Infectious Disease Modelling

Nitrogen dioxide (NO2) is a widespread air pollutant associated with the risk and prevalence of various respiratory infectious diseases. Epidemiological studies have reported associations between NO2 exposure and COVID-19 incidence, and biological evidence indicates that NO2 exposure increases vulnerability to infection. Motivated by these findings and by the scarcity of mechanistic epidemic models that incorporate air pollutant data, here we introduce an NO2- and mobility-informed metapopulation model and study the first COVID-19 wave in Oklahoma. County-level NO2 concentrations enter the infection rates through a first-order parameterization, alongside high-resolution mobility patterns capturing heterogeneous inter-county connectivity. Using numerical simulations, we demonstrate that higher environmental sensitivity produces earlier and more synchronized outbreaks across counties, while lower sensitivity yields delayed, asynchronous trajectories. When fitted to county-level COVID-19 incidence data, our model outperformed the classic SIR model (fitted independently in each county) in over 75% of counties in Oklahoma across all standard error metrics. The improvements were even more pronounced in forecasting tasks across epidemic stages, with county-level win rates against the SIR model of 59.74% (surge), 87.01% (peak), 80.52% (post-peak), and 88.31% (decline) by nRMSE. To isolate the contribution of NO2, we compared our model against a mobility-only model with NO2 removed and homogeneous infection rates across counties. The NO2 term improved county-level forecasts during the surge (94.81% of counties by nRMSE), post-peak (88.31%), and peak (62.34%) stages, though not during the early and decline stages, suggesting that NO2 information can generate better forecasts than mobility alone during periods of high incidence. Our results demonstrate that environmental exposure can enter the transmission structure of a mechanistic epidemic model directly and that doing so yields measurable forecasting gains.

Brazil experienced its worst dengue epidemic in 2024, with an increase in severe outcomes among elderly individuals, highlighting the need for vaccination strategies informed by population immunity. We reconstructed state- and age-specific immune profiles by fitting an age-structured catalytic model to dengue surveillance data from 2000-2025. This inferred immune profile was incorporated into an SIR-type transmission model to project disease burden and to evaluate the public health impact of vaccine allocation strategies. We observed substantial spatial heterogeneity in accumulated immunity, which was closely associated with projected burden and optimal allocation. Strategies prioritizing states with lower historical incidence and larger susceptible populations yielded greater benefits. Under limited vaccine supply, targeting children resulted in the greatest reductions in DALYs, whereas targeting elderly populations achieved smaller DALY reductions but larger reductions in mortality. We further showed that accelerated vaccine supply markedly increased impact, while allocation choices remained critical when availability was constrained.

Introduction: In temperate regions, influenza and respiratory syncytial virus (RSV) typically exhibit seasonal epidemics during the winter months, often concurrently with SARS-CoV-2 epidemics. Monitoring past and real-time trends in infection dynamics is important for informing public health situational awareness, including identifying and characterising unusual epidemic activity. However, data used to monitor these trends can exhibit substantial noise including strong day-of-the-week variation in reporting. In addition, case data for influenza can obscure distinct trends in influenza subtypes if not combined with virological data. Aim: By quantifying trends in respiratory virus activity using statistical methods, the Australia-Aotearoa Consortium for Epidemic Forecasting and Analytics (ACEFA) aims to provide government partners with comprehensive epidemic intelligence. Methods: Analyses were performed on daily case time series data for SARS-CoV-2, RSV and influenza across Australia's eight jurisdictions. Past and real-time trends were inferred for each pathogen and each influenza subtype using Bayesian p-spline models. Results: The 2025 Winter Situational Assessment Program provided weekly reports throughout the winter season and during the out-of-season influenza A H3N2 epidemic, driven by the emergence of the novel influenza subclade ('subclade K'). Despite limited subtyping data, our analysis provided an early signal of increasing influenza A H3N2 during a period in which overall influenza cases were decreasing; increases in influenza A H3N2 activity were first detected in July, and by mid-August, increasing H3N2 activity was estimated across all jurisdictions. Discussion: Our analysis highlights the benefits of incorporating statistical methods into real-time epidemic situational assessment and in consolidating reporting across jurisdictions.

Mechanistic models of infectious disease transmission provide a framework for understanding and predicting disease dynamics, but statistical inference is often computationally prohibitive. This challenge is especially pronounced for models with high-dimensional structures and many unknown parameters, where trajectory matching may require thousands of evaluations of an expensive likelihood function. We developed a surrogate-assisted optimization framework that uses Bayesian optimization to reduce the number of evaluations of the underlying mechanistic model required for parameter inference. We evaluated the approach using simulated fitting problems based on age-structured susceptible-exposed-infected-recovered models with 4, 8, and 16 age classes and a 210-compartment age-structured rotavirus transmission model. Applying surrogate-assisted optimization substantially reduced the number of likelihood evaluations required to identify good parameter estimates, with larger improvements as model dimensionality increased. In the rotavirus model, the surrogate Bayesian optimization approach reached the benchmark after approximately 0.7 hours, compared with approximately 9.4 hours using traditional Nelder-Mead optimization. These results demonstrate that surrogate-assisted optimization can substantially reduce the computational burden of trajectory matching for complex mechanistic transmission models, particularly for higher-dimensional inference problems.

Background. Classical macroparasite models commonly represent worm burden heterogeneity through prescribed Poisson or negative-binomial (NB) distributions, while transmission dynamics are formulated in terms of aggregate quantities such as mean worm burden and prevalence. We revisit these assumptions using a stratified worm burden (SWB) framework that explicitly tracks the distribution of parasite burdens across heterogeneous host classes and allows demographic turnover, transmission, and interventions to be analyzed within a unified model. Methods. We derive equilibrium distributions of the SWB system under homogeneous and heterogeneous infection processes and compare them with the classical Poisson and NB distributions used in macroparasite epidemiology and control policymaking. The framework is then extended to include host demography, coupled human-snail transmission dynamics, and periodic mass drug administration (MDA). Corresponding SWB and NB-type MacDonald models are calibrated to comparable endemic prevalence and mean worm burden, allowing direct comparison of potential transmission breakpoints and intervention outcomes. Results. In the absence of demographic turnover, the SWB framework naturally generates Poisson and NB equilibrium distributions as limiting cases of homogeneous and heterogeneous infection processes. Incorporating demographic recruitment and mortality alters these benchmark distributions, producing endemic burden structures that differ systematically from their classical counterparts. Analysis of coupled human-snail systems reveals that transmission breakpoints and bistable equilibria occur only within a relatively restricted region of SWB parameter space. In contrast, comparable NB-MacDonald formulations exhibit breakpoint behavior over substantially broader parameter ranges with the net effect of overestimating the likelihood of interruption of transmission. Although calibrated models can reproduce near identical endemic prevalence and mean worm burden, they often generate markedly different responses to MDA, including differences in rebound dynamics, persistence, and elimination thresholds. Conclusions. The SWB framework links classical statistical descriptions of worm aggregation to mechanistic burden-resolved population dynamics, clarifying how endemic distributions emerge and how they are modified by demography and control interventions. Our results suggest that transmission breakpoints are not a universal feature of helminth systems but depend strongly on model structure, and that predictions of elimination and post-treatment recovery may be considerably more model-dependent than implied by prevalence-based calibration alone. The SWB approach therefore provides a flexible alternative framework for evaluating parasite transmission dynamics and predicting the outcomes of control strategies.

Host immunity acquired through prior infections is a key factor shaping the spread of emerging variants. However, accounting for complete infection histories in mathematical models is challenging because the number of possible histories grows exponentially with the number of variants. Here, we present a flexible framework for modeling the co-circulation of multiple variants emerging at arbitrary times while incorporating cross-immunity and variant-specific characteristics. We introduce three tractable approximations: truncation, which retains only infection histories up to a given number of strains; one variant at a time, which is exact when variants circulation does not overlap; and two variants at a time, which is exact when at most two strains co-circulate. We systematically evaluate these approximations across a range of cross-immunity levels and inter-variant emergence delays. Truncation approximations perform best under short emergence delays and strong cross-immunity, whereas the other approaches outperform them at longer delays. When applied to SARS-CoV-2 variant dynamics, the second-order truncation and the two variants at a time approximation accurately capture incidence patterns, whereas the other approaches perform well only for specific subsets of variants. These results identify the parameter regimes in which simplifying assumptions remain valid, provide guidance for model selection, and highlight the key role of infection history in shaping epidemic trajectories.

Understanding the relationship between weather conditions and infectious disease transmission has important public health implications; nevertheless, the functional form of this relationship often remains poorly understood. Humidity has previously been suggested to have a U-shaped effect on the transmission of respiratory pathogens, with optimal transmission occurring at both low and high levels. Here, to assess the impact of this functional form and of its potential misspecification, we first simulate transmission rates based on the U-shaped relationship and then test whether an exponential function accurately estimates these rates across a range of climates. We further use a transmission model to simulate case count data for a range of hypothetical pathogens, comparing outbreak dynamics under the U-shaped forcing function to those under the best-fit exponential approximation. In our simulations, the model fit for the exponential function is climate-dependent, performing best in very dry and very humid environments. In locations where specific humidity values centre around the vertex of the U-shaped function, the exponential model underestimates the amplitude and frequency of incidence peaks. These findings demonstrate the potential consequences of assuming an incorrect functional form for weather-infectious disease relationships and highlight the need for more careful consideration of this functional form in modelling practice.

Background. Public-health use of influenza surveillance - situational awareness, planning for clinical surge, and issuing advisories or warnings - needs more than an accurate point estimate of next week's count. What matters operationally is whether a rise is caught early, whether the direction of change is right, and whether an alert threshold is about to be crossed, all with honest uncertainty. For such use a forecast must also respect reporting delay, later data revision, and the calibration of its prediction intervals. Using Japan's sentinel surveillance, we evaluated short-term probabilistic forecasts that respect the data release history, and we measured what adding other surveillance series contributes, focusing on the properties that matter for public-health decisions. Methods. The target was the number of influenza reports per sentinel site in each of the 47 prefectures. For each forecast we predicted, as quantiles, the value 1-4 weeks after the most recent observed week. We compared three models: a ridge regression trained jointly across prefectures; a gradient-boosting quantile regression (GBQR) that learns from many surveillance series; and their equal-weight combination on the log scale. Six seasons were used to develop the models. The 2025/26 season was held out as a temporal evaluation period after the specification was fixed; because its epidemic profile had already been seen, it is not a fully independent prospective test. We scored forecasts with the weighted interval score (WIS) and checked prediction-interval coverage. For the Japanese target series we used the value first published for each week; some auxiliary series were taken as retrieved and therefore include later revisions. Results. In 2025/26, the combination's WIS relative to a persistence forecast was 0.841, 0.811, 0.794 and 0.818 at horizons of 1-4 observed weeks - that is, 16-21% lower than persistence. Coverage of the 80% prediction interval was 79.7%, 77.1%, 74.5% and 75.1%, and of the 95% interval 94.3%, 93.4%, 92.6% and 91.8%. Adding auxiliary series improved the GBQR over the development period. However, on the original scale the combination's edge over ridge regression was not statistically clear, and in some low-level rising phases the combination did worse than ridge. Conclusions. Over the period studied, models that share information across regions scored better than a persistence forecast. For public-health use, however, the operative questions are the real lead time once reporting delay is accounted for, whether rises from low levels and threshold exceedances are caught in time, and whether the upper prediction intervals are wide enough; on these, the present evidence is not yet sufficient. The 2025 change to the sentinel system also means that pre-change advisory and warning thresholds cannot be read mechanically. A prospective evaluation with strictly controlled data availability is needed before such forecasts are used to support advisories or planning.

Brazil has one of the highest dengue burdens globally, with transmission influenced by climate events. Yet, the effects of compound climate extremes on dengue risk and prediction have not been quantified to date. Using a Bayesian spatiotemporal model of monthly dengue cases across 435 health regions from 2010 to 2025, we estimated relative risk associated with interacting long-lag and short-lag drought indicators and temperature anomalies across nine Koppen-Geiger climatic zones, adjusting for absolute temperature and El Nino-Southern Oscillation. Long-lag dry, mid-lag warm and short-lag wet conditions were associated with the highest relative risk in five climatic zones, with temperature anomalies modifying hydrometeorological dynamics more strongly in temperate than tropical areas. Climate covariates improved the prediction of case counts and outbreaks compared to baseline models at lead times of 1, 3, 6 and 12 months, and for the upcoming season 5 months ahead. Improvements were greatest in the Northeast, Southeast, and Midwest. Recent dengue incidence provided the greatest improvement at the 1-month lead, whereas combining incidence with climate information performed best at 3- and 6-month leads. We extended our framework to weekly state-level predictions of the season ahead to align with national public health planning. We proposed a multi-staged dengue forecasting approach to enhance early warning and response across Brazil.

Simulation models are widely used to support counterfactual inference in settings where direct experimental validation is difficult or infeasible. However, for phenomenological models calibrated to observational time series, goodness-of-fit does not necessarily establish that the structural assumptions underlying such inferences are uniquely supported by the data. Here, we examine this issue using a published COVID-19 transmission model for Japan that incorporates empirical inputs including human mobility, variant prevalence, and vaccination and has been used to estimate the effects of alternative vaccination scenarios. Using the publicly archived code together with a reconstructed substitute for a restricted-access input file required by the released implementation, we first reproduced the published model behaviour. We then performed two complementary computational stress tests. First, the vaccination-related input was suppressed and the remaining parameters were re-estimated against the original epidemic trajectory. Second, as a complementary test of trajectory-level flexibility, the model was refitted to synthetic target series with qualitatively different temporal structures while retaining the empirical mobility and variant inputs. The model retained substantial fitting capacity in both tests: suppression of the vaccination-related input caused only a limited deterioration in fit to the original trajectory, while qualitatively different synthetic trajectories could also be reproduced through parameter adjustment. Variation in fitted parameters across these conditions was also consistent with compensatory parameter behaviour. These results do not imply that vaccination had no effect or that the original model is invalid. Rather, they provide empirical evidence that, in this case, goodness-of-fit to the observed epidemic trajectory alone does not uniquely support the structural interpretation required for vaccination-related counterfactual inference. The study illustrates the value of reproducible computational stress testing as a complement to, rather than a substitute for, formal identifiability or model-comparison analyses.

Non-exponential residence times give rise to history-dependent or memory effects in epidemic dynamics, yet their mathematical formulations remain fragmented. Here we develop a unified framework for a history-dependent SEIR model with general latent- and infectious-period distributions. We first establish the equivalence between the stage-age and renewal formulations and show that both admit a unified transition-flow representation. We then introduce the concept of a transition-flow multiplier, which provides a direct link between residence-time distributions and transition flows. When the multiplier is a weighted sum of shifted power functions, the model admits an exact representation in terms of Riemann-Liouville or tempered Riemann-Liouville fractional operators, providing a first-principles foundation for fractional epidemic models. Finally, using Euler-Lotka equations and stochastic-order arguments, we characterize how the latent- and infectious-period distributions shape epidemic growth and decay. A longer latent period slows both early growth and late decay, while, at fixed means, greater variability in the latent period accelerates early growth but slows late decay, whereas greater variability in the infectious period slows both.

The contribution of environmental reservoirs to antimicrobial resistance (AMR) acquisition in humans remains poorly quantified, despite extensive evidence that resistant bacteria and resistance genes circulate through wastewater, surface waters, food and other environmental matrices. The central difficulty is no longer to demonstrate that these reservoirs are connected, but to quantify the direction and relative contribution of the pathways linking environmental AMR to human acquisition. Here, we synthesize the main processes that need to be represented at the interface between the environment and humans and propose a mechanistic framework for Escherichia coli producing extended spectrum beta lactamases (ESBL Ec), linking human, water and food compartments. The framework explicitly represents contamination of the environment by humans and multiple routes of human exposure to environmental reservoirs, including drinking water, recreational water exposure and food contamination mediated by irrigation. We apply the model to two contrasting illustrative settings, France and Madagascar, and use scenarios involving antibiotic use, environmental contamination and climate to explore how changes in specific drivers propagate across compartments. The simulations show responses that depend strongly on the compartment, while sensitivity analyses identify wastewater treatment efficiency and parameters describing transmission, exposure and environmental selection at specific interfaces as influential components of the system. The framework is used to explore how different transmission pathways and environmental drivers can shape AMR dynamics across interconnected human and environmental compartments. By comparing contrasting scenarios and identifying the parameters that most strongly influence model behaviour, we highlight both potential leverage points and critical gaps in current empirical knowledge. More broadly, this work illustrates how decomposing One Health AMR into tractable reservoir interfaces can help structure future data collection and progressively improve our ability to quantify transmission between reservoirs.

Infectious disease forecasting supports public health preparedness and response by informing resource allocation, intervention planning, and situational awareness. Infectious disease forecasting capabilities in the United States have grown over the past decade yet whether forecast quality has improved over this time remains unclear. Current evaluation metrics describe relative performance for a common set of forecast targets but cannot evaluate performance across different outcomes, seasons, and pathogens. We addressed this challenge by developing a novel skill score metric to assess forecasts based on performance relative to two benchmark models: a hindcast as an idealized upper bound of performance and a baseline model as a minimal performance standard. To study the impact of the baseline choice, we considered three types of baseline models with varying levels of complexity. Using this skill score framework, we retrospectively evaluated short-term influenza (influenza-like illness and hospitalization) and COVID-19 (case, death, and hospitalization) forecasts, focusing on how the performance of team and multi-model ensemble forecasts changed over time. The ensemble outperformed 99% of forecasts from a simplistic, naive baseline, and 84-91% of forecasts from more complex baseline models that incorporate recent data. We found consistent improvements in ensemble and team forecast skill over time for both influenza forecast targets, with 4-week horizon forecasts in the most recent year having higher skill scores than 1-week horizon forecasts in the first year of each challenge. Results were mixed for COVID-19 forecasts. This framework provides a comprehensive picture of progress in infectious disease forecasting over the past decade enabled by improved standardization, data, models, and collaboration.

Introduction Despite the significant burden of Shigellosis, routine surveillance systems in resource-limited settings lack predictive capabilities to guide proactive public health responses. Consequently, we developed and evaluated the accuracy, uncertainty and computational cost of multi-horizon forecasting models for Shigella incidence among children <5 years in rural western Kenya using 16-year longitudinal incidence time-series. Methods We estimated monthly Shigella incidence using data from three pediatric enteric studies (2008-2024), supplemented with Kenya Health Information System during inter-study gaps. Incidence estimates incorporated sampling, healthcare-seeking, stool-testing, and pathogen-specific weights, with bootstrap-derived 95% confidence intervals. Predictors included Google Trends health-related indicators, climate, temporal trends, and seasonality. Forecasts at 1-, 3-, and 6-month horizons were evaluated using rolling-origin validation. Following horizon-specific predictor selection using Boruta, 14 state-space, machine-learning, and hybrid models were compared using point-forecast and probabilistic metrics, bootstrap confidence intervals, and Model Confidence Sets. Adaptive conformal prediction recalibrated prediction intervals, while Shapley Additive exPlanations assessed predictor contributions. Results Mean monthly Shigella incidence was 2.51 per 100 child-years (standard deviation=1.85). Shigella dynamics in Kenya can be forecast beyond seasonal persistence alone for up to six months reducing absolute errors by ~ 33%. However, optimal model architectures and predictor sets shift systematically across forecasting horizons. Temporal trend and recent incidence were the dominant predictors, while predictor importance shifted with forecast horizon toward syndromic, pharmaceutical, seasonal, and lagged environmental signals. Extreme Gradient Boosting performed best at 1 month (Root mean squared error [RMSE]=1.86), remained competitive at 3 months (RMSE=1.89), whereas exponential smoothing (ETS) performed best at 6 months (RMSE=2.05). Forecast accuracy declined with increasing horizon. ETS and TBATS provided the best-calibrated prediction intervals, whereas machine-learning models generally under-covered. Model Confidence Sets demonstrated horizon-dependent model distinguishability. Conclusion Horizon-specific model selection balancing predictive accuracy, uncertainty calibration, and computational feasibility could strengthen operational early warning systems for shigellosis.

Implementing vector control interventions against infectious diseases is operationally challenging, with delivery and population uptake taking considerable time within a community. Studies have suggested that the slow mass drug administration (MDA) of the endectocide, ivermectin, may have contributed to the lower epidemiological impact against malaria observed in recent cluster-randomised controlled trials, although evidence supporting this hypothesis is lacking. It is unclear whether interventions which have short but highly effective durations of entomological impact are more impactful than tools which kill the same number of mosquitoes but over a longer period. Transmission dynamics mathematical models can be used to explore the trade-offs between product profiles and investigate how different implementation periods could influence disease control. First, we use a simple vector-borne disease compartmental model to explore the epidemiological impact of killing the same number of mosquitoes over 10, 30 or 90 days. We find that for different mosquito-killing periods, the total number of cases averted is similar, although the observable impact may depend on when efficacy is assessed. Secondly, we adapted an existing malaria transmission model and simulated the fast and slow deployment of an effective endectocide MDA campaign in different seasonal settings. In perennial and seasonal settings and for a given coverage, slow MDA deployment averts more clinical cases of malaria than a fast synchronised distribution (completed in a single day, although the difference was modest). The benefits of the intervention depend on how well-timed a deployment is relative to the peak of the transmission season, with the efficacy of an ivermectin-like endectocide MDA being up to approximately 42% lower if started late. The work indicates that the time taken to complete MDA campaigns is unlikely to drive major differences in the effectiveness of short-lived endectocides and showcases how transmission dynamics models can be used to support epidemiological trial interpretation.

Immunity against Plasmodium falciparum malaria is acquired slowly and incompletely through repeated infections, leading to differing levels of protection among individuals based on their history of exposure to the disease. Since malaria is a climate sensitive vector-borne disease, immunological history is shaped by the local climate. Nonetheless, compartmental malaria models, which may be used to inform policy, implicitly represent immunity as a population-average trait --- an assumption that can lead to incorrect estimates of clinical prevalence by age, and of the overall burden of malaria. As the rate at which immunity is acquired depends on transmission intensity, and therefore on background climatic conditions, the error introduced by this assumption is expected to be climate-sensitive. We quantify how individual exposure histories shape community-level prevalence, and identify the age groups for which resolving immunity individually matters most across climatic regimes. For this, we introduce VECTRI-ABM, a grid-based framework coupling the climate-driven vector component of the VECTRI model to an agent-based representation of the human population, resolving immunity at the individual level. The framework was calibrated against entomological and epidemiological observations from Dielmo and Ndiop (Senegal, 1990--2003) and evaluated against reported entomological inoculation rates, Malaria Atlas Project estimates of parasite rate in children aged 2--10, and confirmed cases from Senegal's National Malaria Control Program (PNLP). We then applied it across Senegal and The Gambia, which encompass a pronounced Sahelian-to-tropical rainfall gradient, and compared simulations in which immunity develops individually against a counterfactual where agents carry the local population-average immunity, thus mimicking the assumption made by standard models. Results show that the averaging approach misallocates burden across age, underestimating clinical prevalence in children, especially so in the wettest high-transmission regions, and overestimating adult asymptomatic reservoirs, with implications for transmission-targeting interventions. We find that the simpler approach to immunity is adequate for immunologically mature populations, typically above 15 years of age, and that the error it introduces elsewhere is climate-dependent.

We propose a stochastic epidemic model to describe the spread of avian influenza in a network of poultry farms. We consider three main paths of infection transmission between poultry farms: from nearby farms, mainly through airborne diffusion; from farms belonging to the same company, through shared veterinarians, forage providers and similar; from undetected small farms or wildlife. From the parameter estimation, based on a modified Expectation-Maximization algorithm, we infer that approximately 63% of the farms were infected from nearby ones, with an infection force declining with distance; of these, more than one third belonged to the same company of the estimated infector. About 20% were infected from premises belonging to the same company but farther away than the distance threshold of 2 km; the remaining ones from wildlife or unidentified sources. These estimates have been validated by a comparison with genetic data, available for a subset of the farms: the genetic distance between two farms identified, with high probability, as an infector-infectee pair is much lower than between random pairs. These results may help in the implementation of tailored prevention and control measures for future outbreaks.

Background: Excess mortality estimates depend on an expected baseline, an unobservable quantity typically derived from models with prescribed functional forms and short reference periods that exclude exceptional events. Singular Spectrum Analysis (SSA) offers a nonparametric alternative but requires subjective component selection and is sensitive to outliers. We combined SSA with automated component selection and outlier interpolation to estimate long-term baselines and compile a catalog of excess mortality events (EMEs). Methods: We analyzed weekly all-cause mortality (2015-2024) for six metropolitan areas with contrasting climatic, demographic, and epidemiological contexts: Brussels, Antwerp, and Liege (Belgium), and Athens, Thessaloniki, and Larisa (Greece). SSA was applied to standardized log-mortality rates; components were selected using Monte Carlo SSA, and COVID-19 periods were interpolated iteratively. EMEs were defined as weeks exceeding a quasi-Poisson two-z-score threshold, then screened for coincidence with heat waves, cold waves, COVID-19 periods, and influenza epidemics. Results: Baselines were consistent within countries: annual cycles in Belgium, with an additional significant semiannual cycle in Greece reflecting summer mortality. We cataloged 170 EMEs spanning 456 weeks and 14,210 excess deaths. Pandemic weeks accounted for 77% of excess mortality, and 96 weeks coincided with temperature extremes; 56 EMEs, representing 3.7% of excess mortality, remained unmatched. Conclusions: SSA yields reproducible, low-assumption baselines that capture country-specific seasonality. Because flexible baselines can absorb recurrent seasonal mortality, component selection is a substantive choice that should be reported explicitly, together with its sensitivity.

We develop an age- and immunity-structured COVID-19 model with setting-specific contacts, calibrated to Ontario incidence and hospital admissions before Omicron. We simulate counterfactual vaccination strategies and contact restrictions in schools, workplaces, and other community settings, evaluating projected severe-case demand against an illustrative healthcare-capacity benchmark. Vaccination combined with workplace and community contact restrictions keeps simulated demand within capacity. In contrast, relaxing restrictions in either setting produces large surges and repeated capacity threats, even when school restrictions remain. School-only changes produce smaller effects on severe disease. These findings highlight the value of vaccination and setting-specific contact restrictions that prioritize workplace and community settings for pathogens with adult-concentrated severe outcomes.

In England and Wales, Usutu virus (USUV) was detected in 2020 and is now established. West Nile virus (WNV) has so far only been detected in a single location, in two Aedes vexans mosquito pools sampled during 2023 surveillance. Given the growing threat to the UK from Culex-borne viruses there is a need to undertake risk assessments to contribute to preparedness and surveillance strategy for both viruses across human and animal health sectors. Here, we present the first application of a mechanistic R0-based model, incorporating temperature dependence and combining data on vector species distribution with projected future climate scenarios, to assess transmission suitability for WNV and USUV across England and Wales. Under current climate conditions, the two viruses showed comparable spatial patterns, with risk concentrated most heavily in the southeast along the Thames estuary and extending into Kent, Essex and Surrey, as well as around Cambridgeshire and adjacent counties, and across Yorkshire, Nottinghamshire and Lincolnshire. This pattern broadly corresponded with the cumulative distribution of Culex mosquito abundance established in our earlier work (Culex pipiens pipiens, Culex pipiens molestus and Culex torrentium). Both viruses were projected to show increasing transmission suitability risk over time, with substantially steeper increases under Representative Common Pathway (RCP) 8.5 then RCP 4.5. Equine WNV cases were predicted to be most likely in southeast England, in particular around Newmarket, driven by the region's dense horse population. Among the human population, London was identified as the highest-risk area, with Liverpool and Manchester also showing elevated risk levels, driven by a combination of high human and mosquito distributions. When human population distributions were age-adjusted to account for the proportion of over 60 years-olds, there was elevated risk in coastal regions, particularly in the east (Norfolk and Suffolk) and southeast (Kent) of England. The geographic distribution of positive mosquito and bird USUV cases recorded in 2023 and 2024 broadly overlapped with areas predicted to have higher USUV suitability, providing validation for the model's spatial outputs. By characterising relative spatial transmission suitability risk, this work adds to a growing understanding of where arbovirus emergence and spread are most likely across England and Wales, and can support decisions around resource allocation, targeted surveillance efforts, and public health readiness. The results also underscore the importance of incorporating climate change and vector ecology into future modelling work, given their likely influence on how arbovirus risk evolves over time.