Abstract
Zoonotic disease outbreaks are an important threat to human health and numerous drivers have been recognized as contributing to their increasing frequency. Identifying and quantifying relationships between drivers of zoonotic disease outbreaks and outbreak severity is critical to developing targeted zoonotic disease surveillance and outbreak prevention strategies. However, quantitative studies of outbreak drivers on a global scale are lacking. Attributes of countries such as press freedom, surveillance capabilities and latitude also bias global outbreak data. To illustrate these issues, we review the characteristics of the 100 largest outbreaks in a global dataset (n = 4463 bacterial and viral zoonotic outbreaks), and compare them with 200 randomly chosen background controls. Large outbreaks tended to have more drivers than background outbreaks and were related to large-scale environmental and demographic factors such as changes in vector abundance, human population density, unusual weather conditions and water contamination. Pathogens of large outbreaks were more likely to be viral and vector-borne than background outbreaks. Overall, our case study shows that the characteristics of large zoonotic outbreaks with thousands to millions of cases differ consistently from those of more typical outbreaks. We also discuss the limitations of our work, hoping to pave the way for more comprehensive future studies.This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.
Highlights
Disease emergence is widely recognized as a major threat to biodiversity and human health [1,2,3]
See online supplementary material for additional details of outbreak sampling procedures. Those outbreaks we considered potentially zoonotic were caused by pathogens that can be transmitted between animals and humans (e.g. West Nile virus, hantavirus, Q fever), though individual outbreaks included were often not of zoonotic origin
In a future in which large zoonotic disease outbreaks will almost certainly continue to occur regularly, a better general understanding of the factors affecting variation in the severity of outbreaks is critical to the wellbeing of the global community
Summary
Disease emergence is widely recognized as a major threat to biodiversity and human health [1,2,3]. Five of the seven covariates that we used to characterize potential sample bias (see below) could be quantified throughout this time interval With this cut-off, we produced a final dataset of 4463 contemporary outbreaks caused by zoonotic pathogens, within which we compared the putative drivers of the 100 largest (defined by minimum estimated number of cases) to those of 200 random background or control outbreaks (electronic supplementary material, figure S1). In a sample completely random with respect to case numbers and twice as large we only observed 33% more diseases (i.e. a sample of 200 outbreaks from a global dataset included 35 diseases) This suggests that specific pathogens with the potential to cause large outbreaks will be hard to anticipate, though they did have a tendency to be viral and use vector-borne transmission more frequently than the diseases of background outbreaks (table 4; electronic supplementary material, tables S10, S41, S42, S45, S46). Broader use of NLP and related machine learning methods (e.g. [117,118]) to generate more detailed and complete databases of outbreak characteristics represents an exciting avenue for future work. 11 The key to leveraging such data effectively will be more collaborative work where statistical models are co-produced by experts in environmental and socioeconomic drivers, stakeholder issues and policy (e.g. [119,120])
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