Epidemics

The basic reproduction number of an infectious disease is known to depend on the structure of contacts between individuals in a population. This relationship has been explored mathematically through two well-known models: one which depends on a matrix of contact rates between different demographic groups, and another which depends on the variability of contact rates over the population. Here we introduce a model that combines and generalises these two approaches. We derive a formula for the basic reproduction number and validate it through comparisons to simulated outbreaks. Applying this method to contact survey data collected in Belgium between 2020 and 2022, we find that our model produces higher estimates of the basic reproduction number and larger relative changes over periods when social contact behaviour was changing during the COVID-19 pandemic. Our analysis suggests some practical considerations when using contact data in models of infectious disease transmission.

Symptom propagation occurs when the symptoms of secondary cases are related to those of the primary case as a result of epidemiological mechanisms. Determining whether - and to what extent - symptom propagation occurs requires data-driven methods. Here we quantify the strength of symptom propagation as the increase in risk of a secondary case developing severe symptoms if the primary case has severe symptoms. We first used synthetic results to determine the data requirements to robustly estimate the strength of symptom propagation and to investigate the effect of severity-dependent reporting bias. Categorising symptom severity into two group (mild or severe; asymptomatic or symptomatic), our estimation requires only four summary statistics - the number of primary-secondary case pairs of each combination of symptom presentations. Our analysis showed that a relatively small number (100) of synthetic primary-secondary case pairs was sufficient to obtain a reasonable estimate of the strength of symptom propagation and 1,000 pairs meant errors were consistently small across replicates. Our estimates were robust to severity-dependent reporting bias. We also explored how symptom propagation can be separated from other individual-level factors affecting severity, using age dependence as an example. Although synthetic data generated from an age-structured model led to overestimations of the strength of symptom propagation, allowing disease severity to be age-dependent restored the accuracy of parameter estimation. Finally, we applied our methodology to estimate the strength of symptom propagation from three publicly available data collected during the COVID-19 pandemic with data on presence or absence of symptoms: England households, Israel households, and Norway contact tracing. Our age-free methodology indicated a 12-17% increase in the risk of being symptomatic if infected by someone symptomatic. Our positive estimates for the strength of symptom propagation persisted when applying our age-dependent methodology to the two household data sets with age-structured information (England and Israel). These findings demonstrate evidence for symptom propagation of SARS-CoV-2 and provide consistent estimates for its strength. Our synthetic data analysis supports the conclusion that these correlations are not a result of reporting bias or age-dependent effects. This work provides a practical tool for estimating the strength of symptom propagation that has minimal data requirements, enabling application across a wide range of pathogens and epidemiological settings.

In dense urban settings, delays between diagnostic sampling and effective isolation can sustain transmission during peak infectiousness. We define a waiting-window transmission externality arising when infectious individuals remain mobile while awaiting results, formalized as E = N{middle dot}P{middle dot}TR{middle dot}D, where N is daily testing volume, P test positivity, TR transmission during the waiting period, and D turnaround time. Using Monte Carlo simulation and a susceptible-infectious-recovered (SIR) framework, we quantify excess infections per 1,000 tests/day under multiple diagnostic workflows. A surge scenario incorporates positive coupling between TR and D ({rho} = 0.45), reflecting co-occurrence of laboratory saturation and elevated contacts during system stress. Under centralized 48-hour workflows, excess infections reach [~]80 at P = 10% and [~]401 at P = 50%, increasing to [~]628 under surge conditions. In contrast, near-patient rapid testing and home sampling reduce this to [~]5 and [~]25-26, respectively. Workflows that eliminate the waiting window--either through immediate isolation at sampling or through home-based PCR that returns results at the point of collection--effectively collapse the transmission term. These findings identify diagnostic delay as a modifiable driver of epidemic dynamics. Operational redesign of testing workflows, including decentralized sampling and home-based molecular diagnostics, offers a scalable pathway to improve epidemic controllability and reduce inequities in dense urban environments.

Tuberculosis (TB) remains a leading cause of death globally, with early mortality often driven by severe malnutrition and human immuno-deficiency virus (HIV) co-infection. Traditional survival analyses identify risk factors but remain associative, failing to capture the dynamic physiological collapse preceding death. In a novel interdisciplinary adaptation, we applied the Merton jump-diffusion structural framework from quantitative finance to model survival as a state of biological solvency, in which mortality occurs when a stochastic health trajectory crosses a critical failure threshold. We analysed a retrospective cohort of 15,182 TB patients in Cameroon over two decades. Adjusted body mass index (BMI) was conceptualized as a proxy for health capital and modeled using a stochastic process accounting for individual recovery trends, physiological instability, and acute clinical shocks. The study included predominantly young adult males (median age: 33 years) with a median BMI of 20.7 kg/m2. HIV co-infection was present in 35% of patients. The overall mortality rate during the 240 days follow-up period was 7.0%, with 55.1% of deaths occurring within the first 30 days. The model identified a critical failure threshold at BMI 17.329 kg/m2. HIV co-infection emerged as a key driver of metabolic instability, significantly increasing physiological volatility. Statistical validation confirmed that sudden clinical shocks were necessary to explain observed mortality patterns. The resulting Distance-to-Death (DtD) metric slightly outperformed standard associative extended Cox models in predicting survival, achieving a higher discriminative ability in testing set (Harrells C-index: 0.781 vs. 0.772; p = 0.038). Patients stratified into the highest-risk category showed a mortality rate of 16.7%, compared with 1.6% in the most stable group.This study bridges financial engineering and clinical epidemiology, offering a mechanistic understanding of how physiological reserves and metabolic instability determine survival. To support clinical application, we developed an interactive digital triage tool enabling identification of high-risk patients in resource-limited settings. Author summaryTuberculosis remains a major cause of death worldwide, particularly in people with poor nutrition or co-infection with HIV. In this study, we explored a new way to understand why some patients survive while others do not. We adapted a method originally used in finance to track the "health reserves" of patients over time, using body weight and related measures to estimate how close someone is to a critical health threshold. Our approach captures both gradual health decline and sudden medical complications, such as severe infections or rapid deterioration. By applying this method to a large group of patients in Cameroon, we found that a very low body weight is a strong warning sign for impending death and that HIV infection makes health outcomes less predictable. We also created a simple scoring tool that can help doctors identify patients at greatest risk, so that life-saving interventions and closer monitoring can be prioritized. This work bridges mathematical modeling and clinical care, offering a new way to assess patient vulnerability and improve outcomes in resource-limited settings.

Background Social contact surveys, which measure who-contacts-whom, are widely used to inform infectious disease transmission models and estimate the reproduction number (R), a key metric for assessing epidemic risk. Despite their widespread use, sample size calculations are not routinely performed. Aims To assess the impact of sample size on estimates of R and determine a practical target sample size for social contact surveys used in epidemic modelling. Methods We conducted a review of social contact surveys (2008-2025) to characterise current practice. We characterised the impact of survey size on epidemic metrics using two social contact surveys, the UK Social Contact Survey and POLYMOD (Europe) and two methods. For each dataset and approach, we generated repeated subsamples and calculated the resulting reproduction numbers, characterised their distributions and measured uncertainty. Results We identified 107 unique social contact surveys from 57 studies. Sample sizes ranged from 30 to more than 10,000 participants, with a median of 1,438. One quarter of surveys contained fewer than 1,000 participants. From our simulations, we find that sample sizes below 200 individuals can result in highly variability reproduction numbers. Increasing sample size increases precision, and the most meaningful gains are up to 1,300 individuals. Increasing sample sizes over 3,000 individuals leads to smaller gains. Conclusions A minimum sample size of approximately 1,200-1,300 participants appears sufficient for general-purpose use. These findings support the inclusion of sample size considerations in the design, reporting and interpretation of social contact surveys used for epidemic intelligence and public health decision-making.

Emerging respiratory disease outbreaks pose a major threat to food production systems. Agricultural workers live in larger, more crowded households than the general population, amplifying their potential exposure to respiratory pathogens, yet the consequences for worker health and food production remain poorly understood. We developed a household-structured susceptible-infectious-recovered (SIR) transmission model to compare disease dynamics between agricultural workers and the general U.S. population across six regions. We simulated outbreaks across a range of epidemiological scenarios and assessed productivity losses in California for three labor-intensive crops (oranges, iceberg lettuce, strawberries) with different harvest seasonalities. For a baseline reproduction number of R0 = 1.5, peak disease prevalence among agricultural workers was 1.23-1.45 times higher than that of the general population across regions, and final outbreak sizes were 1.15-1.28 times higher. Peak productivity losses ranged from 0.50%-0.62% across crops, translating to millions in lost revenue. At higher transmissibility and severity (R0 = 3 and assuming all infections are symptomatic), losses were over 2.5 times higher. Household crowding may lead to disproportionate respiratory disease burden among agricultural workers, highlighting the need for targeted outbreak preparedness and mitigation strategies in the agricultural sector to maintain food system resilience and support public health in these communities.

Interpreting serological data remains challenging, particularly in low prevalence or cross reactive contexts, where antibody responses often show substantial overlap between exposed and unexposed individuals and may depart from normal distributional assumptions. Conventional cutoff based approaches often yield inconsistent or biased estimates of seroprevalence. Here, we present a decisional framework based on finite mixture models (FMMs) that enhances the robustness and interpretability of serological analyses. Beyond simply applying mixture models, our framework integrates multiple methodological innovations : (i) systematic comparison of Gaussian and skew normal mixture models to accommodate asymmetric antibody distributions; (ii) rigorous model selection using the Cramer von Mises test (p > 0.01) combined with a parsimonious score (APS) to prioritize models with well separated clusters; and (iii) hierarchical clustering of posterior probabilities to collapse latent components into biologically meaningful seronegative and seropositive groups. Applied to chikungunya virus (CHIKV) data from Bangladesh, the framework produced prevalence estimates consistent with ROC based methods while probabilistically identifying borderline cases. Validation on SARS CoV 2 and dengue datasets further demonstrated its generalizability: for SARS CoV 2, the approach identified up to five latent clusters with high sensitivity (up to 100%) and specificity (up to 100%), enabling discrimination by disease severity. For dengue, it revealed interpretable subgrouping consistent with background exposure and subclinical infection, despite limited confirmed cases. By integrating distributional flexibility, robust goodness of fit testing, and biologically guided cluster consolidation, this decisional FMM framework provides a reproducible and scalable method for serological interpretation across pathogens and epidemiological settings, addressing key limitations of threshold based classification.

Network-based epidemic models have been instrumental in understanding how contact structure shapes infectious disease dynamics, yet widely used frameworks such as Erd[o]s-Renyi, configuration-model, and stochastic block networks do not explicitly capture the combination of fully accessible (saturated) within-group interactions and constrained between-group connectivity characteristic of many real-world settings. Here, we introduce the Multi-Clique (MC) network model, a generative framework in which individuals are organised into fully connected cliques representing stable contact groups (e.g., households, classrooms, or workplaces), with a limited number of external connections governing inter-group transmission. Using stochastic susceptible-infectious-recovered (SIR) simulations on degree-matched networks, we compare epidemic dynamics on MC networks with those on classical random graph models. Despite having an identical mean degree, MC networks exhibit systematically distinct behaviour, including slower epidemic growth, reduced peak prevalence, increased fade-out probability, and delayed time to peak. These effects arise from rapid within but constrained between clique transmission, creating structural bottlenecks that standard models do not capture. The MC framework provides an interpretable, data-driven representation of recurrent contact structure, with parameters that map directly to observable quantities such as household and classroom sizes. By isolating the role of intergroup connectivity, the model offers a basis for evaluating targeted intervention strategies that reduce between-group mixing while preserving within-group interactions. Our results highlight the importance of explicitly representing the real-life clique-based network structure in epidemic models and suggest that classical degree-matched networks may systematically overestimate epidemic speed and intensity in structured populations.

During the COVID-19 pandemic, reported incidence data played a central role in public health surveillance and in tracking epidemic dynamics, although they provide limited insight into the behavioral, immunological, and socioeconomic drivers of transmission.Population-based seroprevalence studies with linked survey data offer a rich but untapped source of individual-level information that can complement routine surveillance. In this study, we investigate whether aggregated seroprevalence cohort data can be leveraged to predict local COVID-19 incidence and to identify interpretable predictors associated with transmission dynamics. Using data from the Multilocal SeroPrevalence (MuSPAD) study in Germany (2020--2022), we trained multiple machine learning models, including least absolute shrinkage and selection operator (LASSO), vector autoregressive models (VAR), multilayer perceptrons (MLPs), and long short-term memory neural networks (LSTMs), to predict location-specific seven-day incidence rates. Feature importance was assessed using regression coefficients where applicable and model-agnostic explainability methods, including Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). Across model classes, cohort-derived features enabled accurate prediction of local incidence, with time-aware models achieving the strongest performance. Consistent predictors included prior infection and testing history, employment-related changes, vaccination status, and mask-wearing behavior, highlighting the importance of behavioral and reporting-related signals. While differential privacy introduced modest degradation in predictive performance under strict privacy budgets, SHAP-based explanations remained stable, and LIME-based explanations were more sensitive to privacy-induced noise. These results demonstrate that aggregated cohort data encode meaningful and interpretable signals of population-level transmission dynamics. Population-based serosurveys therefore provide a complementary source of information for predicting local COVID-19 incidence and identifying key drivers of transmission beyond routine surveillance data. Our findings show that integrating interpretable machine learning with privacy-aware analysis enables actionable insights from sensitive cohort data, supporting their use in digital epidemiology and informing data-driven public health decision-making.

Non-pharmaceutical interventions (NPIs) have been important for controlling SARS-CoV-2 transmission, particularly before and during initial vaccine rollout. During the pandemic, the US Centers for Disease Control and Prevention issued isolation and masking guidance in case of COVID-19-like illness, a positive SARS-CoV-2 test, or known exposure to SARS-CoV-2. However, the impact of this guidance on mitigating transmission in office workplaces is unclear. We used a network-based mathematical model to estimate the impact of this guidance on SARS-CoV-2 transmission among office workers and their communities. The model represented social contacts in the home, office, and community. We used data from the CorporateMix study to parametrize social contacts among office workers and calibrated the model to represent the COVID-19 epidemic in Georgia, USA from January 2021 through August 2022. In the reference scenario (58% adherence to guidance among office workers and the broader population), workplace transmission accounted for a small fraction of total infections. Reducing adherence among office workers to 0% increased workplace transmissions by 27.1% and increasing adherence to 75% reduced workplace transmission by 7.0%. Increasing adherence to 75% among office workers had minimal impact on symptomatic cases and deaths; increasing it among the broader population was more effective in reducing office worker cases and deaths. In our model, moderate adherence to recommended NPIs in workplaces was effective in reducing transmission, but increasing adherence had limited benefit given workplaces that have low contact intensity and hybrid work arrangements. These results underscore the public health benefits of community-wide adoption of recommended NPIs.

Detailed agent-based simulations are increasingly used to support policy decisions, but their computational cost and complex uncertainty structure make systematic scenario analysis challenging. We present a data-driven, uncertainty-aware decision support (DDUADS) workflow for using stochastic simulation models as decision-support tools under limited computational budgets. The approach combines several established techniques-sensitivity screening, Bayesian calibration using simulation-based inference, and multi-surrogate model integration for translational efficiency-into a coherent pipeline that enables uncertainty-aware policy analysis. Rather than producing a single baseline, the calibration stage yields a posterior distribution over plausible model parameterizations, allowing flexible, uncertainty-aware forward projections. We demonstrate the DDUADS workflow on the INFORM-HIV agent-based model of HIV transmission in Chicago to evaluate potential disruptions in antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) use. While the specific application is HIV modeling, the challenges and techniques described here arise in other simulation studies and can be applied to decision support in other domains.

Background: The annual respiratory season in Europe is marked by the co-circulation of multiple respiratory pathogens, such as influenza viruses, rhinoviruses, and coronaviruses. Effective surveillance is necessary but hampered by heterogeneity of case definitions and limited pathogen specificity in existing systems. This study aims to detect pathogen-specific signals in the participatory surveillance of the Netherlands using a sub-set of samples with virological detection. Additionally, we explore a method to use the findings in the Netherlands to enhance the virological interpretation of participatory surveillance data in Italy. Methods: We analyzed symptom data collected through a participatory surveillance platform in the Netherlands and Italy over five years (2020-2025). Symptom-by-week matrices from the Dutch cohort were aggregated into syndromes and their associated time series using Non-negative Matrix Factorization (NMF). We compared the respective time series of the syndromes with influenza virus, SARS-CoV-2, seasonal coronaviruses, RSV, and rhinovirus incidence estimated from nose- and throat swabs of a subsample of symptomatic participants of the participatory surveillance platform in the Netherlands. We tested the transferability of these components by applying the Dutch-derived components to describe Italian symptom data and extract respective incidences. Results: NMF identified eight symptom clusters in the Dutch cohort, one aligning with SARS-CoV-2, one aligning with rhinovirus and a third component aligning with influenza virus, RSV and seasonal incidences estimated from collected nose- and throat swabs. Transferring Dutch-derived symptom clusters to Italian data showed consistency in key components between Dutch and Italian cohorts, particularly those associated with SARS-CoV-2. Conclusion: This study demonstrates that unsupervised symptom decomposition can identify co-circulating respiratory pathogens trends from syndromic surveillance data, providing timely pathogen circulation insights.

Infectious disease dynamics are strongly shaped by human mobility, social structure, and heterogeneous contact patterns, yet many epidemic models do not jointly capture these features. This study develops a spatial metapopulation epidemic model incorporating recurrent group-switch interactions to represent real-world transmission processes. Building on the Movement-Interaction-Return framework, the model integrates household structure, age-stratified contacts, and mobility between locations within a single SEIR framework. Using UK demographic, mobility, and social contact data, the model quantifies how within- and between-group interactions, mobility rates, and location connectivity influence epidemic spread. Both deterministic and stochastic simulations are implemented to analyse outbreak dynamics, variability, and fade-out probabilities for COVID-19-like and Ebola-like infections. Results shows that highly connected locations drive faster transmission, earlier epidemic peaks, and greater difficulty in containment, whereas larger but less connected locations tend to produce slower, more localised outbreaks despite their population size. Comparative analysis reveals that COVID-19-like infections spread rapidly and remain difficult to control even under interventions, while Ebola-like infections exhibit slower dynamics and are more effectively contained, particularly under targeted measures. Non-pharmaceutical interventions, particularly widespread closures, substantially reduce infections, hospitalisations, and deaths, although effectiveness depends on timing and pathogen characteristics. These findings highlight the importance of integrating mobility, clustering, and demographic heterogeneity to inform targeted and effective epidemic control strategies.

Knowing the mortality rates associated with infection by a pathogen is essential for effective preparedness and response. Here, harnessing the flexibility of a Bayesian approach, we produce an estimate of the Infection Fatality Ratio (IFR) for A(H5N1) conditional on explicit assumptions, and quantify the uncertainty thereof. We also apply the method to first-wave COVID-19 data up to March 2020, demonstrating the estimates that could be obtained were the model available then. Our analysis uses World Development Indicators (WDI) from the World Bank, the A(H5N1) WHO confirmed cases and deaths tracker by country (2003-2024), and COVID-19 cases and deaths data from John Hopkins University (January and February 2020). Since infectious disease dynamics are typically influenced by local socio-economic factors rather than political borders, individual countries are placed within clusters of countries sharing similar WDIs relevant to respiratory viral diseases, with clusters derived by performing Hierarchical Clustering. To estimate the IFR, we fit a Negative Binomial Bayesian Hierarchical Model for A(H5N1) and COVID-19 separately. We explicitly modelled key unobserved parameters with informative priors from expert opinion and literature. By modelling underreporting, our analysis suggests lower fatality (15.3%) compared to WHO's Case Fatality Ratio estimate (54%) on lab-confirmed cases. However, credible intervals are wide ([0.5%, 64.2%] 95% CrI). Therefore, good preparedness for a potential A(H5N1) pandemic implies adopting scenario planning under our central estimate, as well as for IFRs as high as 70%. Our approach also returns a COVID-19 IFR estimate of 2.8% with [2.5%, 3.1%] 95% CrI which is consistent with literature.

Household transmission studies are important for understanding infectious disease transmission and evaluating interventions; however, they are frequently constrained by methodological challenges, including in study design and sample size determination, and in estimating parameters of interest after collecting the data. Existing tools often lack flexibility in modeling age-specific susceptibility, infectivity patterns, and the impact of interventions such as vaccination or prophylaxis. Here, we develop HHBayes, an open-source R package that provides a unified framework for simulating and analyzing household transmission data using Bayesian methods. The package enables researchers to: (1) simulate realistic household transmission dynamics with highly customizable variables; (2) incorporate viral load data (measured in viral copies/mL or cycle threshold values) to model time-varying infectiousness; (3) estimate age-dependent susceptibility and infectivity parameters using Hamiltonian Monte Carlo methods implemented in Stan; and (4) evaluate intervention effects through user-defined covariates that modify susceptibility or infectivity. We demonstrate the capabilities of the package through simulation studies showing accurate parameter recovery and applications to seasonal respiratory virus transmission, including the impact of vaccination and antiviral prophylaxis on household attack rates. HHBayes addresses a critical gap in infectious disease epidemiology by providing researchers with accessible tools for both prospective study design and retrospective data analysis. The flexibility of the package in handling complex household structures, time-varying infectiousness, and intervention effects makes it valuable for studying diverse pathogens.

Justification: Accidental lab-acquired infections (LAIs) with potential pandemic pathogens (PPPs) in high-biosafety research facilities risk causing a pandemic. Routine testing of lab workers for LAIs coupled with isolation of infected workers could reduce the risk, but the impact of such an intervention may depend on pathogens' epidemiological characteristics. Objective: This study aims to understand how the epidemiological characteristics of PPPs moderate the efficacy of a routine testing and isolation intervention in preventing larger outbreaks after an LAI. Methods: We employed a discrete-time stochastic network infectious disease model to run 625,000 epidemic simulations encompassing 625 unique combinations of five parameters of interest: test frequency, pathogen transmissibility, the self-isolation rate for symptomatic cases, the percentage of cases that are asymptomatic, and the percentage of infectious time that is spent in the pre-symptomatic state among those who show symptoms. To summarize the Monte Carlo simulations, we paired visual analysis with logistic regression for formal hypothesis testing, with an emphasis on the interaction terms that capture the moderating effect of epidemiological parameters on the impact of test frequency. Main Results: There were four main findings. First, the relative reductions in risk of outbreak that were caused by increased test frequency were inversely correlated with pathogen transmissibility. Second, the effect of test frequency was magnified at higher asymptomatic shares when the symptomatic self-isolation rate was high, but minimally when the self-isolation rate is low. Third, the direction of how the symptomatic self-isolation rate moderated the effect of increased test frequency depended on the asymptomatic share. Fourth, as the pre-symptomatic share of infectious time increased, the effect of test frequency on the probability of an outbreak was strongly magnified largely independent of symptomatic self-isolation rates. Conclusions: Routine testing and isolation could significantly mitigate the risk of catastrophic PPP escapes, with the intervention's success varying based on pathogen characteristics. High shares of asymptomatic and pre-symptomatic transmission notably increased the relative risk reductions achieved by the intervention. These findings suggest prioritizing testing interventions for pathogens with high asymptomatic and pre-symptomatic transmission and highlight the symptomatic self-isolation rate as a policy intervention target.

Despite the evolution of epidemiological analysis and modeling tools, difficulties still remain, especially in developing countries, regarding the availability and use of these tools. Often expensive, requiring high technical expertise, demanding constant connectivity of several or sometimes even significant resources, these tools, although efficient, present a major gap with the operational realities of health districts. It is in this context that we introduce Episia, an open-source Python library designed and conceived to provide a framework to facilitate epidemiological analysis and modeling. It integrates a suite of compartmental epidemic models (SIR, SEIR, SEIRD) with a sensitivity analysis using the Monte Carlo method, a complete biostatistics suite validated against the OpenEpi reference standard, as well as a native DHIS2 client for automated data ingestion. Developed in Burkina Faso, it is optimized and aims not only to address these health challenges encountered in Africa but also remains a versatile tool for global health informatics.

Background Use of oral cholera vaccine (OCV) is globally recommended as a public health response to cholera outbreaks, alongside water, sanitation and hygiene (WASH) interventions. Estimating vaccine effectiveness during emergencies in low-and middle-income countries is challenging because vaccination campaigns are often implemented over short time frames, while individual-level data are frequently incomplete due to constraints in infrastructure, resources and data systems. There is a need for pragmatic approaches that can generate timely, policy-relevant evidence using routinely collected data. Methods We analysed routine surveillance data from a large 2022-2023 cholera outbreak in Blantyre District, Malawi. The EpiEstim framework was used to generate estimates of the time-varying reproduction number (Rt) from line-listed case data. We modelled changes in Rt as a function of cumulative OCV coverage using a log-linear framework and propagated uncertainty through posterior sampling. Lagged WASH exposure variables were incorporated in the model to generate adjusted vaccine effectiveness estimates and to explore potential interaction effects. Sensitivity analyses assessed robustness to alternative lag structures. Findings The Blantyre outbreak was characterised by an initial period of low-level transmission followed by a sharp increase in cases from late November 2022, after which transmission declined steadily through April 2023. This decline coincided with the implementation of a reactive OCV campaign. The majority of the cases were among middle-aged men living in urban Blantyre. The unadjusted vaccine-associated reduction in transmission was estimated at 53.52% (95% credible interval (CrI):42.5-64.1%). After adjusting for a 7-day rolling average WASH activity, total vaccine effectiveness increased to 62.1% (95% CrI: 49.3-74.9%). Sensitivity analyses using alternative lag structures for WASH exposure produced comparable adjusted estimates. Interpretation Implementation of OCV contributed to a substantial reduction in cholera transmission during the outbreak. This study demonstrates a feasible approach for estimating vaccine-attributable impact whilst accounting for public health and social measures, such as WASH interventions. The methods described will be useful in outbreaks where classical observational designs are not possible, providing actionable evidence to policy makers for outbreak response in resource-limited settings.

Lightweight epidemic calculators are widely used for teaching and rapid scenario exploration, yet many omit the methodological detail needed for scientific reuse. We present a browser-native SIR calculator that exposes forward Euler and classical fourth-order Runge--Kutta (RK4) integration alongside epidemiologically interpretable outputs and a population-conservation diagnostic. The implementation is anchored to analytical properties of the deterministic SIR system, including the epidemic threshold, the peak condition, and the final-size relation. Benchmark experiments show that RK4 is essentially step-size invariant over practical discretizations, whereas Euler at a coarse one-day step overestimates peak prevalence by 3.97% and final size by 0.66% relative to a fine-step RK4 reference. These results demonstrate that browser-based tools can support publication-quality computational narratives when solver choice, diagnostics, and assumptions are treated as first-class outputs.

Medical male circumcision (MMC) is an established HIV prevention intervention, yet concerns persist that circumcised men may adopt higher-risk sexual behaviours following the procedure. Evidence from observational studies has been inconsistent, partly because many analyses do not adequately distinguish behaviours that occur before circumcision from those that occur afterward. This study assessed the association between MMC and subsequent sexual behaviours while demonstrating how population-based cross-sectional survey data can be adapted to address this temporal challenge. We analysed nationally representative data from the 2024 Zambia Demographic and Health Survey (ZDHS), including men aged 15 - 59 years who reported their circumcision status. Men who had undergone medical circumcision were compared with uncircumcised men using a matched pseudo-cohort framework that reconstructed temporal ordering based on age at circumcision. Propensity score overlap weighting was applied to improve comparability between circumcised and uncircumcised men, and odds ratios were estimated using logistic regression models incorporating overlap weights and accounting for the complex survey design. Sexual behaviour outcomes occurring after circumcision included condom non-use at last sexual intercourse, multiple sexual partners in the past 12 months, self-reported sexually transmitted infection (STI) symptoms, and composite measures of sexual risk behaviour. The analysis included 9,609 men, of whom 33.3% were medically circumcised. MMC was associated with lower odds of condom non-use at last sexual intercourse (adjusted odds ratio [aOR] = 0.75, 95% confidence interval [CI]: 0.67 - 0.85) and lower odds of reporting any sexual risk behaviour (aOR = 0.83, 95% CI: 0.72 - 0.95). No meaningful associations were observed between MMC and reporting multiple sexual partners, self-reported STI symptoms, or higher levels of composite sexual risk behaviour. In this population-based study, MMC was not associated with sexual risk compensation under routine programme conditions within the overlap population defined by the weighting scheme, supporting the behavioural safety of MMC and illustrating the value of explicitly addressing temporality when analysing behavioural outcomes using cross-sectional survey data.