Insights from disease ecology: focus on hand, foot and mouth disease in China

We assessed the potential of using associations with landscape pattern to discriminate diseases having a wild animal reservoir (wild-zoonoses) from those that do not, and tested the hypothesis that landscape, as measured by select land cover pattern metrics, is more strongly associated with the incidence of wild-zoonoses than with the incidence of diseases lacking a wild animal reservoir. We obtained county-level incidence data from CDC National Notifiable Disease records data for three wild-zoonoses (Lyme Disease, Rocky Mountain Spotted Fever, and West Nile Virus) and eight diseases lacking a wild animal reservoir (Chlamydia trachomatis, Cryptosporidiosis, E. coli O157:H7, Gonorrhea, Legionellosis, Shigellosis, invasive group A Streptococcal disease, and Syphilis). Using GlobCover 2009 land cover data, we calculated 10 land cover pattern metrics for each county in the contiguous United States and modeled their associations with the county-level incidence of each of the eleven diseases using quasi-Poisson regression. Finally, we compared the absolute strengths of the associations between each pattern metric and each disease to determine whether the strongest associations were observed with wild-zoonoses. When sorted by absolute strength of association, wild-zoonoses had the strongest associations with six of the ten pattern metrics (p=0.008), and with all four of the metrics that measure land cover shape (p=0.002). While more work is needed to refine this method, our results suggest that associations with land cover pattern metrics may offer insight into the existence of a wild animal reservoir for an emerging or otherwise poorly understood infectious agent. 12 Background Sixty to 75% of all emerging infectious diseases (EID) are estimated to be zoonotic,27-29 and undetected animal reservoirs may exist for established infectious agents. Given limited resources and the high cost of large-scale wildlife surveillance, a rapid and inexpensive screening tool may expedite the discovery of animal reservoirs, inform research and surveillance priorities, and allow the public health community to respond to EIDs more effectively. To address this need, we propose a novel method through which surveillance data and remote sensing imagery may be used to investigate the existence of a wild animal reservoir for any poorly understood infectious agent, and conduct an exploratory analysis to ascertain the validity of such a method. We assume that mixing between humans and wild animals increases zoonotic disease transmission, and that mixing is governed—at least partially—by landscape. Most simply, proximity between humans, infectious agents, and natural reservoirs is necessary for zoonotic transmission to occur.35,36 Similarly, Lloyd-Smith et al37 described the rate of zoonotic transmission as being governed by the rate, duration, and proximity of contact between the reservoir and humans, among other things. Because landscape influences the potential for such contact, landscape should, in turn, also be associated with the incidence of zoonotic diseases. Empirically, this assumption is borne-out by the historical evidence of increased mixing between humans and deer ticks resulting in higher rates of Lyme disease.29,35 Likewise, factors including forest fragmentation and agricultural encroachment are associated with increased risk of emerging zoonoses.29 In contrast, such associations would be unlikely between landscape and non-zoonotic diseases.