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When analysing new emerging infectious disease outbreaks one typically hasobservational data over a limited period of time and several parameters toestimate, such as growth rate, R0, serial or generation interval distribution,latent and incubation times or case fatality rates. Also parameters describingthe temporal relations between appearance of symptoms, notification, death andrecovery/discharge will be of interest. These parameters form the basis forpredicting the future outbreak, planning preventive measures and monitoring theprogress of the disease. We study the problem of making inference during theemerging phase of an outbreak and point out potential sources of bias relatedto contact tracing, replacing generation times by serial intervals, multiplepotential infectors or truncation effects amplified by exponential growth.These biases directly affect the estimation of e.g. the generation timedistribution and the case fatality rate, but can then propagate to otherestimates, e.g. of R0 and growth rate. Many of the traditionally usedestimation methods in disease epidemiology may suffer from these biases whenapplied to the emerging disease outbreak situation. We show how to avoid thesebiases based on proper statistical modelling. We illustrate the theory bynumerical examples and simulations based on the recent 2014-15 Ebola outbreakto quantify possible estimation biases, which may be up to 20% underestimationof 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 inconjunction with the Euler-Lotka equation.

Estimation in emerging epidemics: biases and remedies