Small World Model for scaling up prediction result based on SEIR model

TL;DR

This paper introduces a Small World Model that enhances epidemic predictions by scaling simulation results from partial GPS data to real-world scenarios, aiding policy evaluation.

Contribution

The study develops a novel scaling model that maps partial simulation data to real-world epidemic outcomes based on disease transmission principles.

Findings

The model effectively converts small-scale simulations to real-world estimates.

It enables analysis of policy impacts on mobility restrictions.

The approach improves epidemic prediction accuracy with limited data.

Abstract

Data-driven epidemic simulation helps better policymaking. Compared with macro-scale simulations driven by statistical data, individual-level GPS data can afford finer and spatialized results. However, the big GPS data, usually collected from mobile phone users, cannot cover all populations. Therefore, this study proposes a Small World Model, to map the results from the "small world" (simulation with partially sampled data) to the real world. Based on the basic principles of disease transmission, this study derives two parameters: a time scaling factor to map the simulated period to the real period, and an amount scaling factor to map the simulated infected number to the real infected number. It is believed that this model could convert the simulation of the "small world" into the state of the real world, and analyze the effectiveness of different mobility restriction policies.