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Estimating the Containment Effectiveness and Economic Cost of Inner-city Non-Pharmaceutical Interventions

Xihan Zhang, Yuqing Liu, Chen Zhao, Guijun Li

TL;DR

A large dataset of fine-grained real-world individual trajectory data from a major Chinese city is used to examine the trade-off between the epidemic containment effectiveness and economic cost of different NPIs, revealing significant variations in the outcomes of different NPIs across activation mechanisms and initial scales of undetected transmission.

Abstract

Non-pharmaceutical interventions (NPIs) are crucial for controlling pandemics, but existing research often overlooks the heterogeneity of individual behavior, which can lead to inaccurate evaluations of the effectiveness of strategies. In this paper, we use a large dataset of fine-grained real-world individual trajectory data from a major Chinese city to examine the trade-off between the epidemic containment effectiveness and economic cost of different NPIs. Our findings reveal significant variations in the outcomes of different NPIs across activation mechanisms and initial scales of undetected transmission. Based on these results, we construct a two-dimensional evaluation framework that comprehensively evaluates the impact of both the containment effectiveness and economic cost, which suggests that implementing stringent strategies-such as lockdown or contact tracing-at low activation thresholds can achieve optimal epidemic control with minimal economic cost. Our study provides a data-driven decision-making framework for understanding the implementation effectiveness and applicability of emergency management policies within urban systems.

Estimating the Containment Effectiveness and Economic Cost of Inner-city Non-Pharmaceutical Interventions

TL;DR

A large dataset of fine-grained real-world individual trajectory data from a major Chinese city is used to examine the trade-off between the epidemic containment effectiveness and economic cost of different NPIs, revealing significant variations in the outcomes of different NPIs across activation mechanisms and initial scales of undetected transmission.

Abstract

Non-pharmaceutical interventions (NPIs) are crucial for controlling pandemics, but existing research often overlooks the heterogeneity of individual behavior, which can lead to inaccurate evaluations of the effectiveness of strategies. In this paper, we use a large dataset of fine-grained real-world individual trajectory data from a major Chinese city to examine the trade-off between the epidemic containment effectiveness and economic cost of different NPIs. Our findings reveal significant variations in the outcomes of different NPIs across activation mechanisms and initial scales of undetected transmission. Based on these results, we construct a two-dimensional evaluation framework that comprehensively evaluates the impact of both the containment effectiveness and economic cost, which suggests that implementing stringent strategies-such as lockdown or contact tracing-at low activation thresholds can achieve optimal epidemic control with minimal economic cost. Our study provides a data-driven decision-making framework for understanding the implementation effectiveness and applicability of emergency management policies within urban systems.
Paper Structure (4 sections, 5 equations, 5 figures)

This paper contains 4 sections, 5 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Results
  3. Discussion
  4. Materials and Methods

Figures (5)

  • Figure 1: Schematic diagram of the NPI evaluation methodology framework. (A) Individual attributes and their data sources. (B) A data-driven individual-level transmission model for cities. The upper panel shows disease transmission patterns within and between three types of locations, while the lower panel depicts the transition process of an individual's health status. (C) Schematic of individual spatiotemporal trajectories, illustrating a co-location event involving three individuals between 21:00-21:45 on a given day. (D) Five common types NPIs selected for our study. (E) Economic evaluation model for implementing NPIs.
  • Figure 2: Infections and economic cost under baseline scenarios versus NPIs under different activation thresholds.Scenarios represent current positive cases reaching 5%, 15%, and 35% of the city's population. (A)–(C) New cases (main figure) and cumulative infection rate (inset); (D)–(F) Weekly economic cost.
  • Figure 3: Infections and economic cost under baseline scenarios versus NPIs under the dynamic zero-COVID policy. (A) New cases (main figure) and cumulative infection rate (inset); (B) Weekly economic cost.
  • Figure 4: Infections and economic cost under the baseline scenario versus NPIs implemented at a moderate activation threshold, for different initial scales of undetected transmission. (A)(D) Current severe cases; (B)(E) New deaths; (C)(F) Weekly economic cost.
  • Figure 5: Dual impacts of NPIs under varying initial scales of undetected transmission and activation thresholds. Different shapes and colors represent distinct strategies. Shape size and color intensity indicate activation thresholds (0%, 5%, 15%, and 35%), where larger shapes and lighter colors correspond to higher thresholds. Border shading reflects initial scales of undetected transmission (0.05%, 0.5%, and 3.5% of the city's population), with lighter borders indicating a larger initial scale of undetected transmission. Colored bands illustrate how the effectiveness of each NPI varies with the initial scale of undetected transmission and the activation threshold.