Selected article for: "component analysis and principal component analysis"
Author: Semenza, Jan C.; Sewe, Maquines Odhiambo; Lindgren, Elisabet; Brusin, Sergio; Aaslav, Kaja Kaasik; Mollet, Thomas; Rocklöv, Joacim
Title: Systemic resilience to cross-border infectious disease threat events in Europe
  • Document date: 2019_5_17
    • ID: ye02hwhy_22
    Snippet: The principal component analysis revealed that three components explained the majority of variability of the IHR core capacities (Table S5) . However, only the first PCA score was significantly related to IDTE with an estimated 20% decrease in risk for every 10% increase in the core capacity score (95% CI: 0.73, 0.88). The individual core capacity weights related to this component was in line with the univariate analysis by relating mainly to IHR.....
    Document: The principal component analysis revealed that three components explained the majority of variability of the IHR core capacities (Table S5) . However, only the first PCA score was significantly related to IDTE with an estimated 20% decrease in risk for every 10% increase in the core capacity score (95% CI: 0.73, 0.88). The individual core capacity weights related to this component was in line with the univariate analysis by relating mainly to IHR core capacities and less so to hazards. The IRR estimate from the first PCA score was also very similar to the estimate from the average composite measure of the IHR core capacities (decrease 20% vs. 19%), and further indicated that the IRR from the univariate analysis of IHR core capacities cannot be combined, due to the strong inter-core capacity correlations. & Semenza, 2012; Wolicki et al., 2016) . To this end, surveillance needs to be flexible and sensitive and to encompass syndromic, laboratory-based, population-based and sentinel systems (Wolicki et al., 2016) . In our analysis, national surveillance was associated with fewer IDTE, presumably because they were intercepted prior to international spread.

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