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Journal articleTurner H, Rivillas-Garcia JC, Prinja S, et al., 2025,
An introduction to costing and the types of costs used within health economic studies
, PharmacoEconomics - Open, ISSN: 2509-4254The number of published health economic analyses, especially economic evaluations, has rapidly expanded globally since the 1990s, and costs are an essential component of such studies. Cost is a general term that refers to the value of the resources/inputs used to produce a good or service. However, within health economics, there are several different types of costs (such as financial, economic, unit, average, etc). The terminology and application of these cost types often differ, leading to inconsistencies in the health economics literature. These inconsistencies create challenges in comparing studies and hinder the use of health economic analyses to effectively inform policy decisions. This paper aims to provide an up-to-date overview of the cost types, key cost terms, and definitions of different cost measures used within health economics, while highlighting key inconsistencies in the literature. We also discuss common adjustments made to cost data, such as accounting for inflation, discounting, and currency conversions, as well as the influence of economies of scale and scope on cost estimates. We highlight the different definitions/categories for the different types of costs are not mutually exclusive and that the type of cost that should be used will depend on the purpose of the study, highlighting recommendations of what to do in practice where relevant. The content was tailored to be relevant across both high-income and low and middle-income (LMIC) country contexts.
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Journal articleWehrli S, Hartner A-M, Boender TS, et al., 2025,
Information Pathways and Voids in Critical German Online Communities During the COVID-19 Vaccination Discourse: Cross-Platform and Mixed Methods Analysis.
, J Med Internet Res, Vol: 27BACKGROUND: In Germany, the messaging app Telegram (Telegram FZ-LLC) served as a tool to organize protests against public health measures during the COVID-19 pandemic. A community of diverse groups formed around these protests, which used Telegram to discuss and share views outside of the general public discourse and mainstream information ecosystem. This increasingly included conspiracy theories and extremist content, propagated by sources that opposed the mainstream positions of the government and traditional media. While the use of such sources has been thoroughly investigated, the role of mainstream information in these communities remains largely unclear. OBJECTIVE: We aimed to better understand the use of mainstream information, that is, from government actors and established media outlets, within critical Telegram communities in the context of the COVID-19 pandemic in Germany. We focused on the discourse about the COVID-19 vaccination, a key public health measure. As a central element of this study, we compared the Telegram discourse with the discourse on X (formerly Twitter, X Corp) and in online news-this cross-platform analysis aimed to put the results into a broader societal context. METHODS: We analyzed Telegram, X, and news data between 2019 and 2023 for popular topics related to the COVID-19 vaccination discourse. We used a mixed methods approach, including text clustering for the exploration of popular topics, a 2-stage keyword filtering scheme for multitopic classification, link sharing analysis for assessing the prevalence of mainstream information, correlation-based time series analysis for measuring the similarity of discourse dynamics, and thematic analysis to examine the reasons for sharing information. RESULTS: We identified 5 popular vaccination-related topics that were discussed on both Telegram and X, namely death, long COVID, measures in schools, mandatory vaccination, and virus variants. On average per topic, 58% (SD 5.2%) of Telegram post
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Journal articleHancock P, Hui T-Y, Epopa PS, et al., 2025,
Requirements for designing cluster randomised control trials to detect suppression of malaria vector population densities
, BMC Biology, ISSN: 1741-7007Background. Novel interventions for mosquito-borne disease control which release modified mosquitoes that are sterilised or genetically modified to cause offspring inviability are progressing towards field applications. Cluster randomised control trials (CRCTs) could provide robust assessment of intervention efficacy in suppressing mosquito populations in field environments, but guidance on designing CRCTs to detect mosquito suppression impacts is limited.Results. We developed statistical models to simulate CRCTs, informed by a 5-year time series measuring densities of malaria vector species from the Anopheles gambiae complex in four villages in western Burkina Faso. We estimated requirements for parallel and step wedge designs, varying the targeted vector species, the suppression effect, and the monitoring regime. For a suppression effect of 50%, 21-22 clusters were required to detect suppression with 90% power when all An. gambiae complex species were targeted, while 24-26 clusters were required when only An. coluzzii was targeted and 60-66 clusters were required when only An. gambiae was targeted. For stronger suppression effects, required trial sizes depended less on target species, with 9-10 clusters being sufficient to detect a 90% suppression effect. We investigated how reducing sampling effort, by sampling fewer houses and restricting sampling to rainy season months, affected statistical power.Conclusions. Our results provide empirically based guidance for designing CRCTs to evaluate interventions aiming to suppress malaria vector populations.
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Journal articleParag K, Lambert B, Donnelly CA, et al., 2025,
Asymmetric limits on timely interventions from noisy epidemic data
, Communications Physics, ISSN: 2399-3650Deciding on when to initiate or relax an intervention in response to an emerging infectious disease is both difficult and important. Uncertainties from noise in epidemiological surveillance data must be hedged against the potentially unknown and variable costs of false alarms anddelayed actions. Here we clarify and quantify how case under-reporting and latencies in case ascertainment, which are predominant surveillance noise sources, can restrict the timeliness of decision-making. Decisions are modelled as binary choices between responding or not that are informed by reported case curves or transmissibility estimates from those curves. Optimal responses are triggered by thresholds on case numbers or estimate confidence levels, with thresholds set by the costs of the various choices. We show that, for growing epidemics, both noise sources induce additive delays on hitting any case-based thresholds and multiplicative reductions in our confidence in estimated reproduction numbers or growth rates. However, for declining epidemics, these noise sources have counteracting effects on case data and limited cumulative impact on transmissibility estimates. We find this asymmetry persists even if more sophisticated feedback control algorithms that consider the longer-term effects of interventions are employed. Standard surveillance data therefore provide substantially weaker support for deciding when to initiate a control action or intervention than for determining when to relax it.This information bottleneck during epidemic growth may justify proactive intervention choices.
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Journal articleDi Lauro F, Probert WJM, Pickles M, et al., 2025,
Large connected components in sexual networks and their role in HIV transmission in Sub-Saharan Africa: A model-based analysis of HPTN 071 (PopART) data
, JOURNAL OF THEORETICAL BIOLOGY, Vol: 613, ISSN: 0022-5193 -
Journal articleDa Silva Candido D, Dellicour S, Cooper LV, et al., 2025,
Historical and current spatiotemporalpatterns of wild and vaccine-derivedpoliovirus spread
, Nature Microbiology, ISSN: 2058-5276 -
Journal articleSteyn N, Chadeau M, Elliott P, et al., 2025,
A Bayesian model for repeated cross-sectional epidemic prevalence survey data
, PLoS Computational Biology, ISSN: 1553-734XEpidemic prevalence surveys monitor the spread of an infectious disease by regularly testing representative samples of a population for infection. State-of-the-art Bayesian approaches for analysing epidemic survey data were constructed independently and under pressure during the COVID-19 pandemic. In this paper, we compare two existing approaches (one leveraging Bayesian P-splines and the other approximate Gaussianprocesses) with a novel approach (leveraging a random walk and fit using sequential Monte Carlo) for smoothing and performing inference on epidemic survey data. We use our simpler approach to investigate the impact of survey design and underlying epidemic dynamics on the quality of estimates. We then incorporate theseconsiderations into the existing approaches and compare all three on simulated data and on real-world data from the SARS-CoV-2 REACT-1 prevalence study in England. All three approaches, once appropriate considerations are made, produce similar estimatesof infection prevalence; however, estimates of the growth rate and instantaneous reproduction number are more sensitive to underlying assumptions. Interactivenotebooks applying all three approaches are also provided alongside recommendations on hyperparameter selection and other practical guidance, with some cases resulting in orders-of-magnitude faster runtime.
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Journal articleYamey G, McDade KK, Anderson RM, et al., 2025,
Vaccine value profile for schistosomiasis.
, Vaccine, Vol: 64Schistosomiasis is caused by parasitic flatworms (Schistosoma). The disease in humans can be caused by seven different species of Schistosoma: S. mansoni, S. japonicum, S. haematobium, S. malayensis, S. mekongi, S. guineensis and S. intercalatum, as well as by hybrids between species, including livestock schistosome species. People are infected when exposed to infested water and the parasite larvae penetrate the skin. Poor and rural communities are typically the most affected, and the general population who lives in affected areas and is exposed to contaminated water is at risk. Areas with poor access to safe water and adequate sanitation are also at heightened risk. About 236.6 million people required treatment for schistosomiasis in 2019-mostly people living in poor, rural communities, especially fishing and agricultural communities. This 'Vaccine Value Profile' (VVP) for schistosomiasis is intended to provide a high-level, holistic assessment of the information and data that are currently available to inform the potential public health, economic, and societal value of pipeline vaccines and vaccine-like products. This VVP was developed by a working group of subject matter experts from academia, non-profit organizations, public private partnerships, and multi-lateral organizations. All contributors have extensive expertise on various elements of the schistosomiasis VVP and collectively aimed to identify current research and knowledge gaps. The VVP was developed using only existing and publicly available information.
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Journal articleRhodes J, Fisher M, 2025,
Emerging terbinafine-resistant Trichophyton indotineae between 2018 and 2023: a multinational genomic epidemiology study
, The Lancet Microbe, ISSN: 2666-5247 -
Journal articleKang H, Lim A, Auzenbergs M, et al., 2025,
Global, regional and national burden of chikungunya: force of infection mapping and spatial modelling study.
, BMJ Glob Health, Vol: 10, ISSN: 2059-7908INTRODUCTION: Chikungunya virus, an arbovirus transmitted by Aedes mosquitoes, causes epidemics in tropical regions with potential risk in higher latitudes. Our aim is to estimate the global, regional and national burden of chikungunya across affected and environmentally suitable at-risk regions. METHODS: We used a random forest model to predict force of infection and estimate chikungunya burden at high spatial resolution (5×5 km) using covariates from climatic, socioeconomic and ecological domains. We used a focal scenario to estimate the observed burden (lower bound) and an at-risk scenario to estimate the potential burden (upper bound) of chikungunya transmission. RESULTS: We predicted global long-term average annual force of infection at 0.012 (95% UI: 0.007 to 0.019) for focal scenario and 0.013 (95% UI: 0.005 to 0.03) for at-risk scenario in 103 countries. We estimated global chikungunya burden annually of 14.4 million (95% UI: 11.0 to 17.8 million) infections and 0.96 million (95% UI: 0.56 to 1.6 million) disability-adjusted life years (DALYs) in the focal scenario, and 34.9 million infections (95% UI: 26.7 to 43.1 million) and 2.3 million DALYs (95% UI: 1.4 to 3.8 million) in the at-risk scenario for 2020. The chronic phase accounts for 54% of chikungunya burden, with relatively higher burden among 40-60-year-old population, with mortality disproportionately affecting children under 10 and adults over 80. CONCLUSION: While chikungunya transmission has high geographical uncertainty, high force of infection is not limited to tropical regions and is distributed across all continents. Our estimates of chikungunya burden are useful for prioritisation of regions and target age groups for chikungunya vaccine introduction.
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