Modeling the Impact of Misinformation Dynamics on Antimicrobial Resistance

TL;DR

This paper introduces a mathematical model to analyze how misinformation about antibiotics influences behaviors that contribute to antimicrobial resistance, highlighting key factors affecting the spread of false beliefs and resistance.

Contribution

It develops a novel multi-strain misinformation model incorporating behavioral delays to study misinformation's role in antimicrobial resistance dynamics.

Findings

Identifies critical misinformation strains affecting antibiotic use

Demonstrates the impact of delays on misinformation spread

Provides insights for targeted public health interventions

Abstract

Antimicrobial Resistance (RAM) poses a significant threat to global public health, making important medicines less useful. While the medical and biological reasons behind RAM are well studied, we still don't know enough about how false health information affects people's actions, which can speed up RAM. This study presents a new mathematical model to investigate the complex interplay between the spread of misinformation and the dynamics of RAM. We adapt a multi-strain fake news model, including distinct population compartments representing individuals susceptible to, believing in, or skeptical of various ideas related to antibiotic use. The model considers multiple "strains" of misinformation, such as the wrong belief that antibiotics are effective for viral infections or not trusting medical advice regarding prudent antibiotic prescription. Time delays are integrated to reflect the latency in information processing, behavioral change, and the manifestation of resistance. Through stability analysis and numerical simulations, this research aims to identify critical factors and parameters that influence the propagation of harmful beliefs and their consequent impact on behaviors contributing to RAM. The findings could help develop public health campaigns to reduce the negative impact of misinformation on fighting antimicrobial resistance.