Abstract
Developing methods for anticipating the emergence or reemergence of infectious diseases is both important and timely; however, traditional model-based approaches are stymied by uncertainty surrounding the underlying drivers. Here, we demonstrate an operational, mechanism-agnostic detection algorithm for disease (re-)emergence based on early warning signals (EWSs) derived from the theory of critical slowing down. Specifically, we used computer simulations to train a supervised learning algorithm to detect the dynamical footprints of (re-)emergence present in epidemiological data. Our algorithm was then challenged to forecast the slowly manifesting, spatially replicated reemergence of mumps in England in the mid-2000s and pertussis post-1980 in the United States. Our method successfully anticipated mumps reemergence 4 years in advance, during which time mitigation efforts could have been implemented. From 1980 onwards, our model identified resurgent states with increasing accuracy, leading to reliable classification starting in 1992. Additionally, we successfully applied the detection algorithm to 2 vector-transmitted case studies, namely, outbreaks of dengue serotypes in Puerto Rico and a rapidly unfolding outbreak of plague in 2017 in Madagascar. Taken together, these findings illustrate the power of theoretically informed machine learning techniques to develop early warning systems for the (re-)emergence of infectious diseases.
Highlights
Outbreaks of infectious diseases continue to surprise and evade public health control policy. This is due to a combination of (1) the reemergence of familiar vaccine-preventable infectious diseases, such as mumps [1], measles [2], and pertussis [3]; (2) the evolution of resistance to antimicrobials, including methicillin-resistant Staphylococcus aureus (MRSA) [4], malaria [5], and extensively drug-resistant tuberculosis (XDR TB) [6]; (3) pathogen range expansion driven by anthropogenic changes in land use [7] and climate [8]; and (4) the emergence of novel pathogens from a zoonotic reservoir, such as HIV [9], severe acute respiratory syndrome coronavirus (SARS-CoV) [10], and Ebola virus [11]
Logistic regression was carried out on the ensemble of early warning signal (EWS) to assign a weight to each signal (Fig 2D)
Emergence is predicted at any time t when Dt > c, where c is a threshold (Fig 2I). We identified this threshold by minimizing classification error, using the receiver-operator characteristic (ROC) curve (Fig 2E)
Summary
Outbreaks of infectious diseases continue to surprise and evade public health control policy This is due to a combination of (1) the reemergence of familiar vaccine-preventable infectious diseases, such as mumps [1], measles [2], and pertussis [3]; (2) the evolution of resistance to antimicrobials, including methicillin-resistant Staphylococcus aureus (MRSA) [4], malaria [5], and extensively drug-resistant tuberculosis (XDR TB) [6]; (3) pathogen range expansion driven by anthropogenic changes in land use [7] and climate [8]; and (4) the emergence of novel pathogens from a zoonotic reservoir, such as HIV [9], severe acute respiratory syndrome coronavirus (SARS-CoV) [10], and Ebola virus [11]. We propose a mechanism-agnostic approach that harvests information contained in longitudinal epidemiological data
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