Infectious Disease Modelling

Nipah virus (NiV) is a sporadic yet extremely deadly zoonotic pathogen, with reported case fatality rates of 40%-75% in impacted areas. Prolonged incubation, documented relapse, and delayed-onset encephalitis following apparent recovery indicate that NiV dynamics are influenced by intricate temporal processes. However, mechanistic contributions of these processes to epidemic persistence remain poorly understood. In this study, we develop and analyze a delay differential equation model for NiV transmission that explicitly incorporates incubation delay, relapse, and post-recovery delay effects. We compute a primary-transmission reproduction threshold (R0), characterize the disease-free and endemic equilibria, and analyze their stability, including delay-induced Hopf bifurcations. We show that relapse modifies the endemic-equilibrium existence condition, so an endemic equilibrium is not determined solely by the classical threshold criterion R0 = 1. We calibrate the model to NiV incidence data from Bangladesh (2001-2024) and perform simulations and sensitivity analyses to evaluate the effects of relapse and delays across epidemiological scenarios. Results indicate that sustained oscillations occur only under hypothetical parameter regimes, suggesting that delay-induced periodic outbreaks are unlikely under empirically informed conditions. Scenario analyses demonstrate that relapse and encephalitis-related delays predominantly influence post-peak dynamics, while incubation delay alters the time and intensity of the epidemic peak. We also introduce a relapse-driven replenishment fraction to quantify contribution of relapse to continued transmission, demonstrating its growing significance following the first outbreak peak. Overall, our results identify relapse as a key mechanism for epidemic persistence and underscore the importance of incorporating relapse and biological time delays into epidemiological modeling and public health strategies.

As a common vector-borne disease, dengue fever still remains a lot of challenges to forecast for which the significant distinction of epidemic scale is affected by multiple factors, such as mosquito density, meteorological conditions, geographical environment, travel and so on. To track down the epidemic scale and forecast the remaining time of epidemic season, the population size affected by the epidemic is evaluated before the compartmental model is optimized by assimilation observation with filtering method. In retrospective forecast of dengue pandemic for Guangzhou from 2014-2015 seasons, accurate forecast of dengue cases is generated with an accurate prediction of peak time in all time periods. The real-time forecast system shows a good performance on capturing the trajectory of dengue transmission and scale of epidemic.

Since the US reported its first COVID-19 case on January 21, 2020, the science community has been applying various techniques to forecast incident cases and deaths. To date, providing an accurate and robust forecast at a high spatial resolution has proved challenging, even in the short term. Here we present a novel multi-stage deep learning model to forecast the number of COVID-19 cases and deaths for each US state at a weekly level for a forecast horizon of 1 to 4 weeks. The model is heavily data driven, and relies on epidemiological, mobility, survey, climate, and demographic. We further present results from a case study that incorporates SARS-CoV-2 genomic data (i.e. variant cases) to demonstrate the value of incorporating variant cases data into model forecast tools. We implement a rigorous and robust evaluation of our model - specifically we report on weekly performance over a one-year period based on multiple error metrics, and explicitly assess how our model performance varies over space, chronological time, and different outbreak phases. The proposed model is shown to consistently outperform the CDC ensemble model for all evaluation metrics in multiple spatiotemporal settings, especially for the longer-term (3 and 4 weeks ahead) forecast horizon. Our case study also highlights the potential value of virus genomic data for use in short-term forecasting to identify forthcoming surges driven by new variants. Based on our findings, the proposed forecasting framework improves upon the available forecasting tools currently used to support public health decision making with respect to COVID-19 risk. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSA systematic review of the COVID-19 forecasting and the EPIFORGE 2020 guidelines reveal the lack of consistency, reproducibility, comparability, and quality in the current COVID-19 forecasting literature. To provide an updated survey of the literature, we carried out our literature search on Google Scholar, PubMed, and medRxi, using the terms "Covid-19," "SARS-CoV-2," "coronavirus," "short-term," "forecasting," and "genomic surveillance." Although the literature includes a significant number of papers, it remains lacking with respect to rigorous model evaluation, interpretability and translation. Furthermore, while SARS-CoV-2 genomic surveillance is emerging as a vital necessity to fight COVID-19 (i.e. wastewater sampling and airport screening), to our knowledge, no published forecasting model has illustrated the value of virus genomic data for informing future outbreaks. Added value of this studyWe propose a multi-stage deep learning model to forecast COVID-19 cases and deaths with a horizon window of four weeks. The data driven model relies on a comprehensive set of input features, including epidemiological, mobility, behavioral survey, climate, and demographic. We present a robust evaluation framework to systematically assess the model performance over a one-year time span, and using multiple error metrics. This rigorous evaluation framework reveals how the predictive accuracy varies over chronological time, space, and outbreak phase. Further, a comparative analysis against the CDC ensemble, the best performing model in the COVID-19 ForecastHub, shows the model to consistently outperform the CDC ensemble for all evaluation metrics in multiple spatiotemporal settings, especially for the longer forecasting windows. We also conduct a feature analysis, and show that the role of explanatory features changes over time. Specifically, we note a changing role of climate variables on model performance in the latter half of the study period. Lastly, we present a case study that reveals how incorporating SARS-CoV-2 genomic surveillance data may improve forecasting accuracy compared to a model without variant cases data. Implications of all the available evidenceResults from the robust evaluation analysis highlight extreme model performance variability over time and space, and suggest that forecasting models should be accompanied with specifications on the conditions under which they perform best (and worst), in order to maximize their value and utility in aiding public health decision making. The feature analysis reveals the complex and changing role of factors contributing to COVID-19 transmission over time, and suggests a possible seasonality effect of climate on COVID-19 spread, but only after August 2021. Finally, the case study highlights the added value of using genomic surveillance data in short-term epidemiological forecasting models, especially during the early stage of new variant introductions.

The outbreaks of Coronavirus Disease 2019 (COVID-19) have impacted the world significantly. Modeling the trend of infection and realtime forecasting of cases can help decision making and control of the disease spread. However, data-driven methods such as recurrent neural networks (RNN) can perform poorly due to limited daily samples in time. In this work, we develop an integrated spatiotemporal model based on the epidemic differential equations (SIR) and RNN. The former after simplification and discretization is a compact model of temporal infection trend of a region while the latter models the effect of nearest neighboring regions. The latter captures latent spatial information. We trained and tested our model on COVID-19 data in Italy, and show that it out-performs existing temporal models (fully connected NN, SIR, ARIMA) in 1-day, 3-day, and 1-week ahead forecasting especially in the regime of limited training data.

Canine rabies persists in NDjamena (Chad) despite vaccination campaigns exceeding 70% coverage, suggesting a role for dog mobility and spatial heterogeneity. We propose a metapopulation SEIR model incorporating distance-modulated dog movements and an explicit vaccinated class. Analysis of the isolated patch establishes global stability of the disease-free equilibrium via a Lyapunov function. For the metapopulation, a composite Lyapunov function shows that elimination is governed by a reproduction number [R]v. Calibrated with field data (2012-2022), simulations reveal that uniform vaccination of both patches reduces [R]v by 46% (from 2.84 to 1.52) but does not achieve elimination, while targeted strategies are less effective. These results demonstrate that exhaustive vaccination coverage across the entire urban network and increased vaccination intensity are necessary to eliminate canine rabies in NDjamena. Our model provides a quantitative framework for planning effective control strategies.

In this paper, we propose a new model to estimate the impact of an intervention on human hosts of a vector-borne infection, such as dengue, which occurs in yearly outbreaks of different magnitudes. The model applies to these outbreaks and, in fact, is independent of their intensity, that is, it does not require the steady-state assumption. The model takes as input the officially reported age-dependent number of cases of a vector-borne infection. It is deterministic and does not account for stochasticity. Our objective is to estimate the impact of the intervention (the efficacy), and we rely on the observed fact that the age distribution of the proportion of cases of the infections transmitted by the same vector is independent of both the intensity of transmission and the geographic area studied, at least for Brazilian regions. This finding is highlighted in the main text and forms the basis of our calculations. A hypothetical intervention is simulated using a dengue vaccine, which allows the determination of the optimal strategy for a vaccination campaign.

In this study, we extend the universal differential equation (UDE) framework by integrating Kolmogorov-Arnold Network (KAN) with ordinary differential equations (ODEs), herein referred to as KAN-UDE models, to achieve efficient and interpretable deep learning for complex systems. Our case study centers on the epidemiology of emerging infectious diseases. We develop an efficient algorithm to train our proposed KAN-UDE models using time series data generated by traditional SIR models. Compared to the UDE based on multi-layer perceptrons (MLPs), training KAN-UDE models shows significantly improves fitting performance in terms of the accuracy, as evidenced by a rapid and substantial reduction in the loss. Additionally, using KAN, we accurately reconstruct the nonlinear functions represented by neural networks in the KAN-UDE models across four distinct models with varying incidence rates, which is robustness in terms of using a subset of time series data to train the model. This approach enables an interpretable learning process, as KAN-UDE models were reconstructed to fully mechanistic models (RMMs). While KAN-UDE models perform well in short-term prediction when trained on a subset of the data, they exhibit lower robustness and accuracy when real-world data randomness is considered. In contrast, RMMs predict epidemic trends robustly and with high accuracy over much longer time windows (i.e., long-term prediction), as KAN precisely reconstructs the mechanistic functions despite data randomness. This highlights the importance of interpretable learning in reconstructing the mechanistic forms of complex functions. Although our validation focused on the transmission dynamics of emerging infectious diseases, the promising results suggest that KAN-UDEs have broad applicability across various fields.

The first cases of COVID-19 in Londrina-PR were manifested in March 2020 and the disease lasts until the present moment. We aim to inform citizens in a scientific way about how the disease spreads. The present work seeks to describe the behavior of the disease over time. We started from a compartmental model of ordinary differential equations like SEIR to find relevant information such as: transmission rates and prediction of the peak of infected people. We used the data released by city hall of Londrina to carry out simulations in periods of 14 days, applying a parameter optimization technique to obtain results with the greatest possible credibility.

In reaction to the severe socio-economic effects and upheavals that the Covid-19 sickness had on the world within the first few weeks of its introduction, everyone involved had to act quickly to look for possible solutions for preventing the ensuing epidemics. A prompt response is more critical given Nigerias subpar social, economic, and healthcare infrastructure. Investigated was the efficacy of various pharmacological, non-pharmaceutical, or a combination of both therapies in flattening the Covid-19 incidence curve. In order to investigate the impact of these interventions, a deterministic SVEIHR model was created and applied. The Nigerian Center for Disease Control (NCDC) portals Covid-19 data were used to parametrize the model. For simulations using a system dynamic simulation, estimated parameters were employed. The fundamental reproduction number, R0, was used to evaluate the success of our suggested intervention in effectively managing COVID-19 transmission. The simulation results demonstrated that the use of only non-pharmaceutical interventions, such as the use of face masks, a light lockdown, and hand washing at baseline or high levels, is insufficient, with the R0 varying from vaccination at the vaccination rate of 0.5% with non-pharmaceutical interventions at any level of compliance, and a combination of vaccination at 0.05% and high hygiene level were effective in flattening the Covid-19 disease incidence curve in Nigeria, returning a R0 less than 0. Furthermore, maintaining a high level of cleanliness, which includes hand washing and the use of a face mask, would be sufficient to stop the spread of Covid-19 disease and eventually flatten Covid-19 disease incidence curve in Nigeria, given a low turnout of 0.05% for vaccination and the easing of lockdown.

COVID-19 global dynamics is modeled by an adaptation of the deterministic SEIR Model, which takes into account two dominant lineages of the SARS-CoV-2, and a time-varying reproduction number to estimate the disease transmission behavior. Such a methodology can be applied worldwide to predict forecasts of the outbreak in any infected country. The pandemic in Brazil was selected as a first study case. Brazilian official published data from February 25th to August 30th, 2020 was used to adjust a few epidemiologic parameters. The estimated time-dependence mean value to the infected individuals (confirmed cases) presents - in logarithmic scale - standard deviation SD = 0.08 for over six orders of magnitude. Data points for additional three weeks were added after the model was complete, granting confidence on the outcomes. By the end of 2020, the predicted numbers of confirmed cases in Brazil, within 95% credible intervals, may reach 6 Million (5 -7), and fatalities would accounts for 180 (130 - 220) thousands. The total number of infected individuals is estimated to reach 13 {+/-} 1 Million, 6.2% of the Brazilian population. Regarding the original SARS-CoV-2 form and its variant, the only model assumption is their distinct incubation rates. The variant form reaches a maximum of 96% of exposed individuals as previously reported for South America.

Effective management of seasonal diseases such as dengue fever depends on timely deployment of control measures prior to the high transmission season. As the epidemic season fluctuates from year to year, the availability of accurate forecasts of incidence can be decisive in attaining control of such diseases. Obtaining such forecasts from classical time series models has proven a difficult task. Here we propose and compare machine learning models incorporating feature selection,such as LASSO and Random Forest regression with LSTM a deep recurrent neural network, to forecast weekly dengue incidence in 790 cities in Brazil. We use multivariate time-series as predictors and also utilize time series from similar cities to capture the spatial component of disease transmission. Among the compared models, the LSTM recurrent neural network model displayed the smallest predictive errors in predicting incidence of dengue out of sample, in cities of different sizes.

We introduce a deterministic model that partitions the total population into the susceptible, infected, quarantined, and those traced after exposure, the recovered and the deceased. We hypothesize accessible population for transmission of the disease to be a small fraction of the total population, for instance when interventions are in force. This hypothesis, together with the structure of the set of coupled nonlinear ordinary differential equations for the populations, allows us to decouple the equations into just two equations. This further reduces to a logistic type of equation for the total infected population. The equation can be solved analytically and therefore allows for a clear interpretation of the growth and inhibiting factors in terms of the parameters in the full model. The validity of the accessible population hypothesis and the efficacy of the reduced logistic model is demonstrated by the ease of fitting the United Kingdom data for the cumulative infected and daily new infected cases. The model can also be used to forecast further progression of the disease. In an effort to find optimized parameter values compatible with the United Kingdom coronavirus data, we first determine the relative importance of the various transition rates participating in the original model. Using this we show that the original model equations provide a very good fit with the United Kingdom data for the cumulative number of infections and the daily new cases. The fact that the model calculated daily new cases exhibits a turning point, suggests the beginning of a slow-down in the spread of infections. However, since the rate of slowing down beyond the turning point is small, the cumulative number of infections is likely to saturate to about 3.52 x 105 around late July, provided the lock-down conditions continue to prevail. Noting that the fit obtained from the reduced logistic equation is comparable to that with the full model equations, the underlying causes for the limited forecasting ability of the reduced logistic equation are elucidated. The model and the procedure adopted here are expected to be useful in fitting the data for other countries and in forecasting the progression of the disease.

Coinfection of COVID-19 and malaria in endemic regions may generate complex epidemiological interactions that influence susceptibility patterns, disease burden, and outbreak risk. Although malaria-acquired immunity has been hypothesized to modulate host responses to other infections, its population-level implications for COVID-19 transmission under uncertainty remain insufficiently understood. In this study, we develop a deterministic-stochastic compartmental model for the coupled dynamics of COVID-19, malaria, and their co-infection. Malaria-acquired partial immunity is incorporated through a relative susceptibility parameter that reduces the risk of COVID-19 infection among malaria-recovered individuals. For the deterministic system, we establish positivity, boundedness, an invariant feasible region, and basic reproduction numbers for the COVID-19-only and malaria-only subsystems. We then use numerical simulations to examine how immunity-mediated reductions in susceptibility may influence COVID-19 incidence, peak burden, hospitalization, and cumulative mortality. To account for environmental and transmission variability, we extend the deterministic model to an Ito stochastic differential equation framework and use repeated realizations to characterize uncertainty in epidemic trajectories, peak distributions, and outbreak risk. In addition, global sensitivity analysis based on partial rank correlation coefficients (PRCCs) is performed to identify the parameters with the greatest influence on COVID-19 outcomes. Our results suggest that, under the assumed modeling framework, malaria-acquired partial immunity may reduce the peak infectious burden and cumulative mortality associated with COVID-19. The stochastic simulations further show substantial variability around deterministic trajectories and indicate a non-negligible probability of large outbreak events that are not fully captured by mean-field predictions alone. Overall, the proposed framework provides an uncertainty-aware, mechanistic basis for studying COVID-19-malaria co-dynamics and for assessing how interacting disease processes may shape epidemic outcomes in endemic settings.

Following the outbreak of the novel coronavirus SARS-Cov2 in Europe and the subsequent failure of national healthcare systems to sufficiently respond to the fast spread of the pandemic, extensive statistical analysis and accurate forecasting of the epidemic in local communities is of primary importance in order to better organize the social and healthcare interventions and determine the epidemiological characteristics of the disease. For this purpose, a novel combination of Monte Carlo simulations, wavelet analysis and least squares optimization is applied to a known basis of SEIR compartmental models, resulting in the development of a novel class of stochastic epidemiological models with promising short and medium-range forecasting performance. The models are calibrated with the epidemiological data of Greece, while data from Switzerland and Germany are used as a supplementary background. The developed models are capable of estimating parameters of primary importance such as the reproduction number and the real magnitude of the infection in Greece. A clear demonstration of how the social distancing interventions managed to promptly restrict the epidemic growth in the country is included. The stochastic models are also able to generate robust 30-day and 60-day forecast scenarios in terms of new cases, deaths, active cases and recoveries.

ImportanceRemoving the epidemic waves and reducing the instability level of an endemic critical point of COVID-19 dynamics are fundamental to the control of COVID-19 in the US. ObjectiveTo develop new mathematic models and investigate when and how will the COVID-19 in the US be evolved to endemic. Design, Setting, and ParticipantsTo solve the problem of whether mass vaccination against SARS-CoV-2 will ultimately end the COVID-19 pandemic, we defined a set of nonlinear ordinary differential equations as a mathematical model of transmission dynamics of COVID-19 with vaccination. Multi-stability analysis was conducted on the data for the daily reported new cases of infection from January 12, 2021 to December 12, 2022 across 50 states in the US using the developed dynamic model of COVID-19 and limit cycle theory. Main Outcomes and MeasuresEigenvalues and the reproduction number under the disease-free equilibrium point and endemic equilibrium point were used to assess the stability of the disease-free equilibrium point and endemic equilibrium point. Both analytic analysis and numerical methods were used to determine the instability level of new cases of COVID-19 in the US under the different types of equilibrium points and to investigate how the system moves back and forth between stable and unstable states of the system and how the pandemic COVD-19 will evolve to endemic in the US. ResultsMulti-stability analysis identified two types of critical equilibrium points, disease-free endemic equilibrium points in the COVID-19 transmission dynamic system. The transmissional, recovery, vaccination rates and vaccination effectiveness during the major transmission waves of COVID-19 across 50 states in the US were estimated. These parameters in the model varied over time and across the 50 states. The eigenvalues and the reproduction numbers R0 and [Formula] in the disease-free equilibrium point and endemic equilibrium point were estimated to assess stability and classify equilibrium points. They also varied from state to state. The impacts of the transmission and vaccination parameters on the stability of COVID-19 were simulated, and stability attractor regions of these parameters were found and ranked for all 50 states in the US. The US experienced five major epidemic waves, endemic equilibrium points of which across 50 states were all in unstable states. However, the combination of re-infection and vaccination (hybrid immunity) may provide strong protection against COVID-19 infection, and stability analysis showed that these unstable equilibrium points were toward stable points. Theoretical analysis and real data analysis showed that additional epidemic waves may be possible in the future, but COVID-19 across all 50 sates in the US is rapidly moving toward stable endemicity. Conclusions and RelevanceBoth stability analysis and observed epidemic waves in the US indicated that the pandemic might not end with the disappearance of the virus. However, after enough people gained immune protection from vaccination and from natural infection, COVID-19 would become an endemic disease, as the stability analysis showed. Educating the population about multiple epidemic waves of the transmission dynamics of COVID-19 and designing optimal vaccine rollout are crucial for controlling the pandemic of COVID-19 and its evolving to endemic. Key PointsO_ST_ABSQuestioC_ST_ABSThe US has already experienced five waves of the epidemic. We urgently need to know when and how will COVID-19 be evolved into endemic. FindingsTo solve the problem, we developed a mathematical model of transmission dynamics of COVID-19 with vaccination and performed a multi-stability analysis of COVID-19 transmission dynamics in the US. We found that COVID-19 dynamics of all 50 states in the US were getting closer and closer to endemic and stable states. MeaningCOVID-19 dynamics of all 50 states in the US are toward stable states and will be evolved to endemic in the near future.

Forecast models are a key decision-support tool for public health authorities in managing epi- demics, feeding into early warning systems, scenario evaluations, and empirical basis for resource al- location. In Brazil, improving dengue forecasting became a priority in response to the unprecedented increase in cases, which surpassed the total of the previous decade and expanded to new regions. The Infodengue-Mosqlimate consortium launched the Brazilian Dengue 2024 Challenge (IMDC24), or Dengue Forecast Sprint, bringing together six international teams provided with cases and climate covariates data to generate actionable forecasts for 2024 and 2025 seasons in five diverse Brazilian states, leveraging advanced machine learning and classical statistical models. This paper outlines the structure and findings of the IMDC24. Model performance varied between years and locations, and no single model consistently excelled, especially during 2024s atypical, climate-change-driven con- ditions. This performance variability highlighted the need for ensemble approaches. The ensemble models developed are presented as the main results of this collaborative development. As intended, the ensemble models have been adopted by Brazilian public health authorities to help with planning and response to the forecasted 2025 dengue epidemics across the country. Significance StatementThe Dengue Challenge 2024 (IMDC24) was organized by the Mosqlimate-Infodengue consortium, which aims to provide forecasting models as decision support tools for early warning systems, scenario assess- ments, and empirical basis for resource allocation for mosquito-borne diseases. During IMDC24, six international teams, provided with dengue case, sociodemographic, and climate data, developed scenario forecasting models for the 2024 and 2025 dengue seasons in Brazil. In this study, we evaluated the per- formance of each model and built an ensemble model, considering the variation in performance of each model, especially during the atypical climate conditions of 2024. Among the main applications of this work, we highlight the incorporation of the results into the Brazilian Ministry of Healths nationwide dengue epidemic response agenda.

The fast moving post-modern society allows for individuals to move fast in and between different countries, making it a perfect situation for the spread of emerging diseases. COVID-19 emerged with properties of a highly contagious disease, that has spread rapidly around the world. SIR/SEIR models are generally used to explain the dynamics of epidemics, however Coronavirus has shown dynamics with constant non-pharmaceutical interventions, making it difficult to model with these simple models. We extend an SEIR model to include a confinement compartment (SEICR) and use this to explain data from COVID-19 epidemic in Costa Rica. Then we discuss possible second wave of infection by adding a time varying function in the model to simulate cyclic interventions.

The COVID-19 pandemic has presented severe challenges in understanding and predicting the spread of infectious diseases, necessitating innovative approaches beyond traditional epidemiological models. This study introduces an advanced method for automated model discovery using the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, leveraging a dataset from the COVID-19 outbreak in Thuringia, Germany, encompassing over 400,000 patient records and vaccination data. By analysing this dataset, we develop a flexible, data-driven model that captures many aspects of the complex dynamics of the pandemics spread. Our approach incorporates external factors and interventions into the mathematical framework, leading to more accurate modelling of the pandemics behaviour. The fixed coefficient values of the differential equation as globally determined by the SINDy were not found to be accurate for locally modelling the measured data. We therefore refined our technique based on the differential equations as found by SINDy, by investigating three modifications that account for recent local data. In a first approach, we re-optimized the coefficient values using seven days of past data, without changing the globally determined differential equation. In a second approach, we allowed a temporal dependence of the coefficient values fitted using all previous data in combination with regularization. As a last method, we kept the coefficients fixed to the original values but augmented the differential equation with a small neural network, locally optimized to the data of the past week. Our findings reveal the critical role of vaccination and public health measures in the pandemics trajectory. The proposed model offers a robust tool for policymakers and health professionals to mitigate future outbreaks, providing insights into the efficacy of intervention strategies and vaccination campaigns. This study advances the understanding of COVID-19 dynamics and lays the groundwork for future research in epidemic modelling, emphasising the importance of adaptive, data-informed approaches in public health planning.

Dengue fever, a tropical vector-borne disease, is a leading cause of hospitalization and death in many parts of the world, especially in Asia and Latin America. In places where timely and accurate dengue activity surveillance is available, decision-makers possess valuable information that may allow them to better design and implement public health measures, and improve the allocation of limited public health resources. In addition, robust and reliable near-term forecasts of likely epidemic outcomes may further help anticipate increased demand on healthcare infrastructure and may promote a culture of preparedness. Here, we propose ensemble modeling approaches that combine forecasts produced with a variety of independent mechanistic, statistical, and machine learning component models to forecast reported dengue case counts 1-, 2-, and 3-months ahead of current time at the province level in multiple countries. We assess the ensemble and each component models monthly predictive ability in a fully out-of-sample and retrospective fashion, in over 180 locations around the world -- all provinces of Brazil, Colombia, Malaysia, Mexico, and Thailand, as well as Iquitos, Peru, and San Juan, Puerto Rico -- during at least 2-3 years. Additionally, we evaluate ensemble approaches in a multi-model, real-time, and prospective dengue forecasting platform -- where issues of data availability and data completeness introduce important limitations -- during an 11-month time period in the years 2022 and 2023. We show that our ensemble modeling approaches lead to reliable and robust prediction estimates when compared to baseline estimates produced with available information at the time of prediction. This can be contrasted with the high variability in the forecasting ability of each individual component model, across locations and time. Furthermore, we find that no individual model leads to optimal and robust predictions across time horizons and locations, and while the ensemble models do not always achieve the best prediction performance in any given location, they consistently provide reliable disease estimates -- they rank in the top 3 performing models across locations and time periods -- both retrospectively and prospectively.

Vaccination is a cornerstone of infectious disease control, yet vaccines are not fully protective, leaving a fraction of the vaccinated population susceptible to infection. This partial protection can alter behavior, as individuals who perceive themselves as immune may reduce adherence to preventive measures. Motivated by this, we investigate how behavioral changes among non-immune vaccinated individuals influence the dynamics of a directly transmitted disease and the basic reproduction number. We propose a model that incorporates vaccine failure through three facets (take, degree, and duration) alongside a behavioral parameter that modifies contact rates according to compliance with mitigation measures. Our analysis highlights the critical role of the behavioral index in key phenomena, including backward bifurcation and overall disease dynamics. We identify two thresholds. The first specifies the values of the behavioral index for which backward bifurcation does not arise, thereby indicating the conditions under which the disease may persist. The second establishes a relationship between the behavioral index and vaccine efficacy, which allows us to compare the transmission dynamics of our model with those of the classical vaccination model.