ArticleInnovation (Cambridge (Mass.))2025
The effective intervening opportunity model for seasonal human mobility.
Article in Innovation (Cambridge (Mass.)), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
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Who cites it
2 citing papers in PubMed.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Seasonal human mobility plays a crucial role in economic growth, labor market dynamics, and public health management. Traditional mobility models often rely on geographic distance, which fails to capture the influence of destination popularity, such as family gatherings or economic opportunities. To address this, we introduce the effective intervening opportunity (EIO) model, which replaces geographic distance with a more accurate "effective distance" that integrates both destination appeal and proximity. We use mobility flow data from 400 million mobile users over 6 years to simulate China's
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Registered trials
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.