Identification of case clusters and counties with high infective connectivity in the 2001 epidemic of foot-and-mouth disease in Uruguay

被引:13
作者
Chowell, G
Rivas, AL [1 ]
Smith, SD
Hyman, JM
机构
[1] Cornell Univ, Dept Biol Stat & Computat Biol, Ithaca, NY 14853 USA
[2] Cornell Univ, Inst Resource Informat Syst, Coll Agr & Life Sci, Ithaca, NY 14853 USA
[3] Los Alamos Natl Lab, Los Alamos, NM 87545 USA
关键词
D O I
10.2460/ajvr.67.1.102
中图分类号
S85 [动物医学(兽医学)];
学科分类号
0906 ;
摘要
Objective-To evaluate the influence of individual spatial units (ie, counties) on the epidemic spread of foot-and-mouth disease (FMD) virus. Sample Population-163 counties in Uruguay where there was an outbreak of FMD between April 23 and July 11, 2001. Procedure-A geographically referenced database was created, and the distance between counties (13,203 county pairs), road density of counties (163 counties), and time when cases were reported in those counties (11 weeks of the epidemic) were considered to assess global spatial and spatial-temporal autocorrelation, determine the contribution of links connecting pairs of counties with infected animals, and allow us to hypothesize the influence for spread during the epidemic for counties with greater than the mean infective link contributions. Results-Case clusters were indicated by the Moran / and Mantel tests during the first 6 weeks of the epidemic. Spatial lags between pairs of counties with infected animals revealed case clustering before and after vaccination was implemented. Temporal lags predicted autocorrelation for up to 3 weeks. Link indices identified counties expected to facilitate epidemic spread. If control measures had been implemented in counties with a high index link (identifiable as early as week 1 of the epidemic); they could have prevented (by week 11 of the epidemic) at least 2.5 times as many cases per square kilometer than the same measures implemented in counties with average link indices. Conclusions and Clinical Relevance-Analysis of spatial autocorrelation and infective link indices may identify network conditions that facilitate (or prevent) disease spread.
引用
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页码:102 / 113
页数:12
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