PubMed · 14416721
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I M LIBRACH. 1960. Infectious diseases.. https://pubmed.ncbi.nlm.nih.gov/14416721/
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The endemic persistence of infectious diseases can often not be understood without taking into account the relevant heterogeneities of host mixing. Here, we consider spatial heterogeneity, defined as 'patchiness' of the host population. After briefly reviewing how disease persistence is influenced by population size, reproduction number and infectious period, we explore its dependence on the level of spatial heterogeneity. Analysis and simulation of disease transmission in a symmetric meta-population suggest that disease persistence typically becomes worse as spatial heterogeneity increases, although local persistence optima can occur for infections with oscillatory population dynamics. We obtain insight into the dynamics that underlie the observed persistence patterns by studying the infection prevalence correlation between patches and by comparing full-model simulations to results obtained using simplified patch-level descriptions of the interplay between local extinctions and between-patch transmissions. The observed patterns are interpreted in terms of rescue effects for strong spatial heterogeneity and in terms of between-patch coherence and synchronization effects at intermediate and weak levels of heterogeneity.
BACKGROUND: Timeliness is a key performance measure of public health surveillance systems. Timeliness can vary by disease, intended use of the data, and public health system level. Studies were reviewed to describe methods used to evaluate timeliness and the reporting timeliness of National Notifiable Diseases Surveillance System (NNDSS) data was evaluated to determine if this system could support timely notification and state response to multistate outbreaks. METHODS: Published papers that quantitatively measured timeliness of infectious disease surveillance systems operating in the U.S. were reviewed. Median reporting timeliness lags were computed for selected nationally notifiable infectious diseases based on a state-assigned week number and various date types. The percentage of cases reported within the estimated incubation periods for each disease was also computed. RESULTS: Few studies have published quantitative measures of reporting timeliness; these studies do not evaluate timeliness in a standard manner. When timeliness of NNDSS data was evaluated, the median national reporting delay, based on date of disease onset, ranged from 12 days for meningococcal disease to 40 days for pertussis. Diseases with the longer incubation periods tended to have a higher percentage of cases reported within its incubation period. For acute hepatitis A virus infection, which had the longest incubation period of the diseases studied, more than 60% of cases were reported within one incubation period for each date type reported. For cryptosporidiosis, Escherichia coli O157:H7 infection, meningococcal disease, salmonellosis, and shigellosis, less than 40% of cases were reported within one incubation period for each reported date type. CONCLUSION: Published evaluations of infectious disease surveillance reporting timeliness are few in number and are not comparable. A more standardized approach for evaluating and describing surveillance system timeliness should be considered; a recommended methodology is presented. Our analysis of NNDSS reporting timeliness indicated that among the conditions evaluated (except for acute hepatitis A infection), the long reporting lag and the variability across states limits the usefulness of NNDSS data and aberration detection analysis of those data for identification of and timely response to multistate outbreaks. Further evaluation of the factors that contribute to NNDSS reporting timeliness is warranted.
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