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- W4299732559 abstract "In this paper we introduce a novel method to conduct inference with models defined through a continuous-time Markov process, and we apply these results to a classical stochastic SIR model as a case study. Using the inverse-size expansion of van Kampen we obtain approximations for first and second moments for the state variables. These approximate moments are in turn matched to the moments of an inputed generic discrete distribution aimed at generating an approximate likelihood that is valid both for low count or high count data. We conduct a full Bayesian inference to estimate epidemic parameters using informative priors. Excellent estimations and predictions are obtained both in a synthetic data scenario and in two Dengue fever case studies." @default.
- W4299732559 created "2022-10-03" @default.
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- W4299732559 date "2011-11-09" @default.
- W4299732559 modified "2023-09-23" @default.
- W4299732559 title "Towards Uncertainty Quantification and Inference in the stochastic SIR Epidemic Model" @default.
- W4299732559 doi "https://doi.org/10.48550/arxiv.1111.2260" @default.
- W4299732559 hasPublicationYear "2011" @default.
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