Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola

Abstract

As an emergent infectious disease outbreak unfolds, public health response is reliant on information on key epidemiological quantities, such as transmission potential and serial interval. Increasingly, transmission models fit to incidence data are used to estimate these parameters and guide policy. Some widely used modelling practices lead to potentially large errors in parameter estimates and, consequently, errors in model-based forecasts. Even more worryingly, in such situations, confidence in parameter estimates and forecasts can itself be far overestimated, leading to the potential for large errors that mask their own presence. Fortunately, straightforward and computationally inexpensive alternatives exist that avoid these problems. Here, we first use a simulation study to demonstrate potential pitfalls of the standard practice of fitting deterministic models to cumulative incidence data. Next, we demonstrate an alternative based on stochastic models fit to raw data from an early phase of 2014 West Africa Ebola virus disease outbreak. We show not only that bias is thereby reduced, but that uncertainty in estimates and forecasts is better quantified and that, critically, lack of model fit is more readily diagnosed. We conclude with a short list of principles to guide the modelling response to future infectious disease outbreaks.

Cross-immunity between strains explains the dynamical pattern of paramyxoviruses

“…Stochasticity could provide another reason for the better parameter estimates involving HPIV (51). Our deterministic models of unbroken chains of transmission better fit RSV incidence during annual outbreaks, whereas HPIV spends a significant fraction of time in the stochastic regime with low numbers of infections or fluctuations in reporting (7).…”

Transmission network of the 2014–2015 Ebola epidemic in Sierra Leone

“…To further allow for observational error effects, we considered six filter settings, running simulations with three different levels of observation error variance (see Material and methods), and used the agreement of inference among the six settings as an indicator of the confidence, or reliability, of the inference (figure 2). A second concern involves system stochasticity [32], seemingly random processes that arise owing to variations of behaviour or changes in the intensity of intervention measures. To account for these effects, modelling studies have used stochastic model structures [14,17,20,32].…”

“…A second concern involves system stochasticity [32], seemingly random processes that arise owing to variations of behaviour or changes in the intensity of intervention measures. To account for these effects, modelling studies have used stochastic model structures [14,17,20,32]. The SEIR network model used here is deterministic; however, the EAKF, used in conjunction with the model, to some extent introduces stochasticity to the system through its ensemble formulation and the random selection of initial state variable and parameter conditions.…”

Measuring the impact of Ebola control measures in Sierra Leone

“…We used a Bayesian approach to fit the model to weekly EVD confirmed and probable case data reported in each district of Sierra Leone (18,19), and to estimate how community transmission varied over time. We then used the fitted model to simulate multiple stochastic epidemic trajectories, and measured the number of cases that could have occurred in each district had additional beds not been introduced.…”