Estimating age-specific reproductive numbers-A comparison of methods

Abstract

Large outbreaks, such as those caused by influenza, put a strain on resources necessary for their control. In particular, children have been shown to play a key role in influenza transmission during recent outbreaks, and targeted interventions, such as school closures, could positively impact the course of emerging epidemics. As an outbreak is unfolding, it is important to be able to estimate reproductive numbers that incorporate this heterogeneity and to use surveillance data that is routinely collected to more effectively target interventions and obtain an accurate understanding of transmission dynamics. There are a growing number of methods that estimate age-group specific reproductive numbers with limited data that build on methods assuming a homogenously mixing population. In this article, we introduce a new approach that is flexible and improves on many aspects of existing methods. We apply this method to influenza data from two outbreaks, the 2009 H1N1 outbreaks in South Africa and Japan, to estimate age-group specific reproductive numbers and compare it to three other methods that also use existing data from social mixing surveys to quantify contact rates among different age groups. In this exercise, all estimates of the reproductive numbers for children exceeded the critical threshold of one and in most cases exceeded those of adults. We introduce a flexible new method to estimate reproductive numbers that describe heterogeneity in the population.

Keywords: H1N1; Reproductive number; heterogeneity; influenza; social mixing; transmission models.

Figure 1

Epidemic curves for children and adults in a) South Africa and b) Japan.

Figure 2

Contact tracing sample distribution from South Africa.

Figure 3

Estimates of reproductive numbers in a) South Africa and b) Japan for children and adults for each method (M-WP: Moser WP, G-WP: Glass WP, G-WT: Glass WT, W-WT: White WT).

Figure 4

Daily effective reproductive number estimates for WT-based methods in a) South Africa and b) Japan.

Figure 5

Moving average daily effective reproductive numbers compared to basic reproductive numbers in South Africa.