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Researchers outline statistical pitfalls in studies of how parasites and disease risk spread

Study 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. These datasets span local to global host-parasite interactions and often integrate diverse host or parasite traits across evolutionary histories.

Read the study 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…

By the Truvace desk1 min read1 sourceEvidence: Emerging · 30/100 — Commentary, opinion, or narrative review · 1 independent sourcePeer-reviewedTRV-2026-1142· 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/.

In brief

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

  1. Disease macroecology relies on large, complex datasets to understand the biotic and abiotic factors shaping parasite distributions and emerging infectious disease risk.
  2. These datasets span local to global host-parasite interactions and often integrate diverse host or parasite traits across evolutionary histories.
  3. Selecting appropriate statistical approaches requires first asking key questions about the study system: How well is the system understood?

The 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

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

  1. Peer-reviewedJournal of Helminthology2026-09-18

The debate