− HEALTH The FDA received a malfunction report involving Inogen one g5 oxygen concentrator, made by Inogen, Inc.. Rece…+ HEALTH Objective We systematically evaluated the diagnostic performance of artificial intelligence (AI)-assisted int…− HEALTH BackgroundFall-related injuries in older adults remain a major health concern, contributing to functional dep…− CLIMATE Background Chronic inflammation is a major driver of cardiovascular diseases, but mechanisms linking systemic…− HEALTH Disease macroecology relies on large, complex datasets to understand the biotic and abiotic factors shaping p…− HEALTH Background National Surgical Quality Improvement Program (NSQIP) data collection depends on labor-intensive m…+ HEALTH ObjectivePerceptual evaluation by speech-language pathologists (SLPs) is essential for initial evaluation of…− HEALTH Background Heart failure with preserved ejection fraction is a heterogeneous syndrome, and comorbid obstructi…
TruaceTracing the truth around AISaturday, September 19, 2026
Subscribe
Health·P Space·Evidence-backed problem·Published 2026-09-19

Can't see the forest for the trees? Statistical considerations for disease macroecology

Abstract: 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…

TRV-2026-1142Peer-reviewedPermanent record — cite & verify
Can't see the forest for the trees? Statistical considerations for disease macroecology

"Oh say can you see" by merra marie is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/.

The 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.

Main 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?
Problem

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.

The rundown

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

Reader signal

How should this claim be treated?

The debate