# Upscaling effects on infectious disease emergence risk emphasize the need for local planning in primary prevention within biodiversity hotspots

**Authors:** Renata L. Muylaert, David A. Wilkinson, Evita Izza Dwiyanti, David T. S. Hayman

PMC · DOI: 10.1038/s41598-025-21514-4 · 2025-10-27

## TL;DR

This study shows that local planning is crucial for preventing disease outbreaks in biodiversity hotspots, as upscaled data can miss key details.

## Contribution

The study introduces a multi-scale spatial analysis to assess zoonotic disease emergence risks in biodiversity hotspots.

## Key findings

- High-resolution spatial data (around 500 m) is essential for accurate zoonotic risk assessments.
- Jakarta and West Java are identified as high-risk areas for epidemic spread.
- Population centers influence forest management and agroforestry practices affecting disease risk.

## Abstract

Zoonotic risk assessments are increasingly vital in the wake of recent epidemics. The microbial diversity of parasitic organisms correlates with host species richness, with regions of high biodiversity facing elevated risks of emerging zoonotic infections. While habitat loss and fragmentation reduce species diversity, anthropogenic encroachment, particularly in forested areas, amplifies human exposure to novel pathogens. This study integrates host habitat, biodiversity, human encroachment, and population at risk to estimate novel disease emergence and epidemic risk at multiple spatial scales. Using Java, Indonesia, as a case study, we demonstrate that degrading spatial resolution leads to information loss, with optimal resolutions typically below 2000 m, ideally around 500 m when native-resolution processing is unfeasible. Gravity models of epidemic spread highlight Jakarta and West Java as high-risk areas, with varying contributions from surrounding regions. Our spatial analysis underscores the influence of population centers on forest management and agroforestry practices. These findings offer valuable insights for guiding pandemic prevention research and improving pathogen- and driver-based risk monitoring strategies.

The online version contains supplementary material available at 10.1038/s41598-025-21514-4.

## Full-text entities

- **Diseases:** zoonotic infections (MESH:D015047), infectious disease (MESH:D003141)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Figures

5 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12559317/full.md

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Source: https://tomesphere.com/paper/PMC12559317