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

TL;DR

This paper highlights common modeling errors in outbreak analysis, demonstrates their impact through simulations, and proposes simple, effective alternatives for more accurate parameter estimation and forecasting during emerging infectious disease outbreaks.

Contribution

It identifies pitfalls of standard deterministic models, introduces stochastic modeling approaches, and provides practical principles for improved outbreak modeling.

Findings

Deterministic models can produce large biases in parameter estimates.

Stochastic models better quantify uncertainty and improve fit diagnostics.

Applying these methods to Ebola data reduces bias and enhances forecast reliability.

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 modeling 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 over-estimated, 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 modeling response to future infectious disease outbreaks.