Modelling epidemiological and economics processes – the case of cervical cancer

Abstract Background Mathematical models aid in predicting the epidemiological and economic consequences of interventions. While many diseases are clearly infectious or chronic, cervical cancer is a complex disease that is caused by human papillomavirus and its classification varies by disease stage, which complicates the model decision. As low screening coverage is a major barrier to reducing the incidence, especially in resource-limited regions, modelling possible interventions and assessing effectiveness is essential for better care. Methods This study analysed the applicability of different mathematical models for cervical cancer, focusing on epidemiological and economic processes. A scoping review, following PRISMA guidelines, identified models used in cervical cancer research, including screening, vaccination, and treatment interventions. Publications were screened for model type, rationale, and forecasting suitability.. Results Among 71 original articles retrieved, most papers used System Dynamics Models (23), followed by homogenous Markov models (15) and Discrete-Event Simulation (DES) (12). No studies have applied inhomogeneous Markov models. Scenario techniques and sensitivity analyses, including Monte Carlo simulations, were frequent. Advanced models were mainly used in high-income settings, except for DES. Most studies lacked justification for model selection, but publications using readily available models often omitted descriptions or rationales. Conclusions The Model choice depends on study goals, time horizon, and epidemic stage. Homogeneous Markov models suit short-term screening assessments, while system dynamics and inhomogeneous Markov models better capture long-term or lifetime modelling for screening evaluation. System Dynamics models are best suited for diseases like cervical cancer. Given the global burden and the lack of rationale in model selection, interdisciplinary collaboration is needed to enhance forecasting and cost-effectiveness. Key messages • The epidemiology and economics of cervical cancer are complex and require strategic simulations to assess the impact of interventions. • System Dynamics Models are best to model complex diseases, like cervical cancer.