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These often have high-dimensional input and output spaces, with the models exhibiting complex, non-linear dynamics. Coupled with a paucity of necessary data, this results in a large number of non-ignorable hidden states that must be handled by the inference routine. Likelihood-based approaches to this missing data problem are very flexible, but challenging to scale, due to having to monitor and update these hidden states. Methods based on simulating the hidden states directly from the model-of-interest have an advantage that they are often more straightforward to code, and thus are easier to implement and adapt in real-time. However, these often require evaluating very large numbers of simulations, rendering them infeasible for many large-scale problems. We present a framework for using emulation-based methods to calibrate a large-scale, stochastic, age-structured, spatial meta-population model of COVID-19 transmission in England and Wales. By embedding a model discrepancy process into the simulation model, and combining this with particle filtering, we show that it is possible to calibrate complex models to high-dimensional data by emulating the log-likelihood surface instead of individual data points. The use of embedded model discrepancy also helps to alleviate other key challenges, such as the introduction of infection across space and time. We conclude with a discussion of major challenges remaining and key areas for future work.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1014299","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T17:55:21Z","timestamp":1780336521000},"page":"e1014299","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["On real-time calibrated prediction for complex model-based decision support in pandemics: Part 2"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9485-3236","authenticated-orcid":true,"given":"Trevelyan J.","family":"McKinley","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel B.","family":"Williamson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James M.","family":"Salter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Challen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leon","family":"Danon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ben","family":"Youngman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doug","family":"McNeall","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"pcbi.1014299.ref001","volume-title":"Report 9\u2014Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand","author":"NM Ferguson","year":"2020"},{"issue":"1","key":"pcbi.1014299.ref002","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1008619","article-title":"Predictions of COVID-19 dynamics in the UK: short-term forecasting and analysis of potential exit strategies","volume":"17","author":"MJ Keeling","year":"2021","journal-title":"PLoS Comput Biol"},{"issue":"1829","key":"pcbi.1014299.ref003","doi-asserted-by":"crossref","first-page":"20200279","DOI":"10.1098\/rstb.2020.0279","article-title":"Real-time nowcasting and forecasting of COVID-19 dynamics in England: the first wave","volume":"376","author":"P Birrell","year":"2021","journal-title":"Philos Trans R Soc Lond B Biol Sci"},{"issue":"1829","key":"pcbi.1014299.ref004","doi-asserted-by":"crossref","first-page":"20200272","DOI":"10.1098\/rstb.2020.0272","article-title":"A spatial model of COVID-19 transmission in England and Wales: early spread, peak timing and the impact of seasonality","volume":"376","author":"L Danon","year":"2021","journal-title":"Philos Trans R Soc Lond B Biol Sci"},{"issue":"6","key":"pcbi.1014299.ref005","article-title":"A network modelling approach to assess non-pharmaceutical disease controls in a worker population: an application to SARS-CoV-2","volume":"17","author":"EM Hill","year":"2021","journal-title":"PLoS Comput Biol"},{"issue":"6","key":"pcbi.1014299.ref006","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1016\/S1473-3099(21)00143-2","article-title":"Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study","volume":"21","author":"S Moore","year":"2021","journal-title":"Lancet Infect Dis"},{"key":"pcbi.1014299.ref007","first-page":"231832","article-title":"Combining models to generate consensus medium-term projections of hospital admissions, occupancy and deaths relating to COVID-19 in England","volume":"1","author":"H Manley","year":"2021","journal-title":"Royal Society Open Science"},{"issue":"9","key":"pcbi.1014299.ref008","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1177\/09622802221109523","article-title":"Uncertainty quantification for epidemiological forecasts of COVID-19 through combinations of model predictions","volume":"31","author":"DS Silk","year":"2022","journal-title":"Stat Methods Med Res"},{"key":"pcbi.1014299.ref009","unstructured":"Bowman VE. 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