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Journal articleLeuba SI, Verity R, Gutman JR, et al., 2026,
The burden of malaria-attributable maternal anaemia and the impact of preventive treatment across sub-Saharan Africa
, Nature Health, ISSN: 3005-0693Malaria in pregnancy is a major but poorly quantified contributor to maternal anaemia in sub-Saharan Africa. We combined individual-level data on haemoglobin (Hb), gravidity, gestational age and PCR-confirmed Plasmodium falciparum infection from 12,608 pregnancies in 7 African countries with a gravidity-specific model of malaria exposure and immunity linked to contemporary maps of transmission and fertility. For 2023, we estimate that 13.1 million pregnancies in malaria-endemic African regions were exposed to P. falciparum. In the absence of preventive measures, this exposure would have resulted in 2.41 million (95% credible interval 1.98–3.04 million) cases of moderate or severe anaemia (Hb < 9 g dl−1), including 600,000 (408,000–906,000) severe cases (Hb < 7 g dl−1). A counterfactual scenario using 2,000 transmission levels suggests that a 32% reduction in exposure during pregnancy translated into only a 22% decline in intrinsic anaemia burden, reflecting a shift from a concentration of risk in primigravidae to a more even distribution across gravidities as multigravid women acquire less pregnancy-specific immunity. Calibrating our model to randomized trials, we estimate that under current coverage, intermittent preventive treatment of malaria in pregnancy using sulfadoxine-pyrimethamine averted around 1.10 million (0.72–1.61 million) cases of moderate or severe anaemia and 330,000 (225,000–523,000) severe cases in 2023. These findings show that although burden has declined substantially, malaria remains a major driver of maternal anaemia risk. Meanwhile, lower immunity across multigravidae means any interruption to intermittent preventive treatment of malaria in pregnancy using sulfadoxine-pyrimethamine, or other population-based malaria control efforts, risks rapid resurgence of severe maternal anaemia, with substantial consequences for maternal and neonatal
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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 articleFaria N, Zé-Zé L, Borges V, et al., 2026,
Detection of dengue virus serotype 2 in local Aedes aegypti populations, Madeira Island, Portugal, 2025
, Parasites and Vectors, Vol: 19, ISSN: 1756-3305BackgroundSince 2010, dengue virus (DENV) has caused sporadic outbreaks across Europe, namely in Croatia, Spain, France, Italy and the Portuguese island of Madeira. Aedes aegypti mosquito is established in the Autonomous Region of Madeira, and along the eastern Black Sea coast of Cyprus. In Madeira Island, an outbreak of DENV serotype 1 occurred between 2012 and 2013, resulting in 1080 confirmed cases. Despite ongoing entomological surveillance, no further local transmission was detected in the following decade.MethodsIn January 2025, following two suspected dengue cases on Madeira Island, increased entomological surveillance efforts were implemented to confirm a local event transmission of DENV. A network of mosquito traps was complemented by targeted surveillance using 17 BG-PRO traps positioned in the vicinity of suspected human cases. Daily collections of adult A. aegypti, collected from 10 January to 31 March 2025, were screened by reverse transcription polymerase chain reaction (RT-PCR) for Aedes-borne viruses in the reference laboratory. Viral sequencing was performed using target enrichment and bioinformatics with INSaFLU-TELEVIR. The climate-driven suitability for dengue transmission by A. aegypti was also investigated. Serological and molecular tests were conducted on samples from suspected human cases.ResultsOut of 80 analysed A. aegypti pools (N = 393 mosquitoes), 1 pool, with 9 mosquitoes collected near the home of suspected human cases, tested positive for DENV. The dengue whole genome sequence from this sample was determined and classified as DENV-2 lineage 2II_F.1.1.3. The same virus was retrospectively confirmed in one of the clinical cases. Analysis of mosquito abundance and climate data confirmed the occurrence of this local transmission event during a period of low mosquito abundance and low climatic suitability.ConclusionsHere, we report an in-depth analysis of a local dengue transmission event that occurred in Funchal, the capital
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Journal articleChindelevitch L, Hedman ÅK, Bichko D, et al., 2026,
ReverseGWAS identifies combined phenotypes associated with a genotype in GWA studies
, Bioinformatics, ISSN: 1367-4803MotivationTraditional genome-wide association studies (GWAS) aim to uncover the genetic variants associated with a single phenotype of interest (typically a disease), and to elucidate its genotypic architecture. However, many of today’s GWAS simultaneously measure multiple related phenotypes, leading to the possibility of pursuing the reverse aim of elucidating the “phenotypic architecture” of a single genetic variant. In other words, we may ask what combination of measured phenotypes is associated with a given genotypic variant. ReverseGWAS is an algorithmic platform for answering such questions in the context of large-scale multi-phenotype GWAS.ResultsWe demonstrate the effectiveness of ReverseGWAS on simulated data, showing its ability to identify logical combinations of phenotypes with a reasonable amount of noise. We then apply it to a selection of combined phenotypes from the UK Biobank, obtaining 719 candidate associations using autoimmune diseases and 205 using common ICD10 codes. We find that the majority of these associations (546/719 and 111/205, respectively) successfully replicate in an independent cohort, FinnGen.AvailabilityThe source code of ReverseGWAS is freely available to non-commercial users as an installable R package at https://github.com/Leonardini/rgwas.Supplementary informationSupplementary data are available at Bioinformatics online.
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Journal articleTwumasi-Ankrah S, Owusu M, Owusu-Ansah M, et al., 2026,
Statistical methods for predicting the presence of Salmonella Typhi in wastewater samples at Asante Akyem Agogo, Ghana
, PLoS Neglected Tropical Diseases, Vol: 20, Pages: e001973-e001973, ISSN: 1935-2727BackgroundMonitoring wastewater is vital for tracking typhoid fever in endemic areas. This study evaluated the performance of both spatial and non-spatial models in predicting Salmonella Typhi detection in wastewater from the Asante Akim North district in Ghana and identified key environmental risk factors.MethodsWe collected wastewater samples of Moore swabs at 40 sites across Agogo, Juansa, Hwidiem, and Domeabra over a period of 27 months. Multiplex PCR was used to detect Salmonella Typhi, focusing on the ttr, tviB, and staG genes. An Aquaprobe AP-2000 was also used to measure different physicochemical factors, such as pH, temperature, dissolved oxygen, and salinity. Three non-spatial models, namely Generalized Estimating Equations (Logistic), Mixed-Effects Models, and Random Forest, as well as four spatial models, including Bayesian Generalized Additive Models (GAM) and Spatial Generalized Linear Mixed Models (GLMM), were fitted to the wastewater dataset. Model fitting was done using 5-fold cross-validation, stratified by site. Model performance was evaluated using accuracy, sensitivity, and specificity. We also used SHapley Additive exPlanations (SHAP) analysis to find the most important predictors.FindingsIn general, 44.13% of the samples tested positive for S. Typhi. Detection was much higher during wet seasons (50.17% vs. 35.11%; p < 0.001), with fast flows (64.45%), and in channels that were 1–2 meters wide (58.70%). Positive samples had relatively higher pH (7.46 vs. 7.40; p < 0.001), dissolved oxygen (46.97% vs. 36.77%; p < 0.001), and rainfall (3.92mm vs. 3.30mm; p = 0.022). In comparing both non-spatial and spatial models, the non-spatial Random Forest model demonstrated the highest performance with an accuracy of 0.993, sensitivity of 0.997, and specificity of 0.989. In the SHAP analysis of the preferred non-spatial random forest model, it was found that pH, season, dissolved oxygen
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Journal articleGrant R, Zanella M-C, Gan C, et al., 2026,
Wastewater-based surveillance of respiratory viruses in a geriatric hospital: a pilot study.
, J Hosp Infect, Vol: 171, Pages: 1-10OBJECTIVE: While wastewater-based surveillance (WBS) has informed our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza and respiratory syncytial virus (RSV) circulation in the community, its use in hospital settings remains limited. We aimed to use WBS to understand the dynamics between SARS-CoV-2, influenza (A/B) and RSV loads in the hospital wastewater and infections among patients in a geriatric hospital in Switzerland. METHODS: We conducted a prospective WBS study which involved the collection of 24-h composite samples 7 days/week from the centralised hospital wastewater of the geriatric hospital. We measured SARS-CoV-2, influenza A/B and RSV RNA concentrations using digital reverse transcription polymerase chain reaction. Kendall's rank correlation (τ) and cross-correlation function were used to assess the correlation and lag time between the viral RNA loads and SARS-CoV-2, influenza A/B and RSV infections among hospitalised patients. RESULTS: Between 14th October 2024 and 19th April 2025, we collected 166 wastewater samples. The associations between wastewater loads and infections were significant and positive for SARS-CoV-2 (τ = 0.29, 95% confidence interval [CI]: 0.19-0.39, P < 0.01), influenza A (τ = 0.36, 95% CI: 0.25-0.44, P < 0.01) and RSV (τ = 0.48, 95% CI: 0.38-0.57, P < 0.01). In sensitivity analyses restricted to healthcare-associated infections, the associations between wastewater loads and infections among patients remained significant and positive for SARS-CoV-2 (τ = 0.32, 95% CI: 0.23-0.41, P < 0.01), influenza A (τ = 0.24, 95% CI: 0.12-0.34, P < 0.01) and RSV (τ = 0.37, 95% CI: 0.27-0.47, P < 0.01). Increases in wastewater viral load preceded increases in reported SARS-CoV-2 and influenza A healthcare-associated infections by approximately three and five days, respectively. CONCLUSIONS: WBS could be used as a complementary early warning system for the circulati
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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 articleLal Z, Silva L, Alam N, et al., 2026,
Mapping the barriers and facilitators of oral healthcare access for vulnerable migrants across high-income countries: a scoping review.
, BDJ Open, Vol: 12BACKGROUND: According to the World Health Organisation, oral health (OH) diseases are a major global health issue and outcomes are consistently poorer among refugees and migrants than host populations in many high-income countries (HICs). In the UK, the Office for Health Improvement and Disparities recognises asylum seekers, refugees, undocumented migrants, low-wage migrants, unaccompanied minors, and victims of trafficking as vulnerable migrants. These groups face worse OH outcomes due to systemic, socio-economic, cultural, and lifestyle-related factors, alongside barriers to accessing dental services. This scoping review explores the barriers and facilitators to oral healthcare experienced by vulnerable migrants in HICs. METHODS: We conducted a scoping review using the Arksey and O'Malley framework and reported findings in line with PRISMA-ScR. Embase and MEDLINE were searched from inception until April 30th 2024, for studies examining factors influencing access to oral healthcare services. Data were charted and thematically mapped onto the Dahlgren and Whitehead model of Social Determinants of Health (SDH). RESULTS: Of 3894 identified records, 17 studies (10 qualitative, 5 quantitative, and 2 mixed-methods) were included, covering 2653 participants across 8 HICs (USA, UK, Australia, Austria, Germany, Finland, Saudi Arabia and Canada). Barriers and facilitators were present across all SDH layers. At the socio-economic, cultural, and environmental level, financial barriers were most commonly reported (12/17 studies). Language difficulties, low awareness of services, and mistrust of healthcare providers mapped to living and working conditions, while acculturation and social support aligned with the social and community networks layer. Limited knowledge of prevention was noted under lifestyle factors, and lastly, gender roles under personal characteristics. Cultural and religious norms also shaped care-seeking, with spirituality and religious trad
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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, Vol: 26, ISSN: 1471-2334BackgroundDengue, 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.MethodsWe 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.ResultsFifty-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.ConclusionsCost 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 intervention
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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.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.
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