Development, Testing, and Calibration of LINCS: A New Microsimulation Model of Maternal and Fetal Cytomegalovirus Infection in the US

ABSTRACT Background Congenital cytomegalovirus (cCMV) is a leading cause of birth defects and the most common cause of non-genetic sensorineural hearing loss in children. There is a lack of decision modeling frameworks that can project cytomegalovirus (CMV)-related patient outcomes and inform health policy. We created, tested, and calibrated a model of CMV acquisition and transmission in pregnancy using linked mother-infant dyads. Methods We developed the L inking IN fants and Mothers in C ytomegalovirus S imulation (LINCS) dyad-level Monte-Carlo microsimulation model of CMV infection among pregnant people and fetuses throughout pregnancy. We parameterized the model with data from the US, Canada and the EU from the existing literature, implemented rigorous code testing procedures, and calibrated a key set of parameters to match model output to external data on cCMV prevalence and symptom risk. Results A fully parameterized model for CMV among pregnant people and fetuses was developed, and the model code was confirmed to perform as specified. The calibration procedure identified parameter sets that generated model output closely matching the target values from the available data on cCMV prevalence and symptom risk. Conclusions The LINCS model’s ability to simulate the natural history of CMV infection during pregnancy was described and demonstrated, and the model was tested and calibrated to ensure proper functioning. Base case parameters were derived for CMV infection natural history to be used in future decision analyses of CMV testing and treatment strategies.