BACKGROUND: The 2022 Ebola Virus Disease (EVD) outbreak occurred at a time when Uganda was still battling the social and psychological challenges of the COVID-19 pandemic; placing health care professionals (HCPs) at a much higher risk of developing psychological distress. Psychological distress among HCPs can cause decreased workplace productivity and ineffective management of their patients. The current study aimed to investigate and understand psychological distress among HCPS in Mbarara city in Southwestern Uganda following the 2022 EVD outbreak.
METHODS: We enrolled 200 HCPs through convenient sampling from one private and one public health facility in Mbarara city in Southwestern Uganda, in a cross-sectional convergent parallel mixed method approach where qualitative and quantitative data were collected concurrently. Quantitative data, utilizing the Kessler Psychological Distress (K10) Scale, provided us with a quantitative measure of the prevalence of psychological distress among HCPs, and were analyzed using STATA version 16. Qualitative data, on the other hand, offered deeper insights into the nature, perceptions, and contextual factors influencing this distress, and were analyzed using emergent theme analysis.
RESULTS: The prevalence of psychological distress was 59.5% and it was higher among females (63.9%) compared to males (36.1%). HCPs vividly expressed distress and anxiety, with heightened suspicion that every patient might be an EVD carrier, creating a pervasive sense of unsafety in the workplace. However, the outbreak had an educational affect where concerns about the announcement of another EVD outbreak were diverse, with HCPs expressing anxiety, despair, and dissatisfaction with the country\'s management of potential outbreaks.
CONCLUSIONS: High levels of psychological distress were experienced by HCPs in Southwestern Uganda as a result of the 2022 EVD pandemic. HCPs express a wide range of feelings, such as dread, anxiety, despair, pessimism, and discontent with the way the outbreaks are handled throughout the nation. We recommend implementation of comprehensive psychosocial support programs tailored to the unique needs of HCPs, including counseling services, stress management workshops, and peer support networks.

BACKGROUND: Cooling towers containing Legionella spp are a high-risk source of Legionnaires\' disease outbreaks. Manually locating cooling towers from aerial imagery during outbreak investigations requires expertise, is labour intensive, and can be prone to errors. We aimed to train a deep learning computer vision model to automatically detect cooling towers that are aerially visible.
METHODS: Between Jan 1 and 31, 2021, we extracted satellite view images of Philadelphia (PN, USA) and New York state (NY, USA) from Google Maps and annotated cooling towers to create training datasets. We augmented training data with synthetic data and model-assisted labelling of additional cities. Using 2051 images containing 7292 cooling towers, we trained a two-stage model using YOLOv5, a model that detects objects in images, and EfficientNet-b5, a model that classifies images. We assessed the primary outcomes of sensitivity and positive predictive value (PPV) of the model against manual labelling on test datasets of 548 images, including from two cities not seen in training (Boston [MA, USA] and Athens [GA, USA]). We compared the search speed of the model with that of manual searching by four epidemiologists.
RESULTS: The model identified visible cooling towers with 95·1% sensitivity (95% CI 94·0-96·1) and a PPV of 90·1% (95% CI 90·0-90·2) in New York City and Philadelphia. In Boston, sensitivity was 91·6% (89·2-93·7) and PPV was 80·8% (80·5-81·2). In Athens, sensitivity was 86·9% (75·8-94·2) and PPV was 85·5% (84·2-86·7). For an area of New York City encompassing 45 blocks (0·26 square miles), the model searched more than 600 times faster (7·6 s; 351 potential cooling towers identified) than did human investigators (mean 83·75 min [SD 29·5]; mean 310·8 cooling towers [42·2]).
CONCLUSIONS: The model could be used to accelerate investigation and source control during outbreaks of Legionnaires\' disease through the identification of cooling towers from aerial imagery, potentially preventing additional disease spread. The model has already been used by public health teams for outbreak investigations and to initialise cooling tower registries, which are considered best practice for preventing and responding to outbreaks of Legionnaires\' disease.
BACKGROUND: None.

BACKGROUND: The World Health Organization declared mpox an international public health emergency. Since January 1, 2022, China has been ranked among the top 10 countries most affected by the mpox outbreak globally. However, there is a lack of spatial epidemiological studies on mpox, which are crucial for accurately mapping the spatial distribution and clustering of the disease.
OBJECTIVE: This study aims to provide geographically accurate visual evidence to determine priority areas for mpox prevention and control.
METHODS: Locally confirmed mpox cases were collected between June and November 2023 from 31 provinces of mainland China excluding Taiwan, Macao, and Hong Kong. Spatiotemporal epidemiological analyses, including spatial autocorrelation and regression analyses, were conducted to identify the spatiotemporal characteristics and clustering patterns of mpox attack rate and its spatial relationship with sociodemographic and socioeconomic factors.
RESULTS: From June to November 2023, a total of 1610 locally confirmed mpox cases were reported in 30 provinces in mainland China, resulting in an attack rate of 11.40 per 10 million people. Global spatial autocorrelation analysis showed that in July (Moran I=0.0938; P=.08), August (Moran I=0.1276; P=.08), and September (Moran I=0.0934; P=.07), the attack rates of mpox exhibited a clustered pattern and positive spatial autocorrelation. The Getis-Ord Gi* statistics identified hot spots of mpox attack rates in Beijing, Tianjin, Shanghai, Jiangsu, and Hainan. Beijing and Tianjin were consistent hot spots from June to October. No cold spots with low mpox attack rates were detected by the Getis-Ord Gi* statistics. Local Moran I statistics identified a high-high (HH) clustering of mpox attack rates in Guangdong, Beijing, and Tianjin. Guangdong province consistently exhibited HH clustering from June to November, while Beijing and Tianjin were identified as HH clusters from July to September. Low-low clusters were mainly located in Inner Mongolia, Xinjiang, Xizang, Qinghai, and Gansu. Ordinary least squares regression models showed that the cumulative mpox attack rates were significantly and positively associated with the proportion of the urban population (t0.05/2,1=2.4041 P=.02), per capita gross domestic product (t0.05/2,1=2.6955; P=.01), per capita disposable income (t0.05/2,1=2.8303; P=.008), per capita consumption expenditure (PCCE; t0.05/2,1=2.7452; P=.01), and PCCE for health care (t0.05/2,1=2.5924; P=.01). The geographically weighted regression models indicated a positive association and spatial heterogeneity between cumulative mpox attack rates and the proportion of the urban population, per capita gross domestic product, per capita disposable income, and PCCE, with high R2 values in north and northeast China.
CONCLUSIONS: Hot spots and HH clustering of mpox attack rates identified by local spatial autocorrelation analysis should be considered key areas for precision prevention and control of mpox. Specifically, Guangdong, Beijing, and Tianjin provinces should be prioritized for mpox prevention and control. These findings provide geographically precise and visualized evidence to assist in identifying key areas for targeted prevention and control.

UNASSIGNED: Pandemics of life-threatening viruses have detrimental impacts on the motivation of healthcare workers to work. However, no study has examined this impact during different pandemics with the same setting and design. This study aimed to reveal similarities and differences in factors associated with willingness and hesitation to work between two recent pandemics, H1N1 influenza and COVID-19, in the same hospital, using the same questionnaire.
UNASSIGNED: Healthcare workers in one hospital in Japan completed a questionnaire on basic characteristics and stress-related questions during the H1N1 influenza (n = 1061) and the COVID-19 (n = 1111) pandemics. Logistic regressions were performed to ascertain the effect of personal characteristics and stress-related questions on the likelihood that employees showed strong or weak motivation or hesitation to work.
UNASSIGNED: The feeling of being protected by the hospital was the only factor that significantly decreased hesitation and increased motivation to work, and females felt significantly more hesitation to work than males did in both pandemics.
UNASSIGNED: Hospital managers and government officers should focus on increasing organizational support and caring for female workers to maintain healthcare workers\' motivation to work during future pandemics.

UNASSIGNED: A disproportionate number of COVID-19 deaths occur in Residential Aged Care Facilities (RACFs), where better evidence is needed to target COVID-19 interventions to prevent mortality. This study used an agent-based model to assess the role of community prevalence, vaccination strategies, and non-pharmaceutical interventions (NPIs) on COVID-19 outcomes in RACFs in Victoria, Australia.
UNASSIGNED: The model simulated outbreaks in RACFs over time, and was calibrated to distributions for outbreak size, outbreak duration, and case fatality rate in Victorian RACFs over 2022. The number of incursions to RACFs per day were estimated to fit total deaths and diagnoses over time and community prevalence.Total infections, diagnoses, and deaths in RACFs were estimated over July 2023-June 2024 under scenarios of different: community epidemic wave assumptions (magnitude and frequency); RACF vaccination strategies (6-monthly, 12-monthly, no further vaccines); additional non-pharmaceutical interventions (10, 25, 50% efficacy); and reduction in incursions (30% or 60%).
UNASSIGNED: Total RACF outcomes were proportional to cumulative community infections and incursion rates, suggesting potential for strategic visitation/staff policies or community-based interventions to reduce deaths. Recency of vaccination when epidemic waves occurred was critical; compared with 6-monthly boosters, 12-monthly boosters had approximately 1.2 times more deaths and no further boosters had approximately 1.6 times more deaths over July 2023-June 2024. Additional NPIs, even with only 10-25% efficacy, could lead to a 13-31% reduction in deaths in RACFs.
UNASSIGNED: Future community epidemic wave patterns are unknown but will be major drivers of outcomes in RACFs. Maintaining high coverage of recent vaccination, minimizing incursions, and increasing NPIs can have a major impact on cumulative infections and deaths.

BACKGROUND: Detection and containment of hospital outbreaks currently depend on variable and personnel-intensive surveillance methods. Whether automated statistical surveillance for outbreaks of health care-associated pathogens allows earlier containment efforts that would reduce the size of outbreaks is unknown.
METHODS: We conducted a cluster-randomized trial in 82 community hospitals within a larger health care system. All hospitals followed an outbreak response protocol when outbreaks were detected by their infection prevention programs. Half of the hospitals additionally used statistical surveillance of microbiology data, which alerted infection prevention programs to outbreaks. Statistical surveillance was also applied to microbiology data from control hospitals without alerting their infection prevention programs. The primary outcome was the number of additional cases occurring after outbreak detection. Analyses assessed differences between the intervention period (July 2019 to January 2022) versus baseline period (February 2017 to January 2019) between randomized groups. A post hoc analysis separately assessed pre-coronavirus disease 2019 (Covid-19) and Covid-19 pandemic intervention periods.
RESULTS: Real-time alerts did not significantly reduce the number of additional outbreak cases (intervention period versus baseline: statistical surveillance relative rate [RR]=1.41, control RR=1.81; difference-in-differences, 0.78; 95% confidence interval [CI], 0.40 to 1.52; P=0.46). Comparing only the prepandemic intervention with baseline periods, the statistical outbreak surveillance group was associated with a 64.1% reduction in additional cases (statistical surveillance RR=0.78, control RR=2.19; difference-in-differences, 0.36; 95% CI, 0.13 to 0.99). There was no similarly observed association between the pandemic versus baseline periods (statistical surveillance RR=1.56, control RR=1.66; difference-in-differences, 0.94; 95% CI, 0.46 to 1.92).
CONCLUSIONS: Automated detection of hospital outbreaks using statistical surveillance did not reduce overall outbreak size in the context of an ongoing pandemic. (Funded by the Centers for Disease Control and Prevention; ClinicalTrials.gov number, NCT04053075. Support for HCA Healthcare\'s participation in the study was provided in kind by HCA.).

OBJECTIVE: To investigate the distribution and prevalence of Japanese encephalitis virus (JEV) antibody (as evidence of past infection) in northern Victoria following the 2022 Japanese encephalitis outbreak, seeking to identify groups of people at particular risk of infection; to investigate the distribution and prevalence of antibodies to two related flaviviruses, Murray Valley encephalitis virus (MVEV) and West Nile virus Kunjin subtype (KUNV).
METHODS: Cross-sectional serosurvey (part of a national JEV serosurveillance program).
METHODS: Three northern Victorian local public health units (Ovens Murray, Goulburn Valley, Loddon Mallee), 8 August - 1 December 2022.
METHODS: People opportunistically recruited at pathology collection centres and by targeted recruitment through community outreach and advertisements. People vaccinated against or who had been diagnosed with Japanese encephalitis were ineligible for participation, as were those born in countries where JEV is endemic.
METHODS: Seroprevalence of JEV IgG antibody, overall and by selected factors of interest (occupations, water body exposure, recreational activities and locations, exposure to animals, protective measures).
RESULTS: 813 participants were recruited (median age, 59 years [interquartile range, 42-69 years]; 496 female [61%]); 27 were JEV IgG-seropositive (3.3%; 95% confidence interval [CI], 2.2-4.8%) (median age, 73 years [interquartile range, 63-78 years]; 13 female [48%]); none were IgM-seropositive. JEV IgG-seropositive participants were identified at all recruitment locations, including those without identified cases of Japanese encephalitis. The only risk factors associated with JEV IgG-seropositivity were age (per year: prevalence odds ratio [POR], 1.07; 95% CI, 1.03-1.10) and exposure to feral pigs (POR, 21; 95% CI, 1.7-190). The seroprevalence of antibody to MVEV was 3.0% (95% CI, 1.9-4.5%; 23 of 760 participants), and of KUNV antibody 3.3% (95% CI, 2.1-4.8%; 25 of 761).
CONCLUSIONS: People living in northern Victoria are vulnerable to future JEV infection, but few risk factors are consistently associated with infection. Additional prevention strategies, including expanding vaccine eligibility, may be required to protect people in this region from Japanese encephalitis.

The E/S (exposed/susceptible) ratio is analyzed in the SEIR model. The ratio plays a key role in understanding epidemic dynamics during the 2014-2016 Ebola outbreak in Sierra Leone and Guinea. The maximum value of the ratio occurs immediately before or after the time-dependent reproduction number (Rt) equals 1, depending on the initial susceptible population (S(0)). It is demonstrated that transmission rate curves corresponding to various incubation periods intersect at a single point referred to as the Cross Point (CP). At this point, the E/S ratio reaches an extremum, signifying a critical shift in transmission dynamics and aligning with the time when Rt approaches 1. By plotting transmission rate curves, β(t), for any two arbitrary incubation periods and tracking their intersections, we can trace CP over time. CP serves as an indicator of epidemic status, especially when Rt is close to 1. It provides a practical means of monitoring epidemics without prior knowledge of the incubation period. Through a case study, we estimate the transmission rate and reproduction number, identifying CP and Rt = 1 while examining the E/S ratio across various values of S(0).

BACKGROUND: Surveillance systems revealed that the prevalence of vancomycin-resistant Enterococcus faecium (VREfm) has increased. We aim to investigate the epidemiological and genomic characteristics of VREfm in China.
METHODS: We collected 20,747 non-redundant E. faecium isolates from inpatients across 19 hospitals in six provinces between January 2018 and June 2023. VREfm was confirmed by antimicrobial susceptibility testing. The prevalence was analyzed using changepoint package in R. Genomic characteristics were explored by whole-genome sequencing.
RESULTS: 5.59% (1159/20,747) of E. faecium isolates were resistant to vancomycin. The prevalence of VREfm increased in Guangdong province from 5% before 2021 to 20-50% in 2023 (p < 0.0001), but not in the other five provinces. Two predominant clones before 2021, ST17 and ST78, were substituted by an emerging clone, ST80, from 2021 to 2023 (88.63%, 195/220). All ST80 VREfm from Guangdong formed a single lineage (SC11) and were genetically distant from the ST80 VREfm from other countries, suggesting a regional outbreak. All ST80 VREfm in SC11 carried a new type of plasmid harbouring a vanA cassette, which was embedded in a Tn1546-like structure flanked by IS1678 and ISL3. However, no conjugation-related gene was detected and no transconjugant was obtained in conjugation experiment, indicating that the outbreak of ST80 VREfm could be attributed to clonal transmission.
CONCLUSIONS: We revealed an ongoing outbreak of ST80 VREfm with a new vanA-harbouring plasmid in Guangdong, China. This clone has also been identified in other provinces and countries, foreboding a risk of wider spreading shortly. Continuous surveillance is needed to inform public health interventions.

Correctional centres (termed here \'prisons\') are at high risk of COVID-19 and have featured major outbreaks worldwide. Inevitable close contacts, frequent inmate movements, and a disproportionate burden of co-morbidities mean these environments need to be prioritised in any public health response to respiratory pathogens such as COVID-19. We developed an individual-based SARS-CoV-2 transmission model for the prison system in New South Wales, Australia - incorporating all 33 correctional centres, 13,458 inmates, 578 healthcare and 6,909 custodial staff. Potential COVID-19 disease outbreaks were assessed under various mitigation strategies, including quarantine on entry, isolation of cases, rapid antigen testing of staff, as well as immunisation.Without control measures, the model projected a peak of 472 new infections daily by day 35 across the prison system, with all inmates infected by day 120. The most effective individual mitigation strategies were high immunisation coverage and prompt lockdown of centres with infected inmates which reduced outbreak size by 62-73%. Other than immunisation, the combination of quarantine of inmates at entry, isolation of proven or suspected cases, and widespread use of personal protective equipment by staff and inmates was the most effective strategy. High immunisation coverage mitigates the spread of COVID-19 within and between correctional settings but is insufficient alone. Maintaining quarantine and isolation, along with high immunisation levels, will allow correctional systems to function with a low risk of outbreaks. These results have informed public health policy for respiratory pathogens in Australian correctional systems.