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Journal articleFeng H, Marini G, Barabás É, et al., 2026,
Modelling the risk of West Nile virus infection in seven European countries from published serological and case notification data, 2008 to 2022.
, Euro Surveill, Vol: 31BACKGROUNDWest Nile virus (WNV) is a zoonotic mosquito-borne pathogen increasingly reported in Europe.AIMWe aimed to characterise heterogeneities in the average annual human risk of WNV infection (force of infection, FOI) and in WNV surveillance across Europe.METHODSWe conducted a systematic review following the PRISMA guidelines to identify serological studies on WNV in humans with IgG-based assays in Europe. We then used mathematical models fitted to both age-stratified serosurvey and case data to reconstruct spatially explicit FOI estimates, the sensitivity of syndromic surveillance and age-dependent trends in case reporting.RESULTSWe extracted 92 serosurvey datasets from 21 countries. Based on 10 age-stratified serosurvey datasets from Greece, Hungary, Italy, Romania and Spain and case data from seven countries (Austria, Cyprus, Greece, Hungary, Italy, Romania and Spain), we estimated the WNV FOI for 119 European nomenclature of territorial units for statistics level (NUTS) 0-3 regions. We found evidence of spatial heterogeneities in transmission intensity and estimated that on average less than 0.2% of human WNV infections were notified, with country variability and age-dependent trends in the propensity of reporting WNV disease.CONCLUSIONThis study shows that the intensity of WNV transmission, the average annual incidence of infection and the sensitivity of surveillance are heterogeneous across Europe. Due to differences in case reporting across countries, the incidence of reported WNV cases does not necessarily reflect the same proportion of WNV infections and hence the actual infection incidence, which highlights the importance of conducting WNV seroprevalence surveys.
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Journal articleDiaz AV, Diouf ND, Léger E, et al., 2026,
Correction: 'Variable efficacy of praziquantel among Schistosoma-infected ruminants of northern Senegal-a drug trial and population genetic study across two contrasting epidemiological regions' (2026), by Diaz et al.
, Philos Trans R Soc Lond B Biol Sci, Vol: 381 -
Journal articleChacky F, Rumisha SF, Remiji MJ, et al., 2026,
Trends in prevalence and predictors of anemia among school-aged children in Tanzania: analysis of 2019-2023 cross-sectional surveys using a multilevel logistic regression model.
, BMC Public Health, Vol: 26BACKGROUND: Anemia constitutes a major global public health challenge among school-aged children with documented impacts on psychomotor development, academic achievement, susceptibility to infections, and long-term economic productivity. This study presents nationally representative findings on the prevalence and determinants of anemia among school children aged 5-16 years in mainland Tanzania. METHODS: Data were collected as part of the cross-sectional Tanzania School Malaria Parasitological Survey (SMPS) in 2019, 2021, and 2023. Hemoglobin concentration was assessed using the HemoCue Hb 201 + analyzer, and anemia status was classified according to the 2024 WHO thresholds. Candidate predictors associated with anemia (p < 0.2 in univariable analyses) were included in a multivariable multilevel logistic regression model to assess the contribution of predictor variables to anemia prevalence and to calculate adjusted odds ratios (AOR) and 95% confidence intervals (CI). RESULTS: The study surveyed 47,366 school-aged children from 810 schools across three survey rounds. Generally, anemia prevalence was 45.5% (mild: 18.5%, moderate: 25.4%, severe: 1.5%). On multivariable regression of anemia risk, a statistically significant interaction was observed between age and sex. While odds of anemia were similar between sexes in lower age groups (5-11 and 12-14 years), male children aged 15-16 years old had almost three times the odds of anemia compared to female children in the same age group (AOR 2.96 [95%CI 2.18-4.02]). Malaria infection significantly increased anemia odds (AOR 1.88 [95%CI 1.75-2.01]). Regional disparities were noted, with the Lake [AOR 1.77 (95%CI 1.38-2.26)] and Western [AOR 1.78 (95%CI 1.34-2.37)] zones having highest odds of anemia compared to the Central zone. Over time, anemia risk decreased, with a notable reduction in 2023 (AOR 0.75 [95%CI 0.70-0.80]) compared to reference year (2019). CONCLUSIONS: Anemia in school-aged ch
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Journal articleSoe KM, Hauck K, Jiamton S, et al., 2026,
Correction: The cost of community outreach HIV interventions: a case study in Thailand.
, BMC Public Health, Vol: 26 -
Journal articleParag K, Santillana M, Cori A, et al., 2026,
The R = 1 threshold can misclassify epidemic stability
, Communications Physics, Vol: 9, ISSN: 2399-3650The effective reproduction number, R, is a predominant statistic for tracking infectious disease spread and informing health policies. An estimated R = 1 is universally interpreted as a stability threshold distinguishing epidemic growth (R > 1) from control (R < 1). We demonstrate that this interpretation frequently fails because R typically averages over groups with heterogeneous characteristics. We find that R = 1 conceals valuable early-warning signals of resurgence and misclassifies complex dynamics as noise, generating false positive stability thresholds that diminish predictive and policymaking value. We further illustrate that a popular alternative transmissibility definition (using next-generation matrices) overcorrects this issue, producing false negative stability signals by amplifying stochastic variation. We address these limitations by adapting a recently developed statistic, E, derived from R using experimental design theory. We show that E tightly constrains the set of scenarios consistent with stability, while remaining robust to noise and establish E = 1 as a more practical and meaningful real-time threshold.
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ReportMcCabe R, Ebbarnezh L, Okware S, et al., 2026,
Estimation of the size of the Ebola outbreak caused by Bundibugyo virus in the Democratic Republic of the Congo: May 20, 2026 update
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Journal articleDarko E, Akortia D, Nkrumah G, et al., 2026,
Environmental surveillance of pathogens in Africa.
, Appl Environ Microbiol, Vol: 92The control of infectious diseases depends on effective diagnostics and interventions. In Africa, resource limitations hinder clinical surveillance. Environmental surveillance (ES), particularly wastewater surveillance, offers a cost-effective alternative. While globally expanding, its application in Africa remains limited. This review aimed to describe published studies in Africa that have utilized ES for the detection of infectious pathogens of public health importance in Africa. The study employed a rapid review approach to synthesize evidence on ES of infectious pathogens in Africa, following guidance from the Cochrane Rapid Reviews Methods. Articles from major databases, including Scopus, PubMed, Science Direct, and Cochrane, were screened using Catchii.org. Duplicates were removed, and data were extracted into Excel, and study quality was appraised using the AXIS tool and a modified Newcastle-Ottawa Scale. The search strategy identified 2,189 articles, of which 90 were found to be eligible. We identified 47 microbial species that have been reported across studies. These consisted of 46.8% bacteria (n = 22), 36.2% viruses (n = 17), 4.3% fungi (n = 2), and 12.7% parasites (n = 6). Among viruses identified, SARS-CoV-2 was the most common, followed by rotaviruses and polioviruses. Vibrio cholerae was mostly reported among bacterial pathogens. The most common sampling method was grab sampling (n = 85, 94.4%), while two-phase separation (n = 22, 37.3%) and filtration (n = 11, 39.3%) were the most frequently used concentration methods for viral and bacterial detection, respectively. ES of infectious pathogens in Africa remains limited. There is a need to expand this to enhance pathogen monitoring, transmission insights, and preparedness for emerging variants.
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ReportMcCabe R, Ebbarnezh L, Okware S, et al., 2026,
Estimation of the size of the Ebola outbreak caused by Bundibugyo virus in the Democratic Republic of the Congo [Estimation de l’ampleur de l’épidémie de maladie à d’Ebola causée par le virus Bundibugyo en République démocratique du Congo]
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Journal articleGayawan E, Egbon OA, Utazi CE, et al., 2026,
The influence of women's empowerment on childhood vaccination coverage in Nigeria: a spatio-temporal analysis.
, Sci Rep, Vol: 16Immunization is one of the most impactful public health achievements, significantly reducing childhood morbidity and mortality worldwide. However, gender disparity and women's disempowerment constitute structural barriers in accessing vaccine services in low- and middle-income countries. In Nigeria, widespread differences in social norms and cultural values affect gender roles and influence women's ability to decide their own healthcare needs and participate in household decision-making. This leads to attitudinal differences in uptake of immunization depending on the child's location of residence. Using data from four waves of the Nigeria Demographic and Health Survey, we constructed two empowerment indices that determine whether caregivers participate in household decision-making and have the ability to decide on their healthcare needs. We used a structured spatiotemporal statistical model to determine whether a significant part of childhood vaccination coverage disparities can be attributed to these women's empowerment measures and predicted events at the third administrative level of the country. We considered five vaccination indicators: Bacillus Calmette-Guerin (BCG), zero-dose, receiving a complete dose of DPT, MCV-1 (first dose of measles-containing vaccine), and receipt of all basic vaccinations. The adopted model was validated by comparing the empirical estimates of vaccination coverage level from the data with model projections at the second administrative level. The findings indicate that although empowerment regarding participation in household decision-making and agency over healthcare access is generally associated with increased vaccine uptake, their effects vary considerably across locations and notably among the highly empowered category of women. Although there are efforts to bridge immunization gaps within the country, the study emphasizes the need for tailored strategies that target up-scaling the ability of women and the wider community to parti
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Journal articleManley H, Leber W, Smith K, et al., 2026,
Application of machine-learning algorithms to identify the key determinants of risk for HIV, hepatitis C and hepatitis B in primary care settings.
, BMC Infect Dis, Vol: 26BACKGROUND: Testing for Blood-Borne-Viruses (BBVs) such as the human immunodeficiency virus (HIV), hepatitis C virus (HCV) and hepatitis B virus (HBV) is generally focused on specialist settings. However, people with undiagnosed infections are also present within the general population. We explore whether using machine-learning algorithms (MLAs) can identify people at heightened risk of HIV, HBV, HCV, or a composite 'any BBV' (defined as positivity for one or more of the three infections) in primary care settings. METHODS: From de-identified electronic health records data from 165 general practices in North East London we extracted risk factors for HIV, HCV and HBV and used them to train (75% data) and test (25% data) three MLAs: Logistic Regression (LR), AdaBoost with random under sampling (RUSBoost) and Balanced Random Forest classifier (BRFC). The ROC curves, ROC AUC, sensitivity and specificity values quantified the models' performance. Across the models the key features for each outcome were identified. RESULTS: A total of 1,987,954 patients were included in the study with no inclusion or exclusion criteria, from whom 75 predictive features were selected for HIV, 24 for HCV, 37 for HBV and 88 for any BBV outcome. Different models were optimal for individual BBVs positivity classification, depending on the accuracy metric. As a single infection, HCV was predicted most accurately across models and accuracy metrics. When targeting any BBV outcome, LR was the model with highest AUC value, BRFC was the most sensitive model and RUSBoost was the most specific model. The key identified features were similar across models with age the strongest predictor for both individual positivity and the composite outcome. A number of features were important for two of the BBV positive groups: Black African ethnicity (HIV and HBV), liver disease (HBV and HCV) and opiate and cocaine use (HBV and HCV). A number of individual features were important for individual BBVs positivity. CON
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