Mathematical models to characterize early epidemic growth: A review.

摘要: There is a long tradition of using mathematical models to generate insights into the transmission dynamics infectious diseases and assess potential impact different intervention strategies. The increasing use for epidemic forecasting has highlighted importance designing reliable that capture baseline characteristics specific pathogens social contexts. More refined are needed however, in particular account variation early growth real epidemics gain better understanding mechanisms at play. Here, we review recent progress on modeling characterizing patterns from disease outbreak data, survey types formulations most useful capturing diversity profiles, ranging sub-exponential exponential dynamics. Specifically, incorporate spatial details or realistic population mixing structures, including meta-population models, individual-based network simple SIR-type effects reactive behavior changes inhomogeneous mixing. In this process, also analyze simulation data stemming detailed large-scale agent-based previously designed calibrated study how networks shape patterns, general dynamics, control international emergencies such as 2009 A/H1N1 influenza pandemic 2014-2015 Ebola West Africa.