Historical data are essential for development of detection algorithms. Spatio-temporal data, however, are difficult to come by due to variety of issues concerning patient confidentiality. Several approaches have been used to generate benchmark data using statistical methods. Here, we demonstrate how to generate benchmark data using a discrete event model simulating inter- and intra-contact network transmission dynamics of infectious diseases in space and time using publicly available population data.
OBJECTIVE
The objective of this study is to generate benchmark data from a discrete event model simulating the transmission dynamics of an infectious disease within and between contact networks in urban settings using real population data. Such data can be used to test the performance of various temporal and spatio-temporal detection algorithms when real data are scarce or cannot be shared.