# Impact of misinformation in temporal network epidemiology

## TL;DR

This paper studies how misinformation about contact networks affects disease outbreak predictions, revealing that errors cause exponential-like prediction inaccuracies influenced by network structure.

## Contribution

It introduces a systematic analysis of misinformation effects on epidemic modeling using empirical temporal networks and identifies structural factors affecting prediction accuracy.

## Key findings

- Prediction errors follow a stretched exponential decay with error frequency.
- Node identity and timestamp errors similarly impact outbreak size and duration predictions.
- Network structural factors influence the severity of misinformation effects.

## Abstract

We investigate the impact of misinformation about the contact structure on the ability to predict disease outbreaks. We base our study on 31 empirical temporal networks and tune the frequencies in errors in the node identities or timestamps of contacts. We find that for both these spreading scenarios, the maximal misprediction of both the outbreak size and time to extinction follows an stretched exponential convergence as a function of the error frequency. We furthermore determine the temporal-network structural factors influencing the parameters of this convergence.