Characterizing the Transmission Potential of Zoonotic Infections from Minor Outbreaks

Abstract

The transmission potential of a novel infection depends on both the inherent transmissibility of a pathogen, and the level of susceptibility in the host population. However, distinguishing between these pathogen- and population-specific properties typically requires detailed serological studies, which are rarely available in the early stages of an outbreak. Using a simple transmission model that incorporates age-stratified social mixing patterns, we present a novel method for characterizing the transmission potential of subcritical infections, which have effective reproduction number R<1, from readily available data on the size of outbreaks. We show that the model can identify the extent to which outbreaks are driven by inherent pathogen transmissibility and pre-existing population immunity, and can generate unbiased estimates of the effective reproduction number. Applying the method to real-life infections, we obtained accurate estimates for the degree of age-specific immunity against monkeypox, influenza A(H5N1) and A(H7N9), and refined existing estimates of the reproduction number. Our results also suggest minimal pre-existing immunity to MERS-CoV in humans. The approach we describe can therefore provide crucial information about novel infections before serological surveys and other detailed analyses are available. The methods would also be applicable to data stratified by factors such as profession or location, which would make it possible to measure the transmission potential of emerging infections in a wide range of settings.

The pandemic potential of avian influenza A(H7N9) virus: a review

“…These R 0 estimates are very similar to values estimated for the H5N1 strain, which also generally centre close to 0•1 [20], with confidence ranges spanning up to near 0•4 [21]. However, there is evidence that levels of pre-existing immunity to H5N1 in the human population is significantly higher than it is for H7N9 [23,103], which means that H7N9 could pose a higher risk of larger outbreaks despite a similar R 0 [20]. By contrast, R 0 estimates for the 2009 pandemic H1N1 strain were in the range of 1•2-1•7 [104][105][106].…”

“…By contrast, the H7N9 case count has risen to nearly 80% of the current H5N1 case count in only 2 years. The H7N9 and H5N1 viruses share many similar characteristics, including incubation time [4][5][6], host and human tissue tropism [7][8][9][10][11], treatments [12], antiviral sensitivity [12,13], reservoirs [14][15][16][17], reproductive numbers [18][19][20][21], and low levels of population immunity to both viruses [22][23][24]. There are also some significant differences between the two viruses, including epidemiological risk factors [4,12,25], case-fatality rates [1,3], geography of human cases [3,26], vaccine status [27,28], and the degree of pathogenicity in humans and poultry [9,29].…”

Going through the motions: incorporating movement analyses into disease research

“…Some tools exist in epidemiology to address model building based on limited data (e.g. fitting R 0 for rare spillover diseases; Blumberg & Lloyd-Smith 2013;Kucharski & Edmunds 2015), but this problem requires special attention in the context of movement research and given the ongoing anthropogenic changes to local and global environments.…”

Going through the motions: incorporating movement analyses into disease research

“…This could be problematic for predicting pathogen dynamics in response to rare movement events (e.g., atypical long-distance dispersal events) or transmission (e.g., cross-species spillover events). Some tools exist in epidemiology to address model building based on limited data (e.g., fitting R 0 for rare spillover diseases; Blumberg & Lloyd-Smith 2013; Kucharski & Edmunds 2015), but this problem requires special attention in the context of movement research, and given the ongoing anthropogenic changes to local and global environments.…”