Real-time decision-making during emergency disease outbreaks

Abstract

In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect.Our results show that the control policy that would have been chosen using all the data is also identified from an early stage in an outbreak using only the available data, despite high variability in projections of epidemic size. Critically, we find that it is an improved understanding of the locations of infected farms, rather than improved estimates of transmission parameters, that drives improved prediction of the relative performance of control interventions. However, the ability to estimate undetected infectious premises is a function of uncertainty in the transmission parameters. Here, we demonstrate the need for both real-time model fitting and generating projections to evaluate alternative control interventions throughout an outbreak. Our results highlight the use of using models at outbreak onset to inform policy and the importance of state-dependent interventions that adapt in response to additional information throughout an outbreak.

Context matters: using reinforcement learning to develop human-readable, state-dependent outbreak response policies

“…Parameters governing both the seeding of the outbreak and of control interventions were similar to previous modelling studies (e.g. [8,35,36]). Each simulation used the same four seeding premises.…”

Assessing the performance of real-time epidemic forecasts: A case study of Ebola in the Western Area region of Sierra Leone, 2014-15

“…At the same time, the model was flexible enough to visually fit a wide variety of time series, and this flexibility might mask underlying misspecifications. Whenever possible, the guiding principle in assessing real-time models and predictions for public health should be the quality of the recommended decisions based on the model results [61].…”

Dynamics of the 2004 avian influenza H5N1 outbreak in Thailand: The role of duck farming, sequential model fitting and control

“…For the future, it is important to explore mechanisms to improve the predictive power of infectious disease models during the early stages of disease outbreaks. It is simply not possible for policy makers to wait for uncertainty to resolve after the first few weeks before employing an intervention and therefore seeking methods to reduce uncertainty in model predictions at epidemic onset is vital ( Probert et al, 2018 ). It is also important to state that a model that has been fitted to a previous outbreak may not necessarily be able to predict the spatiotemporal dynamics of future outbreaks.…”

“…As optimal control actions for control of disease outbreaks may depend on objectives ( Probert et al, 2016 ), we evaluated the effectiveness of local control by analysing the projected number of infected flocks, the number of culled flocks and the duration of the outbreak. Real-time decision-making capability was tested by evaluating interventions based on the models fitted using outbreak data throughout the course of an epidemic ( Probert et al, 2018 ).…”