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Journal articleSilva L, Gogoi M, Lal Z, et al., 2026,
Antibiotic knowledge among ethnic minority groups in high-income countries: A mixed-methods systematic review.
, Public Health Pract (Oxf), Vol: 11OBJECTIVES: Antimicrobial resistance (AMR) is a major global public health concern. Although low-income countries are disproportionately affected by AMR, certain underserved groups in high-income countries (HICs), such as migrants and ethnic minorities, disproportionately bear the burden of AMR. This may be driven by socio-cultural factors including differences in health literacy. This review aimed to investigate the level of antibiotic knowledge amongst different ethnic minority groups in HICs. STUDY DESIGN: This was a mixed-methods systematic literature review. METHODS: We searched four databases (MEDLINE, EMBASE, the Cochrane library, CINAHL) to May 5, 2023, for primary studies on knowledge of antibiotics in different ethnic groups in HICs. We included studies in English using qualitative, quantitative and/or mixed-methods approaches and reporting on antibiotic knowledge by ethnicity. We used the convergent integrated approach for data synthesis and the Mixed-Methods Appraisal tool for quality assessment. RESULTS: 3935 articles were screened and 24 studies (17 quantitative, 5 qualitative, and 2 mixed-methods) were included, comprising 52778 participants from 8 countries (USA, UK, Australia, New Zealand, Netherlands, Greece, Sweden, Germany). Overall, participants from ethnic minority groups were able to identify common names of antibiotics and were aware of risks of antibiotics and side effects. However, participants thought antibiotics would treat viral-type illnesses. Ethnic minority groups generally had lower levels of knowledge compared to ethnic majority groups. CONCLUSIONS: Although ethnic minority communities possessed good levels of knowledge on certain aspects of antibiotics (e.g. being able to identify names of antibiotics), there were gaps in other areas (e.g. misperception that antibiotics are used for viral infections). The lower level of knowledge in ethnic minority groups compared to majority groups may be a contributing factor to health inequaliti
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Journal articleKoemen S, Faria NR, Bastos LS, et al., 2026,
Fast and trustworthy nowcasting of dengue fever: a case study using attention-based probabilistic neural networks in São Paulo, Brazil
, Epidemics, Vol: 54, ISSN: 1755-4365Nowcasting methods are crucial in infectious disease surveillance, as reporting delays often lead to underestimation of recent incidence and can impair timely public health decision-making. Accurate real-time estimates of case counts are essential for resource allocation, policy responses, and communication with the public. In this paper, we propose a novel probabilistic neural network (PNN) architecture, named NowcastPNN, to estimate occurred-but-not-yet-reported cases of infectious diseases, demonstrated here using dengue fever incidence in São Paulo, Brazil. The proposed model combines statistical modelling of the true number of cases, assuming a Negative Binomial (NB) distribution, with recent advances in machine learning and deep learning, such as the attention mechanism. Uncertainty intervals are obtained by sampling from the predicted NB distribution and using Monte Carlo (MC) Dropout. Using proper scoring rules for the prediction intervals, NowcastPNN achieves nearly a 30% reduction in losses compared to the second-best model among other state-of-the-art approaches. While our model requires a large training dataset (equivalent to two to four years of incidence counts) to outperform benchmarks, it is computationally cheap and outperforms alternative methods even with significantly fewer observations as input. These features make the NowcastPNN model a promising tool for nowcasting in epidemiological surveillance of arboviral threats and other domains involving right-truncated data.
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Journal articleHowes A, Stringer A, Flaxman SR, et al., 2026,
Fast approximate Bayesian inference of HIV indicators using PCA adaptive Gauss-Hermite quadrature
, Journal of Theoretical Biology, Vol: 618, ISSN: 0022-5193Naomi is a spatial evidence synthesis model used to produce district-level HIV epidemic indicators in sub-Saharan Africa. Multiple outcomes of policy interest, including HIV prevalence, HIV incidence, and antiretroviral therapy treatment coverage are jointly modelled using both household survey data and routinely reported health system data. The model is provided as a tool for countries to input their data to and generate estimates with during a yearly process supported by UNAIDS. Previously, inference has been conducted using empirical Bayes and a Gaussian approximation, implemented via the TMB R package. We propose a new inference method based on an extension of adaptive Gauss-Hermite quadrature to deal with more than 20 hyperparameters. Using data from Malawi, our method improves the accuracy of inferences for model parameters, while being substantially faster to run than Hamiltonian Monte Carlo with the No-U-Turn sampler. Our implementation leverages the existing TMB C++ template for the model’s log-posterior, and is compatible with any model with such a template.
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Journal articleGrégoire V, Zhu AW, Brown CM, et al., 2026,
Public reporting guidelines for outbreak data: Enabling accountability for effective outbreak response by developing standards for transparency and uniformity.
, Public Health, Vol: 251OBJECTIVES: There are few standards for what information about an infectious disease outbreak should be reported to the public and when. To address this problem, we undertook a consensus process to develop recommendations for what epidemiological information public health authorities should report to the public during an outbreak. STUDY DESIGN: We conducted a Delphi study following the steps outlined in the ACcurate COnsensus Reporting Document (ACCORD) for health-related activities or research. METHODS: We assembled a steering committee of nine experts representing federal and state public health, academia, and international partners to develop a candidate list of reporting items. We then invited 45 experts, 35 of whom agreed to participate in a Delphi panel. Of those, 25 participated in voting in the first round, 25 in the second round, and 25 in the third round, demonstrating consistent engagement in the consensus-building process. The final stage of the Delphi process consisted of a hybrid consensus meeting to finalize the voting items. RESULTS: The Delphi process yielded nine core reporting items representing a minimum standard for public outbreak reporting: numbers of new confirmed cases, new hospital admissions, new deaths, cumulative confirmed cases, cumulative hospital admissions, and cumulative deaths, each reported weekly and at Administrative Level 1 (typically state or province), and stratified by sex, age group, and race/ethnicity. CONCLUSIONS: This minimum reporting standard creates a strong framework for uniform sharing of outbreak information and promotes consistency of data between jurisdictions, enabling effective response by promoting access to information about an unfolding epidemic.
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Journal articleThompson RN, Bansal S, Clapham H, et al., 2026,
Infectious disease outbreak controllability: biological, social and public health factors
, Proceedings of the Royal Society B: Biological Sciences, Vol: 293<jats:title>Abstract</jats:title> <jats:p>Early in an infectious disease outbreak, key policy questions include whether and how the outbreak can be brought under control. In the epidemiological modelling literature, analyses of outbreak controllability have often focused on metrics such as reproduction numbers (which quantify the number of infections generated by each infected individual). However, whether an outbreak can be controlled is a complex question, depending on both the precise definition of ‘under control’ used and numerous factors affecting decision-makers’ ability to implement transmission-reducing measures. Here, based on discussions at the Isaac Newton Institute’s ‘Modelling and inference for pandemic preparedness’ programme (5–30 August 2024), we describe a wide range of factors affecting outbreak controllability in practice. Programme participants came from institutions in ten countries, enabling discussions to reflect experiences of using models to inform policy in different settings. We divide the factors according to whether they relate predominantly to characteristics of the pathogen, host population or available interventions, and describe policy considerations when assessing whether an outbreak is controllable.</jats:p>
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Journal articleHemmings S, Varaden D, Barnes J, et al., 2026,
Diversity analysis of indoor and outdoor fungal bioaerosols in UK households: a longitudinal study
, The Lancet Microbe, ISSN: 2666-5247 -
Journal articleFaria N, 2026,
Detection of dengue virus serotype 2 in local Aedes aegypti populations, Madeira Island, Portugal, 2025
, Parasites and Vectors, ISSN: 1756-3305 -
Journal articleMorel G, Pham A, Morgenstern C, et al., 2026,
An outbreak of highly pathogenic avian influenza H5N1 could impact the dairy cattle sector and the broader economy in the United States
, Communications Earth & Environment, ISSN: 2662-4435The outbreak of Highly Pathogenic Avian Influenza H5N1 in U.S. dairy cattle poses substantial risks to public health, economic sustainability of farming, and global food systems. Using a Computable General Equilibrium model, we simulate its short- to medium-term impacts on Gross Domestic Product and other macro-economic outcomes for the US and its main trading partners. We simulate impacts under the current situation and realistic and reasonable worst-case scenarios. We estimate domestic economic losses ranging between 0.06% and 0.9% of US GDP, with losses to the dairy sector ranging between 3.4% and 20.6%. Trading partners increase dairy production to compensate for the loss. Current government subsidies are about 1.2% (95% HDI: 1% to 1.4%) of output losses, and likely insufficient to incentivise farmers to step up surveillance and biosecurity for mitigating the possible emergence of H5N1 strains with pandemic potential into human populations.
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Journal articleBellekom B, Troman C, Fitz S, et al., 2026,
Comparison of the sensitivity of targeted and untargeted (metagenomic) methods for the detection of viral pathogens in wastewater.
, Sci Total Environ, Vol: 1013Timely and accurate pathogen detection is critical for the successful implementation of wastewater surveillance and has broad implications for public health. A wide range of surveillance tools are currently available, offering both quantitative and qualitative insights into the wastewater virome. Careful consideration of molecular methodology is required to successfully implement an effective wastewater surveillance scheme. Using SARS-CoV-2 as a model organism, we compared detection success across multiple approaches, including targeted (RT-PCR, qPCR, random priming RT-PCR) and target-agnostic (Rapid SMART-9N metagenomics) methods. We also estimated the copy number required for reliable detection, examined how the ratio of target to off-target genomes in wastewater affects detection and genome coverage using metagenomics, and assessed the efficacy of hybrid capture enrichment of target genomes in improving metagenomic detection. Our results show significant differences between methods, targeted RT-PCR and qPCR were more likely (68 % and 65 % respectively) to detect SARS-CoV-2 than target agnostic approaches. The inclusion of carrier RNA during extraction significantly increased the likelihood of target detection. Our target-agnostic metagenomic approach was consistently unable to detect our target, and, even in the presence of high concentrations that are atypical for wastewater, detection was limited. Target enrichment increased SARS-CoV-2 detection and maximum coverage by metagenomics (SMART-9N), though was outperformed by targeted amplicon sequencing. Overall, our findings support the use of targeted approaches for the routine surveillance of viral pathogens in wastewater. Whilst metagenomics provides broad insights into the virome, enrichment strategies are essential when using it to detect specific viruses, particularly in complex wastewater matrices.
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Journal articleLeelavanich D, Dorigatti I, Turner H, 2026,
The economic burden of dengue: a systematic literature review of unit costs for non-fatal episodes treated in the formal healthcare system
, BMC Infectious Diseases, ISSN: 1471-2334Background: Dengue, a vector-borne disease caused by the dengue virus, has emerged as a global public health concern, given the tenfold rise in reported cases over the last two decades. In light of the upcoming dengue interventions, country-specific cost-of-illness estimates are required to evaluate the cost-effectiveness of new interventions against dengue. This study aims to conduct an updated systematic review of dengue cost-of-illness studies, extracting the relevant data, and conducting regression analysis to explore potential factors contributing to the cost variations among countries. Methods: We used the MEDLINE, EMBASE, PubMed, and Web of Science databases to systematically search for published dengue cost-of-illness studies reporting primary data on costs per dengue episode. A descriptive analysis was conducted across all extracted studies. Linear regression analysis was performed to investigate the association between the GDP per capita and cost per episode. The quality of the included studies was also assessed. Results: Fifty-six studies were included, of which 22 used the societal perspective. The reported total cost per episode ranged from $15.0 for outpatients in Burkina Faso to $9,386.1 for intensive care unit patients in Mexico. Linear regression analysis revealed that the cost of dengue illness varies significantly across countries and regions, and was positively related to the setting’s GDP per capita. The quality assessment demonstrated that improvements are needed in future studies, particularly in the reporting of the methodology. Conclusions: Cost of dengue illness varies widely across countries and regions. Future research should focus on understanding other drivers of cost variations beyond GDP per capita to improve the cost estimates for economic evaluation studies. The results presented in this study can serve as crucial input parameters for future economic evaluations, supporting decision makers in allocating resources for dengue in
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