACE RECORD VERSION record: TRV-2026-1142 version: 1 kind: certified reason: Certified into the record timestamp: 2026-09-19T06:53:35.438819Z status: published lens: p_space sector: health headline: Can't see the forest for the trees? Statistical considerations for disease macroecology dek: Disease macroecology relies on large, complex datasets to understand the biotic and abiotic factors shaping parasite distributions and emerging infectious disease risk. These datasets span local to global host-parasite interactions and often integrate diverse host or parasite traits across evolutionary histories. Selecting appropriate statistical approaches requires first asking key questions about the study system: How well is the system understood? How important is predictor accuracy? How much bias is present… gain_title: (none) problem_title: Can't see the forest for the trees? Statistical considerations for disease macroecology: Disease macroecology relies on large, complex datasets to understand the biotic and abiotic factors shaping parasite distributions and emerging infectious disease risk. trace_subject: (none) gain_reading: (none) gain_evidence: (none) problem_reading: Can't see the forest for the trees? Statistical considerations for disease macroecology: Disease macroecology relies on large, complex datasets to understand the biotic and abiotic factors shaping parasite distributions and emerging infectious disease risk. problem_evidence: (none) quick_read: Disease macroecology relies on large, complex datasets to understand the biotic and abiotic factors shaping parasite distributions and emerging infectious disease risk. These datasets span local to global host-parasite interactions and often integrate diverse host or parasite traits across evolutionary histories. We outline three broad analytical pathways commonly used in disease macroecology (frequentist models, Bayesian approaches, and machine learning) and link them to typical data contexts, providing R coding examples using current packages. limitation: tag: Evidence-backed problem key_points: Disease macroecology relies on large, complex datasets to understand the biotic and abiotic factors shaping parasite distributions and emerging infectious disease risk. | These datasets span local to global host-parasite interactions and often integrate diverse host or parasite traits across evolutionary histories. | Selecting appropriate statistical approaches requires first asking key questions about the study system: How well is the system understood? rundown: Disease macroecology relies on large, complex datasets to understand the biotic and abiotic factors shaping parasite distributions and emerging infectious disease risk. These datasets span local to global host-parasite interactions and often integrate diverse host or parasite traits across evolutionary histories. Selecting appropriate statistical approaches requires first asking key questions about the study system: How well is the system understood? How important is predictor accuracy? sources: - peer_reviewed | Journal of Helminthology | https://doi.org/10.1017/s0022149x26101874 | 2026-09-18 prev: 0000000000000000000000000000000000000000000000000000000000000000
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