Simulating Delay in Seeking Treatment for Stroke due to COVID-19 Concerns with a Hybrid Agent-Based and Equation-Based Model

TL;DR

This study models how COVID-19 concerns affect delays in stroke treatment using a hybrid simulation approach, revealing that pandemic peaks influence treatment delays and patient outcomes.

Contribution

It introduces a novel hybrid agent-based and equation-based model to simulate behavioral delays in seeking stroke treatment during pandemics.

Findings

Multiple smaller pandemic peaks reduce treatment delays.

Behavioral changes significantly impact treatment timing.

Delays may increase healthcare costs and worsen patient outcomes.

Abstract

COVID-19 has caused tremendous strain on healthcare systems worldwide. At the same time, concern within the population over this strain and the chances of becoming infected has potentially reduced the likelihood of people seeking medical treatment for other health events. Stroke is a medical emergency and swift treatment can make a large difference in patient outcomes. Understanding how concern over the COVID-19 pandemic might impact the time delay in seeking treatment after a stroke can be important in understanding both the long term cost implications and how to target individuals during another pandemic scenario to remind them of the importance of seeking treatment immediately. We present a hybrid agent-based and equation-based model to simulate the delay in seeking treatment for stroke due to concerns over COVID-19 and show that even small changes in behaviour impact the average delay in seeking treatment for the population. This delay could potentially impact the outcomes for stroke patients and future healthcare costs to support them. We find that introducing control measures and having multiple smaller peaks of the pandemic results in less delay in seeking treatment compared to a scenario with one large peak.