Newly emerging pandemics like COVID-19 call for predictive models to implement precisely tuned responses to limit their deep impact on society. Standard epidemic models provide a theoretically well-founded dynamical description of disease incidence. For COVID-19 with infectiousness peaking before and at symptom onset, the SEIR model explains the hidden build-up of exposed individuals which creates challenges for containment strategies. However, spatial heterogeneity raises questions about the adequacy of modeling epidemic outbreaks on the level of a whole country. Here, we show that by applying sequential data assimilation to the stochastic SEIR epidemic model, we can capture the dynamic behavior of outbreaks on a regional level. Regional modeling, with relatively low numbers of infected and demographic noise, accounts for both spatial heterogeneity and stochasticity. Based on adapted models, short-term predictions can be achieved. Thus, with the help of these sequential data assimilation methods, more realistic epidemic models are within reach. Electronic supplementary material The online version of this article (10.1007/s11538-020-00834-8) contains supplementary material, which is available to authorized users.
【저자키워드】 COVID-19, Stochastic epidemic model, Sequential data assimilation, Ensemble Kalman filter, 【초록키워드】 heterogeneity, Epidemic, Predictive model, outbreak, response, Pandemics, Standard, symptom onset, supplementary material, individual, help, containment strategies, SEIR, disease incidence, Regional, country, limit, raise, peaking, question, explain, hidden, 【제목키워드】 Dynamics, Model, assimilation, SEIR, Regional, stochastic,