From behavioral to structural correlates: a multi-phase analysis of global reported mpox

Area of Science:

• Epidemiology
• Public Health
• Machine Learning

Background:

• Mpox (monkeypox) epidemiology evolved from zoonotic spillover to sustained human transmission.
• Macro-level correlates of mpox burden across epidemic phases are not well understood, hindering targeted responses.

Purpose of the Study:

• To quantify macro-level correlates of mpox burden across distinct epidemic phases.
• To develop a phase-sensitive analytical framework for understanding mpox epidemiology.

Main Methods:

• Utilized World Health Organization (WHO) global daily mpox surveillance data (Jan 2022 - Apr 2025).
• Employed a phase-sensitive framework integrating multi-domain macro-level variables.
• Optimized five ensemble machine learning models and interpreted using SHAP values.

Main Results:

• Identified four temporal-epidemiological phases of mpox incidence: emergence, spread, dissemination, and resurgence.
• Revealed a shift in correlates from behavioral (contact networks, mobility) to structural (socioeconomic disparities, health capacity) factors.
• The LGBT+ Legal Index was the strongest predictor, with context-dependent associations reflecting reporting visibility.

Conclusions:

• Global mpox burden is characterized by phase-specific and geographic heterogeneity, transitioning from behavioral to structural drivers.
• Supports phase-tailored interventions including prevention, health-system strengthening, equitable surveillance, and stigma reduction.