Evidence map›Paper›PMID 39977440›Full record

ArticlePLoS computational biology2025

Resilience of mobility network to dynamic population response across COVID-19 interventions: Evidences from Chile.

Pasquale Casaburi, Lorenzo Dall'Amico, Nicolò Gozzi, Kyriaki Kalimeri, Anna Sapienza, Rossano Schifanella, T Di Matteo, Leo Ferres, Mattia Mazzoli

Abstract read
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Pasquale CasaburiISI Foundation, Turin, Italy.
Lorenzo Dall'AmicoISI Foundation, Turin, Italy.ORCID 0000-0002-7493-6421
Nicolò GozziISI Foundation, Turin, Italy.
Kyriaki KalimeriISI Foundation, Turin, Italy.
Anna SapienzaISI Foundation, Turin, Italy.ORCID 0000-0002-0842-7987
Rossano SchifanellaISI Foundation, Turin, Italy.
T Di MatteoDepartment of Mathematics, King's College London, London, United Kingdom.ORCID 0000-0002-5899-9051
Leo FerresISI Foundation, Turin, Italy.ORCID 0000-0002-5899-9051
Mattia MazzoliISI Foundation, Turin, Italy.ORCID 0000-0002-8756-5535

Funding

Fondation BotnarFondazione CRTLagrange Project of the ISI Foundation
6 · The paper itself

Abstract

The COVID-19 pandemic highlighted the importance of non-traditional data sources, such as mobile phone data, to inform effective public health interventions and monitor adherence to such measures. Previous studies showed how socioeconomic characteristics shaped population response during restrictions and how repeated interventions eroded adherence over time. Less is known about how different population strata changed their response to repeated interventions and how this impacted the resulting mobility network. We study population response during the first and second infection waves of the COVID-19 pandemic in Chile and Spain. Via spatial lag and regression models, we investigate the adherence to mobility interventions at the municipality level in Chile, highlighting the significant role of wealth, labor structure, COVID-19 incidence, and network metrics characterizing business-as-usual municipality connectivity in shaping mobility changes during the two waves. We assess network structural similarities in the two periods by defining mobility hotspots and traveling probabilities in the two countries. As a proof of concept, we simulate and compare outcomes of an epidemic diffusion occurring in the two waves. While differences exist between factors associated with mobility reduction across waves in Chile, underscoring the dynamic nature of population response, our analysis reveals the resilience of the mobility network across the two waves. We test the robustness of our findings recovering similar results for Spain. Finally, epidemic modeling suggests that historical mobility data from past waves can be leveraged to inform future disease spatial invasion models in repeated interventions. This study highlights the value of historical mobile phone data for building pandemic preparedness and lessens the need for real-time data streams for risk assessment and outbreak response. Our work provides valuable insights into the complex interplay of factors driving mobility across repeated interventions, aiding in developing targeted mitigation strategies.

Indexed as

COVID-19Population DynamicsCell PhoneChileComputational BiologyHumansPandemicsSARS-CoV-2SpainTravel

Identifiers

PMID39977440
PMCPMC11870358

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.