# Study on the impact of COVID-19 epidemic and agent disease risk simulation model based on individual factors in Xi’an City

## TL;DR

This study develops an agent-based model to simulate the spread of COVID-19 in Xi’an, using individual-level factors to predict epidemic trends and support public health decisions.

## Contribution

The paper introduces an agent-based model incorporating individual-level factors for simulating disease risk and epidemic trends in Xi’an.

## Key findings

- The model shows strong consistency with official data, validating its accuracy and adaptability.
- It effectively simulates epidemic impacts and disease risks using real-world population and mobility data.
- The model supports scenario testing and aids in adjusting public health and individual behavioral strategies.

## Abstract

Since the first discovery and reporting of the COVID - 19 pandemic towards the end of 2019, the virus has rapidly propagated across the world. This has led to a remarkable spike in the number of infections. Even now, doubt lingers over whether it has completely disappeared. Moreover, the issue of restoring normal life while ensuring safety continues to be a crucial challenge that public health agencies and people globally are eager to tackle.

To thoroughly understand the epidemic’s outbreak and transmission traits and formulate timely prevention measures to fully safeguard human lives and property, this paper presents an agent - based model incorporating individual - level factors.

The model designates Xi'an—where a characteristic disease outbreak occurred—as the research area. The simulation results demonstrate substantial consistency with official records, effectively validating the model’s applicability, adaptability, and generalizability. This validated capacity enables accurate prediction of epidemic trends and comprehensive assessment of disease risks.

From late 2021 to early 2022, it employs a one - to - one population simulation approach and simulates epidemic impacts and disease risks. Initially, using building statistical data in the study area, the model reconstructs the local real - world geographical environment. Leveraging data from the seventh national population census, it also replicates the study area’s population characteristics. Next, the model takes into account population mobility, contact tracing, patient treatment, and the diagnostic burden of COVID - 19 - like influenza symptoms. It integrates epidemic transmission impact parameters into the model framework. Eventually, the model’s results are compared with official data for validation, and it’s applied to hypothetical scenarios. It provides scientific theoretical tools to support the implementation of government - driven prevention and control measures. Additionally, it facilitates the adjustment of individual behavioral guidelines, promoting more effective epidemic management.