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Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates
[article]
2020
medRxiv
pre-print
Hawkes processes are used in machine learning for event clustering and causal inference, while they also can be viewed as stochastic versions of popular compartmental models used in epidemiology. Here we show how to develop accurate models of COVID-19 transmission using Hawkes processes with spatial-temporal covariates. We model the conditional intensity of new COVID-19 cases and deaths in the U.S. at the county level, estimating the dynamic reproduction number of the virus within an EM
doi:10.1101/2020.06.06.20124149
fatcat:dxqpaofamnenrhkh7mnz5cvchi