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Epidemic models play a critical role in understanding transmission dynamics, generating forecasts, and informing public health interventions when they are properly calibrated to epidemiological data. Traditional Bayesian inference methods rely on the likelihood function to update prior knowledge using observed data. However, for realistic epidemic models, likelihood functions are often analytically intractable or computationally prohibitive, which can limit the applicability of these methods. Simulation-based inference provides a promising alternative by approximating posterior distributions through forward simulations rather than an explicit likelihood evaluation. In this study, we present a systematic comparison of four approaches: Approximate Bayesian Computation (ABC), Neural Posterior Estimation (NPE), a neural method with temporal embedding, and Preconditioned Neural Posterior Estimation (PNPE), which integrates elements of both classical and neural techniques. These methods are evaluated across epidemic models of increasing complexity under fixed simulation budgets and varying levels of observational noise, with explicit attention to both structural and practical identifiability. Our results show that neural methods generally improve posterior fidelity and predictive accuracy compared with ABC under constrained simulation budgets. PNPE achieved strong performance in several simulation settings, whereas temporal embeddings improved inference in models with complex epidemic dynamics by capturing sequential dependencies. These gains come with important trade-offs: PNPE required substantially greater computational resources and, unlike fully amortized NPE-based methods, may require reconditioning for each new observation. In contrast, ABC remained computationally efficient and provided reasonable, though often more conservative, posterior estimates. Overall, our findings highlight trade-offs among computational efficiency, posterior accuracy, uncertainty calibration, and inference reusability, suggesting that method selection should depend on model complexity, data quality, identifiability, and available computational resources. 

Abstract BackgroundThe COVID-19 pandemic disrupted global tuberculosis (TB) control efforts, leading to a surge in TB-related excess mortality, particularly in low- and middle-income countries. Pandemic mitigation measures, such as lockdowns, reallocation of healthcare resources, and reduced access to diagnosis and treatment, contributed to delayed TB care and disease progression. Quantifying this collateral damage is crucial to bolstering health system resilience. MethodsWe estimated country-level excess TB mortality between 2020 and 2023 using annual TB mortality data reported by the World Health Organization (WHO). Our approach leverages the SubEpiPredict toolbox of the ensemble n-sub-epidemic modeling framework, calibrated to pre-pandemic trends (2010–2019) to forecast expected TB deaths in the absence of COVID-19 disruptions. We selected the best-fitting model based on AICc and compared projected and reported deaths to quantify excess mortality, incorporating both normal and Poisson error structures. We further examined associations between excess TB mortality and country-level indicators, including the COVID-19 Stringency Index, Global Health Security (GHS) Index, and Socio-demographic Index (SDI). It is also noteworthy that for each estimate of excess TB deaths, we provide an associated uncertainty. ResultsOur global estimate of 755,876 excess TB deaths (95%CI: 591,099 to 965,015) aligns closely with the WHO estimate of approximately 700,000 excess deaths. Therefore, it can be estimated a global relative excess mortality of 12.8% (95%CI: 11.6% to 14.2%), compared to the WHO estimate of 14.6% (95% CI: 5.9% to 26.7%). We found substantial geographic heterogeneity, with the highest TB excess mortality rates observed in southern Africa, South Asia, and parts of South America. Countries with high GHS or SDI scores did not necessarily exhibit lower excess TB mortality, suggesting that pandemic-specific disruptions and competing priorities may have overridden structural advantages. Weak-to-moderate correlations were observed between excess mortality and pandemic stringency, peaking in 2021 and waning by 2022, possibly reflecting health system adaptation. ConclusionThis study presents a systematic, model-based analysis of global excess TB mortality during the COVID-19 pandemic, emphasizing disparities in pandemic response impacts across countries. The findings highlight the need for integrated and resilient public health systems capable of maintaining essential services amid global crises. Our methodology can support real-time monitoring of collateral effects of pandemics on endemic diseases and guide strategic investments in TB surveillance and care continuity. 

BackgroundMany disciplines, such as public health, rely on statistical time series models for real-time and retrospective forecasting efforts; however, effectively implementing related methods often requires extensive programming knowledge. Therefore, such tools remain largely inaccessible to those with limited programming experience, including students training in modeling, as well as professionals and policymakers seeking to forecast an epidemic’s trajectory. To address the need for accessible and intuitive forecasting applications, we presentStatModPredict, an R-Shiny dashboard for conducting robust forecasting analysis utilizing auto-regressive integrated moving average (ARIMA), generalized linear models (GLM), generalized additive models (GAM), and Meta’s Prophet model. MethodsStatModPredictsupports robust real-time forecasting and retrospective model analysis, including fitting, forecasting, evaluation, visualization, and comparison of results from four popular models. After loading an incident time series data set into the interface, users can easily customize model parameters and forecasting options to obtain the desired output. Additionally,StatModPredictoffers multiple editable figures for, but not limited to, the time series data, the forecasts, and model fit and forecast metrics. Users can also upload external forecasts produced elsewhere and evaluate their performance alongside the dashboard’s built-in models, thereby enabling direct comparisons. We provide a detailed demonstration of the dashboard’s features using publicly available annual HIV case data in the US. A video tutorial is available athttps://www.youtube.com/watch?v=zgZOvqhvqw8. ConclusionsBy eliminating programming barriers,StatModPredictfacilitates exploration and use by students training in forecasting, as well as professionals and policymakers aiming to forecast epidemic trajectories. Additionally, the flexibility in the required input data structure and parameter specification process extends the application ofStatModPredictto any discipline that employs time series data. By offering this open-source interface, we aim to broaden access to forecasting tools, promote hands-on learning, and foster contributions from users across disciplines. 

Environmental transmission is a critical driver of cholera dynamics and a key factor influencing model-based inference and forecasting. This study focuses on stable parameter estimation and forecasting of cholera outbreaks using a compartmental SIRB model informed by three formulations of the environmental transmission rate: (1) a pre-parameterized periodic function, (2) a temperature-driven function, and (3) a flexible, data-driven time-dependent function. We apply these methods to the 1991–1997 cholera epidemic in Peru, estimating key parameters; these include the case reporting rate and human-to-human transmission rate. We assess practical identifiability via parametric bootstrapping and compare the performance of each transmission formulation in fitting epidemic data and forecasting short-term incidence. Our results demonstrate that while the data-driven approach achieves superior in-sample fit, the temperature-dependent model offers better forecasting performance due to its ability to incorporate seasonal trends. The study highlights trade-offs between model flexibility and parameter identifiability and provides a framework for evaluating cholera transmission models under data limitations. These insights can inform public health strategies for outbreak preparedness and response. 

This study utilized a clustering-based approach to investigate whether countries with similar COVID-19 dynamics also share similar public health and selected sociodemographic factors. The pairwise distances between 42 European countries for six characteristics were calculated, including COVID-19 incidence, mortality, vaccination, SARS-CoV-2 genetic diversity, cross-country mobility and sociodemographic data. Hierarchical clustering trees were constructed, and the strengths of association between the pairs of trees were quantified using cophenetic correlation and Baker’s Gamma correlation measures. The analysis revealed distinct patterns of agreement between clusterings. Vaccination clusterings showed moderate agreement with incidence but no strong agreement with mortality. Mortality-based clustering only agreed with population health clustering. Incidence-based clustering aligned with population health, genetic diversity and selected sociodemographic parameters. Genetic diversity clusterings agreed with mobility and related sociodemographic characteristics. The utility of the cluster-based methods for the time-series is illustrated, and these findings provide insights into the underlying mechanisms driving epidemiological disparities across localities and subpopulations.