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Infectious disease models are widely used by epidemiologists to improve the understanding of transmission dynamics and disease natural history, and to predict the possible effects of interventions. As the complexity of such models increases, however, it becomes increasingly challenging to robustly calibrate them to empirical data. History matching with emulation is a calibration method that has been successfully applied to such models, but has not been widely used in epidemiology partly due to the lack of available software. To address this issue, we developed a new, user-friendly R package hmer to simply and efficiently perform history matching with emulation. In this paper, we demonstrate the first use of hmer for calibrating a complex deterministic model for the country-level implementation of tuberculosis vaccines to 115 low- and middle-income countries. The model was fit to 9-13 target measures, by varying 19-22 input parameters. Overall, 105 countries were successfully calibrated. Among the remaining countries, hmer visualisation tools, combined with derivative emulation methods, provided strong evidence that the models were misspecified and could not be calibrated to the target ranges. This work shows that hmer can be used to simply and rapidly calibrate a complex model to data from over 100 countries, making it a useful addition to the epidemiologist's calibration tool-kit., Competing Interests: Declaration of interests All authors declare no conflicts of interest., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Introduction: Age-mixing patterns can have substantial effects on infectious disease dynamics and intervention effects. Data on close contacts (people spoken to and/or touched) are often used to estimate age-mixing. These are not the only relevant contacts for airborne infections such as tuberculosis, where transmission can occur between anybody 'sharing air' indoors. Directly collecting data on age-mixing patterns between casual contacts (shared indoor space, but not 'close') is difficult however. We demonstrate a method for indirectly estimating age-mixing patterns between casual indoor contacts from social contact data., Methods: We estimated age-mixing patterns between close, casual, and all contacts using data from a social contact survey in South Africa. The age distribution of casual contacts in different types of location was estimated from the reported time spent in the location type by respondents in each age group., Results: Patterns of age-mixing calculated from contact numbers were similar between close and all contacts, however patterns of age-mixing calculated from contact time were more age-assortative in all contacts than in close contacts. There was also more variation by age group in total numbers of casual and all contacts, than in total numbers of close contacts. Estimates were robust to sensitivity analyses., Conclusions: Patterns of age-mixing can be estimated for all contacts using data that can be easily collected as part of social contact surveys or time-use surveys, and may differ from patterns between close contacts., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)