American Journal of Infection Control

Background: Privacy curtains represent a uniquely difficult infection control surface because they are frequently touched by patients, visitors, and healthcare workers, yet are difficult to disinfect using routine environmental cleaning protocols. We sought to quantify the performance of a novel automated aerosolized hydrogen peroxide system against pathogens on privacy curtains. Methods: This study was performed in an enclosed office space ({approx} 65 m^3), approximating a patient exam room. Four Bacillus atrophaeus (ATCC 9372) biological indicators (BIs) were affixed to a standard polyester privacy curtain. A single one-minute fogging phase was followed by a ten-minute contact/aeration phase (total = 11 min). Four corresponding chemical indicators measured peak airborne aerosolized hydrogen peroxide concentration, and two control BIs were placed outside of the room. Results: All four BIs demonstrated complete inactivation (no growth after seven-day incubation), representing [≥] 4-log10 spore reduction. Peak dosages reached 42 ppm nearest the aerosolized hydrogen peroxide jet outlet, with the lowest value at the near point on the back side of the curtain reaching 27 ppm (exceeding the target dosage of 25 ppm). Control exposures failed to achieve sterilization. Conclusions: A next-generation aerosolized hydrogen peroxide dry-fogging disinfection system achieved full inactivation of resistant spores on both sides of privacy curtains within a short 11-minute automated cycle.

Purpose: Videourodynamics (VUDS) are used to assess bladder dysfunction and risk of upper urinary tract deterioration in patients with spina bifida (SB), but interpretation is subject to interrater variability. Machine learning models have previously classified bladder dysfunction severity and predicted incident hydronephrosis using VUDS data. This study evaluated the performance of adapted versions of these models in an independent external cohort. Materials and Methods: VUDS data were collected from SB patients who underwent VUDS at a single institution between 2016 and 2025. Previously developed machine learning frameworks were adapted for implementation in the independent dataset. Three models were utilized: a deep learning convolutional neural network model using pressure-volume data, a deep learning imaging model using fluoroscopic imaging data, and an ensemble model that averaged the risk data from the pressure-volume and fluoroscopic data. The models were evaluated for prediction of incident hydronephrosis and classification of bladder dysfunction severity compared to expert pediatric urologist reviewers. Results: 70 patients were included in the hydronephrosis cohort, of whom 13 (18%) developed incident hydronephrosis. The ensemble model outperformed individual modalities, with a concordance index of 0.77 (pressure-volume only: 0.70, imaging only: 0.72) and an overall AUROC of 0.75 (pressure-volume: 0.67, imaging: 0.74). For bladder dysfunction classification in 95 VUDS studies, the ensemble model achieved 71% accuracy compared to 60% for pressure-volume data alone and 66% for imaging alone, with no substantial disagreements with expert reviewers. Conclusions: The machine learning models that were developed to predict incident hydronephrosis and classify the severity of bladder dysfunction using VUDS data were able to be adapted to an external, independent dataset and perform with similar discrimination and accuracy as previously reported.

Background Mortality in sepsis is often considered a direct consequence of infection and organ dysfunction, although many deaths occur after withdrawal of life sustaining treatment. The clinical factors leading to these decisions and their implications for interpreting sepsis mortality are not well characterized. Methods We performed a secondary analysis of a prospective observational cohort of adults with physician adjudicated sepsis at a tertiary academic medical center. Patients who transitioned to comfort measures only (CMO) during the index hospitalization underwent structured chart review to characterize baseline health and functional status, clinical severity, care preferences, anticipated outcomes following survival, and the primary reason for CMO transition. Results Among 239 patients with adjudicated sepsis, 42 (17.6%) transitioned to CMO. These patients had substantial underlying disease, including malignancy in 76% and immunocompromised status in 62%. However, 88% lived at home before hospitalization, 52% were independent in activities of daily living, an additional 26% were partially independent, and 90% were full code on admission. Progressive multi organ failure (19%), refractory septic shock (19%), and irreversible respiratory failure (12%) accounted for half of CMO transitions, while advanced frailty (5%) and poor baseline functional status (2%) were infrequent primary reasons. Median time from admission to CMO was 16 days (IQR 7 to 33), and median time from admission to death was 18 days (IQR 11 to 35). Anticipated ventilator dependence (62%), dialysis dependence (36%), and cognitive impairment frequently influenced goals of care discussions. Overall, 38 patients died during the index hospitalization, and 35 (92%) of these deaths followed transition to CMO. Conclusions Most CMO transitions in this cohort occurred after an evolving course of severe sepsis and were driven primarily by progressive organ dysfunction and failure to recover rather than advanced frailty or poor baseline function. These findings identify failure to recover after the acute septic insult as an important clinical trajectory and highlight the need to account for treatment limitation when interpreting sepsis mortality. Understanding why some patients recover from sepsis associated organ dysfunction while others progress to irreversible organ failure may help identify patients and mechanisms for future therapeutic intervention.

PurposeLong COVID literature often describes new-onset symptoms or conditions following SARS-CoV-2 infection; however, Long COVID also encompasses the exacerbation of pre-existing conditions. We describe health conditions reportedly worsened by COVID-19 illness and associated concerns in U.S. adults. MethodsWe collected longitudinal data from adults 18 years and older participating in the Track Post COVID-19 Conditions, an active Long COVID cohort surveillance project. Respondents completed surveys 3-, 6-, and 12- months after COVID-19 illness, beginning in July 2023. In each survey, respondents were asked whether they had any health conditions or concerns that were made worse by COVID-19. Those with an affirmative response were asked to specify the worsening condition(s) via free text. Some respondents used this free text to describe challenges or experiences related to their worsening conditions. This report categorizes and quantifies reported conditions and concerns by body system categories and qualitatively describes associated challenges and experiences using direct quotes from respondents free text responses. ResultsAmong 2,227 adults, 263 (11.8%) respondents reported at least one worsening condition or concern following COVID-19 illness. Of these, nearly 32% reported a worsening respiratory condition or concern. Adults with worsening pre-existing conditions reported challenges accessing adequate care, completing daily activities, and managing their symptoms following COVID-19. DiscussionWhile Long COVID literature often focuses on the development of new-onset long-term symptoms, more than 1 in 10 adults reported worsening of a pre-existing condition following COVID-19. Reported challenges with accessing care and daily activities highlight potential need for accommodations or support for adults with Long COVID.

In 2024, Chicago experienced a measles virus outbreak. Air samples collected as part of routine respiratory virus surveillance from three hospitals that received measles patients were retrospectively analyzed. Measles virus was detected in air samples from one emergency department across five weeks when measles patients were present and another when no known cases were present. Two of three viral sequences derived from air samples were identical to measles patients. The source of one diverging sequence could not be identified and may represent a case not identified by traditional surveillance methods. Air sampling may supplement measles investigation, surveillance and response.

Background: Quantifying behavioral drivers of protective action during infectious disease outbreaks remains challenging. App-based experimental epidemic games (epigames) provide a framework for studying these decisions in naturalistic settings, but correspondence between in-game measures and real-life beliefs or behaviors requires evaluation. We examined whether an activity-based opportunity cost influenced voluntary quarantine decisions within an epigame and whether real-life health beliefs transferred into the game and predicted observed choices. Methods: We conducted a two-week randomized controlled trial from May 4 to May 17, 2026, at VinUniversity (VinUni) in Hanoi, Vietnam. Participants using the Epigames app installed in their smartphone were randomized to a low-barrier group, earning 1 point per qualifying contact with other participant when remaining active, or a high-barrier group, earning 2 points per contact. Of 439 enrolled participants, 283 were successfully randomized to one of the two groups, made at least one valid daily quarantine decision, and their data was used in the analysis. Real-life (S1) and in-game (S2) beliefs measured susceptibility, severity, quarantine self-efficacy, and perceived benefits. Belief correspondence was assessed using Holm-adjusted Spearman correlations and prospective cumulative-logit models. Quarantine choices were analyzed using participant-day binomial generalized estimating equations (GEE) and participant-level quasi-binomial models. Results: Participants made 1,823 valid daily decisions and chose quarantine on 458 participant-days (25.1%). Quarantine rates were 30.1% in the low-barrier group and 20.2% in the high-barrier group. The high barrier was associated with 41% lower population-averaged odds of quarantine (GEE odds ratio [OR] 0.59, 95% confidence interval [CI] 0.40-0.86, = 0.0059); the quasibinomial estimate was identical (OR 0.59, 95% CI 0.40-0.86, = 0.0055). Among 251 participants with matched S1-S2 data, all four corresponding beliefs were positively associated (Spearman's = 0.259-0.418; all Holm-adjusted < 0.001). Prospective ordinal models confirmed positive matched-domain associations (ORs 1.39-2.18 per one-point S1 increase). Beliefs showed little robust association with quarantine behavior, and neither S1 nor S2 beliefs jointly modified the randomized effect. Self-efficacy, network degree, and the preregistered gender main effect were also unsupported. Conclusion: A higher activity-based opportunity cost reduced voluntary quarantine within the VinUni epigame, while participants carried relative differences in stated health beliefs from real-life to in-game framing. These beliefs did not reliably predict observed quarantine choices within the game framing. Epigames can support controlled study of incentives and belief correspondence in naturalistic settings such as college campuses, but behavioral validity or real-world generalizability requires further work.

BackgroundMethicillin-resistant Staphylococcus aureus (MRSA) is one the most common cause of post-surgical infections in Malawi and poses a significant public health challenge due to resistance to commonly used antibiotics. We isolated and evaluated the stability and lytic activity of a MRSA-specific bacteriophage (Staphylophage) from Mzuzu Central hospital (MZCH) sewage against MRSA strains isolated from surgical wards. MethodsA prospective in vitro experimental study was conducted using 56 pus swabs collected from post-surgical patients at Mzuzu Central Hospital (MZCH), Malawi. Standard microbiological methods and VITEK analysis were used for bacterial isolation, identification and antimicrobial susceptibility testing of Staphylococcus aureus and MRSA according to the 2024 EUCAST guidelines. A lytic Staphylophage was isolated and purified from sewage wastewater using filtration, enrichment, and double-layer agar techniques. The purified Staphylophage was evaluated for its lytic activity through host range testing and stability under ultraviolet (UV) exposure (up to 60 minutes), different pH levels (2-12), and temperatures (4{degrees}C, -20{degrees}C, and -80{degrees}C). ResultsOut of 56 bacterial isolates, 18 (32.1%) were S. aureus, of which 11 (61.1%) were MRSA. The isolated Staphylophage showed lytic activity against 87% of Staphylococcus species tested. The phage remained stable at temperatures between -80{degrees}C and 4{degrees}C and across a pH range of 2- 12. It also retained viability after UV exposure for up to 15 minutes, although a reduction in phage titre was observed after 30 minutes. ConclusionsA lytic Staphylophage isolated from hospital sewage at MZCH demonstrated activity against Staphylococcus species, including MRSA strains from post-surgical wound infections. These findings illustrate the feasibility of generating candidate phages for alternative therapeutic approaches in a central hospital setting in Malawi.

Abstract Background: Rising global temperatures are intensifying exposure to extreme heat, yet access to air conditioning (AC) remains unequal which leaves vulnerable populations without protection against the heat. Household survey data on AC ownership is scarce in low- and middle-income countries limiting the ability to identify areas of cooling gaps. Methods: In this study, we develop a geospatial machine learning framework that integrates household data from the Demographic and Health Surveys (DHS) program with high resolution climatic, socioeconomic and demographic covariates to generate a 5km resolution prediction of AC ownership across low- and middle-income countries in Africa and Asia for the year 2023. Results: The resulting map reveals strong spatial inequalities with high predicted prevalence in wealthier and urban regions, and near zero prevalence across much of Central and West Africa and rural areas. These patterns reflect the adaptation cooling gap where those most exposed to extreme heat are least able to adapt through access to cooling. Conclusion: By producing the first continental-scale, high resolution map of AC ownership, this study lays the foundation for designing and targeting equitable cooling strategies, support heat-health planning particularly in communities most affected and informing sustainable energy policy.

Predicted probabilities of antibiotic susceptibility to known or suspected pathogens can be used to support antibiotic selection. Patient demographic, clinical, and prior microbiologic history can be used to generate location-specific prediction models. For three Canadian tertiary care institutions we fit and evaluated models for predicting Gram-negative antibiotic susceptibilities to common antimicrobial agents based on previously established model structures. A web-based application was developed to permit visualization of antibiotic susceptibility predictions and support antibiotic decision-making.

Pyrogenic black carbon, such as biochar and wildfire char, possesses a reversible electron storage capacity (ESC), allowing it to serve as an electron donor or acceptor for microorganisms. However, not all the ESC is bioavailable, and the proportion available varies by culture. We hypothesized microbes capable of producing soluble redox mediators could access the ESC in biochar interior and thus utilize a higher percentage of the ESC than organisms without a mediator. To test this hypothesis, we grew wild-type Shewanella oneidensis (MR-1) and {Delta}bfe MR-1, a mutant incapable of exporting flavins, on air-oxidized wood biochar. Cell counts and redox titration show that both strains of MR-1 grew to similar cell densities and accessed comparable amounts of biochar ESC (~19%). The addition of exogenous flavin mononucleotide (FMN) yielded no measurable increase in final cell density, due to the strong adsorption of FMN to biochar. SEM images show attachment of MR-1 and {Delta}bfe to biochar surfaces and the presence of nanowires. This study demonstrates MR-1 can utilize biochar ESC for growth, through direct physical contact without the involvement of flavins. Understanding how microbes metabolize biochar provides insight into the impacts and biogeochemical roles of pyrogenic carbon.

Wearable light loggers are increasingly used to assess light exposure in everyday environments in studies of circadian physiology and sleep, yet their accuracy under diverse lighting conditions remains insufficiently understood. This study evaluated three commonly used wearable light loggers (ActLumus, Lido and OcuWEAR) by comparing measurements of photopic illuminance, melanopic equivalent daylight illuminance (m-EDI), and correlated colour temperature (CCT) against a reference spectroradiometer under controlled laboratory conditions and in the field. Measurements were conducted across fluorescent, incandescent, and multiple LED sources, as well as under dynamic daylight exposure. Device performance depended strongly on both light source characteristics and metric type. Illuminance exhibited lower variability, whereas m-EDI and CCT showed substantial device- and condition-specific biases, particularly under spectrally atypical illumination conditions. These differences were consistent with spectral mismatch effects arising from standard photometric calibration practices. Importantly, the impact of measurement bias varied by exposure context: under dim light conditions, small absolute deviations led to substantial misclassification relative to currently recommended thresholds, whereas brighter electric light and daylight conditions were more robust. These findings highlight that device- and context-dependent biases can influence the interpretation of light exposure in circadian and sleep research, underscoring the need for careful device selection and data interpretation.

Consumer-grade wearables can continuously and longitudinally measure health signals such as heart rate (HR), activity, and sleep. While changes in these signals have been implicated in the early detection of infection (e.g., influenza, SARS-CoV-2) and real-time alerting systems have been developed, evidence from prospective deployment in pediatric transplant households remains limited. Here, we study the application of a resting heart rate (RHR)-based real-time alerting algorithm to detect respiratory virus infection in a cohort of solid organ transplant recipients and their household members (n=146) in a Phase IV clinical trial (NCT06161454). Reliable and efficient alerting for infection events may be particularly relevant for this population, enabling isolation, clinical management, and/or treatment. Of 31 antigen-test outcomes, nearly all (29/31, 93.5%) correspond to either influenza, COVID, or other patient-reported symptoms. We describe RHR, HR, activity, and sleep/circadian rhythm features around clinical alarm events, identifying candidate features for further investigation and algorithm improvement.

Background: Host response and biomarker research to advance sepsis therapeutics requires well-characterized cohorts with longitudinal biospecimens across a range of hosts and infections. Automated screening based on SIRS criteria is sensitive but nonspecific and identifies more candidates than research teams can approach, sample, and follow longitudinally. We evaluated a structured enrichment review by non-physician study staff, done after automated screening in a hospital-wide prospective sepsis cohort. Methods: The CISID cohort is a prospective observational study of hospitalized patients with suspected sepsis, enrolling adults from the ED, inpatient wards, and ICUs at a tertiary academic medical center. Patients were identified through: (1) automated electronic health record screening for two or more SIRS criteria with a 24-hour lookback, documentation of microbiologic culture or PCR, and receipt of a new antibiotic; (2) a sepsis enrichment step of structured chart review by trained non-physician study staff; and (3) discussion with the primary clinical team. Enrolled patients underwent physician adjudication into four diagnostic categories. To evaluate the enrichment step, we analyzed all patients screened during a 9-week period, assigned a composite "sepsis-likely" variable (CDC Adult Sepsis Event criteria or ICD-10 code R65.20/R65.21 within 48 hours before or after screening), and assessed mortality across each stage of screening. We also assessed concordance of the composite with physician adjudication among enrolled patients. Results: From November 2023 through February 2026, 4,318 patients screened in through EHR-based automated screening; 1,220 (28%) were flagged for enrollment, and 288 were enrolled. Physician adjudication classified 209 (73%) as sepsis, 46 (16%) as infection without sepsis, 14 (5%) were classified as undifferentiated critical illness, and 19 (6.6%) as non-infectious sepsis mimics. Patients with sepsis were severely ill (median SOFA score, 9; ICU admission, 80%; shock, 48%), and 51% were immunocompromised; mortality was 14% in hospital and 27% at 90 days. In a 9-week sample of 907 screened encounters, 21% of those deferred and 70% of those flagged for enrollment met the sepsis-likely composite, a 3.3-fold increase in prevalence after structured study team review. Conclusions: A brief structured enrichment review after automated screening tripled the prevalence of EHR-based sepsis indicators among patients selected for enrollment, while concentrating enrollment efforts on roughly one-quarter of screened-in patients in this hospital-wide, all-source suspected-sepsis cohort. This approach may help prospective cohorts direct limited biospecimen resources toward patients most informative for host-response and biomarker research.

Background: Conventional discrimination metrics, such as the area under the receiver operating characteristic curve (AUROC), do not characterize the composition of outcomes among patients flagged as high risk by clinical prediction models. This limitation is particularly relevant in critical care, where competing deaths and incomplete follow-up can complicate the interpretation of prediction alerts. We developed the Balanced Risk Integrity Index (BRII), a stratum-level metric defined as the proportion of target events among target events, competing deaths, and early-censored observations in a flagged high-risk stratum. BRII is algebraically related to positive predictive value (PPV) and is reported alongside its component decomposition to characterize the outcome composition of flagged patients. Methods: We evaluated BRII using seven acute kidney injury (AKI) prediction models developed in MIMIC-IV and externally validated in MIMIC-III and eICU-CRD (116,010 patients in total). Additional simulations examined the effects of competing deaths and early censoring on BRII and conventional model performance metrics. Results: For 48-hour AKI prediction in the top 10% risk stratum, BRII ranged from 0.90 to 0.96 in MIMIC-III, compared with less than 0.26 across all models in eICU-CRD, where 61.5% of evaluated patients were censored before the prediction horizon. In the eICU-CRD top 10% strata for the same target, 37.0% to 50.2% of patients were censored early, 12.2% to 15.5% had AKI, and 3.4% to 8.1% died before AKI. BRII and PPV ordered at least one model pair differently in 34 of 48 cohort-outcome-top-k groups. Under simulated random censoring for 48-hour AKI prediction, BRII declined by up to 0.80 while binary-label AUROC changed by less than 0.02. Conclusions: BRII provided complementary information on high-risk stratum composition, yielding different model rankings from PPV and revealing substantial differences across external validation cohorts. Interpreted alongside PPV and its component decomposition, BRII offers a standardized approach to evaluating clinical prediction alerts across models and observation environments.

Background/Aims: In medical practice, the rising prevalence of infections in kidney transplant recipients (KTRs) is a notable concern. However, understanding the incidence and risk factors associated with infections in KTRs remains limited. This study aims to assess the incidence and factors associated infection among KTRs. Additionally, we also investigate the median time of each infection among KTRs. Methods: A retrospective cohort study was conducted from 1 January 2018- 31 December 2022 at Thailand's tertiary hospital. We performed univariable and multivariable logistic regression analyses to identify potential risk factors related infection and investigated for the incidence and median time of each infection among KTRs. Results: A total of 179 participants were included in this study. Our findings reveal the kidney from living donor was protective factor for infection among KTRs (aOR 0.063, 95% CI 0.005-0.792; p = 0.032). Urinary tract infections (UTIs)showed as the predominant pathogenic manifestation among KTRs (43%). The median time of UTIs, cytomegalovirus (CMV) infection and pneumocystis pneumonia (PCP) were 16, 57 and 94.5 days, respectively. Conclusions: Our study establishes that receiving a kidney from a living donor is a protective factor against post-kidney transplant infections. Particularly urinary tract infections were most common of infection. Monitoring urinary tract infections post-transplantation is recommended. Moreover, awareness of UTI, CMV infection and PCP should be concerned during 2-3 months after transplantation. However, due to a small sample size; we recommend to increase the number of particular participants in further investigations.

Current health risk assessments from thirdhand exposure to methamphetamine (meth) to occupants of contaminated residences when no cooking or use is still occurring assume that inhalation exposure is insignificant. After Wright et al. (2021) reported substantial meth concentrations in a contaminated residence, the present study was conducted to further test the hypothesis that concentrations were high enough to contribute to total occupant dose. Air sampling was conducted in contaminated residences that had been previously sampled for surface contamination. Samples were collected on personal sampling pumps in fixed locations on treated glass fiber filters is cassettes and analyzed by gas or liquid chromatography with mass spectroscopy. Sixty-nine samples were collected from eighteen residences for the study. Meth concentrations ranged from 0.02 to 15 ug/m3. Concentrations in supply air vents were significantly higher than in return vents or in ambient air. Residences with no HVAC systems (baseboard heat) had airborne concentrations similar to residences with HVAC systems turned off, and lower than when HVAC was on. Whether a residence was occupied or not did not show differences. All data from residences with HVAC had a significant correlation with surface concentrations (R2 = 0.61) but some factors that affected results were not determined in the study. In the context of the risk assessment and regulatory standard established by California and adopted in several other states, the inhalation exposure would approximately double total occupant dose for infants, and this population would exceed the California reference dose by approximately 100%. Further studies should be conducted to refine these estimates and determine the factors that influence airborne concentrations.

Introduction: In temperate regions, influenza and respiratory syncytial virus (RSV) typically exhibit seasonal epidemics during the winter months, often concurrently with SARS-CoV-2 epidemics. Monitoring past and real-time trends in infection dynamics is important for informing public health situational awareness, including identifying and characterising unusual epidemic activity. However, data used to monitor these trends can exhibit substantial noise including strong day-of-the-week variation in reporting. In addition, case data for influenza can obscure distinct trends in influenza subtypes if not combined with virological data. Aim: By quantifying trends in respiratory virus activity using statistical methods, the Australia-Aotearoa Consortium for Epidemic Forecasting and Analytics (ACEFA) aims to provide government partners with comprehensive epidemic intelligence. Methods: Analyses were performed on daily case time series data for SARS-CoV-2, RSV and influenza across Australia's eight jurisdictions. Past and real-time trends were inferred for each pathogen and each influenza subtype using Bayesian p-spline models. Results: The 2025 Winter Situational Assessment Program provided weekly reports throughout the winter season and during the out-of-season influenza A H3N2 epidemic, driven by the emergence of the novel influenza subclade ('subclade K'). Despite limited subtyping data, our analysis provided an early signal of increasing influenza A H3N2 during a period in which overall influenza cases were decreasing; increases in influenza A H3N2 activity were first detected in July, and by mid-August, increasing H3N2 activity was estimated across all jurisdictions. Discussion: Our analysis highlights the benefits of incorporating statistical methods into real-time epidemic situational assessment and in consolidating reporting across jurisdictions.

International travel can increase infection risk and spread antimicrobial resistance (AMR), yet clinical systems rarely capture travel history in a structured format. Extracting this information from free-text clinical documentation at scale could improve alerts for high-consequence infections and AMR surveillance. We developed an annotation framework covering key aspects of travel history, including destination, exposures, travel duration, and multiple temporal variables. The framework was used to annotate a corpus of 100 clinical case reports by five clinicians, yielding over 5,000 clinician annotations. Several LLMs were evaluated on the same extraction task, with their outputs compared against clinician consensus. Agreement among clinicians was high for straightforward variables such as presence of travel and destination, but dropped substantially for temporal variables such as time since return and symptom onset relative to return. LLMs showed a similar pattern. They performed well on high-level variables but struggled with the same temporal fields that challenged clinicians. Model errors were also concentrated in cases where clinicians disagreed. Across models, outputs were more sensitive to sampling temperature than to prompt wording. A smaller open-weight model performed similarly to substantially larger models on several variables but remained weaker on temporal fields. Overall, LLMs could extract some travel history variables, but reliable extraction of temporal information remained difficult.

Background: Amplicon-based next-generation sequencing is a cornerstone of viral genomic surveillance, but it often produces datasets with highly uneven read coverage due to PCR amplification bias. This leads to excessive data generation and substantial computational overhead for downstream bioinformatic analysis. Results: We evaluated the efficacy of two data reduction strategies on 220 SARS-CoV-2 clinical specimens and 12 wastewater samples: random subsampling with SeqKit and read depth normalization with BBNorm. FASTQ files were reduced to target depths from 10X to 10,000X to compare genome coverage, lineage assignment concordance, and computational resource usage. Both strategies markedly reduced total processing time. For clinical isolates, read depth normalization corrected for amplification bias resulting in highly concordant lineage assignments at a target depth of just 200X. Random subsampling preserved inherent bias but produced reliable assignments at depths over 500X. Reducing target depths below 500X in complex wastewater mixtures severely degraded variant deconvolution accuracy and heavily skewed lineage abundance profiles for both methods. Conclusion: Both random subsampling and read depth normalization successfully reduce computational overhead while preserving key clinical sequencing metrics at appropriate reduction settings. The optimal method depends on the analytical goal, ranging from computationally lightweight random subsampling for rapid lineage assignment, to read depth normalization for generating high-quality consensus sequences. However, aggressive data reduction should be avoided in environmental surveillance, where maintaining a high baseline sequencing depth is critical for accurate mixed-variant deconvolution.

Background: Health Information Exchanges (HIEs) offer a potential resource for timely, population-level infectious disease surveillance by integrating electronic health record data across healthcare systems. Methods: We developed an end-to-end informatics pipeline for near-real-time community-level health surveillance based in a regional HIE serving Southeast Texas. We identified deidentified individual-level COVID-19, influenza, and RSV cases using diagnosis codes and laboratory results and assessed demographic and temporal trends from January 2022 through August 2026. Results: We identified 492,242 COVID-19 cases, 343,977 influenza cases, and 33,159 RSV cases during the study period. Surveillance data captured expected seasonal patterns, including recurring winter COVID-19 and influenza peaks and shifting RSV epidemiology, while enabling monitoring of laboratory test positivity metrics and hospitalization trends across age groups. Conclusions: HIE-based surveillance provides scalable, near-real-time monitoring of disease targets across large populations. HIE infrastructure can enhance public health situational awareness and complement traditional surveillance systems by harmonizing vital health data across systems and providers and delivering timely, standardized, and representative population health indicators.