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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 articleKhurana MP, Brünnich Sloth MM, Scheidwasser N, et al., 2026,
SARS-CoV-2 reinfections and subsequent risk of hospital-diagnosed post-acute sequelae in Denmark (2020-2022): a nationwide cohort study.
, Lancet Reg Health Eur, Vol: 63BACKGROUND: Post-acute sequelae of COVID-19 (PASC), or long COVID, are a public health concern. While most recover from SARS-CoV-2 infections within weeks, some experience persistent symptoms. Here, we quantified the association between repeated SARS-CoV-2 infections and the risk of hospital-diagnosed PASC. METHODS: We conducted a nationwide register-based cohort study of all adults in Denmark (≥18 years) with at least one SARS-CoV-2 PCR or antigen test between April 1, 2020, and December 31, 2022. Participants were followed from first test until long COVID diagnosis (ICD-10: B948A), death, emigration, three SARS-CoV-2 infections, or end of study. Risk of long COVID diagnosis was estimated at three timepoints after study entry (180 days, 1 year, 2 years) and the outcomes were assessed during the 180 days after each timepoint. Cause-specific Cox models treated death as a competing risk, with number of infections and vaccination status as time-varying covariates. Absolute risks and differences were estimated using G-computation. Analyses were stratified by sex, income, and vaccination status. Secondary analyses assessed fatigue and headache (ICD-10), excluding individuals with prior diagnoses. FINDINGS: Of 4,418,544 individuals, 6942 (0.16%) were diagnosed with long COVID. The absolute risk of a diagnosis increased following reinfection (0.73% [95% CI 0.69-0.77] after one infection vs. 1.16% [1.05-1.30] after two infections at 180 days), but differences were small and decreased over time. Risks following reinfection were similar across sex and income strata. Absolute risk decreased with prior vaccinations. Secondary analyses showed no increased risk of fatigue or headache after primary infection. A small increase in fatigue risk was observed after reinfection at 1 year (RD 0.03% [0.01-0.05]), but not for headache. INTERPRETATION: Reinfection increases long COVID risk; however, the absolute increase after reinfection is smaller than that observed after a primary inf
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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 articleDixon-Zegeye M, Walker M, Ramani A, et al., 2026,
HISTONCHO: A dataset of intervention histories for onchocerciasis control & elimination in sub-Saharan Africa
, Scientific Data, ISSN: 2052-4463In sub-Saharan Africa (SSA), onchocerciasis control has been implemented for many decades, beginning in 1974 under the Onchocerciasis Control Programme in West Africa (OCP) and in 1995 in Central and East Africa (plus Liberia) under the African Programme for Onchocerciasis Control (APOC). Since the establishment of the Expanded Special Project for Elimination of Neglected Tropical Diseases (ESPEN) in 2016, data on mass drug administration (MDA) with ivermectin has been centrally compiled for all endemic countries at implementation unit (IU) level, beginning in 2013. This paper presents HISTONCHO, a dataset collating detailed information on interventions, including vector control, from 1975 through to 2022, using the ESPEN portal (2013-2022), regional and country reports, implementation partners’ records, and published literature. Reconstructing such intervention histories is crucial for an understanding of their evolution, modelling their impact, and tailoring future interventions. We discuss strengths and limitations associated with the ESPEN database, and how HISTONCHO can be improved to support modelling of intervention strategies as well as onchocerciasis control and elimination efforts by endemic country programmes.
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Journal articleDixon-Zegeye M, Ramani A, Walker M, et al., 2026,
Modelling of onchocerciasis-associated skin and ocular disease and the impact of ivermectin treatment
, Communications Medicine, ISSN: 2730-664XBackground Despite decades of control interventions in sub-Saharan Africa, morbidity associated with Onchocerca volvulus infection still exerts a substantial burden of disease, arising from cutaneous, ocular and neurological manifestations.Methods We developed and integrated a morbidity sub-model into our previously published individual-based, stochastic transmission model, EPIONCHO-IBM, including both reversible (severe itch, reactive skin disease (RSD)), and irreversible (skin atrophy, depigmentation, hanging groin) cutaneous sequelae, and eye disease (blindness, visual impairment). We modelled the relationship between onchocerciasis skin disease (OSD) and infection prevalence using pre intervention data from northern Nigeria, and between onchocerciasis ocular disease (OOD) and infection intensity using data from the Onchocerciasis Control Programme in West Africa. We simulated the impact of ivermectin mass drug administration (MDA) upon OSD and OOD using data from Cameroon, Central African Republic, Nigeria, Sudan and Uganda.Results Modelled age-specific OSD and OOD prevalence at baseline align well with reported prevalence estimates across the simulated range of endemicity levels but underestimate irreversible OSD in older age groups. Under MDA, we capture trends in infection prevalence, severe itch and irreversible OSD but underestimate reductions in RSDand blindness prevalence.Conclusions Integrating morbidity outcomes into transmission dynamics modelling will help improve estimates of onchocerciasis disease burden and inform the effectiveness and cost effectiveness of current and alternative interventions.
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Journal articleNgwili N, Kachepa U, Korir M, et al., 2026,
Spatial and temporal risk mapping of human and porcine Taenia solium infections in Malawi: a systematic review and geostatistical approach
, One Health Outlook, ISSN: 2524-4655Background Taenia solium, colloquially called the pork tapeworm, is a zoonotic parasite with a human definitive host and a porcine intermediate host. Humans can become an aberrant intermediate host due to accidental ingestion of parasite eggs from the environment or through autoinfection, resulting in human cysticercosis (HCC), neurocysticercosis (NCC) if the central nervous system is infected. Pigs become infected with the larval stage, porcine cysticercosis (PCC), through the ingestion of parasite eggs shed by humans through defecation. Malawi has been classified as endemic for T. solium by the WHO based on the presence of key risk factors; however, the subnational distribution is not known. To ensure the appropriate resources are mobilized to support targeted future T. solium control measures in Malawi, there is a need to understand the variation in T. solium endemicity status across the country.Methods The current study uses a systematic literature review (SLR) using a pre-registered protocol; (PROSPERO CRD42023411044) to collate all available evidence on T. solium in Malawi. A geospatial risk mapping approach was conducted based on data from Malawi demographic health surveys (MDHS), and pig density data from the Food and Agriculture Organization (FAO) database to create geospatial risk maps of endemic subnational areas for 2000, 2004, 2010, and 2016. To create a single composite risk factor map for the four years from the MDHS, each parameter was plotted as a binary variable with the high or low risk categories and overlaid into a single composite risk factor classification. Additional data from hospital records on NCC and meat inspection records across several Agricultural Development Divisions (ADDs) were also collected.
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Journal articleMcCain K, Vicco A, Morgenstern C, et al., 2026,
A systematic review and meta-analysis of Zika virus epidemiology
, Nature Health, ISSN: 3005-0693Zika virus (ZIKV), classified as a priority pathogen by the World Health Organization, is an Aedes-borne arbovirus that can cause neurological complications and birth defects in newborns of mothers infected during pregnancy. We conducted a systematic review of peer-reviewed studies reporting ZIKV epidemiological parameters, transmission models and outbreaks (PROSPERO CRD42023393345) to characterize its transmissibility, seroprevalence, risk factors, disease sequelae and natural history. We performed meta-analyses of the proportions of congenital Zika syndrome, pregnancy loss among ZIKV-infected mothers and symptomatic cases. We extracted information from 574 studies. Across 418 included studies assigned a high-quality score, we extracted 969 parameters, 127 outbreak records and 154 models. Using random-effects models, we estimated proportions of congenital Zika syndrome (4.65%, 95% confidence interval (CI): 3.38–6.67%), pregnancy loss (2.48%, 95% CI: 1.62–3.78%) and symptomatic cases (51.20%, 95% CI: 38.00–64.23%). Seroprevalence estimates (n = 354) were retrieved beyond South America and French Polynesia. Basic reproduction number estimates (n = 77) ranged between 1.12 and 7.4. We found 66 human epidemiological delay estimates, including the intrinsic incubation period (n = 11, range: 4–12.1 days), infectious period (n = 15, range: 3–50 days), extrinsic incubation period (n = 22, range: 5.1–24.2 days) and serial interval (n = 27, range: 7.4–32.9 days). These data are available in the R package ‘epireview’ (version 1.4.5). We provide a comprehensive systematic summary of ZIKV epidemiology, revealing large heterogeneities and inconsistencies in the reporting of parameter estimates, study designs and parameter definitions and underscoring the need for standardized epidemiological definitions.
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Journal articleCliff M, Jahn S, Bita Fouda A, et al., 2026,
New title: Re-evaluating environmental associations with meningitis risk across Africa
, Wellcome Open Research, Vol: 10, Pages: 497-497<ns3:p>Background Previous analytical work, defining the distribution of meningitis epidemics in Africa is over 20 years old, with climate change representing an ongoing issue. We aim to update this analysis and determine if the meningitis belt geography and associated environmental risk factors have changed in the last two decades. Methods Epidemic bacterial meningitis data from 2003–2022 were provided by WHO-AFRO. Districts across Africa were coded 1 if they experienced a meningitis outbreak and 0 if not. Monthly means of windspeed, rainfall, dust, and humidity were processed into climatic profiles using k-means clustering. We undertook logistic regression with meningitis epidemic history as the dependent variable and k-means clusters of rainfall, dust, humidity, and windspeed, alongside land-cover type as independent variables. A sensitivity analysis was conducted, excluding the Democratic Republic of Congo (DRC), due to limited laboratory confirmation of cases. Results Rainfall, dust, windspeed and humidity demonstrated the strongest statistical association with outbreaks and were included in our final model. With a probability cut-off >0.4, our model had specificity and sensitivity of 82.07% and 82.22%, respectively, in identifying districts having experienced a meningitis epidemic. The Sahelian region had the highest risk of meningitis outbreaks (probability >0.8), consistent with previous findings. The inclusion/exclusion of the DRC had a significant impact on our model. In the full model the Republic of the Congo, Gabon, and Angola had a moderate risk of meningitis (probability >0.4), suggesting a possible expansion of the belt. However, when the DRC was excluded, no countries surrounding the meningitis belt were at risk for outbreaks, highlighting the importance of laboratory testing and case confirmation. Conclusions The apparent extension of risk beyond the belt possibly reflects surveillance limitations rather than altera
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Journal articleWilliams TJ, Griffiths JS, Gonzales-Huerta LE, et al., 2026,
Selective Targeting of IL-1RAP-Dependent Eosinophilic Inflammation in Allergic Fungal Airway Disease.
, Allergy -
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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