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Background   Cryptosporidium parvum  is one of the most important biological contaminants in drinking water that produces life threatening infection in people with compromised immune systems. Dairy calves are thought to be the primary source of  C. parvum  contamination in watersheds. Understanding the spatial and temporal variation in the risk of  C. parvum  infection in dairy cattle is essential for designing cost-effective watershed management strategies to protect drinking water sources. Crude and Bayesian seasonal risk estimates for  Cryptosporidium  in dairy calves were used to investigate the spatio-temporal dynamics of  C. parvum  infection on dairy farms in the New York City watershed.    Results  Both global (Global Moran's I) and specific (SaTScan) cluster analysis methods revealed a significant (p < 0.05) elliptical spatial cluster in the winter with a relative risk of 5.8, but not in other seasons. There was a two-fold increase in the risk of  C. parvum  infection in all herds in the summer (p = 0.002), compared to the rest of the year. Bayesian estimates did not show significant spatial autocorrelation in any season.    Conclusions  Although we were not able to identify seasonal clusters using Bayesian approach, crude estimates highlighted both temporal and spatial clusters of  C. parvum  infection in dairy herds in a major watershed. We recommend that further studies focus on the factors that may lead to the presence of  C. parvum  clusters within the watershed, so that monitoring and prevention practices such as stream monitoring, riparian buffers, fencing and manure management can be prioritized and improved, to protect drinking water supplies and public health.

international journal of health geographics

Temporal and spatial dynamics of Cryptosporidium parvum infection on dairy farms in the New York City Watershed: a cluster analysis based on crude and Bayesian risk estimates