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Journal articleLang MM, Lyne B, Donnelly CA, et al., 2026,
Shared risk factors for malaria and schistosomiasis co-infection: A systematic review and meta-analysis.
, PLoS Negl Trop Dis, Vol: 20BACKGROUND: Malaria and schistosomiasis are co-endemic across sub-Saharan Africa, where both diseases often co-occur, yet the shared risk factors for co-infection remain poorly synthesized. METHODS: We conducted a systematic review and meta-analysis to identify shared risk factors for malaria-Schistosoma co-infection and to narratively synthesize the statistical methodologies applied in the literature. We searched PubMed/MEDLINE, Embase, Web of Science, Global Index Medicus, and Global Health from inception to February 19, 2025 (PROSPERO CRD420250648824). We pooled effect sizes for risk factors across sociodemographic, environmental, and behavioral dimensions. Fixed-effects meta-analysis with inverse variance weighting was used to calculate pooled Odds Ratios (OR) and 95% confidence intervals (CIs). Study quality was assessed using a modified version of the Quality Assessment tool for Observational Cohort and Cross-Sectional Studies by the National Institutes of Health. RESULTS: We screened 1,345 records and included 30 studies conducted across 12 African countries. A meta-analysis of 23 studies showed that schistosomiasis infection was associated with 1.27 times higher odds of malaria (OR 1.27; 95% CI: 1.17-1.39). Narrative synthesis identified age as an important predictor, with risk consistently peaking in older children and adolescents (typically 8-17 years). Associations with sex were setting-dependent: males had significantly higher odds of co-infection in community-based studies (OR 2.08; 95% CI: 1.64-2.63), whereas no significant association was found in school-based studies (OR 0.87; 95% CI: 0.64-1.19). Direct water contact was strongly associated with co-infection (OR 2.53; 95% CI: 1.60-4.00). Heterogeneity was high (I2 > 80%), warranting caution during interpretation. Only one study was categorized as high risk of bias. CONCLUSION: The association between malaria and schistosomiasis appears to be associated with overlapping environmental
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Journal articleJombart T, Kada S, Chakraborty D, et al., 2026,
A stochastic meta-population model of Ebola virus disease transmission for informing public health decisions.
, Epidemics, Vol: 55Ebola virus disease (EVD) remains a constant international public health threat. Developing models that integrate the complex transmission dynamics of EVD is essential for informing evidence-based strategies for outbreak preparedness and response. Here, we introduce a stochastic, meta-population, compartmental model of EVD epidemics which accounts for key stages of the disease transmission including ecologically-driven zoonotic introductions, person-to-person transmission, spatial spread, and potentially complex interventions. Our model can distinguish between different transmission modes (direct transmission from contact with infectious cases, funeral exposures, or sexual transmission from contact with convalescent individuals) as well as different intervention mechanisms (overall reduction of contacts, safe and dignified burials, and vaccination). We illustrate our approach by simulating EVD epidemics in an area at high risk of zoonotic introduction in the Democratic Republic of the Congo, and show how it can be used to identify potential future transmission hotspots and help assess the scaling of future responses. Our model is implemented in a computer-efficient, free, open-source software, and can be used for informing public health policies.
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Journal articleGeismar C, White PJ, Cori A, et al., 2026,
A statistical framework for comparing epidemic forests.
, PLoS Comput Biol, Vol: 22Inferring who infected whom in an outbreak is essential for characterising transmission dynamics and guiding public health interventions. However, this task is challenging due to limited surveillance data and the complexity of immunological and social interactions. Instead of a single definitive transmission tree, epidemiologists often consider multiple plausible trees forming epidemic forests. Various inference methods and assumptions can yield different epidemic forests, yet no formal test exists to assess whether these differences are statistically significant. We propose such a framework using a chi-square test and permutational multivariate analysis of variance (PERMANOVA). We assessed each method's ability to distinguish simulated epidemic forests generated under different offspring distributions. While both methods achieved perfect specificity for forests with 100+ trees, PERMANOVA consistently outperformed the chi-square test in sensitivity across all epidemic and forest sizes. Implemented in the R package mixtree, we provide the first statistical framework to robustly compare epidemic forests.
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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 articleChristen P, Ahmed MHA, Bing BCW, et al., 2026,
Measuring the growth of infectious disease modelling publications and their impact on policymaking: A large language model-assisted bibliometric review
, Epidemics, Pages: 100926-100926, ISSN: 1755-4365 -
Journal articleAhmed S, Mangal TD, Hallett TB, et al., 2026,
When is elimination of an infectious disease cost- effective? An analytical framework to guide elimination priorities
, Cost Effectiveness and Resource Allocation, ISSN: 1478-7547BackgroundElimination targets for infectious diseases are increasingly common in global health, yet the economic rationale for pursuing elimination is often assumed rather than rigorously assessed. Existing evaluations frequently emphasise future cost savings or broader economic benefits while overlooking health opportunity costs—the health that could have been gained had resources been allocated elsewhere. This study aimed to develop an analytical framework to investigate when disease elimination generates positive net health benefit (NHB) and to illustrate how key factors interact to shape this assessment.MethodsWe constructed a generalisable analytical framework incorporating ten factors related to intervention and disease costs, intrinsic disease/intervention characteristics, and stakeholder viewpoints and evaluation parameters. The framework was applied to create an exemplar model that showed how these factors jointly influence the NHB of achieving elimination. This was evaluated across wide parameter ranges informed by the literature, using different cost-effectiveness thresholds, discount rates, and time horizons. ResultsThe framework revealed distinct regions of parameter space in which elimination yield positive NHB. The cost effectiveness threshold, discount rates, disease burden, and intervention impact were strong determinants of NHB. In particular, lower thresholds, higher discount rates, and shorter time horizons reduced the likelihood that elimination would generate positive NHB. The framework also showed that elimination may be cost effective in some settings but not in others, even for the same disease, due to differences in costs, burden, and opportunity costs.ConclusionsDisease elimination is not always a good investment; its value depends on the interplay between disease characteristics, programme costs, and the health opportunity costs of resource use. The proposed framework provides a transparent, health opportunity cost-based structure f
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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 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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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 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.
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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