An objective reconstruction of the chain of contagion.
North's clustering method, which is based on a much used ecological model, the nearest neighbor distance, was applied to the objective reconstruction of the chain of household-to-household transmission of variola minor (the mild form of smallpox). The discrete within-household outbreaks were considered as points which were ordered in a time sequence using a 10-40 day interval between introduction of the disease into a source household and a receptor household. The closer points in the plane were assumed to have a larger probability of being links of a chain of household-to-household spread of the disease. The five defining distances (Manhattan or city-block distance between presumptive source and receptor dwellings) were 100, 200, 300, 400 and 500 m. The subchain sets obtained with the five defining distances were compared with the subchains empirically reconstructed during the field study of the epidemic through direct investigation of personal contacts of the introductory cases with either introductory or subsequent cases from previously affected households. The criteria of fit of theoretical to empirical clusters were: (a) the number of clustered dwellings and of subchains , (b) number of dwellings in a subchain and (c) position of dwellings in a subchain . The defining distance closest to the empirical findings was 200 m, which fully agrees with the travelling habits of the study population. Less close but acceptable approximations were obtained with 100, 300, 400 and 500 m. The latter two distances gave identical results, as if a clustering ceiling had been reached. It seems that North's clustering model may be used for an objective reconstruction of the chain of contagious whose links are discrete within-household outbreaks.
D001938:Brazil; D004196:Disease Outbreaks / Q000453:epidemiology; D005260:Female; D006798:Housing; D006801:Humans; D008297:Male; D008954:Models, Biological; D012899:Smallpox / Q000453:epidemiology / Q000635:transmission*; D013029:Space-Time Clustering