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TRUVACE RECORD VERSION
record: TRV-2026-1142
version: 2
kind: revised
reason: Direction audit: the problem line stated no AI consequence and was removed. Filed as context; not counted as a gain or a problem.
timestamp: 2026-10-26T06:38:43.916864Z
status: published
lens: general
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: Context
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
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