Infectious Disease Modelling

Conventional compartmental models may become difficult to parameterize over multi-wave epidemic horizons when susceptibility and transmission conditions change between successive epidemic regimes. This study introduces a reduced macroscopic framework that changes the scale of epidemic description from individual-level transmission structure to effective population-level dynamics. Epidemic waves are formulated as transport processes governed by deterministic boundaries, mass balance, and a small set of effective macroscopic parameters. The susceptible population is represented by a dynamic Effective Susceptible Pool that can be re-initialized at transitions between biologically distinct epidemic regimes, while transmission resistance and external control measures are incorporated at the macroscopic level. The resulting equations admit a dimensionless similarity representation and closed-form analytical solutions, enabling analytical estimation of epidemic trajectories and peak healthcare demand without computationally intensive numerical simulation. The framework is evaluated using comparative time-series data for SARS-CoV-2 and seasonal influenza A within the geographically and demographically consistent setting of Rhode Island. Despite their different biological and immunological regimes, the analyzed trajectories exhibit a common reduced asymptotic scaling form. The results support the use of a macroscopic, scale-reduced representation for cross-calibration of heterogeneous surveillance signals and analytical assessment of healthcare-system demand. Further validation across pathogens, populations, and open-system settings is required.

In climate-health impact projection studies, projected impacts from multiple general circulation models (GCMs) are commonly aggregated by reporting the mean of GCM-specific impacts as the point estimate alongside a 95% empirical confidence interval (eCI) constructed from the 2.5th and 97.5th percentiles of the simulated pooled distribution of GCM-specific impacts. This study shows that the eCI generally does not yield the nominal coverage probability for the GCM-ensemble mean and constructs an interval aligned with the estimand. In a simulation study, the coverage of the eCI for the GCM-ensemble mean deviates from the nominal level in both directions, whereas the aligned interval yields coverage near 95% across all considered settings. The exact coverages derived analytically under a location-shift model agree with the simulation results. In a reanalysis of a heat-related mortality projection in London, the eCI is consistently wider. The eCI should be distinguished from confidence intervals for the GCM-ensemble mean; rather, the interval may be better described as a simulation-based approximate prediction interval for a GCM-specific impact under the uniformly randomly selected GCM from the considered GCM set.

Dengue transmission is shaped by multiple viral serotypes, temporary cross-immunity (TCI), antibody-dependent enhancement (ADE), and repeated exposure in endemic populations. Classical multi-strain models usually assume lifelong protection against reinfection with the same serotype. However, recent evidence suggests that homologous dengue reinfections, although rare, can occur. Their population-level consequences remain poorly understood. We extend a two-infection, two-strain dengue model with TCI and ADE-mediated transmission differences to include homologous reinfections. Homologous reinfection is represented by two exploratory parameters: relative susceptibility to reinfection with the same serotype and relative infectiousness during homologous reinfection. Using equilibrium analysis, bifurcation diagrams, simulations, and phase-space projections, we examine how these parameters affect dengue dynamics and interact with TCI duration and seasonal forcing under intermediate and long TCI durations, with and without seasonality. The extended model shows that qualitative dynamics characteristic of endemic dengue transmission are reproduced mainly when susceptibility to homologous reinfection is low, so that homologous reinfections remain rare but dynamically influential. Longer TCI broadens regions of complex oscillatory dynamics, while seasonality shifts the bifurcation structure and makes torus bifurcations a central route to complex behavior. Although backward bifurcation can occur when homologous susceptibility exceeds the biologically meaningful range, this result should be interpreted as a mathematical mechanism rather than a realistic dengue scenario. These results indicate that rare homologous reinfection pathways can influence long-term dengue dynamics when interacting with immune history, TCI, ADE-mediated transmission differences, and seasonal variation. Incorporating such pathways may improve understanding of recurrent outbreaks and irregular incidence patterns in highly exposed populations.

Background: Tuberculosis remains a persistent infectious disease whose control is complicated by latent infection, delayed treatment, incomplete recovery, reinfection, and continuing transmission from infectious individuals. Although treatment is central to tuberculosis management, it is frequently represented only as a transition parameter in mathematical models rather than as a separate epidemiological state. In this study, treatment was therefore incorporated explicitly as an independent compartment so that its influence on transmission, recovery, disease-induced mortality, and long-term disease persistence could be evaluated. Methods: A deterministic nonlinear compartmental model was formulated by dividing the total population into susceptible, exposed, actively infected, treated, and recovered classes. Reinfection of recovered individuals, progression from latent infection to active disease, movement of infectious individuals into treatment, treatment-associated recovery, natural mortality, and disease-induced mortality were included. Positivity and boundedness of the solutions were examined to establish biological validity. The basic reproduction number, R0, was derived through the next-generation matrix approach. Disease-free and endemic equilibria were determined, and their local and conditional global stability properties were investigated using Jacobian analysis, the Routh-Hurwitz criterion, center manifold theory, Lyapunov functions, and LaSalles invariance principle. Normalized sensitivity indices, Latin hypercube sampling, partial rank correlation coefficients, and numerical simulations were also applied. Results: The disease-free equilibrium was shown to be locally asymptotically stable when ,R0<1 whereas sustained transmission and a unique endemic equilibrium were associated with R0>1. Under the stated reduced-model assumptions, stability of the endemic equilibrium was established. Transmission-related parameters were identified as the strongest positive contributors to disease persistence. In contrast, treatment and recovery parameters were found to reduce the reproduction number and infectious burden. Numerical simulations indicated that stronger treatment implementation and reduced transmission opportunities produced substantial reductions in active tuberculosis cases. Conclusion: Treatment was shown to function as both a clinical pathway and an epidemiological control mechanism. The proposed framework may support the design of treatment-centered strategies for reducing tuberculosis prevalence and preventing long-term endemic persistence.

The initial response of populations to the SARS-CoV-2 virus reduced the incidence of COVID-19 cases. However, this success was shorted lived once most populations relaxed most restrictions, resulting in an increase in infections. This feedback contributed to additional pandemic waves. The temporal unfolding of behavioural changes in populations present a challenge to mathematical models for disease dynamics. Coupled behaviour-disease models with varying levels of complexity accounting for several factors have been used to capture behavioural dynamics and SARS-CoV-2 transmission, with varying results. To study the impact of model complexity on the predictive power of models, here we formulate five coupled behaviour-disease models with varying structure and number of parameters. We fit the models to SARS-CoV-2 infection incidence and stringency of control interventions from five European countries in the first wave, and study how well these fitted models predict the second wave. We show that models with more parameters do not necessarily have a greater ability to explain and predict key features of a pandemic wave. Hence, our results show that a relatively simple coupled behaviour-disease model with important parameters can do an adequate job of providing information about the pandemic wave. Additionally, our findings show that complex models can be country-specific, working better for some countries and poorly for others. We conclude that modellers should not always opt for the most complicated possible models, if the data do not support their use.

Malaria is still one of the most dangerous causes of morbidity and mortality in tropical and subtropical regions even though it has been actively combated for many years. In 2023, there were approximately 263 million malaria cases and 597,000 deaths from this disease on a global scale, with sub-Saharan Africa being the region most affected by it [24]. Climate factors affect mosquito biology, including their abundance, survival, and biting rates, as well as parasite development, while human awareness plays a crucial role in adopting preventive measures and effective treatments. Despite the progress in both climate- and awareness-based malaria modelings, few studies integrate these factors in one comprehensive model that involves the detailed mechanisms of transmission processes. The current study develops a deterministic climate-driven SEAIR-SEI malaria transmission model that includes the impact of temperature, rainfall, and humidity on mosquito biology and endogenous community awareness. The model was proven to be well-posed by showing the positivity and boundedness of its solution and through the demonstration of the existence and uniqueness of its solution. The malaria-free equilibrium was determined, and the basic reproduction number was calculated using the next-generation matrix method. The model underwent local and global stability analyses to characterise the diseases persistence in the population. Additionally, a normalized forward sensitivity analysis was conducted, revealing the mosquito biting rate as the key force driving malaria transmission. Four time-dependent malaria interventions, namely, long-lasting insecticidal nets, community awareness campaigns, indoor residual spraying, and prompt treatment, were included in the model through optimal control theory and analysed using Pontryagins Maximum Principle. The numerical results for the optimal control problem showed that employing all four interventions leads to the best outcome by decreasing the objective functional value by 88.17%, reducing the total number of infected humans by 92.49%, and minimizing the total number of infectious mosquitoes by 93.87%. Interestingly, combining two interventions, indoor residual spraying, and prompt treatment, also yielded nearly optimal results. Therefore, the designed control strategy can serve as an efficient and affordable framework for malaria control in sub-Saharan Africa.

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.

Background Dengue is a major public health challenge, and predictive models are crucial for early warning systems. However, many current modeling practices rely exclusively on climatic factors or employ complex algorithms that lack the interpretability needed for informed public health decision-making. To address these shortcomings, we developed and validated a multidimensional, interpretable statistical model to predict monthly dengue incidence. Methodology/Principal Findings We used a Generalized Linear Mixed Model (GLMM) with a Negative Binomial distribution to analyze 14 years (2010-2023) of spatiotemporal data from 37 municipalities in Huila, Colombia, an endemic region. The model integrates non-linear and lagged effects of climatic, demographic, and socioeconomic factors. The final model underwent rigorous external validation on an independent test set (2021-2023). Our model demonstrated high predictive discrimination (R2 = 0.743, Spearman's {rho} = 0.657), accurately capturing the timing of epidemic outbreaks. Key findings include the identification of an optimal thermal window for transmission at 27-28{degrees}C, a threshold effect for precipitation above 800 mm, and a saturation dynamic in outbreak autocorrelation. Conclusions/Significance This mechanistically-informed statistical approach provides a robust and transparent tool for epidemiological surveillance, successfully balancing high predictive performance with the explanatory power needed for effective, data-driven public health interventions.

Mosquito-borne diseases pose a growing public health challenge as climate change reshapes vector population dynamics. West Nile virus (WNV), transmitted between birds and Culex mosquitoes, disproportionately affects Maricopa County, Arizona, one of the nation's highest-burden counties, yet whether models that include weather and avian dynamics improve forecast accuracy remains unclear. Using a 15-year weekly time series of mosquito abundance, mosquito infection prevalence, and human cases, we developed four mechanistic model configurations of varying complexity, from mosquito-human dynamics alone to full models incorporating avian dynamics and weather forcing. We fitted each model to the weekly-observed data, generated probabilistic 1- and 2-week-ahead forecast horizons, and evaluated forecasts against a historical baseline. All configurations fit the data equally regardless of weather or avian dynamics. However, models incorporating both birds and weather created more accurate forecasts of mosquito abundance and mosquito infection prevalence, and all configurations outperformed the baseline for forecasting human cases. Forecast accuracy was highest in summer and fall, and ensemble aggregation sometimes outperformed every individual model, stabilizing predictions across the 15-year record. These findings indicate that avian and weather dynamics are most critical for predicting mosquito-specific data, positioning this framework as a scalable tool for public health planning for WNV surveillance under climate change.

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.

Japanese encephalitis virus (JEV) transmission involves complex interactions among Culex mosquitoes, amplifying pig hosts, reservoir wading birds, humans, and environmental conditions, complicating quantitative assessment of transmission dynamics and intervention effectiveness. We developed a deterministic, fourteen-compartment One Health mathematical framework that integrates these interconnected host vector populations and their epidemiological states. The model incorporates temperature-dependent mosquito biting, seasonal transmission, human vaccination, pig biosecurity, environmental barriers, and mosquito-control interventions. Mathematical properties were established through analyses of non-negativity, boundedness, biologically feasible equilibria, local and global stability, and optimal control. District-specific simulations were conducted for Bellary, Udupi, Kolkata, and Purba Bardhaman during the August transmission period. Intervention scenarios were evaluated, and global sensitivity analysis was performed using 500 Latin hypercube samples with partial rank correlation coefficients. Model outputs were also compared with district-level surveillance observations. Vaccination-adjusted basic reproduction numbers were 0.905 in Bellary, 0.965 in Udupi, 1.817 in Kolkata, and 0.885 in Purba Bardhaman, with only Kolkata exceeding the epidemic threshold. Under maximum intervention, total infections decreased by 80.6%, 96.8%, 80.5%, and 72.2%, respectively, while infected mosquito populations declined to zero across all four settings. In Kolkata, vaccinating 3.6 million individuals with dose series II reduced the reproduction number from 1.817 to 0.9846, whereas population-wide dose series I vaccination alone was insufficient to reduce it below unity. Sensitivity analysis identified mosquito recruitment, temperature-dependent biting, carrying capacity, mosquito mortality, density-dependent regulation, and mosquito-to-human transmission as major determinants of peak human infection. Overall, the framework demonstrates heterogeneity in JEV transmission and intervention effectiveness and provides a mathematically grounded One Health approach for comparative evaluation of integrated control strategies.

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.

Vaccines, by reducing bacterial infection, transmission and/or colonisation, are promising investments against the global rise of antibiotic resistance (ABR). From a public health perspective, while efforts are put in developing bacterial vaccines, anticipating their potential impact on ABR is essential. We developed a compartmental model formalising inter-individual transmission and selection pressure through both bystander and targeted antibiotic exposure. Following a mathematical analysis of the model's equilibrium points, we explored the impact of different vaccines through simulations for two bacterial types. In simulations, vaccines consistently reduced infection incidence, although to varying extents. For S. aureus, a vaccine reducing acquisition rate, infection rate and colonisation duration by 60% at 70% coverage reduced total infections by 80%, while this reduction was only of 48% for E. coli. The impact on the resistance proportion among colonised differed markedly: this same vaccine increased it by 11% for S. aureus, while decreasing it by 8% for E. coli. Overall, our results highlight that population level impact on ABR strongly depends on the vaccine mechanism of action. The proposed model, which gathers the main drivers involved, provides a general framework that can be adapted to a wide range of bacterial pathogens and vaccines.

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: Dengue fever has become an epidemiological concern worldwide, with 531,617 cases reported in Argentina during the 2023-2024 season. The El Nino Southern Oscillation (ENSO) phenomenon has been linked to changes in dengue dynamics. This study assessed the relationships among ENSO, local climatic variables, and dengue outbreaks in Argentina from 2018 to 2024. Methods: A nationwide spatiotemporal analysis was conducted using dengue cases and monthly mean temperature, relative humidity, and rainfall. The Southern Oscillation Index (SOI) was used as the ENSO indicator (El Nino phase=< minus0.5, neutral phase= minus0.5 to 0.5, and La Nina phase= > 0.5). We fitted Hierarchical Bayesian models with Integrated Nested Laplace Approximation (INLA). A Negative Binomial distribution of dengue cases was used to account for overdispersion. Spatial dependence was modeled by using the Besag York Mollie 2 (BYM2) structure, adjusted for population. Results: The final model explained 53.7% of the variation in Argentinas departmental monthly dengue cases during the study period. Exposure response curves of posterior probability and 95% credible intervals suggested that dengue risk increased when monthly mean temperatures were between 20 and 30 degrees Celsius during the El Nino or neutral phase, with less consistent effects during the La Nina phase. Humidity >50% was associated with increased dengue risk during the La Nina phase, with less steep or absent associations during El Nino or neutral phases. Negative SOI scores were associated with an increased risk of dengue after controlling for the covariates. Conclusion: Dengue outbreaks in Argentina were independently associated with temperatures between 20 and 30 degrees celsius during the El Nino and neutral phases, humidity > 50% during the La Nina phase, and the El Nino phase of the ENSO. Incorporating ENSO indices into predictive models could enhance early warning systems and timely public health interventions in Argentina.

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.

In 2026, a Bundibugyo ebolavirus (BDBV) outbreak emerged in the Democratic Republic of the Congo (DRC), with 4,566 confirmed cases and 2,128 deaths reported as of 11 August, potentially becoming the largest Ebola outbreak on record globally. We developed a susceptible-exposed-infectious-deceased-recovered (SEIDR) model incorporating incorporating three categories of interventions, public self-protection, safe burial, and treatment and convalescence, to assess early transmission dynamics, the current epidemic trajectory, and cross-border spillover risk, and to inform the formulation of control strategies. Based on cumulative confirmed case data up to 31 July, sensitivity analyses across multiple candidate start dates identified 28 March as the optimal start date of sustained transmission, with 31 March to 3 April as the most likely onset window. As of 31 July, the basic reproduction number (R0) was 1.83 (95% CI: 1.81-1.84). When 58.12% of the susceptible population adopted protective behaviours, the transmission chain could be effectively interrupted. By integrating the non-dominated sorting genetic algorithm II (NSGA-II) with Pontryagin's minimum principle (PMP), we derived a time-varying optimal control strategy, with adjustments every two weeks, that could shorten the epidemic duration by approximately 7 months. Using International Migrant Stock data and Facebook IP-based mobility data with the Prophet forecasting model, we assessed spillover risk. Four countries were identified as very high risk at the end of July. Compared with the status quo scenario, the optimised control strategy could substantially reduce global importation risk. Enhanced entry screening and preparedness are warranted in neighbouring countries of the DRC in Africa, France in Europe, and Canada in North America.

Since the COVID-19 pandemic, forecasting hubs and non-traditional respiratory disease surveillance streams have become increasingly common. However, many forecasting approaches assume that relationships between surveillance predictors and disease outcomes remain stable over time and that incorporating additional historical data will improve forecast performance. To evaluate these assumptions in a real-world setting, we developed and evaluated forecasts of SARS-CoV-2 and influenza hospitalizations in Utah using syndromic surveillance, test positivity, and wastewater data. Rather than identifying a single, best-performing model, we examined whether relationships between surveillance predictors and hospitalization outcomes remained stable across seasons and whether longer historical training periods consistently improved forecast accuracy. Relationships between surveillance predictors and hospitalizations varied substantially by pathogen and season. Analyses using pooled data across multiple years suggested strong positive correlations between predictors and outcomes, but these aggregated patterns often obscured weak or negative correlations observed during SARS-CoV-2 variant waves and influenza seasons. Forecast performance similarly varied over time. Models that performed well during some seasons, transmission phases, or under certain training strategies frequently performed worse than benchmark models in others. Training on additional historical data generally reduced forecast accuracy, though this varied by disease and transmission phase. Forecasting groups should prioritize continual evaluation of surveillance predictors, adaptive strategies, and diverse ensembles, rather than relying on a single model, data stream, or historical training framework each year.

Violent conflicts increasingly involve multiple armed actors competing for influence over shared civilian populations, creating complex dynamics that challenge conventional security analysis and policy design. We present a framework that adapts epidemiological methods informed by the conflict landscape in Nigeria to model multi-actor violent conflict as an epidemic process. We derive a basic insecurity reproduction number ($R_0$), identify violence-free and persistent-violence equilibria, and introduce a novel Civilian Harm Index (CHI) to quantify humanitarian impact. Sensitivity analyses identify recruitment, ideological support from civilian populations, and abduction as the key drivers of conflict persistence and civilian harm. The framework reveals several counterintuitive findings. Interventions that most effectively suppress violence transmission are not necessarily those that minimise civilian harm, demonstrating that epidemic control and humanitarian protection may require distinct optimisation criteria. Likewise, interventions effective against one armed actor may be ineffective, or even counterproductive, when applied uniformly across groups. In addition, prisoner exchange and ransom payments increase violence persistence and civilian harm. Although developed as an illustrative rather than predictive framework, our results show that epidemiological methods provide quantitative metrics for evaluating intervention priorities and trade-offs in complex multi-actor conflicts.