Abstract
BackgroundAccurately assessing the transmissibility and serial interval of a novel human pathogen is public health priority so that the timing and required strength of interventions may be determined. Recent theoretical work has focused on making best use of data from the initial exponential phase of growth of incidence in large populations.MethodsWe measured generational transmissibility by the basic reproductive number R0 and the serial interval by its mean Tg. First, we constructed a simulation algorithm for case data arising from a small population of known size with R0 and Tg also known. We then developed an inferential model for the likelihood of these case data as a function of R0 and Tg. The model was designed to capture a) any signal of the serial interval distribution in the initial stochastic phase b) the growth rate of the exponential phase and c) the unique combination of R0 and Tg that generates a specific shape of peak incidence when the susceptible portion of a small population is depleted.FindingsExtensive repeat simulation and parameter estimation revealed no bias in univariate estimates of either R0 and Tg. We were also able to simultaneously estimate both R0 and Tg. However, accurate final estimates could be obtained only much later in the outbreak. In particular, estimates of Tg were considerably less accurate in the bivariate case until the peak of incidence had passed.ConclusionsThe basic reproductive number and mean serial interval can be estimated simultaneously in real time during an outbreak of an emerging pathogen. Repeated application of these methods to small scale outbreaks at the start of an epidemic would permit accurate estimates of key parameters.
Highlights
Uncertainty dominates important early policy decisions for emerging respiratory pathogens such as Severe Acute Respiratory Syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and pandemic influenza A/H1N1
The model was designed to capture a) any signal of the serial interval distribution in the initial stochastic phase b) the growth rate of the exponential phase and c) the unique combination of R0 and Tg that generates a specific shape of peak incidence when the susceptible portion of a small population is depleted
In our simulated population of 1,000 people for an influenza-like pathogen (Scenario 1) there was an average delay of only 16.5 days from the introduction of 10 infectious individuals to peak incidence
Summary
Uncertainty dominates important early policy decisions for emerging respiratory pathogens such as Severe Acute Respiratory Syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and pandemic influenza A/H1N1. This uncertainty is reduced greatly once the basic reproductive number and the serial interval of infection are known. We present a temporal likelihood model that allows real-time simultaneous estimation of R0 and the average of serial interval Tg in a small well-observed population, so that it provides timely information for informed public health responses. Our two illustrative scenarios were designed to have similar exponential growth phases and are based loosely on pandemic influenza A/H1N1 and SARS-CoV infections. Recent theoretical work has focused on making best use of data from the initial exponential phase of growth of incidence in large populations.
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