Algorithm-driven HPV vaccine allocation paradigm for cervical cancer elimination targeting the Chinese population.

Abstract

This study identifies optimal, fiscally sustainable HPV vaccination strategies for China using Bayesian optimization and transmission dynamics modeling. By integrating a demographic-based HPV transmission model with a decision-analysis framework, we simulated cervical cancer burden across varying time horizons (30-100 years). Our budget impact analysis, incorporating willingness-to-pay data from a multi-center contingent valuation survey of 787 parents, reveals critical funding dynamics. We found that achieving cervical cancer elimination within 40 years necessitates a 23.97% vaccination coverage among 15-17-year-old girls using the domestic nonavalent vaccine. Conversely, a rapid 30-year elimination target requires 76.66% coverage. At current market prices, autonomous consumer demand falls short, exposing a ¥35.93 billion funding gap for the 40-year target. To mitigate these financial barriers and ensure long-term fiscal resilience, we propose a tripartite financing mechanism-allocating costs among consumers (45.90%), the government (26.19%), and medical insurance (27.91%). These algorithm-driven findings provide an actionable, evidence-based framework for optimizing multi-party health financing and accelerating cervical cancer elimination in China.