2017/08/02 by Atiye Alaeddini, Alaeddini, Atiye, Daniel J. Klein +1
Biochemistry, Genetics and Molecular Biology · Mathematics · #COVID-19 epidemiological studies #Computation (stat.CO) #FOS: Biological sciences #FOS: Computer and information sciences #Quantitative Methods (q-bio.QM) #q-bio.QM #stat.CO
paper · pdf · doi:10.48550/arxiv.1708.00886
Proceedings of the 2017 Winter Simulation Conference
arxiv created 2017/08/02 · openalex publication_date 2017/08/02 · arxiv updated 2017/08/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Epidemiological models have tremendous potential to forecast disease burden and quantify the impact of interventions. Detailed models are increasingly popular, however these models tend to be stochastic and very costly to evaluate. Fortunately, readily available high-performance cloud computing now means that these models can be evaluated many times in parallel. Here, we briefly describe PSPO, an extension to Spall's second-order stochastic optimization algorithm, Simultaneous Perturbation Stochastic Approximation (SPSA), that takes full advantage of parallel computing environments. The main focus of this work is on the use of PSPO to maximize the pseudo-likelihood of a stochastic epidemiological model to data from a 1861 measles outbreak in Hagelloch, Germany. Results indicate that PSPO far outperforms gradient ascent and SPSA on this challenging likelihood maximization problem.