Search Results (902)

Background: Wastewater-based surveillance (WBS) has emerged as a valuable tool for population-level monitoring of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmission, yet the interplay between sampling strategies and disease prevalence in shaping detection performance remains ambiguous. We investigated how grab and composite sampling influence SARS-CoV-2 ribonucleic acid (RNA) detection dynamics and predictive lag times across high- and low-prevalence communities in Selangor, Malaysia. Methods: A 28-week longitudinal study was conducted in Selangor, Malaysia, comparing grab and composite wastewater sampling in communities with high and low Coronavirus disease 2019 (COVID-19) prevalence. SARS-CoV-2 RNA in 348 samples was quantified using digital Reverse Transcription Polymerase Chain Reaction (RT-dPCR), and viral lineages were characterized by Nanopore sequencing. Detection sensitivity and lead times relative to reported cases were evaluated. Results: In low-prevalence settings, grab sampling showed higher detection sensitivity than composite sampling (92.0% vs. 70.0%), whereas both methods achieved similarly high detection in high-prevalence areas (>97.0%). Lag-time analysis indicated that grab sampling in high-prevalence settings was significantly associated with case trends at potential two-week lead (p = 0.024), while composite sampling in low-prevalence settings showed the strongest association at a potential one-week lead (p = 0.0022). Overall, lag structures varied by both sampling strategy and prevalence context. Both sampling approaches captured the replacement of Omicron sublineages (XBB.1.5, XBB.1.9.1, XBB.1.16) and identified additional circulating variants, including EG.5, that were not captured in the available clinical sequencing dataset during the same period. Conclusions: These findings reveal that local transmission intensity is associated with the utility of different sampling designs. Context-specific optimization of WBS sampling strategies enhances sensitivity, reduces detection lag, and strengthens early warning and genomic-tracking capacity in public health surveillance frameworks. Full article

A three-year (January 2020–December 2022) daily dataset of 16 polycyclic aromatic hydrocarbons (PAHs) collected in parallel with PM_2.5 and a suite of meteorological variables at a coastal Mediterranean urban site in southern Italy (Pomigliano d’Arco, Campania) is presented and analysed. Raw PAH time series were decomposed into a long-term trend component (LT), a seasonal component (ST), and a residual component (RT) using an iterative missing-value-robust Kolmogorov–Zurbenko (KZ) moving-average filter. Spearman rank correlations between PAH concentrations and four meteorological predictors (mean temperature, relative humidity, mean wind speed, and maximum wind speed) were computed for each congener. Diagnostic molecular ratios—Fla/(Fla + Pyr), BaP/BghiP, Indeno[1,2,3-cd]pyrene/(IcdP + BghiP), and BaA/(BaA + Chr)—were evaluated seasonally and interpreted jointly with an information-theoretic Bayesian mixture modelling procedure (SNOB/MML) and with the documented susceptibility of some PAH ratios, especially BaP-containing ratios, to atmospheric ageing, phase repartitioning and summer photodegradation. Total PAH concentrations (sum of 16 congeners) ranged from <1 ng m⁻³ in summer to 46 ng m⁻³ during winter high-pollution episodes, with BaP peaking at ≈6.7 ng m⁻³. Because BaP was measured in the PM_2.5 fraction, comparisons with the EU annual target value of 1 ng m⁻³ established for PM₁₀-bound BaP are treated as indicative context only, not as formal compliance statements. Pronounced seasonal variability was driven primarily by residential heating emissions, and the incremental lifetime cancer risk (ILCR) for inhalation exposure reached $1.03\times {10}^{-4}$ (95% CI: $0.88$–$1.20\times {10}^{-4}$) during the heating season under a continuous outdoor-exposure worst-case scenario. The absolute ILCR magnitude is conditional on the selected TEF scheme and on the adopted BaP unit-risk coefficient; under an additional indoor-dominated scenario (16 h day⁻¹, infiltration factor 0.6), the corresponding risk remained above the conventional ${10}^{-6}$ benchmark. An anomalous near-background PAH signal during spring 2020 is attributed to the COVID-19 national lockdown, which reduced total PAH concentrations by approximately 85% relative to the seasonal component predicted by the iterative moving-average filter for the same calendar window. Source apportionment via diagnostic ratios identifies residential/biomass combustion as the dominant cold-season source and vehicular emissions as the prevailing warm-season source. These results provide a novel characterisation of PAH pollution dynamics in the undersampled southern Mediterranean and provide evidence to support targeted abatement policies. Full article

Wastewater-based epidemiology has emerged as a valuable complementary tool for population-level monitoring. This study evaluated the early warning value of wastewater surveillance for monitoring SARS-CoV-2 and its correlation with COVID-19 infection trends. From May 2024 to December 2025, 526 wastewater samples were collected from five treatment plants. Spearman correlation and a quasi-Poisson generalized additive model (adjusting for wastewater temperature) were used to assess relationships between SARS-CoV-2 RNA concentration, the number of reported cases, and lag associations. Wastewater viral loads (copies/mL) significantly correlated with reported cases. Wastewater temperature was positively correlated with both viral concentrations and case numbers. A significant lagged association was observed for the N gene, with relative risk peaking at a 10-day lag. Although the ORF1ab gene was not significant for most lag periods, its temporal trend was consistent with that of the N gene. Wastewater surveillance of SARS-CoV-2, particularly targeting the N gene, can effectively predict COVID-19 infection dynamics with a 10-day lead time, thereby supporting wastewater surveillance as an early warning tool for public health monitoring. Full article

Traditional and COVID-19-specific severity scores are applied in intensive care units (ICUs) to guide decision-making and predict mortality. Since traditional severity scores (APACHE II, SAPS II, SAPS 3, and SOFA) were not originally designed for SARS-CoV-2, this study compared their performance with COVID-19–specific models (Shang-COVID and SEIMC), including a novel distinction between early (≤7 days) and late (>7 days) ICU mortality. Adult ICU COVID-19 patients from the first two pandemic waves in Portugal were included (n = 286). Six scores were calculated, and four outcomes assessed: hospital, ICU, early ICU, and late ICU mortality. Discrimination was assessed using ROC curves with AUCs, 95% CIs, and p-values. AUCs were compared using the Delong test (early vs. late ICU mortality and across scores within each wave) and the Hanley & McNeil test (between waves for each score). Traditional scores demonstrated robust mortality prediction. SEIMC performed well for hospital (AUC_wave1 = 0.808; AUC_wave2 = 0.724) and ICU mortality (AUC_wave1 = 0.805; AUC_wave2 = 0.706). SEIMC (AUC_wave1 = 0.786; AUC_wave2 = 0.800) and Shang-COVID (AUC_wave1 = 0.617; AUC_wave2 = 0.736) showed potential for early mortality prediction but require further validation and recalibration. Overall performance was superior during the first wave, likely reflecting differences in patient characteristics, viral variants, and health measures. Traditional severity scores demonstrated stable robust prediction of ICU and hospital mortality in COVID-19 cases. Disease-specific scores did not significantly outperform established models, though also showed good predictive ability in some contexts, particularly early ICU mortality. These findings highlight the need for continuous validation and recalibration of predictive tools as clinical contexts evolve. Full article

Evidence on hospitalised COVID-19 patients during the Omicron BA.5 wave from Eastern European, vaccine-heterogeneous cohorts remains limited. We conducted a retrospective single-centre cohort study of 395 consecutive adults admitted with laboratory-confirmed COVID-19 to a tertiary infectious-diseases unit in western Romania between 1 July and 31 October 2022. Median age was 72 years (IQR 65–81); 33.2% were unvaccinated, 42.8% had documented prior SARS-CoV-2 infection, and 41.3% were obese. Multivariable logistic regression identified independent predictors of in-hospital mortality and post-acute symptom burden. In-hospital mortality was 15.7% (62/395). Vaccination was independently associated with lower mortality (adjusted odds ratio [aOR] 0.55, 95% CI 0.30–0.99; p = 0.048), as was each 1% increase in admission SpO₂ (aOR 0.83, 95% CI 0.76–0.92; p < 0.001), whereas COPD independently increased mortality risk (aOR 2.42, 95% CI 1.15–5.10; p = 0.020). Interleukin-6 was the most discriminating admission biomarker for in-hospital mortality (AUROC 0.70). Bloodstream bacterial co-infection, detected in 22.5% of patients tested on clinical suspicion, was dominated by gut-derived organisms with case-fatality ≥30%. At discharge, 90.1% reported persistent symptoms, most commonly cognitive (24.6%). Prior SARS-CoV-2 infection independently predicted post-acute symptom burden (aOR 2.96, 95% CI 1.75–5.01; p < 0.001), with a specific cardiopulmonary signature. In this BA.5 cohort, vaccination remained protective; IL-6 was the most informative admission biomarker; bloodstream infections suggested gut translocation; and prior infection was an independent determinant of early post-acute symptom burden. Full article

The effectiveness of infection prevention depends on not only uptake but also the timing of adoption. Vaccination studies typically treat uptake as binary, overlooking when while investigating why individuals get vaccinated. Using the novel mRNA COVID-19 vaccines as a case study, the influences of risk perceptions and social norms on vaccination timing were examined through an Innovation Diffusion framework. An online survey was conducted in November 2021 to assess vaccination behaviors, attitudes, and peer expectations of 1710 U.S. residents (51.64% females, 31.23% minorities, with a relatively balanced distribution across age and income brackets). Participants were classified by vaccination timing and intentions as early adopters, early majority, late majority, or laggards for comparative analyses. One year after vaccine rollout, 64.3% had received at least one dose; 20.1% reported no intention to vaccinate, and this resistance persisted through May 2023 when the pandemic ended. Vaccine confidence and prior behavior (e.g., influenza vaccination) demonstrated strong gradients across adoption timing. Earlier uptake was associated with higher perceived vaccine importance, infection risk, and peer uptake, whereas age and education effects diminished over time. Perceived illness severity and disease knowledge showed inconsistent influences. Later adopters anticipated higher post-vaccination infection risk and greater peer non-vaccination, reinforcing hesitancy. Social norms (but not risk perception) mediated the relationship between confidence and timing; earlier adoption further predicted booster acceptance. These findings highlight the importance of trust, correcting efficacy misperceptions, and leveraging positive peer norms to promote timely vaccination and inform strategies for other infectious diseases. Full article

Background: Post-COVID-19 condition (PCC) affects many survivors, with evidence of sex-specific differences in prevalence and symptom profiles. However, few prediction studies have examined whether sex-stratified models improve prediction or generalize across sexes. This study aimed primarily to develop and compare sex-stratified machine learning models for PCC prediction using routinely available baseline variables, and secondarily to assess cross-sex generalizability and adversarial robustness. Methods: We analyzed a prospective longitudinal cohort of 1006 adults hospitalized with COVID-19 at Sechenov University Hospital Network (Moscow, Russia). Demographics, smoking status, and pre-existing comorbidities were extracted from medical records, and PCC status was assessed at 6-month follow-up. Machine learning models—including classical algorithms and graph-based neural networks—were trained separately for males and females. Cross-sex validation evaluated generalizability, variable importance aided interpretation, and adversarial perturbations assessed model robustness. Results: PCC prevalence was higher in females (53.9%) than males (39.1%). Overall predictive performance was modest across all models, with AUC values ranging approximately 0.50–0.61. Graph-based models achieved the highest discrimination, with the best AUC reaching approximately 0.61, while classical approaches provided limited predictive value. Cross-sex validation showed minor asymmetry: models trained on male data performed slightly better on female cases than vice versa. Adversarial testing revealed sensitivity of all models to input perturbations. Conclusions: Demographics and comorbidities alone provide insufficient information for reliable PCC prediction. Modest sex-specific differences in model generalizability suggest distinct, sex-associated PCC phenotypes, but richer multimodal data—including clinical biomarkers, wearable-derived measures, and patient-reported outcomes—will be required to develop clinically useful and equitable predictive models. Sex-stratified approaches should be considered in future post-viral syndrome prediction studies. Full article

Accurate forecasting of respiratory virus-associated hospitalization rates per 100,000 population is essential for healthcare capacity planning, yet remains challenging during the COVID-19 era due to abrupt distribution shifts and symptom overlap among influenza-like illnesses caused by multiple pathogens. We propose a two-stage deep learning framework that disentangles stable pre-pandemic seasonal dynamics from COVID-19-induced excess hospitalizations. A lightweight GRU is first trained on pre-pandemic surveillance data to model baseline influenza/RSV-driven seasonality, after which an excess model learns from the residual series and integrates multiple online search trends (flu, COVID-19, and fever) using a standard multi-head self-attention mechanism. While we use COVID-19-era data as a case study, the proposed baseline–excess decomposition is not disease-specific and is intended to generalize to future large-scale respiratory outbreaks or pandemics that induce abrupt regime shifts. Experiments on U.S. weekly respiratory hospitalization rate data curated from CDC surveillance networks (AME) show that the proposed approach achieves strong accuracy on a chronological COVID-era split (2020–2025), reaching $R^2=0.907$ with MAPE = 19.22%. Beyond point forecasts, we further evaluate an expanding-window rolling-origin protocol and report calibrated prediction intervals via split conformal prediction, supporting deployment-oriented uncertainty quantification. By decoupling baseline and excess components and fusing behavioral trend signals in a disciplined manner, this framework improves predictive performance under regime shift while providing interpretable excess estimates for timely situational awareness and healthcare resource planning. Full article

The inflation rate has shown an upward trend globally, specifically after COVID-19, and the economies of the Gulf Cooperation Council (GCC) are not an exception. A heightened inflation in the modern globalized world is indeed undesirable due to its enormous adverse consequences on all sectors of the economy. However, the true determinants of the inflation rate, particularly in the case of GCC economies, are not well-explored. Accordingly, this research paper attempts to see whether the inflation rate in GCC economies is driven by internal factors or global factors. This paper focuses on data for the period 1998 to 2023 and applies the PMG-ARDL methodology for the estimation. The results confirmed that money supply, oil prices, GDP, and global supply chain pressure are the key inflationary drivers in the long run. In contrast, trade openness has reduced the inflation rate in the long run, which is consistent with the prediction of Romer’s hypothesis. In the short run, we found that real GDP and trade openness are the main driving forces behind the heightened inflation rate. Furthermore, the causality findings indicated several unidirectional and bidirectional relationships among the variables under consideration. Our results are robust to alternative econometric estimators and hence offer valuable policy implications for the consideration of policymakers. Full article

The aim of this research is to employ improved machine learning techniques to determine the best Bitcoin trading positions in response to sudden price changes caused by global emergencies such as pandemics, conflicts, and economic disputes. Specifically, this study examines price fluctuations during the COVID pandemic as a case study to evaluate the performance of the algorithms investigated. We present a novel hybrid approach that merges Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Decision Tree (DT) classification to effectively eliminate noisy data and extract pertinent information for accurate position forecasting. The DBSCAN algorithm organizes the data to reveal important patterns, while the DT classifier sorts the trading signals. The performance of the proposed DBSCAN-DT model is rigorously compared with established alternatives, including the Multi-Layer Perceptron (MLP), Support Vector Classifier (SVC), and traditional Decision Trees. Findings from the experiments show that the DBSCAN-DT hybrid consistently outperforms these benchmarks during the outbreak, epidemic, and pandemic phases of COVID, attaining greater accuracy in forecasting both trading positions and market trends. These findings emphasize the essential importance of incorporating pandemic-related disruptions into cryptocurrency price prediction models and showcase the flexibility of our method in addressing sudden market changes. Full article

Background: The factors driving Coronavirus disease 2019 (COVID-19) severity and its long-term respiratory sequelae remain poorly understood. This study evaluates whether baseline lung function (LF) influences COVID-related clinical outcomes, mortality, and post-infection LF decline. Methods: Data from 602 participants in the Prospective Urban Rural Epidemiology (PURE)-Colombia study were analyzed. Among these, 200 with confirmed SARS-CoV-2 infection and 402 controls (65% women; 68% aged ≥60 years). All underwent baseline spirometry prior to 2010 and follow-up testing 1–40 months post-recovery. Among infected individuals, 51 (26%) died. Spirometric parameters Forced Expiratory Volume in 1 Second (FEV₁), Forced Vital Capacity (FVC), and Peak Expiratory Flow (PEF) were compared using paired t-tests and Cohen’s d. Non-parametric data were compared using Wilcoxon s (z statistic). Results: Compared to baseline LF, hospitalized COVID-19 patients showed significant declines in follow-up LF: FEV₁ (2.84 vs. 2.34 L; p = 0.002), FVC (3.01 vs. 2.53 L; p = 0.006), and PEF (399 vs. 328 L; p = 0.001). Non-hospitalized COVID-19 cases showed a non-significant downward trend, while controls maintained stable LF. Risk factors for post-COVID FEV₁ < 80% predicted included hospitalization, elevated waist-to-hip ratio, and incomplete or absent COVID-19 vaccination. Moderate-to-high physical activity was protective. Post-COVID PEF < 80% predicted was associated with female sex, diabetes mellitus, and subsidized healthcare enrollment. Mortality risk was elevated among individuals with low baseline LF, age > 65, male sex, hypertension, obesity, low physical activity, and reduced handgrip strength. Conclusions: Significant LF decline was observed in hospitalized COVID-19 patients, with minimal changes in outpatients and controls. Identifying clinical and demographic predictors of post-COVID LF impairment may inform targeted interventions to mitigate long-term pulmonary complications. Full article

Wastewater testing for SARS-CoV-2 provided useful public health information during the COVID-19 pandemic yet was underutilized in rural communities. We addressed this gap by implementing wastewater surveillance and assessing its performance in 10 communities in Eastern Kentucky. We collected wastewater samples 1–2 times weekly at 10 wastewater treatment plants (WWTPs) from May 2021 until April 2023 and measured SARS-CoV-2 RNA concentrations using polymerase chain reaction testing. We calculated time-lagged correlations between wastewater concentrations and county-level reported COVID-19 cases by site. We developed a generalized linear model to estimate COVID-19 incidence from wastewater SARS-CoV-2 concentrations. The 10 participating WWTPs served 2430 to 35,575 customers, and 90% were in rural counties. We cumulatively analyzed 818 wastewater samples. Correlations between wastewater SARS-CoV-2 concentrations and COVID-19 cases were significant at seven of the WWTPs and were strongest during the Delta variant period. The incidence density model predicted more COVID-19 cases during the latter study period (May 2022–April 2023) than were officially reported. Wastewater surveillance data in these rural communities corroborated clinical case data and may have more accurately described community disease levels later in the pandemic. Full article

Background: COVID-19, caused by SARS-CoV-2, exhibits highly variable severity, from mild symptoms to respiratory failure and multiorgan dysfunction. Traditional risk factors incompletely explain this heterogeneity, highlighting the potential role of gut microbiota and host metabolomics in modulating immune responses. Methods: Thus, this study investigates how gut microbiota variations are associated with plasma metabolite profiles in COVID-19, exploring relationships between microbial and metabolic signatures and disease severity and potential therapeutic targets. In a prospective cohort of 55 patients, stool and plasma samples were analyzed using 16S rRNA sequencing and untargeted LC-HRMS metabolomics. Results: Severe COVID-19 was associated with reduced microbial diversity and enrichment of pro-inflammatory taxa, including Prevotella, Alistipes, Dialister, and Lachnoclostridium, whereas mild cases showed higher abundance of protective commensals such as Bacteroides, Faecalibacterium, and Blautia. Metabolomic profiling revealed alterations in bile acids, unsaturated fatty acids, tryptophan, and inositol phosphate pathways. Notably, linoleate levels were elevated in severe cases, showing correlations with pro-inflammatory microbes, while acylcarnitines and inositol derivatives were enriched in mild disease. Predictive functional analysis suggested that severe-associated microbes showed enhanced amino acid catabolism, oxidative glucose metabolism, and xenobiotic degradation, which may be linked to host inflammation. Conclusions: These findings highlight associations between gut microbiota composition, microbial metabolism, and circulating metabolites in COVID-19 severity. Identified microbial and metabolomic signatures may represent potential candidates to be considered biomarkers and therapeutic targets to modulate disease progression. Full article

Background/Objectives: COVID-19 has made a tremendous impact, causing a massive number of deaths worldwide. The inadequacy of health facilities resulted in shortage of resources and exhaustion of frontline workers who had to manage in a short time many patients with no tools to prioritize those at high risk. This study intended to disclose the architecture of such complex disease and enhance the management of hospitalized patients, preventing severe outcomes. Methods: We performed a retrospective multicenter study aimed at refining the best predictive model for COVID-19 mortality, integrating 19 genetic and 13 clinical features. We trained three machine learning (ML) models (GBM, XGB and RF) on a dataset of 532 COVID-19 hospitalized Italian patients, among the 605 recruited during the first wave of the pandemic, when vaccines were not available. Results: All the models achieved great values for accuracy, AUROC, f1, f2 and PR-AUC metrics. XGB f1 optimization resulted in better performance providing fewer false positives (N_f1 = 26 versus N_f2 = 27, N_PR-AUC = 29), and mostly false negatives (N_f1 = 63 versus N_f2 = 69, N_PR-AUC = 69), being the main goal to answer. We next delved into the feature importance to understand which features contribute to the model decision: age was the main driver of mortality prediction, followed by ventilation. The remainder was equally distributed between genetic (HLA-DRA rs3135363, PPARGC1A rs192678, CRP rs2808635, ABO rs657152) and other clinical features, demonstrating that genetic data did not confound, but rather implemented, the power of the model. Conclusions: Our results suggest that integrating genetic and clinical data into ML models is crucial for identifying high-risk cases within the vast disease heterogeneity, enabling the P4-medicine approach to improve patient outcomes and support the healthcare system. Full article

Conventional Safety Performance Functions (SPFs) primarily rely on static exposure measures such as Annual Average Daily Traffic (AADT), often failing to capture real-time, individual-level risky driving behaviors. To address this gap, this study proposes a Risky Driving Indicator (RDI) that integrates large-scale smartphone-based hard braking event data with traffic detector occupancy measures. The RDI was evaluated against traditional models across three specific aggregation levels: AADT, Annual Average Weekday Daily Traffic (AAWDT), and AAWDT excluding the overnight period. A case study was conducted using data from 2021 to 2022, a period coinciding with the COVID-19 pandemic, on South Korea’s busiest freeway to evaluate RDI-based SPFs. The results showed that models using the COM-Poisson framework outperformed traditional volume-based versions, showing superior performance across Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Akaike Information Criterion (AIC) values. These findings confirm that integrating crowdsourced behavioral data enhances predictive accuracy, offering transportation agencies a cost-effective, scalable solution for proactive hotspot identification and dynamic safety monitoring. By improving safety management through scalable and cost-effective mobile sensing, this study contributes to the development of more sustainable highway transportation systems. Full article