Estimation in emerging epidemics: biases and remedies

TL;DR

This paper examines biases in estimating key epidemiological parameters during emerging outbreaks and proposes statistical methods to correct these biases, with illustrations from the Ebola outbreak.

Contribution

It identifies sources of bias in early outbreak parameter estimation and introduces statistical remedies to improve inference accuracy.

Findings

Biases can cause up to 20% underestimation of R0.

Estimation biases may lead to 62% overestimation of growth rate.

Proper modeling reduces bias in early outbreak analysis.

Abstract

When analysing new emerging infectious disease outbreaks one typically has observational data over a limited period of time and several parameters to estimate, such as growth rate, R0, serial or generation interval distribution, latent and incubation times or case fatality rates. Also parameters describing the temporal relations between appearance of symptoms, notification, death and recovery/discharge will be of interest. These parameters form the basis for predicting the future outbreak, planning preventive measures and monitoring the progress of the disease. We study the problem of making inference during the emerging phase of an outbreak and point out potential sources of bias related to contact tracing, replacing generation times by serial intervals, multiple potential infectors or truncation effects amplified by exponential growth. These biases directly affect the estimation of e.g. the generation time distribution and the case fatality rate, but can then propagate to other estimates, e.g. of R0 and growth rate. Many of the traditionally used estimation methods in disease epidemiology may suffer from these biases when applied to the emerging disease outbreak situation. We show how to avoid these biases based on proper statistical modelling. We illustrate the theory by numerical examples and simulations based on the recent 2014-15 Ebola outbreak to quantify possible estimation biases, which may be up to 20% underestimation of R0, if the epidemic growth rate is fitted to observed data or, conversely, up to 62% overestimation of the growth rate if the correct R0 is used in conjunction with the Euler-Lotka equation.