Tuberculosis (TB) is one of the infectious diseases of public health concern globally. Kenya is ranked 15th among the 22 high TB burden countries worldwide, which collectively contribute to 80% of the world's TB cases. TB Treatment failure is one of the threats to the control of TB. The research aimed at determining affordable predictors of TB treatment failure in a resource limited setting to inform policy in designing public health interventions that are best suited to the country's needs. To determine the predictors of treatment failure among patients with sputum smear positive pulmonary TB attending selected public health facilities in Nairobi Count. Data was abstracted and summarized from both patients and their medical records, focusing on socio-demographic, behavioral, and clinical exposure data. Data was collected from 4 Sub-counties, a total of 21 public health facilities with high case load of pulmonary TB were reached. Utilizing an unmatched case-control design, the study enrolled 81 patients diagnosed with TB treatment failure (cases) and 162 patients who were declared cured after completing their anti-TB treatment (controls. Strengthen contact tracing, screening, and documentation of TB treatment failure cases. Conduct further studies to elucidate the association between HIV and TB treatment failure. The factors significantly associated with treatment failure in this study encompassed prior exposure to first-line anti-Tuberculosis drugs, positive sputum smear at 2 months of treatment, and suboptimal adherence to anti-TB treatment. These findings contribute valuable insights into the identification of simple predictors of TB treatment failure such as utilizing sputum microscopy or gene expert testing at 2 months of treatment to detect individuals at risk and strengthen the implementation of DOT and TB treatment failure contact tracing protocol.

Epidemiological investigations of the foodborne parasitic illness cyclosporiasis can be aided by molecular techniques that enable the identification of genetically related clusters of Cyclospora isolates. At the Centers for Disease Control and Prevention (CDC), routine Cyclospora genotyping for the purpose of informing epidemiological outbreak investigations has occurred since 2018 using clinical stool specimens from case patients diagnosed with cyclosporiasis. This approach involves targeted amplicon deep sequencing of eight genotyping markers, followed by bioinformatic processing through a custom clustering algorithm. However, not all stool specimens submitted to the CDC for genotyping successfully amplify for at least five of the eight genotyping markers, the minimum required to be bioinformatically processed through the clustering algorithm. In this study, we utilized information from clinical stool specimens sent to the CDC from the years 2019 to 2023 to assess if the type of preservative, the age of the specimen, or the method used to diagnose the patient influenced the probability of successfully genotyping parasites from a fecal specimen. Additionally, we assessed the analytical specificity of the Cyclospora genotyping workflow by analyzing samples positive for other intestinal parasites, including closely related non-human infecting Cyclospora species and other coccidia. We found that stool specimens stored in preservatives had a greater likelihood of sequencing success over time relative to specimens without preservatives or those stored in non-nutritive transport media. Additionally, stool specimens from case patients diagnosed via microscopy-based methods were more likely to yield DNA of sufficient quality and quantity for genotyping compared to PCR or multiplex panels. Lastly, we determined that the genotyping workflow has an analytical specificity of 100%, as no non-human-infecting Cyclospora or other parasites yielded sequence data at >1 of the genotyping markers. This knowledge will help strengthen the quality of Cyclospora genotyping data produced in the future, improving the utility of this data for supporting epidemiological investigations.IMPORTANCEDetermining the genetic relatedness among parasites causing foodborne illness, such as Cyclospora, is a valuable tool to complement outbreak investigations. However, this molecular genotyping approach is limited by the quality and quantity of genetic data obtained from the samples being investigated. In this study, we demonstrate that the storage conditions of clinical stool specimens are correlated to the quality of sequence data produced for Cyclospora genotyping. Our insights can be used to guide storage recommendations for stool specimens, which can improve the quality of foodborne illness outbreak investigations conducted in the future. Additionally, we showed that the current Cyclospora genotyping tool used by the Centers for Disease Control (CDC) is highly specific to human-infecting Cyclospora parasites; this valuable information indicates that the CDC's Cyclospora investigations are not negatively impacted by false-positive detections.

Bacterial meningitis is a significant public health concern, with over 1.2 million cases reported globally each year. Rwanda is at increased risk of meningitis outbreaks due to its proximity to countries that lie in the meningitis belt. Rwanda has been conducting surveillance and recording meningitis outbreak cases across the country since 2012. We evaluated the meningitis surveillance system at Kibogora Level Two Teaching hospital, Nyamasheke district of Rwanda to assess whether the surveillance objectives were being met. The study was cross-sectional, using purposive sampling to select healthcare providers participating in the meningitis surveillance. Rwanda's bacterial meningitis data from 2017 to 2021 was collected from clinical registers and Rwanda's electronic integrated disease surveillance system (eIDSR) from Kibogora Level Two Teaching Hospital catchment area, Nyamasheke district, Rwanda. The study area was chosen because a meningitis outbreak was recorded in the area and its bordering country namely Democratic of Republic of Congo (DRC) prior to the current study period. Information on the participant's demographics, occupation, training, professional experience, and their perception on the surveillance system were gathered using a structured questionnaire. Meningitis surveillance systems attributes including usefulness, acceptability, and flexibility were assessed and categorized as poor (< 50% score), reasonable (50-69%), good (70-90%), or excellent (> 90%) in reference to the study conducted on the evaluation of the meningitis surveillance system in Luanda Province, Angola in March 2017. Data collected from clinical registers and eIDSR were used to assess core functions of the meningitis surveillance system including accuracy in detection of cases, laboratory confirmation of cases, and availability of evaluation reports. Descriptive statistics were analyzed using Microsoft Office Excel. Thirty-one healthcare providers working on meningitis surveillance in the Kibogora Level Two Teaching Hospital were interviewed. During the period under evaluation, 48 suspected cases of meningitis were identified; 43 (90%) met the surveillance case definition, and only 10 (21%) were reported to eIDSR (completeness). Attributes such as flexibility scored good while stability and acceptability scored reasonable. Out of 48 suspected meningitis cases, only 2 (4%) samples were collected from patients and sent to the hospital laboratory for analysis. This study found a good knowledge level of the meningitis surveillance system among healthcare workers; however, the system's core functions, such as notification rate and laboratory confirmation were found to have gaps. The notification rate could be improved by conducting regular refresher courses for healthcare workers supporting surveillance system. Moreover, MoH could enhance the implementation of a national policy requiring mandatory CSF sample testing to confirm pathogens for all suspected cases. Future studies should explore performance-based incentives to improve reporting completeness. Rwanda's experience could provide insights for other low-resource settings facing similar surveillance challenges.

Human adenovirus (HAdV), especially HAdV species F (HAdV-F) is recognized as a cause of acute gastroenteritis (AGE) worldwide. To assess the global prevalence of HAdV in case-patients of all ages with AGE, we conducted a systematic literature search for studies published in English during 2015-2022. We generated pooled prevalence estimates using generalized linear mixed models. Using data from 147 included articles, the overall pooled prevalence among AGE case-patients of any species of HAdV (pan-HAdV) was 5.8 % and 6.0 % for HAdV-F. The prevalence of HAdV was significantly higher among case-patients < 5 years of age compared with case-patients ≥ 5 years of age (pan-HAdV: 6.6 % vs. 2.0 %, p < 0.0001; HAdV-F: 8.7 % vs. 2.3 %, p = 0.04). Prevalence was significantly higher in high mortality developing countries and lowest in developed countries (pan-HAdV: 9.4 % vs. 4.0 %, p < 0.0001; HAdV-F: 11.6 % vs. 3.2 %, p = 0.0003). Understanding the burden of HAdV-associated AGE may be useful for targeted interventions, including future vaccine development.

BACKGROUND: Expanding malaria community case management (mCCM) to all ages could shift the point-of-care to the community leading to improved healthcare access in underserved populations. This study assesses the economic viability of such an expansion in Farafangana district, Madagascar. METHODS: A cluster-randomized trial was conducted across 30 health centres and the 502 community health workers (CHW) in their catchment areas, with the intervention arm implementing the age-expanded mCCM intervention. CHWs across both arms received training, supplies, and supervision to manage malaria. An economic evaluation assessed cost-effectiveness from health sector and societal perspectives, measuring outcomes in disability-adjusted life years (DALYs) averted. The impact of CHW compensation and economic risks were evaluated using sensitivity analyses. RESULTS: Without CHW compensation, annual costs were $794,000, primarily for antimalarials and diagnostic tests. Incremental cost-effectiveness ratios (ICERs) per DALY averted ranged from -$21.86 to $212.42. From a societal perspective, the ICER was -$135.64, and -$243.29 including mortality benefits, meaning the intervention was cost-saving. The programme could avert 99.6 deaths and 3,721.7 DALYs annually, yielding $1,172,283 in net economic benefits. Sensitivity analyses supported these findings. CONCLUSIONS: Age-expanded mCCM is highly cost-effective and can enhance malaria treatment access in resource-limited settings.

BACKGROUND: Crimean-Congo hemorrhagic fever (CCHF) is a tick-borne zoonotic disease characterized by a high case fatality rate of ~30%. CCHF is endemic in Kyzylorda Oblast, Kazakhstan, which has a population of 800,000, with approximately 10 cases reported annually. In 2022, by end of July, 15 cases had been reported. We conducted an investigation to identify the risk factors associated with CCHF and to recommend preventive measures. METHODS: We conducted a case-control study. Case-patients were defined as individuals hospitalized between April and July 2022, showing signs consistent with CCHF and having a history of exposure-contact with ticks or animals and sudden onset of unexplained bleeding-within 2 weeks before the onset of illness. Confirmed case-patients additionally tested positive for CCHF-using both polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) for both immunoglobulin M (IgM) and immunoglobulin G (IgG) tests. For every case-patient, two people from neighboring households were selected as controls. We used logistic regression to assess the factors associated with CCHF. Ticks collected from animals residing on the case-patient's property were tested for CCHF. We also reviewed public environmental and livestock data. RESULTS: We studied 17 suspected, 7 probable, and 14 confirmed case-patients, along with 71 controls. Case-patients were predominantly male (74%), 47% were livestock workers and 37% were agricultural workers. Among the 14 confirmed CCHF case patients, 4 died from the illness (case fatality rate: 29%). Among the all case-patients, 100% reported weakness, 97% headaches, and 84% fevers. Over half (53%) of case-patients reported ticks on their bodies and clothing ≤2 weeks before the onset of the illness compared to 1% of controls (p < 0.001). Nearly half (47%) of the case-patients visited or lived in a high-risk area for tick bites ≤2 weeks before the onset of the illness compared to 6% of controls (p < 0.001). Livestock and agricultural workers had higher odds of CCHF compared to those not in these professions (odds ratios and 95% confidence interval [CI]: 3.0 [1.3-7.2] and 4.0 [1.5-10.5], respectively). Among the 55 control persons tested for CCHF, 1 (2%) tested IgG-positive. Of 163 ticks tested, 0.6% were PCR positive. In 2022, Kyzylorda had increased livestock numbers, above-average temperatures in February and March, and a delayed acaricidal treatment for livestock and pastures. CONCLUSION: We found a high occupational risk for CCHF. The prevalence of CCHF in ticks in our study was 0.6%, which is consistent with regional tick surveillance data. Increased tick control measures and personal protective measures for people with occupational exposure to ticks may help reduce cases.

Understanding disparities in salmonellosis burden is critical for developing effective, equitable prevention programs. Past efforts to characterize disparities were limited in scope and by the analytical methods available when the study was conducted. We aim to address this gap by identifying disparities in salmonellosis incidence between counties with different determinants of health (DOH) profiles. Using national U.S. Laboratory-based Enteric Disease Surveillance (LEDS) data for 1997-2019, age-adjusted county-level salmonellosis incidence/100,000 persons was calculated and linked to publicly available DOH data. We used hurdle counterfactual random forest (CFRF) to quantify, for each DOH, the risk that (i) ≥1 versus no cases were reported by a county, and (ii) when ≥1 case was reported, whether a high (≥16 cases/100,000 persons) or low incidence (≥1 & <4 cases/100,000 persons) was reported. Risk in both models was significantly associated with demographic DOH, suggesting a disparity between counties with different demographic profiles. Risk was also significantly associated with food, healthcare, physical, and socioeconomic environment. The risk was generally greater for counties with more negative food resources, and for under-resourced counties (e.g., fewer healthcare and social services, fewer grocery stores). Risk was also significantly higher if any extreme weather event occurred. The study also found that underreporting and underascertainment appeared to result in underestimation of salmonellosis incidence in economically marginalized and under-resourced communities. Overall, our analyses indicated that, regardless of other county characteristics, extreme weather was associated with increased salmonellosis incidence, and that certain communities were differentially disadvantaged toward a higher incidence. This information can facilitate the development of community-specific prevention efforts.

BACKGROUND: The advanced disease management (ADM) package, which aims to reduce morbidity and mortality in people with Advanced HIV disease (AHD, WHO stage III/IV and/or CD4 count < 200 cells/mm(3) or age < 5 years), is not fully implemented in India. We assessed the feasibility of implementing the full WHO ADM package as part of routine HIV care under the programmatic setting in antiretroviral therapy centers of Mumbai. METHODS: We implemented the ADM package (screening, treatment, and prophylaxis for major opportunistic infections, rapid ART initiation, and ART adherence support) in 17 ART centers from October 2020 to December 2021. Treatment naïve and experienced persons with AHD, including children, were enrolled. We assessed the feasibility through coverage of ADM package components and reported the proportion of rapid ART initiation (≤ 7 days), cotrimoxazole prophylaxis, TB preventive treatment (TPT) for those eligible [(excluded active TB disease (n = 280) and those completed TPT prior to enrolment (n = 1,186)], TB-LAM screening (excluded current TB disease), and cryptococcal antigen (CrAg) assay (excluded children < 10 years of age). We used a point of care test for TB (LAM) and cryptococcus (CrAg) screening. We followed the prospective cohort for one year (through 31 July 2022) to document outcomes for survival and lost to follow- up (LTFU). RESULTS: We identified 4,334 PLHIV with AHD and provided the full ADM package to 64% (2,779/4,334); 297 did not receive ADM (146 died, 151 LTFU), and 1,258 received routine standard of care (587 had TB, 366 were at decentralized sites, and 305 LAM/CrAg kits were not available) with existing ART center staff. Nearly 78% (385/494) of treatment naïve were rapidly initiated on ART. Nearly 82% (1,129/1,383) and 99% (2,751/2,779) received TPT and cotrimoxazole prophylaxis, respectively. Of the eligible, 99% (2,508/2,524) and 98% (2,715/2,758) were screened for TB and cryptococcal infection, respectively. At the end of 12 months, 88% (2,458/2,779) were alive, 8% (210/2,779) died, and 4% (111/2,779) were LTFU. Mean survival time was significantly (p < 0.001) higher among treatment experienced people; 11.6 months (95% CI: 11.5,11.7) compared to treatment naïve people 10.8 months (95% CI: 10.5,11.0). CONCLUSION: With careful anticipatory planning, stakeholder engagement, and training, implementing the full ADM package is feasible in a routine program setting with existing human resources. Additional intensive case management may be necessary for the reduction of mortality among treatment naïve PLHIV.

BACKGROUND: Patients hospitalised with influenza have heterogeneous clinical presentations and disease severity, which may complicate epidemiologic study design or interpretation. We applied latent class analysis to identify clinically distinct subgroups of adults hospitalised with influenza. METHODS: We analysed cross-sectional study data on adults (≥18 years) hospitalised with laboratory-confirmed influenza from the population-based U.S. Influenza Hospitalization Surveillance Network (FluSurv-NET) including 13 states during 2017-2018 and 2018-2019 influenza seasons (October 1 through April 30). Adults were included if they were residents of the FluSurv-NET catchment area, hospitalised with laboratory-confirmed influenza during these two seasons, and had both the main case report form and the supplemental disease severity case report form completed. We constructed a latent class model to identify subgroups from multiple observed variables including baseline characteristics (age and comorbidities) and clinical course (symptoms at admission, respiratory support requirement, and development of new complications and exacerbations of underlying conditions). FINDINGS: Among the 43,811 influenza-associated hospitalizations reported during the 2017-2018 and 2018-2019 influenza seasons, 15,873 (36.2%) were included in our analytic population: among them, 7069 (44.5%) were male and 8804 (55.5%) were female. We identified five subgroups. Subgroup A included persons of all ages with few comorbidities and 87.9% (255/290) of pregnant women. Subgroup B included older adults with comorbidities (cardiovascular disease (79.7% [3650/4581]) and diabetes (50.6% [2320/4581])). Almost all patients in subgroups C and D had asthma or chronic lung disease and high proportions with exacerbations of underlying conditions (59.7% [889/1489] and 65.1% [2274/3496], respectively). Subgroup E had the highest proportion with new complications (90.3% [1383/1531]). Subgroups D and E had the highest proportions with severe disease indicators: 21.0% (733/3496) and 50.4% (771/1531) required ICU admission, 7.2% (253/3496) and 28.0% (428/1531) required invasive mechanical ventilation, and 3.3% (116/3496) and 11.4% (174/1531) died in-hospital, respectively. INTERPRETATION: The five identified subgroups of adults hospitalised with influenza had varying distributions of age, comorbid conditions, and clinical courses characterized by new complications versus exacerbations of existing conditions. Stratifying by these subgroups may strengthen analyses that assess the impact of influenza vaccination and antiviral treatment on risk of severe disease. Limitations included that results were based on a convenience sample within FluSurv-NET sites and were likely not representative of all adults hospitalised with influenza in the United States. Influenza testing was also clinician-driven, likely leading to under-ascertainment. FUNDING: Centers for Disease Control and Prevention.

A multistate measles outbreak, predominantly affecting members of close-knit communities with low measles vaccination coverage in New Mexico, Oklahoma, and Texas began in January 2025. As of April 17, a total of 800 cases have been reported in the United States in 2025; 654 (82%) cases in New Mexico, Oklahoma, and Texas have been associated with the ongoing outbreak. These cases represent an approximately 180% increase over the 285 measles cases reported in the United States during all of 2024, and the second highest annual case count in the United States in 25 years. Overall, 771 (96%) patients have been unvaccinated or had unknown vaccination status (77% were unvaccinated, and 14% had unknown vaccination status when excluding 590 cases reported by Texas, which requires explicit consent by law [i.e., opt-in] to enroll in the Texas Immunization Registry), 85 (11%) patients have been hospitalized, and three patients have died. Among 48 (6%) internationally imported cases, 44 (92%) occurred among U.S. residents. Endemic measles was declared eliminated in the United States in 2000 as a direct result of high 2-dose childhood coverage with the measles, mumps, and rubella (MMR) vaccine. However, measles cases and outbreaks continue to occur when travelers with measles return to the United States while they are infectious; larger U.S. outbreaks typically follow importation into close-knit communities with low vaccination coverage. Nationally, risk for widespread measles transmission remains low because of high population-level immunity. To prepare for and prevent measles cases and outbreaks, public health departments should continue working with trusted community messengers on culturally competent community engagement, education, vaccination efforts, and other community infection prevention approaches (e.g., case isolation, contact monitoring, and post-exposure prophylaxis) and coordinating with health care facilities and schools. Increasing national and local MMR vaccination coverage is essential to preventing measles cases and outbreaks.

BACKGROUND: Split-hand/foot malformation (SHFM) is a rare, genetically heterogeneous, congenital limb defect. Some but not all associated genes are known; therefore, the aim was to identify genes underlying SHFM. METHODS: Buccal cell-derived DNA from 26 children with SHFM and their parents who participated in the National Birth Defects Prevention Study was exome sequenced. Family-based trio analyzes prioritized rare coding variants by inheritance patterns, predicted pathogenicity, and location within putative limb development genes. Copy-number variants in SHFM candidate genomic regions were also examined. Case-control analyzes compared coding variants between case children and 1191 controls (parents of children with non-limb birth defects). Variant validation was by Sanger sequencing or droplet digital polymerase chain reaction. RESULTS: In family-based analyzes, the prioritized and validated variants (each in a single family) included likely damaging variants that were heterozygous and de novo in speckle type BTB/POZ protein (SPOP) and ubiquitin-like modifier activating enzyme 2 (UBA2), X-linked recessive in fibroblast growth factor 13 (FGF13) and RNA binding motif protein 10 (RBM10), and compound heterozygous in interleukin enhancer binding factor 3 (ILF3). Validation assays did not confirm predicted de novo copy-number gains at chromosomes 10q24 and 19p13.11. Case-control analyzes did not identify statistically significant associations. CONCLUSION: Exome analysis nominated new susceptibility genes (FGF13, ILF3, RBM10, SPOP) and detected a variant in a known candidate gene (UBA2). Follow-up investigation is needed to ascertain damaging variants in these genes in additional cases with SHFM and evaluate the impact of the variants on gene expression, protein function, and limb development.

BACKGROUND: A multistate outbreak of e-cigarette, or vaping, product use-associated lung injury (EVALI) occurred in 2019. Because of EVALI's novelty and severity, the New York City (NYC) Department of Health and Mental Hygiene (DOHMH) prospectively assessed sequelae among NYC residents who received an EVALI diagnosis in 2019. METHODS: Using existing NYC EVALI surveillance data, DOHMH attempted contact with all living residents who received an EVALI diagnosis in 2019 and conducted 3 waves of telephone interviews during April 2020-March 2021. Interview questions were adapted from the Centers for Disease Control and Prevention's EVALI case report form and validated surveys. Baseline differences between respondents and nonrespondents were assessed with Chi-square and Fisher's exact tests; clinical and behavioral characteristics and open-ended responses were summarized. RESULTS: In 2019, 53 NYC residents received an EVALI diagnosis; 33 (67%), 14 (29%), and 18 (37%) of 49 living residents participated in the first, second, and third interviews, respectively. Interviews occurred after outpatient diagnosis (6%) or hospital discharge (94%), at a median of 8, 11, and 17 months for each wave. Respondents (N = 33) and nonrespondents (N = 16) did not differ by sex, age, hospitalization status or length. Respondents were mostly male (70%), had a median age of 23 years (range: 16-63 years), and all reported using vaping or e-cigarette products (vaping) with tetrahydrocannabinol (88%), nicotine (49%), or cannabidiol (9%) before diagnosis. Respiratory (first and second interviews) and gastrointestinal (third interviews) symptoms were most commonly reported. Sixteen respondents (49%) reported any new diagnosis during follow-up. Fifteen to 29% of respondents reported vaping at each interview; 58%-93% reported recent non-vaped cannabinoid use. CONCLUSION: NYC residents with EVALI reported symptoms throughout the follow-up period, and approximately half reported newly diagnosed health conditions. Further studies are needed to understand EVALI's relationship with symptoms and health conditions.

BACKGROUND: West Nile virus (WNV) is the most common cause of mosquito-borne disease in the continental USA, with an average of ~1200 severe, neuroinvasive cases reported annually from 2005 to 2021 (range 386-2873). Despite this burden, efforts to forecast WNV disease to inform public health measures to reduce disease incidence have had limited success. Here, we analyze forecasts submitted to the 2022 WNV Forecasting Challenge, a follow-up to the 2020 WNV Forecasting Challenge. METHODS: Forecasting teams submitted probabilistic forecasts of annual West Nile virus neuroinvasive disease (WNND) cases for each county in the continental USA for the 2022 WNV season. We assessed the skill of team-specific forecasts, baseline forecasts, and an ensemble created from team-specific forecasts. We then characterized the impact of model characteristics and county-specific contextual factors (e.g., population) on forecast skill. RESULTS: Ensemble forecasts for 2022 anticipated a season at or below median long-term WNND incidence for nearly all (> 99%) counties. More counties reported higher case numbers than anticipated by the ensemble forecast median, but national caseload (826) was well below the 10-year median (1386). Forecast skill was highest for the ensemble forecast, though the historical negative binomial baseline model and several team-submitted forecasts had similar forecast skill. Forecasts utilizing regression-based frameworks tended to have more skill than those that did not and models using climate, mosquito surveillance, demographic, or avian data had less skill than those that did not, potentially due to overfitting. County-contextual analysis showed strong relationships with the number of years that WNND had been reported and permutation entropy (historical variability). Evaluations based on weighted interval score and logarithmic scoring metrics produced similar results. CONCLUSIONS: The relative success of the ensemble forecast, the best forecast for 2022, suggests potential gains in community ability to forecast WNV, an improvement from the 2020 Challenge. Similar to the previous challenge, however, our results indicate that skill was still limited with general underprediction despite a relative low incidence year. Potential opportunities for improvement include refining mechanistic approaches, integrating additional data sources, and considering different approaches for areas with and without previous cases.

BACKGROUND: Marburg virus disease (MVD) is a severe viral infection caused by the Marburg marburgvirus species. In February 2023, Equatorial Guinea declared its first outbreak. This case series describes the natural history of MVD in five laboratory confirmed patients. METHODS: Patients with confirmed MVD admitted to the national treatment center in Bata, Equatorial Guinea, were monitored for vital signs and symptoms. Comprehensive clinical data was collected to understand the progression and outcome of the disease. RESULTS: Five patients were confirmed to have MVD. Three male healthcare workers were diagnosed early in their disease and subsequently survived. The other two patients, both females, were admitted later in their disease progression and died within 24 hours of admission. Four patients received remdesivir under a protocol for the Monitored Emergency Use of Unregistered and Experimental Interventions. The early symptoms were non-specific, with rapid progression to more severe conditions in the later stages of the disease. Early treatment with remdesivir showed the drug to be well tolerated. CONCLUSIONS: Contrary to some reports and the recommended case definition for MVD, our patients presented with a rash but did not exhibit vomiting or diarrhea. Hemorrhagic signs were solely observed in the terminal stage, preceding demise. Despite the limited sample size, these findings emphasize the importance of tailoring the case definition to the specific outbreak. Further evidence on the efficacy and safety of therapeutics for MVD, including remdesivir, should be gathered through well-designed trials during future epidemic responses.

Marburg virus (MARV) is a highly pathogenic filovirus and a causative agent of sporadic zoonotic viral hemorrhagic fever outbreaks with high case fatality rates. In humans, filoviruses like MARV and Zaire Ebola virus (EBOV) target, among others, innate immune cells like dendritic cells and macrophages (MΦs). Filovirus-infected dendritic cells display impaired maturation and antigen presentation, while MΦs become hyper-activated and secrete proinflammatory cytokines and chemokines. Our current understanding of human macrophage responses to MARV remains limited. Here, we used human monocyte-derived macrophages (moMΦs) to address how their phenotype, transcriptional profile, and protein expression change upon an in vitro infection with a bat isolate of MARV. Confirming its tropism for macrophages, we show that MARV induces notable shifts in their transcription distinct from responses induced by lipopolysaccharide (LPS), marked by upregulated gene expression of several chemokines, type I interferons, and IFN-stimulated genes. MARV infection also elicited pronounced inter-individually different transcriptional programs in moMΦs, the induction of Wnt signaling-associated genes, and the downregulation of multiple biological processes and molecular pathways.

Death from myocarditis due to Lyme disease is uncommon but may be under-recognized. Myocarditis can lead to sudden and/or unexpected death due to fatal cardiac arrhythmias, which may be misattributed to more common causes, such as coronary artery disease. We report an unexpected death in a 69-year-old man from a Lyme-endemic area who had multiple cardiovascular risk factors. His death was initially attributed to coronary artery disease by the local coroner's office but was later confirmed to be due to Lyme disease, based on positive serological testing, characteristic cardiac histopathology findings, and the detection of Borrelia burgdorferi spirochetes, antigens, and DNA in the heart using various methods. Forensic pathologists should maintain a high suspicion of Lyme carditis, particularly in cases of sudden/unexpected death in Lyme-endemic areas. A history of rash or recent insect bites should prompt serologic testing for Lyme disease.

On September 20, 2022, the Uganda Ministry of Health declared an outbreak of Sudan Virus Disease (SVD). As the outbreak grew, it became imperative to quickly visualize and analyze chains of disease transmission. Determining epidemiological links between cases is critical for outbreak control as incorrect linkages may result in missed case detection and undetected disease transmission. We describe the Uganda Ministry of Health's experience using Chainchecker, a computer application designed to visualize and verify transmission chain data. To use Chainchecker, a line list documenting the epidemiological details associated with individual cases is uploaded to the application. To verify epidemiologic linkages, the application calculates the exposure windows for each case based on user-defined incubation periods and dates of symptom onset. If genetic sequencing data is available, Chainchecker can overlay genetic distance data on top of the epidemiologic data. Chainchecker can also provide visualizations of hospitalization data, which can highlight potential instances of nosocomial disease transmission. Using the Chainchecker application, the case investigation team was able to connect 11 previously unlinked cases to the larger chain of disease transmission. The use of the application also led to the identification and correction of transmission chain errors for 13 SVD cases and the identification of 5 potential instances of nosocomial transmission. The use of the Chainchecker application in Uganda during the 2022 SVD outbreak allowed the response teams to rectify critical errors in transmission chains. Countries prone to Ebola Disease (EBOD) outbreaks should consider incorporating Chainchecker as an element of EBOD preparedness and response.

BACKGROUND: Limited estimates exist on population-level impact of partner notification (PN) for gonorrhea with uncertainty in the influence of local epidemiology on PN effectiveness. An ecological study in New York found a 6% reduction in diagnoses with a 10% increase in PN coverage. We estimated gonorrhea incidence reductions via partner notification across different epidemiological conditions to compare effects to the prior finding and understand key determinants of variation. METHODS: We developed a stochastic network model of men who have sex with men and calibrated gonorrhea transmission dynamics to varied epidemiological conditions. Population level impact of increasing partner notification was summarized by incidence rate ratios (IRRs), and relative importance of explanatory variables (including network density, baseline burden, natural history parameters) was assessed via linear regression modeling of IRR, and bootstrapping to evaluate uncertainty in estimation. RESULTS: We estimated IRR of 0.97 (95% range 0.93--0.99) for a 10% relative increase in partner notification coverage, comparable to the IRR of 0.94 (0.91--0.97) identified in the empirical study. Partner notification retained effectiveness under diverse epidemiological conditions. In a univariate sensitivity analysis, the strongest influence on IRR came from parameters governing index case testing probability with IRR 0.93 when testing was at its highest. Other factors such as network density, baseline incidence, and various natural history parameters had relatively minor effects on the IRR. We observed larger individual-level benefits from partner notification for individuals with higher number of partners. CONCLUSIONS: Our findings support prior population-level estimates of the impact of partner notification on gonorrhea incidence.

Marburg virus (MARV) and Ravn virus (RAVV), the only two known members of the species Orthomarburgvirus marburgense (family Filoviridae), are causative agents of Marburg virus disease, a severe viral disease that typically emerges in sub-Saharan Africa and is characterized by human-to-human transmission and high case fatalities. Despite the robust characterization of MARV experimental infection in Egyptian rousette bats (ERBs; Rousettus aegyptiacus; common name: Egyptian rousettes), a natural MARV reservoir, experimental infection with RAVV in ERBs has not been completed. Here, we experimentally infect 12 ERBs with RAVV and quantify viral loads in blood, oral swabs, and rectal swabs over a 21-day timeline with serological and cumulative shedding data and baseline clinical parameters. Compared to previously described experimental MARV infection in ERBs, these bats experimentally inoculated with RAVV had significantly higher and prolonged rectal viral shedding loads, as well as significantly prolonged oral shedding and higher peak viremia. All ERBs seroconverted by 21 days post-infection. Additionally, all ERBs demonstrated marked heterogeneity in RAVV viral shedding loads consistent with the Pareto Principle and viral "supershedders." Our results introduce the possibility of variation in transmission dynamics and subsequent spillover differences between RAVV and MARV.IMPORTANCERavn virus, along with Marburg virus, causes severe viral disease in humans with high fatality but little to no clinical disease in its reservoir host, the Egyptian rousette bat. Our findings provide important insights into how Ravn virus behaves in its natural reservoir host, showing that Ravn virus infection followed a similar timeline to Marburg virus infection, with virus detected in blood, saliva, and feces. However, Ravn virus-infected bats had higher levels of viral shedding and shed the virus for a longer period, particularly in feces, compared to Marburg virus. These differences in viral shedding may impact the spread of the virus within bat populations and potentially alter the likelihood of spillover into humans, non-human primates, and other animal species. These insights are crucial for understanding Ravn virus maintenance in its bat reservoir and improving our ability to mitigate or prevent future human outbreaks.

PROBLEM/CONDITION: Autism spectrum disorder (ASD). PERIOD COVERED: 2022. DESCRIPTION OF SYSTEM: The Autism and Developmental Disabilities Monitoring Network is an active surveillance program that estimates prevalence and characteristics of ASD and monitors timing of ASD identification among children aged 4 and 8 years. In 2022, a total of 16 sites (located in Arizona, Arkansas, California, Georgia, Indiana, Maryland, Minnesota, Missouri, New Jersey, Pennsylvania, Puerto Rico, Tennessee, Texas [two sites: Austin and Laredo], Utah, and Wisconsin) conducted surveillance for ASD among children aged 4 and 8 years and suspected ASD among children aged 4 years. Surveillance included children who lived in the surveillance area at any time during 2022. Children were classified as having ASD if they ever received 1) an ASD diagnostic statement in a comprehensive developmental evaluation, 2) autism special education eligibility, or 3) an ASD International Classification of Diseases, Ninth Revision (ICD-9) code in the 299 range or International Classification of Diseases, Tenth Revision (ICD-10) code of F84.0, F84.3, F84.5, F84.8, or F84.9. Children aged 4 years were classified as having suspected ASD if they did not meet the case definition for ASD but had an evaluator's suspicion of ASD documented in a comprehensive developmental evaluation. RESULTS: Among children aged 8 years in 2022, ASD prevalence was 32.2 per 1,000 children (one in 31) across the 16 sites, ranging from 9.7 in Texas (Laredo) to 53.1 in California. The overall observed prevalence estimate was similar to estimates calculated using Bayesian hierarchical and random effects models. ASD was 3.4 times as prevalent among boys (49.2) than girls (14.3). Overall, ASD prevalence was lower among non-Hispanic White (White) children (27.7) than among Asian or Pacific Islander (A/PI) (38.2), American Indian or Alaska Native (AI/AN) (37.5), non-Hispanic Black or African American (Black) (36.6), Hispanic or Latino (Hispanic) (33.0), and multiracial children (31.9). No association was observed between ASD prevalence and neighborhood median household income (MHI) at 11 sites; higher ASD prevalence was associated with lower neighborhood MHI at five sites.Record abstraction was completed for 15 of the 16 sites for 8,613 children aged 8 years who met the ASD case definition. Of these 8,613 children, 68.4% had a documented diagnostic statement of ASD, 67.3% had a documented autism special education eligibility, and 68.9% had a documented ASD ICD-9 or ICD-10 code. All three elements of the ASD case definition were present for 34.6% of children aged 8 years with ASD.Among 5,292 (61.4% of 8,613) children aged 8 years with ASD with information on cognitive ability, 39.6% were classified as having an intellectual disability. Intellectual disability was present among 52.8% of Black, 50.0% of AI/AN, 43.9% of A/PI, 38.8% of Hispanic, 32.7% of White, and 31.2% of multiracial children with ASD. The median age of earliest known ASD diagnosis was 47 months and ranged from 36 months in California to 69.5 months in Texas (Laredo).Cumulative incidence of ASD diagnosis or eligibility by age 48 months was higher among children born in 2018 (aged 4 years in 2022) than children born in 2014 (aged 8 years in 2022) at 13 of the 15 sites that were able to abstract records. Overall cumulative incidence of ASD diagnosis or eligibility by age 48 months was 1.7 times as high among those born in 2018 compared with those born in 2014 and ranged from 1.4 times as high in Arizona and Georgia to 3.1 times as high in Puerto Rico. Among children aged 4 years, for every 10 children meeting the case definition of ASD, one child met the definition of suspected ASD.Children with ASD who were born in 2018 had more evaluations and identification during ages 0-4 years than children with ASD who were born in 2014 during the 0-4 years age window, with an interruption in the pattern in early 2020 coinciding with onset of the COVID-19 pandemic.Overall, 66.5% of children aged 8 years with ASD had a documented autism test. Use of autism tests varied widely across sites: 24.7% (New Jersey) to 93.5% (Puerto Rico) of children aged 8 years with ASD had a documented autism test in their records. The most common tests documented for children aged 8 years were the Autism Diagnostic Observation Schedule, Autism Spectrum Rating Scales, Childhood Autism Rating Scale, Gilliam Autism Rating Scale, and Social Responsiveness Scale. INTERPRETATION: Prevalence of ASD among children aged 8 years was higher in 2022 than previous years. ASD prevalence was higher among A/PI, Black, and Hispanic children aged 8 years than White children aged 8 years, continuing a pattern first observed in 2020. A/PI, Black, and Hispanic children aged 8 years with ASD were also more likely than White or multiracial children with ASD to have a co-occurring intellectual disability. Identification by age 48 months was higher among children born in 2018 compared with children born in 2014, suggesting increased early identification consistent with historical patterns. PUBLIC HEALTH ACTION: Increased identification of autism, particularly among very young children and previously underidentified groups, underscores the increased demand and ongoing need for enhanced planning to provide equitable diagnostic, treatment, and support services for all children with ASD. The substantial variability in ASD identification across sites suggests opportunities to identify and implement successful strategies and practices in communities to ensure all children with ASD reach their potential.

OBJECTIVE: The objective of this study was to generate updated estimates for the incidence rate, cost burden, and case fatality rate (CFR) of neonatal herpes simplex virus (nHSV) infections in the US in 2019. METHODS: A nationally representative sample of US pediatric discharges was assessed using data from the Healthcare Cost and Utilization Project Kids' Inpatient Database to estimate the incidence, costs, and fatality of nHSV in 2019. Cases were estimated using herpes simplex virus International Classification of Diseases, Tenth Revision, Clinical Modification codes (B00.xx, A60.xx, or P35.2) among infants aged 28 days or younger admitted to the hospital and with hospital stays more than 5 days or resulting in death. A matching algorithm was developed to deduplicate records of readmissions or transfers from another hospital. Estimates were generated overall and by sociodemographic factors including race, US region, primary payer, and median household income. RESULTS: In total, 561 nHSV cases were estimated in the US in 2019, resulting in an incidence rate of 15.7 per 100 000 hospital births. The highest incidence rate was in the South (21.3; 95% confidence interval [CI], 19.0-23.9) and in infants born to Black birth parents (27.3; 95% CI, 22.8-32.4). The total cost to the US health care system was estimated at $28.9 million. The CFR among infants with nHSV was estimated to be 4.6%. CONCLUSION: This study updates the incidence rate, cost burden, and CFR of nHSV in 2019, an increase compared with past estimates, and highlights the racial and geographic disparities across the US. Public health interventions for early detection and prevention are critical to mitigate these disparities.

Leptospirosis (caused by pathogenic bacteria in the genus Leptospira) is prevalent worldwide but more common in tropical and subtropical regions. Transmission can occur following direct exposure to infected urine from reservoir hosts, or a urine-contaminated environment, which then can serve as an infection source for additional rats and other mammals, including humans. The brown rat, Rattus norvegicus, is an important reservoir of Leptospira spp. in urban settings. We investigated the presence of Leptospira spp. among brown rats in Boston, Massachusetts and hypothesized that rat population dynamics in this urban setting influence the transportation, persistence, and diversity of Leptospira spp. We analyzed DNA from 328 rat kidney samples collected from 17 sites in Boston over a seven-year period (2016-2022); 59 rats representing 12 of 17 sites were positive for Leptospira spp. We used 21 neutral microsatellite loci to genotype 311 rats and utilized the resulting data to investigate genetic connectivity among sampling sites. We generated whole genome sequences for 28 Leptospira spp. isolates obtained from frozen and fresh tissue from some of the 59 positive rat kidneys. When isolates were not obtained, we attempted genomic DNA capture and enrichment, which yielded 14 additional Leptospira spp. genomes from rats. We also generated an enriched Leptospira spp. genome from a 2018 human case in Boston. We found evidence of high genetic structure among rat populations that is likely influenced by major roads and/or other dispersal barriers, resulting in distinct rat population groups within the city; at certain sites these groups persisted for multiple years. We identified multiple distinct phylogenetic clades of L. interrogans among rats that were tightly linked to distinct rat populations. This pattern suggests L. interrogans persists in local rat populations and its transportation is influenced by rat population dynamics. Finally, our genomic analyses of the Leptospira spp. detected in the 2018 human leptospirosis case in Boston suggests a link to rats as the source. These findings will be useful for guiding rat control and human leptospirosis mitigation efforts in this and other similar urban settings.

In 2017, Uganda implemented an electronic Integrated Disease Surveillance and Response System (eIDSR) to improve data completeness and reporting timelines. However, the eIDSR system had limited functionality and was implemented on a small scale. The Ministry of Health, with support from the Infectious Disease Institute, Makerere University, and Health Information Systems Program Uganda, upgraded the system functionality and scaled up its implementation. This study describes the process and impact of upgrading eIDSR functionality and expanding its implementation across additional districts. The Ministry of Health, through its Integrated Epidemiology, Surveillance & Public Health Emergency Department, coordinated the implementation of the eIDSR. User requirements were identified through consultations with national surveillance stakeholders. The feedback informed the design and development of the upgraded eIDSR functionalities. The eIDSR rollout followed a consultative workshop to create awareness of the system among stakeholders. A curriculum was developed, and a national training of trainers was conducted. These trainers cascaded the training to the district health teams, who later cascaded the training to health workers. The training adopted an on-site training approach, where a group of national or district trainers would train new users at their desks. The eIDSR system was upgraded to the District Health Information Software 2 (DHIS2) 2.35 platform featuring faster reading and writing tracker data, handling over 100 concurrent users and enhanced case-based surveillance features on Android and web platforms. From October 2020 to September 2022, the eIDSR was rolled out in 68% (100/146) of districts. Additionally, the system permitted prompt reporting of signals of epidemic-prone diseases. Improving the functionality and the expanded geographical scope of the eIDSR system enhanced disease surveillance. Stakeholder commitment and leveraging existing structures will be needed to scale up eIDSR.

We report the genome of a case of mpox detected in Kenya involving a truck driver with travel history to Uganda. Whole genome sequencing and phylogenetic analysis of the mpox virus (MPXV) showed that the genome clustered with clade Ib, which was recently identified in the Democratic Republic of Congo.

Introduction: Emergency 9-1-1 incident data are recorded voluntarily within fire-department-specific computer-aided dispatch systems. The National Fire Incident Reporting System serves as a repository for these data, but inconsistency and variability in reporting practices across departments often lead to challenges in data quality and utility. This study aims to enhance emergency incident categorization and explore the feasibility of an automated system using free-text incident data from the National Fire Operations Reporting System (NFORS). Method: Researchers extracted and standardized 3,564 unique 9–1-1 incident descriptions from six fire departments using NFORS data, including narrative fields from emergency reports. The data were preprocessed using natural language processing (NLP) techniques, such as tokenization, stop word removal, and feature extraction (e.g., TF-IDF and n-grams). These features were used to train and evaluate Machine Learning (ML) models, including Naïve Bayes, Random Forest, and Support Vector Machine, to classify incidents into nine categories. The NLP techniques prepared the text data for the ML models, which performed the classification and assessed the automated system's performance. Results: The study demonstrated significant improvements in incident categorization accuracy using the NLP and ML approach. Unigram models achieved 93% accuracy when applied to 3,564 unique incident descriptions. This performance was evaluated by comparing the automated classifications to manually assigned categories, which served as the reference. Mis-categorizations primarily occurred with “Emergency Medical Services (EMS).” Conclusions: Standardized and consistent incident categorization is vital for informed decision-making, efficient resource allocation, and effective emergency response. Our findings suggest that adopting a robust categorization system, such as the nine-category model using NLP and ML, can improve categorization accuracy and enhance data quality and utility for decision-making. Practical Applications: Public safety agencies can leverage these insights to modernize data systems, strengthen occupational surveillance, and create more resilient and sustainable public safety data systems. © 2025

IMPORTANCE: Invasive group A Streptococcus (GAS) infections are associated with substantial morbidity, mortality, and economic burden. OBJECTIVE: To update trends in invasive GAS disease incidence rates in 10 US states between 2013 and 2022. DESIGN, SETTING, AND PARTICIPANTS: Clinical, demographic, and laboratory data for invasive GAS cases were collected as part of population-based surveillance in the Active Bacterial Core surveillance network covering 34.9 million persons across 10 US states. A case was defined as isolation of GAS from a normally sterile site or from a wound in a patient with necrotizing fasciitis or streptococcal toxic shock syndrome between January 1, 2013, and December 31, 2022. Demographic and clinical data were collected from medical record review. From 2013 to 2014, available isolates were emm typed and antimicrobial susceptibilities determined using conventional methods; from 2015 onward, whole-genome sequencing was used. MAIN OUTCOMES AND MEASURES: Incidence rates by sex, age, race, and selected risk factors; clinical syndromes, outcomes, and underlying patient conditions; and isolate characteristics, including antimicrobial susceptibility. RESULTS: Surveillance in 10 US states identified 21 312 cases of invasive GAS from 2013 through 2022, including 1981 deaths. The majority of cases (57.5%) were in males. Among case-patients, 1272 (6.0%) were aged 0 to 17 years, 13 565 (63.7%) were aged 18 to 64 years, and 6474 (30.4%) were 65 years or older; 5.5% were American Indian or Alaska Native, 14.3% were Black, and 67.1% were White. Incidence rose from 3.6 per 100 000 persons in 2013 to 8.2 per 100 000 persons in 2022 (P < .001 for trend). Incidence was highest among persons 65 years or older; however, the relative increase over time was greatest among adults aged 18 to 64 years (3.2 to 8.7 per 100 000 persons). Incidence was higher among American Indian or Alaska Native persons than in other racial and ethnic groups. People experiencing homelessness, people who inject drugs, and residents of long-term care facilities had substantially elevated GAS incidence rates. Among tested isolates, those nonsusceptible to macrolides and clindamycin increased from 12.7% in 2013 to 33.1% in 2022. CONCLUSIONS: Invasive GAS infections increased substantially in 10 US states during a surveillance period from 2013 to 2022. Accelerated efforts to prevent and control GAS are needed, especially among groups at highest risk of infection.

BACKGROUND: Child survival rates have improved globally, but neonatal mortality due to infections, such as group B Streptococcus (GBS), remains a significant concern. The global burden of GBS-related morbidity and mortality is substantial. However, data from low and middle-income countries are lacking. Vaccination during pregnancy could be a feasible strategy to address GBS-related disease burden. METHODS: We assessed maternal rectovaginal GBS colonization and neonatal disease rates in a prospective cohort of 6062 women-infant pairs. Surveillance for invasive infant disease occurred in parallel at 2 Kampala hospital sites. In a nested case-control study, we identified infants <90 days of age with invasive GBS disease (iGBS) (n = 24) and healthy infants born to mothers colonized with GBS (n = 72). We measured serotype-specific anticapsular immunoglobulin G (IgG) in cord blood/infant sera using a validated multiplex Luminex assay. RESULTS: We found a high incidence of iGBS (1.0 per 1000 live births) within the first 90 days of life across the surveillance sites, associated with a high case fatality rate (18.2%). Maternal GBS colonization prevalence was consistent with other studies in the region (14.7% [95% confidence interval, 13.7%-15.6%]). IgG geometric mean concentrations were lower in cases than controls for serotypes Ia (0.005 vs 0.12 µg/mL; P = .05) and III (0.011 vs 0.036 µg/mL; P = .07) and in an aggregate analysis of all serotypes (0.014 vs 0.05 µg/mL; P = .02). CONCLUSIONS: We found that GBS is an important cause of neonatal and young infant disease in Uganda and confirmed that maternally derived antibodies were lower in early-onset GBS cases than in healthy exposed controls.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been widespread since 2020 and will likely continue to cause substantial recurring epidemics. However, understanding the underlying infection burden and dynamics, particularly since late 2021 when the Omicron variant emerged, is challenging. Here, we leverage extensive surveillance data available in New York City (NYC) and a comprehensive model-inference system to reconstruct SARS-CoV-2 dynamics therein through August 2023. METHODS: We fit a metapopulation network SEIRSV (Susceptible-Exposed-Infectious-(re)Susceptible-Vaccination) model to age- and neighborhood-specific data of COVID-19 cases, emergency department visits, and deaths in NYC from the pandemic onset in March 2020 to August 2023. We further validate the model-inference estimates using independent SARS-CoV-2 wastewater viral load data. RESULTS: The validated model-inference estimates indicate a very high infection burden-the number of infections (i.e., including undetected asymptomatic/mild infections) totaled twice the population size ( > 5 times documented case count) during the first 3.5 years. Estimated virus transmissibility increased around 3-fold, whereas estimated infection-fatality risk (IFR) decreased by >10-fold during this period. The detailed estimates also reveal highly complex variant dynamics and immune landscape, and higher infection risk during winter in NYC over the study period. CONCLUSIONS: This study provides highly detailed epidemiological estimates and identifies key transmission dynamics and drivers of SARS-CoV-2 during its first 3.5 years of circulation in a large urban center (i.e., NYC). These transmission dynamics and drivers may be relevant to other populations and inform future planning to help mitigate the public health burden of SARS-CoV-2. | Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019, causing the COVID-19 pandemic and multiple epidemics since. Using comprehensive surveillance data and mathematical tools, this study estimated SARS-CoV-2 infection burden and severity over time as well as examined key factors affecting the epidemic patterns, during its first 3.5 years of circulation in New York City. Study findings highlight the emergence of new SARS-CoV-2 strains and higher infection risk in winter as key epidemic drivers during the study period; these may be observed in other populations and could inform future planning to help mitigate the public health burden of SARS-CoV-2. | eng

We used metagenomic next-generation sequencing (mNGS) to investigate an outbreak of Fusarium solani meningitis in US patients who had surgical procedures under spinal anesthesia in Matamoros, Mexico, during 2023. Using a novel method called metaMELT (metagenomic multiple extended locus typing), we performed phylogenetic analysis of concatenated mNGS reads from 4 patients (P1-P4) in parallel with reads from 28 fungal reference genomes. Fungal strains from the 4 patients were most closely related to each other and to 2 cultured isolates from P1 and an additional case (P5), suggesting that all cases arose from a point source exposure. Our findings support epidemiologic data implicating a contaminated drug or device used for epidural anesthesia as the likely cause of the outbreak. In addition, our findings show that the benefits of mNGS extend beyond diagnosis of infections to public health outbreak investigation.