ArticleChina CDC weekly2026
Assessment of Cross-Border Monkeypox Importation Risk: A Modeling Study - Worldwide, 2022-2025.
Article in China CDC weekly, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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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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Authors and funding
10 authors.
Funding
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Abstract
Introduction: Monkeypox (mpox) has re-emerged as a significant global public health threat. This study developed a practical, rapid early-warning tool to quantify country-level mpox importation risk and identify potential sources of cross-border transmission. Methods: We obtained weekly confirmed mpox case data and international passenger volume data spanning January 2022 through November 2025. We constructed an importation risk index (RI) that integrated epidemic intensity in source countries, passenger flows, and public health response capacity. Using a simplified ordinary differential equation framework, we derived a regional RI to estimate time-varying growth and acceleration parameters from surveillance time series. We then computed both real-time and baseline RI values and evaluated their lagged correlations with reported case counts. Results: The RI demonstrated consistently strong lagged correlations with subsequent reported case counts. The real-time RI provided earlier alerts in 93.3% of countries, with a mean lead time of 4.23 weeks - approximately 3.7 weeks ahead of the baseline RI. The dominant sources of importation risk varied across countries, suggesting that this model can inform tailored, country-specific prevention and control strategies. Conclusion: This cross-border risk assessment model provides an actionable framework for early warning and source attribution, supporting targeted surveillance and proactive allocation of public health resources. These findings underscore the critical importance of global health collaboration and timely data sharing.
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