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Accurate pandemic caseload forecasting allows informed policy decisions on the adoption of non-pharmaceutical interventions (NPIs) to reduce disease transmission. Using COVID-19 as an example, we present Pandemic conditional Ordinary Differential Equation (PAN-cODE), a deep learning method to forecast daily increases in pandemic infections and deaths. By using a deep conditional latent variable model, PAN-cODE can generate alternative caseload trajectories based on alternate adoptions of NPIs, allowing stakeholders to make policy decisions in an informed manner. PAN-cODE also allows caseload estimation for regions that are unseen during model training. We demonstrate that, despite using less detailed data and having fully automated training, PAN-cODE\u2019s performance is comparable to state-of-the-art methods on 4-week-ahead and 6-week-ahead forecasting. Finally, we highlight the ability of PAN-cODE to generate realistic alternative outcome trajectories on select US regions.<\/jats:p>","DOI":"10.1093\/jamia\/ocac160","type":"journal-article","created":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T20:35:51Z","timestamp":1661978151000},"page":"2089-2095","source":"Crossref","is-referenced-by-count":1,"title":["PAN-cODE: COVID-19 forecasting using conditional latent ODEs"],"prefix":"10.1093","volume":"29","author":[{"given":"Ruian","family":"Shi","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Toronto , Toronto, Canada"},{"name":"Vector Institute for Artificial Intelligence , Toronto, Ontario, Canada"}]},{"given":"Haoran","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Toronto , Toronto, Canada"},{"name":"Vector Institute for Artificial Intelligence , Toronto, Ontario, 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