Calibration of transmission-dynamic infectious disease models: A scoping review and reporting framework.

OBJECTIVE/BACKGROUND: Transmission-dynamic models are commonly used to study infectious disease epidemiology. Calibration involves identifying model parameter values that align model outputs with observed data or other evidence. Inaccurate calibration and inconsistent reporting produce inference errors and limit reproducibility, compromising confidence in the validity of modeled results. No standardized framework exists for reporting on calibration of infectious disease models, and an understanding of current calibration approaches is lacking. METHODS: We developed the Purpose-Inputs-Process-Outputs (PIPO) framework for reporting calibration practices and applied it in a scoping review to assess calibration approaches and evaluate reporting comprehensiveness in transmission-dynamic models of tuberculosis, HIV and malaria published between January 1, 2018, and January 16, 2024. We searched relevant databases and websites to identify eligible publications, including peer-reviewed studies where these models were calibrated to empirical data or published estimates. RESULTS: We identified 411 eligible studies encompassing 419 models, with 74% (n = 309) being compartmental models and 20% (n = 81) individual-based models (IBMs). The predominant analytical purpose was to evaluate interventions (71% of models, n = 298). Parameters were calibrated mainly because they were unknown or ambiguous (40%, n = 168), or because determining their value was relevant to the scientific question beyond being necessary to run the model (20%, n = 85). The choice of calibration method was significantly associated with model structure (p-value<0.001) and stochasticity (p-value = 0.006), with approximate Bayesian computation more frequently used with IBMs and Markov-Chain Monte Carlo with compartmental models. Regarding reporting comprehensiveness, all PIPO framework items were reported in 4% (n = 18) of models; 11-14 items in 66% (n = 277), and 10 or fewer items in 28% (n = 124). Implementation code was the least reported, available in only 20% (n = 82) of models. CONCLUSIONS: Reporting on calibration is heterogeneous in recent infectious disease modeling literature. Our proposed framework for reporting of calibration approaches could support improved reproducibility and credibility of modeled analyses.