Infections

ObjectivesPyogenic vertebral osteomyelitis (PVO) is a severe infection with increasing incidence. We aimed to describe long-term population-based temporal trends in incidence, clinical presentation, diagnostic approaches, microbiology, and outcomes.Materials and methodsWe conducted a retrospective population-based cohort study including 1,017 adults with multidisciplinary team-verified PVO diagnosed at a tertiary infectious diseases referral centre in the Region of Southern Denmark between 2009 and 2022. Incidence rates (IRs) per 100,000 person-years (PY) were calculated using regional population data and compared across three calendar periods (2009-2013, 2014-2018, 2019-2022). Temporal changes in clinical characteristics, microbiology, diagnostic strategies, and outcomes were assessed.ResultsIn total, 1,017 patients were included. The crude IR increased from 4.7 per 100,000 PY in 2009-2013 to 11.0 in 2019-2022, with the largest increase among patients aged ≥70 years. Mean age and comorbidity burden rose over time. Pain was common, fever was often absent, and neurological deficits occurred in 33.5% (n = 341). Postsurgical infection was the most frequent primary focus, and concomitant endocarditis occurred in 8.3% (n = 84). A microbiological diagnosis was established in 64.8% (n = 635) of cases, most commonly Staphylococcus aureus. Use of Fluorine-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography ([18F]FDG-PET/CT) increased over time. Median hospital stay remained 33 days. One-year mortality increased from 9.5% to 18.2%, while relapse declined, resulting in a stable composite risk of death or relapse (20.7%).ConclusionsThe incidence of PVO increased markedly from 2009 to 2022, predominantly in older patients with comorbidities. Despite improved diagnostics, microbiologically-negative cases remain frequent. Mortality remained high and increased over time.

The second WHO Public Health Emergency of International Concern (PHEIC) for mpox was declared on 14 August 2024 after clade Ib monkeypox virus emerged in the Democratic Republic of the Congo (DRC) and spread rapidly across Africa and internationally. The PHEIC ended on 5 September 2025, and the Africa CDC Public Health Emergency of Continental Security was lifted on 22 January 2026. This study characterised global and Africa-specific mpox epidemiology across the full second PHEIC cycle and immediate post-emergency period, from January 2024 to February 2026. A retrospective surveillance analysis was conducted using the WHO Global Mpox Trends dashboard exported data, supplemented by WHO situation reports S32-S64 and international and national surveillance reports. Analyses included negative binomial segmented regression, travel-linked pathway analysis, exploratory analysis of documented secondary transmission after travel-linked detection, and descriptive statistics. Models used reported case counts with log population offsets; reported case-rate ratios therefore reflect reported case rates rather than true infection incidence. Between January 2024 and February 2026, 82,738 confirmed cases and 306 deaths were reported to WHO globally (case fatality ratio 0.37%). The PHEIC period accounted for 74.0% of cases, peaking at 7,405 cases in May 2025. In total, 163 clade-specific importation or travel-linked detection events were documented across 41 countries within the primary analytic cutoff; documented secondary transmission occurred in 25 (15.3%) events across 16 countries. Within DRC, 200 health zones recorded 144,035 total reported cases and 2,047 deaths (case fatality ratio 1.42%); these aggregate suspected/clinically compatible and confirmed cases. The top 20 health zones accounted for 45.4% of national burden. Reported transmission declined after the May 2025 peak but persisted beyond PHEIC termination. Emergency termination marked an administrative transition, not the end of mpox transmission. The post-emergency period requires sustained surveillance, maintained diagnostic capacity, targeted response in persistent transmission settings, and equitable access to vaccines and countermeasures.

The COVID-19 pandemic underscored the urgent need for early detection of local outbreaks to enable better resource allocation and effective mitigation. Outbreak identification largely depended on local infection rates, which were highly variable and influenced by daily testing volumes. Effective pandemic management requires accurate, adaptive methods that reflect the dynamic and localized nature of these rates, ensuring timely and appropriate interventions. This study seeks to improve the early detection and localization of COVID-19 outbreaks through Statistical Process Control (SPC) and health data analytics. We propose an adaptive outbreak-detection framework that combines a normalized p-control chart with an Amplitude-based labeler (ABL). For each area and day, the monitored statistic is a standardized positivity-rate score, computed from the observed daily positivity rate, the number of tests performed that day, and an area-specific baseline positivity estimate derived from recent historical observations. This calibration is followed by Phase II sequential monitoring using fixed three-sigma limits on the normalized scale and Western Electric decision rules. The framework accommodates varying daily sample sizes, captures regional interconnectedness, and supports parameter tuning using a temporally ordered train/test design. Accounting for daily testing fluctuations and spatial variability, it enhances the accuracy of local outbreak detection. Compared with the benchmark outbreak-detection methods considered in this study, the proposed framework was evaluated on its ability to predict outbreak onset within the following three days, achieving overall accuracy of 0.81 and averaged precision and recall of 0.83 and 0.79, respectively. Performance was further evaluated using the Fβ-measure, enabling a flexible trade-off between precision and recall depending on the relative cost of errors. SPC-based methods like ours can significantly improve early outbreak detection and localization. In the post-vaccination era, when targeted responses are key to controlling localized spread while avoiding widespread restrictions, health data analytics offer a valuable tool for informed decision-making.