Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome

Changing epidemiology of parvovirus B19 in the Netherlands since 1990, including its re-emergence after the COVID-19 pandemic

Parvovirus B19V (B19V) infection during pregnancy can be complicated by potentially life-threatening fetal hydrops, which can be managed by intrauterine transfusion (IUT). This study investigates the long-term temporal patterns in the epidemiology of B19V and evaluates the impact on fetal hydrops, by combining data on B19V infections from the Dutch Sentinel Surveillance system in the period 1990 to 2023, Dutch blood banking data and hospital data on fetal hydrops. Using wavelet analysis, we identified annual epidemic cycles in the Netherlands in the period 1990-2019 and we identified superimposed multiannual cycles in the period 1990-2009. After 2009, no multiannual cycle could be identified, although the incidence fluctuated and correlates with number of IUT performed. As of 2020, weekly reports of B19V infection demonstrated a historically low incidence and B19V-DNA positive blood donors were nearly absent. From May 2020 to May 2023, no IUT for B19V-related hydrops was performed. In the spring of 2023, B19V infections re-emerged, reaching pre-pandemic epidemic levels. Due to the changes in B19V epidemiology over the last 30 years and the near-absence of B19V during the COVID-19 pandemic, the resulting low immunity levels may lead to rebound outbreaks. Alertness to severe complications such as fetal hydrops is warranted.

Methodological Considerations in Longitudinal Analyses of Microbiome Data: A Comprehensive Review

Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and compare available approaches and methods to overcome them. Appropriate handling of longitudinal microbiome data can shed light on important roles, functions, patterns, and potential interactions between large numbers of microbial taxa or genes in the context of health, disease, or interventions. We present a comprehensive review and comparison of existing microbiome time-series analysis methods, for both preprocessing and downstream analyses, including differential analysis, clustering, network inference, and trait classification. We posit that the careful selection and appropriate utilization of computational tools for longitudinal microbiome analyses can help advance our understanding of the dynamic host-microbiome relationships that underlie health-maintaining homeostases, progressions to disease-promoting dysbioses, as well as phases of physiologic development like those encountered in childhood.