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Journal articleNgwili N, Kachepa U, Korir M, et al., 2026, , One Health Outlook, Vol: 8, ISSN: 2524-4655
Background 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 articleCidade JP, Taccone FS, Reyes LF, et al., 2026, , Intensive Care Medicine Experimental, Vol: 14
Objective: Immune dysregulation plays a pivotal role in the pathophysiology of sepsis and COVID-19, with lymphopenia emerging as a consistent marker of severity and poor prognosis. However, most existing studies have assessed lymphocyte counts at isolated time points, limiting insights into their temporal behavior and prognostic value. The dynamics of lymphocyte recovery or persistence of lymphopenia remain largely unexplored in large populations, as well as the impact of adjunctive therapies such as corticosteroids. We hypothesized that the persistence or recovery of lymphopenia may be key to understanding disease progression and predicting outcomes. Using the multinational ISARIC cohort, we investigated longitudinal lymphocyte trajectories in hospitalized patients and the clinical determinants associated with their evolution over time. Methods: We conducted a multinational prospective observational cohort study using data from the ISARIC-WHO Clinical Characterization Protocol. Patients with confirmed SARS-CoV-2 infection and at least four lymphocyte measurements during the first 28 days of hospitalization were included. We analyzed lymphocyte trajectories, Cox regression survival analyses and multivariable linear regression modelling. We also applied multistate models and joint modeling to assess the association between lymphocyte trajectories and 28-day mortality, incorporating corticosteroid use as a time-varying covariate. Results: Of 945,317 screened patients, 231,933 hospitalized adults with confirmed COVID-19 and sufficient lymphocyte data were included, with 56.6% classified as lymphopenic. Lymphopenia was independently associated with higher rates of ICU admission, organ support, and in-hospital mortality (OR = 1.52, 95% CI 1.48–1.55), and lower absolute lymphocyte counts were strongly linked to worse survival in adjusted Cox models (HR = 1.33 per 1 × 10鈦 cells/L decrease, 95% CI 1.28–1.38). Multistate modeling revealed that lymphope
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Journal articleMcCain K, Topazian HM, Challenger JD, et al., 2026, , BMC Medicine, Vol: 24, ISSN: 1741-7015
Background The malaria vaccine R21/Matrix-M is recommended for young children in malaria-endemic regions. However, the small vaccine-eligible population and waning vaccine efficacy mean that routine vaccination is unlikely to prevent severe cases in older children who experience significant malaria burden. As R21/Matrix-M vaccination expands, targeting older age groups may be warranted, depending on funding. Methods Using a stochastic, individual-based P. falciparum malaria transmission model, we estimate the impact of 1) one-off catch-up campaigns with R21/Matrix-M to previously unvaccinated age groups between age 6 months and 14 years, and/or 2) extra boosters at 2, 5 and/or 10 years after the primary series in low, moderate, and high transmission settings. We assume that vaccine immunogenicity in older children is equivalent to that of the standard target age group, though clinical trials have shown lower immunogenicity in older children.ResultsCatch-up campaigns in moderate-to-high transmission settings targeting younger children averted the most uncomplicated cases per 1000 additional doses (358 (95% Credible Interval (CI) 113-570) in children aged 6 months-2 years at 45% PfPR2-10), compared with targeting older children. In low transmission settings, the impact was similar across age groups, with a slightly higher impact when targeting school-aged children (373 (95% CrI 240-518) in children aged 5-9 years at 5% PfPR2-10). Across extra booster strategies, an extra booster 10 years post-primary series averted the most severe cases per 1000 additional doses at low transmission (12 (95% CrI 6-18) at 5% PfPR2-10), but the least at high transmission (-4 (95% CrI -11-3) at 45% PfPR2-10). Expanding the vaccine-eligible population in areas of moderate-to-high transmission often had higher incremental efficiency than routine age-based vaccination at low transmission. Sensitivity analyses assuming lower immunogenicity in older children modestly reduced the per-dose impac
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Journal articleTopazian HM, Morgan CE, Goel V, 2026, , One Health, Vol: 23, Pages: 101499-101499, ISSN: 2352-7714
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Journal articleLopes BC, Dutra JVR, Moreira FRR, et al., 2026, , Water Res, Vol: 300
This study evaluated wastewater-based epidemiology (WBE) for monitoring dengue virus (DENGV) and chikungunya virus (CKV) during Brazil's most severe arbovirus epidemic, focusing on the city of Belo Horizonte, Minas Gerais. From March 2022 to August 2024, 24-hour composite raw sewage samples were collected weekly from two major wastewater treatment plants, encompassing over 80% of the city's population. Viral RNA was quantified via RT-qPCR and positive samples underwent genome sequencing for genotype characterization. DENGV and CKV RNA were detected in over 90% of samples across both wastewater treatment plants (WWTPs), demonstrating sustained and widespread viral circulation throughout epidemic and inter-epidemic periods. Although CHIKV concentrations varied significantly across years, DENGV concentrations remained statistically stable, and no significant correlations were observed between wastewater viral loads and reported clinical cases. A considerable proportion of samples presented concentrations below the limit of quantification, indicating that while WBE is highly sensitive for qualitative detection of arboviruses, quantitative interpretation remains methodologically constrained. Sequencing confirmed the presence of DENGV-1 sorotype I and CKV genotype V, clustering with contemporaneous Brazilian strains and reflecting regional transmission dynamics. Wastewater-based modelling further suggested that reported clinical cases may substantially underestimate true infection burden, although quantitative estimates were highly sensitive to assumptions regarding viral shedding variability. These findings demonstrate that WBE provides a sensitive, non- invasive, population level approach for tracking arboviral circulation and viral diversity during large-scale outbreaks and could complement public health surveillance frameworks, especially in regions with limited diagnostic capacity or high levels of underreporting, to enhance epidemic response and control strategies.
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Journal articleSteyn N, Mills C, Shirvaikar V, et al., 2026, , Am J Epidemiol
Estimating, understanding, and communicating uncertainty is fundamental to statistical epidemiology, where model-based estimates regularly inform real-world decisions. However, sources of uncertainty are rarely formalised, and existing classifications are often inconsistent. This lack of structure hampers interpretation, model comparison, and targeted data collection. Connecting ideas from machine learning, information theory, experimental design, and health economics, we present a first-principles decision-theoretic framework that defines uncertainty as the expected loss incurred by making an estimate using incomplete information, arguing that this is a practically relevant definition for epidemiology. We show how reasoning about future data leads to a notion of expected uncertainty reduction, which induces formal definitions of reducible and irreducible uncertainty. We illustrate our approach with a simple worked example and a case study of SARS-CoV-2 wastewater surveillance in Aotearoa New Zealand, estimating the uncertainty reduction if wastewater surveillance were expanded to the full population. We then connect our framework to relevant literature from adjacent fields, showing how it unifies and extends many of these ideas. Our article serves as a gateway for applying a wide range of approaches to epidemiological models. Altogether, our framework provides a foundation for more reliable, consistent, and policy-relevant uncertainty quantification in infectious disease epidemiology.
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Journal articleSheppard R, Charles GD, Ciavarella C, et al., 2026,
Integrating parallel Plasmodium falciparum and Plasmodium vivax malaria models in a unified framework to capture co-endemic prevalence patterns
, Communications Health, ISSN: 3091-4841Background Plasmodium falciparum and Plasmodium vivax cause most malaria cases worldwide and are co-endemic in many countries, yet differ substantially in biology and in their responses to common interventions. As programmes drive down P. falciparum, P. vivax is a growing challenge for elimination, but most modelling tools assess the species separately, limiting coordinated policy. We aimed to build a unified malaria transmission modelling framework for co-endemic settings and assess how well it reflects global prevalence.Methods We integrated an established P. vivax model into a flexible P. falciparum modelling platform, enabling parallel simulation of both species within a shared biological, demographic, and intervention environment. Modelled equilibrium prevalences, matched by mosquito density, were compared with 19,225 yearly co-prevalence estimates from the Malaria Atlas Project (769 sub-national regions, 33 co-endemic countries, 2000–2024); uncertainty was represented by 95% quantile-based regions from 50 parameter draws. We assessed how this fit was modified by biological factors and simulated interventions.Results Here we show that the framework captures 51% of co-prevalence estimates within its uncertainty regions, rising to 65.5% when country-specific P. vivax relapse rates and human Duffy negativity are included. P. falciparum predominates where mosquito densities are high, whereas P. vivax is relatively more prevalent at lower densities. Simulated interventions produce larger relative reductions in P. falciparum prevalence, while P. vivax shows greater rebounds after intervention withdrawal, particularly at low mosquito densities.Conclusions This unified framework provides a quantitative tool to support coordinated, species-specific intervention strategies in co-endemic settings, a step toward sustainable malaria elimination.
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Journal articleSimmonds O, Maddren R, Collyer B, et al., 2026, , Trans R Soc Trop Med Hyg
OBJECTIVES: Predisposition to reinfection is characterized by certain individuals repeatedly acquiring infection at higher rates than others in the population despite treatment. As endemic regions approach low prevalence levels, such individuals will act as reservoirs of infection and contribute to sustaining transmission cycles in communities. This study investigated evidence for individual-level predisposition to Ascaris lumbricoides infections using longitudinal data from the Geshiyaro project in southern Ethiopia. METHODS: Longitudinal parasitological data collected over 7 years were analysed. Kendall's tau (τ) correlation coefficient was used to assess temporal persistence of infection between successive survey rounds, and Kendall's coefficient of concordance (W) was used to evaluate long-term stability in individuals' relative infection risk over many years of survey rounds. RESULTS: Positive correlations in infection status between survey rounds were observed (τ = 0.10-0.30, P < .001). Moderate concordance in infection ranking across the study period was also detected (W = 0.43, 95% CI: 0.42-0.45; P < .001), indicating persistent individual-level predisposition over many years of treatment and reinfection. CONCLUSION: These findings demonstrate sustained heterogeneity in infection risk within endemic communities. As transmission declines, identifying individuals predisposed to reinfection may help inform targeted or complementary 'test-and-treat' strategies to support interruption of A. lumbricoides transmission.
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Journal articleGrant R, Zanella MC, Gan C, et al., 2026, , Journal of Hospital Infection, Vol: 173, ISSN: 0195-6701
The publisher regrets that an error was introduced during the production process which affected the CRediT authorship contribution statement for this paper. The correct CRediT authorship contribution statement for M.-C. Zanella is: “Writing – review & editing, Supervision, Funding acquisition, Conceptualization”. The publisher would like to apologise for any inconvenience caused.
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Journal articleMcCabe R, Ebbarnezh L, Okware S, et al., 2026, , Lancet Infect Dis, Vol: 26, Pages: e279-e280
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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