PubMed · 8128374
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A M Jørgensen. 1993-06-23. [When everything goes wrong].. https://pubmed.ncbi.nlm.nih.gov/8128374/
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BACKGROUNDIncreasing incidence rates of invasive Streptococcus dysgalactiae subspecies equisimilis (iSDSE) have been detected worldwide.AIMWe aimed to investigate iSDSE infection incidence rates in Denmark during 2014-2024, and characterise the genomic population structure of a subset of iSDSE isolates and their antimicrobial resistance (AMR).METHODSUsing national register data, we estimated overall and sex-/age-stratified iSDSE incidences during 2014-2024, by retrospectively identifying cases of invasive infections with group C and G streptococci or S. dysgalactiae (including specified as subspecies equisimilis). From the voluntary national beta-haemolytic streptococci laboratory surveillance system, whole-genome-sequenced isolates from August 2020-September 2022 were used to investigate the iSDSE genomic population structure. Susceptibility to penicillin, erythromycin and clindamycin was determined and AMR genes identified.RESULTSDuring 2014-2024, iSDSE incidence rates increased significantly (linear trend analysis p < 0.001) with mean annual incidence ranging between 10.3 and 16.4 per 100,000, peaking in 2023. Incidence was higher in males, increasing with older age. Nearly 75% of the 1,223 iSDSE isolates belonged to four of 14 genetic clusters. Sequence types (STs) ST20 and ST17 were most prevalent, while emm-type stG62647, a variant associated internationally with higher virulence, dominated. All isolates were phenotypically susceptible to penicillin but approximately 10% were respectively erythromycin and clindamycin resistant. High erythromycin resistance prevalence (57%; 39/68), coinciding with gene ermA, occurred in one genetic cluster.CONCLUSIONThe findings illustrate the need for national registry-based surveillance to detect epidemiological changes and potential outbreaks. Further, continuous genomic surveillance can monitor the occurrence and expansion of genetic clades and AMR genes.
Temporal changes in the incidence of measles virus infection within large urban communities in the developed world have been the focus of much discussion in the context of the identification and analysis of nonlinear and chaotic patterns in biological time series. In contrast, the measles records for small isolated island populations are highly irregular, because of frequent fade-outs of infection, and traditional analysis does not yield useful insight. Here we use measurements of the distribution of epidemic sizes and duration to show that regularities in the dynamics of such systems do become apparent. Specifically, these biological systems are characterized by well-defined power laws in a manner reminiscent of other nonlinear, spatially extended dynamical systems in the physical sciences. We further show that the observed power-law exponents are well described by a simple lattice-based model which reflects the social interaction between individual hosts.