Risk prediction models for imported infectious diseases: A systematic review

Accurate prediction models for imported infectious diseases are essential for early warning, cross-border surveillance, and resource allocation. Despite the growing number of predictive models for imported infectious diseases, substantial heterogeneity in model structure, reporting, and performance across studies has led to confusion for researchers and limited practical uptake, even for the same disease.

We conducted a systematic review of predictive models for imported infectious diseases, following PRISMA guidelines. Searches in PubMed, Scopus, and Embase (through July 2025) identified studies developing or applying risk prediction models involving cross-border populations. Data were extracted using the CHARMS framework, focusing on model type, predictors, transmission routes, predicted outcomes, performance metrics, and validation methods.

From 4685 records, 273 studies (308 models) met inclusion criteria. Models were categorized by transmission route (respiratory, vector-borne, fecal-oral, others) and modelling approach (transmission dynamics model, classical statistical model, machine learning). Most models focused on respiratory or vector-borne diseases, and were predominantly used in international airport settings. Disease burden indicators were the most common estimation targets. Only 54.6% of studies reported model predictive performance, while 38.5% conducted internal validation and only 9.9% conducted external validation. Predictors varied by transmission route: environmental and climatic factors dominated vector-borne models, while mobility-related factors were central to respiratory and fecal-oral models. Modelling techniques varied in complexity, interpretability, and data dependency, each with specific advantages and limitations.

Imported infectious disease models suffer from limited scenario diversity and insufficient reporting of model performance and validation. Future modelling efforts should emphasize broader cross-border contexts, improved transparency, and robust external validation to enhance predictive model utility in global public health decision-making.