On the predictability of infectious disease outbreaks

Abstract

Infectious disease outbreaks recapitulate biology: they emerge from the multi-level interaction of hosts, pathogens, and environment. Therefore, outbreak forecasting requires an integrative approach to modeling. While specific components of outbreaks are predictable, it remains unclear whether fundamental limits to outbreak prediction exist. Here, adopting permutation entropy as a model independent measure of predictability, we study the predictability of a diverse collection of outbreaks and identify a fundamental entropy barrier for disease time series forecasting. However, this barrier is often beyond the time scale of single outbreaks, implying prediction is likely to succeed. We show that forecast horizons vary by disease and that both shifting model structures and social network heterogeneity are likely mechanisms for differences in predictability. Our results highlight the importance of embracing dynamic modeling approaches, suggest challenges for performing model selection across long time series, and may relate more broadly to the predictability of complex adaptive systems.

The intrinsic predictability of ecological time series and its potential to guide forecasting

“…). In epidemiology, a recent study on the information‐theoretic limits to forecasting of infectious diseases concluded that, for most diseases, the forecast horizon is often well beyond the time scale of outbreaks, implying that prediction is likely to succeed (Scarpino and Petri ).…”

Comparative Analysis of Early Dynamic Trends in Novel Coronavirus Outbreak: A Modeling Framework

“…The 24 transmissibility depends on the biological properties of the coronavirus, as 25 well as the contact patterns which can be intervened at the national or social 26 levels in populations. We hypothe- 28 sized that there could be a significant reduction of transmissibility with time 29 which is in accordance with the public health interventions in Wuhan and 30 other provinces. We hypothe- 28 sized that there could be a significant reduction of transmissibility with time 29 which is in accordance with the public health interventions in Wuhan and 30 other provinces.…”

Spreading predictability in complex networks

“…25 Zhang et al [31] proposed a measurement to state the efforts of users on Twitter to get 26 their information spreading. They found that small fraction of users with special 27 performance on participation can gain great influence, while most other users play a 28 role as middleware during the information propagation. 29 Up to now, most researches are focused on macro level of spreading prediction, but 30 few on micro level.…”

“…In [28], the researchers studied the predictability of a 15 Many state-of-the-art researches focus on predicting infection scale or threshold.…”

“…In [28], the researchers studied the predictability of a 15 The main difference between mean-field 13 and pair approximation is that the former(latter) approximates high-order moments in 14 term of first (second) order ones.…”