# Where to refine spatial data to improve accuracy in crop disease modelling: an analytical approach with examples for cassava

## TL;DR

This paper introduces a method to identify where improving spatial data on cassava crops can most effectively enhance disease spread predictions.

## Contribution

A novel analytical method to prioritize spatial data refinement for better crop disease modeling accuracy.

## Key findings

- The method identifies areas where host data errors most impact pathogen invasion predictions.
- Applying the method in sub-Saharan Africa could improve cassava brown streak virus spread modeling.
- Spatial prioritization helps optimize data refinement efforts for epidemic prediction accuracy.

## Abstract

Epidemiological modelling plays an important role in global food security by informing strategies for the control and management of invasion and spread of crop diseases. However, the underlying data on spatial locations of host crops that are susceptible to a pathogen are often incomplete and inaccurate, thus reducing the accuracy of model predictions. Obtaining and refining datasets that fully represent a host landscape across territories can be a major challenge when predicting disease outbreaks. Therefore, it would be an advantage to prioritize areas in which data refinement efforts should be directed to improve the accuracy of epidemic prediction. In this paper, we present an analytical method to identify areas where potential errors in mapped host data would have the largest impact on modelled pathogen invasion and short-term spread. The method is based on an analytical approximation for the rate at which susceptible host crops become infected at the start of an epidemic. We show how implementing spatial prioritization for data refinement in a cassava-growing region in sub-Saharan Africa could be an effective means for improving accuracy when modelling the dispersal and spread of the crop pathogen cassava brown streak virus.