Prospects for detecting early warning signals in discrete event sequence data: application to epidemiological incidence data

Emma Southall; Michael J. Tildesley; Louise Dyson

using spatial variation [17, 18] , explored the 44 effects of detrending [8, 15] using the ensemble of multiple EWS [12, 13, 30] ; and datasets known to be going through a critical transition: from climate systems to stock 48 markets, to applications with ecological field data [16, 22, 23] . These studies observed 49 unexpected characteristic traits of common EWS, such as identifying a decreasing trend 50 in variance or standard deviation, leading to a discussion on the robustness of indicators. 51 It is therefore highly important to understand analytically how EWS are expected to 52 change on the approach to a critical transition for different data types to avoid any 53 misleading results. 54 The initial development of EWS in epidemiology focused on prevalence data, 55 producing analytical solutions and numerically testing the capabilities for statistical 56 indicators of emergence and elimination of infectious diseases [5] [6] [7] [8] . Analysis of 57 computer simulations of well-studied epidemiological systems have highlighted 58 challenges such as seasonality [6] or detrending of epidemiological time series data [8] .