Infectious Disease Modelling

This study is based on the design and analysis of a novel age- and dose-structured model for assessing the population-level impact of the recently-approved R21/Matrix-M malaria vaccine (which is administered in three doses followed by a booster dose) on controlling the spread of malaria in children under five in Burkina Faso. While the current malaria vaccination program in Burkina Faso prioritizes children 0-3 years of age (Group 1 in our model), we also assessed a hypothetical scenario where children 3-5 years of age (Group 2 in our model) are also vaccinated (since children under five years of age suffer the brunt of malaria morbidity and mortality). The vaccination-free version of the model was calibrated using yearly cumulative malaria mortality data for children in Burkina Faso. In addition to establishing well-posedness, we showed that the disease-free equilibrium of the model is locally-asymptotically stable whenever the control reproduction number ([R]v) is below one. Conditions for achieving vaccine-induced herd immunity (needed for disease elimination) under varying age-group structures and dosage schedules were derived, and a global sensitivity analysis was conducted to identify the parameters of the model that most strongly influence [R]v. Simulations of a homogeneous model including only Group 1 indicate that administering only the first dose of the vaccine with baseline bednet usage requires an impractically high herd immunity threshold of 97%. However, with all four doses, herd immunity is achievable without bednet when the required coverage ratios receiving doses 2, 3, and the booster dose are 73% to 90%. With baseline bednets, these ratios drop to just 10%-30%, dramatically improving elimination prospects. In a heterogeneous setting incorporating both Groups 1 and 2, herd immunity can be achieved (with bednet at baseline) by vaccinating either 46% of the total population of Groups 1 and 2 or 75% of individuals in Group 1 alone. Simulations of the full two-group model (with bednet at baseline) show that vaccinating only children in Group 1 with the first dose reduces the cumulative number of new malaria cases and malaria-induced deaths in Group 1 by about 19%-20%, and produces spillover reductions of about 11%-12% in the unvaccinated Group 2, indicating a moderate indirect benefit across groups. If children in Group 1 receive all four doses, the reductions in Group 1 increase to about 36%-38%, with larger spillover reductions of about 25%-26% in Group 2. When both groups receive only the first dose, the malaria burden decreases by about 24%-26% in each group. The greatest reductions occur when both groups receive all four doses, yielding decreases of about 43%-46%. These results show that extending Burkina Fasos current vaccination program to include children in the 3-5-year age group can substantially improve malaria elimination prospects, particularly when combined with bednet usage at baseline levels or higher.

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.

With significant population fractions in many societies who refuse vaccines, it is important to reconsider how vaccination is incorporated into compartmental epidemiology models. It is still most common to apply the vaccination rate to the entire class of susceptibles, rather than to use the more realistic assumption that the vaccination rate function should depend only on the population of susceptibles who are willing and able to receive a vaccination. This study uses a simple generic disease model to address two questions: (1) How much error is introduced in key model outcomes by neglecting vaccine unwillingness?, and (2) Can the error be reduced by incorporating vaccine unwillingness into the vaccination rate constant rather than the rate diagram? The answers depend greatly on the time scale of interest. For the endemic time scale, where longterm behavior is studied with equilibrium point analysis, the error in neglecting unwillingess is large and cannot be improved upon by decreasing the vaccination rate constant. For the epidemic time scale, where the first big epidemic wave is studied with numerical simulations, the error can still be significant, particularly for diseases that are relatively less infectious and vaccination programs that are relatively slow.

This study presents a theoretical and mathematical framework for understanding the dynamical behavior of infectious disease spread using a compartmental modeling approach. The proposed model incorporates memory effects to capture temporal dependencies that are not adequately represented by classical formulations. Qualitative analysis is employed to investigate the stability properties of the system and the role of key mechanisms in shaping long term dynamics. Publicly available surveillance information is used only to illustrate the consistency of the model behavior with observed trends. The results highlight the value of memory based modeling structures for describing complex biological processes and provide a general mathematical perspective for studying epidemic dynamics.

Malaria persists worldwide, exerting its greatest impact in sub-Saharan Africa. This study develops and uses a mathematical model to assess how sub-optimum versus optimum treatment of malaria drives asymptomatic infections, immunity build-up, and sustained transmission, providing insights for effective control. Fitted to case data from Kenya and Nigeria, the framework is used to quantify the burden of malaria and the additional cost associated with sub-optimum treatment. Global sensitivity analysis identifies mosquito demographic parameters, biting rates, and malaria treatment rate among major disease drivers under sub-optimum treatment, emphasizing the need for integrated strategies that improve access to optimum treatment and reduce mosquito-human contact. Model simulations show that sub-optimum treatment amplifies asymptomatic prevalence, sustaining/increasing malaria transmission and burden. Further simulations reveal that optimum treatment could avert more than one-third of infections and deaths, while asymptomatic infections contribute up to $96%$ ($75%$) of malaria-related Years Lived with Disability in Kenya (Nigeria). Cost analysis shows that optimum treatment lowers malaria burden significantly and can reduce annual total treatment costs by $\approx $12$ million, underscoring the substantial economic and public health gains of limiting sub-optimum care. This study demonstrates that effective and sustained malaria control requires strengthening adherence to treatment, minimizing sub-optimum treatment, reducing mosquito-human contact, and targeting asymptomatic carriers to curb hidden transmission and reduce long-term health and economic losses.

Visceral leishmaniasis (VL) is considerably more severe among individuals infected with human immunodeficiency virus (HIV), leading to higher parasite loads, frequent relapse, and increased mortality. To examine the epidemiological interaction between the two diseases, we develop a comprehensive VL-HIV co-infection model that incorporates transmission pathways, treatment effects, and relapse dynamics. The model is parameterized using real-time data from Bihar, India, including monthly VL-only and VL-HIV co-infected cases and annual HIV prevalence data. Our analysis shows that HIV infection drives the resurgence and persistence of VL even in settings where VL alone would not sustain transmission, underscoring the amplifying effect of HIV-induced immunosuppression on VL dynamics. We further demonstrate that increasing HIV treatment coverage substantially reduces co-infection prevalence and lowers VL relapse rates. Numerical simulations and optimal control analysis highlight the effectiveness of integrated intervention strategies that combine awareness, treatment enhancement, and vector control. Overall, this study emphasizes the need for coordinated VL and HIV control programs and provides data-driven guidance for designing sustainable intervention strategies in endemic regions.

Vaccine-acquired immunity plays an important role in controlling the spread of many infectious diseases; however, vaccine efficacy can diminish over time. This work uses a mathematical model to study the effects of waning vaccination-acquired immunity on infection incidence. With an SEIR-type compartmental model that considers both vaccinated and unvaccinated populations (and their mixing), we present mathematical conditions under which vaccinated individuals drive ongoing growth in infections, i.e., over half of the daily incidence arises from vaccinated individuals. Analysis of a mathematical model of COVID-19 spread in the state of Colorado suggests how and for what duration vaccinated individuals could have sustained such growth. Importantly, our model demonstrates that, despite potential for brief vaccinated-driven periods of growth in infections, which occur among unvaccinated-driven periods of growth in infections, increased vaccination coverage always reduces total cases and total hospitalizations. This work provides insight into how waning immunity in vaccinated populations can contribute to ongoing infection incidence and demonstrates the value of complementary interventions to prevent disease spread in vaccinated populations.

Schistosomiasis is a neglected parasitic disease caused by various trematode species of the genus Schistosoma for which 251 million people needed treatment in 2021. Many mathematical models of Schistosoma mansoni transmission incorporate the effect of chemoprophylaxis on parasite burden within the human host. While praziquantel is the most commonly implemented pharmaceutical used to control schistosomiasis, due to its applicability over several species and its negligible side effects, it is not very effective against juvenile schistosomes in humans. This limited efficacy on the juvenile life-stage of the parasite may be an important factor in the persistence of the disease. The demographic consequences of praziquantel use on schistosome population age and sex composition within the human host may obfuscate the effectiveness of these chemoprophylactic control strategies. Furthermore, the effectiveness of this treatment is heavily dependent on the force of infection to humans and the frequency at which these pharmaceuticals are administered. Using a stochastic mechanistic model, we investigated the effects of inconsistent drug efficacy among parasite life stages, varying parasite population structure within the human host, and alternative treatment regimes to the prevailing once-yearly strategy. This allowed us to identify the reduction in infection prevalence under differing infection risk scenarios, parasite population structures at the time of treatment, and treatment schedules. Our results indicate that if elimination is the goal, then widespread (>75% of the population) treatment should be the target and that more frequent treatment schedules are useful up to several treatments a year.

Tetanus is a severe disease of the nervous system, transmitted through bacteria in the environment. In the absence of medical attention, case fatality rates are extremely high. Despite progress towards maternal and neonatal tetanus elimination targets, tetanus remains a serious public health problem. Routine infant and maternal vaccination have contributed to considerable reduction in cases and deaths from tetanus globally. However, protective immunity wanes over time. To increase duration of protection, the World Health Organization recommends three diphtheria-tetanus-pertussis-containing vaccine booster doses be given in early childhood, childhood, and adolescence. Evidence to support country-level decision-making about the introduction of these booster doses is critical. We have developed a novel age-structured, deterministic compartmental model of tetanus transmission and vaccination. The model is driven by environmental transmission and incorporates interventions like hygiene and safe birth practices to reduce the magnitude of environmental transmission. It explicitly models vaccination, separating each dose of the primary series, booster doses, and maternal vaccination to capture dose-specific effectiveness and duration of protection. The model captures heterogeneous immunity profiles by dose and age, and the cumulative nature of vaccine-derived protection. The immune dynamics follow the patterns described in literature and can replicate seroprevalence studies, although the exact characterisation of immunity in the literature still has gaps. This model presents a substantial advancement on previously published models and is well positioned to inform tailored vaccination strategies to reduce neonatal and non-neonatal tetanus.

1Parameter estimation is often necessary to inform transmission models of infectious diseases. This estimation requires choosing an observation model that links the model outputs to the observed data. Although potentially consequential, this choice has received little attention in the literature. Here, we aimed to compare eight observation models, including common distributions such as the Poisson, binomial, negative binomial, and normal (equivalent to least-squares estimation). Using Bayesian inference methods, we fit an SIR-like model to daily case reports during the first wave of COVID-19 in Belgium, Finland, Germany, and the UK. We found considerable differences in the log-likelihoods of the observation models, spanning three orders of magnitude between the best and the worst. Compared with the best models, the binomial, Poisson, and normal models received no support due to their rigid variance structures. Additionally, the binomial and Poisson models produced overly narrow prediction and confidence intervals, especially for key parameters such as the basic reproduction number. The other five models--each with a free dispersion parameter scaling the variance to the mean--performed significantly better, with the negative binomial model ranking first in three countries. We conclude that flexible observation models are essential for transmission models to accurately capture all sources of uncertainty.

Monkeypox viral disease has been and continues to be a global public health concern. Currently, there are existing, though minimal measures to manage mpox and any future outbreaks. Relying on data-driven modeling for early detection of mpox and prediction of possible cases and deaths in the presence of an outbreak is thus imperative. The present study forecasted global mpox virus cases and deaths in Asia, Africa, Australia, Europe, North America, Oceania, and South America. Three forecasting models (deep neural network, gradient boosting, and polynomial regression) were trained on data from the seven geographical regions. The performance of the three models was assessed using coefficient of determination, mean squared error, root mean squared error, and mean absolute scaled error across each region. Prediction using the deep neural network revealed a potential of higher mpox deaths in Africa and higher mpox cases in South America. Prediction using gradient boosting showed a potential of mpox deaths in Africa and higher mpox cases in Asia and North America. Prediction using polynomial regression revealed a potential of higher mpox deaths in Africa and Asia while rapid rises in mpox cases from 2025 to 2028 were anticipated in all regions except Asia in case of a monkeypox outbreak. For the three models, the tree-based ML model (gradient boosting) outperformed the statistical model and deep learning model by R2 and MSE in predicting mpox case counts across all the seven geographical regions. This study showcases the worth in using data-driven modelling to predict emerging and re-emerging infectious diseases such as mpox.

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

Malaria transmission is strongly modulated by climate, yet most mechanistic models used for health policy evaluation do not explicitly account for climate-driven variability in mosquito dynamics. Here, we present a novel modelling framework that couples VECTRI, a climate-sensitive model for malaria, with OpenMalaria, a detailed individual-based model of malaria epidemiology and intervention impact. This integrated approach enables simulation of transmission processes that respond to interannual climate fluctuations while retaining the capacity to evaluate realistic intervention strategies. We apply this framework to Mozambique, a high-burden country for malaria, with pronounced climatic seasonality and extensive routine surveillance data. Using district-level incidence time series from the ten highest-incidence districts, we validate the modelling framework and compare its performance with the standalone versions of VECTRI and OpenMalaria. The joint modelling framework reproduces malaria seasonality more accurately than the two single models, with improved timing of incidence peaks in 7 out of 10 districts and a closer representation of evolution of transmission throughout the season, as measured by the seasonality index, in 7 out of 10 districts. We then use the coupled system to assess how climate-driven interannual variability affects the predicted effectiveness of indoor residual spraying, a key seasonal malaria control intervention in Mozambique. Integrating climate-driven interannual variability into OpenMalaria substantially impacts the modeled effectiveness of indoor residual spraying. The joint modelling framework increases the estimated protective effectiveness of indoor residual spraying in reducing incidence by up to 11% and delayed the optimal deployment window by two weeks. Our results demonstrate that climate-informed mechanistic models can meaningfully alter estimates of intervention impact and improve the realism of malaria predictions. The joint OpenMalaria-VECTRI modelling framework provides a flexible tool for national malaria programs seeking to evaluate seasonal interventions under varying climatic scenarios. Author summaryMalaria transmission in Mozambique changes from year to year because mosquito populations strongly depend on rainfall and temperature. However, most models used to guide malaria control planning do not fully capture these climate-driven fluctuations. We developed a new modelling approach that links two existing tools: VECTRI, which simulates climate-sensitive mosquito and malaria dynamics, and OpenMalaria, which simulates malaria infections and the effects of interventions. We validated this joint modelling framework using routine surveillance malaria data from Mozambique and found that it better captures observed seasonal and interannual patterns than standalone models. We then used it to explore how climate variability influences the effectiveness of indoor residual spraying, an important seasonal malaria control strategy. Accounting for climate-driven mosquito dynamics reduced the predicted protective effectiveness of indoor residual spraying by 11% and shifted the optimal deployment timing by approximately two weeks. These results show that integrating climate information into malaria models can improve the accuracy and usefulness of intervention planning.

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.

Background: Accurate estimation of respiratory syncytial virus (RSV) disease burden is challenged by the imperfect testing performance that varies by clinical specimens, diagnostic tests, and timing of specimen collection. Although the use of multiple testing approaches (such as testing multiple clinical specimens or additional diagnostic tests) could increase the RSV detection, there is absence of a modelling framework to fully incorporate the complexity of heterogeneous diagnostic data. In this study, we proposed a novel Bayesian latent class model that accounted for heterogeneous data on the number of RSV tests and variable specimen collection time among individual patients, imperfect testing sensitivity and specificity of different combinations of clinical specimen and diagnostic test (i.e., testing approaches), and RSV seasonality. Methods: Using simulated datasets consisting of four different testing approaches that mimic real-world RSV epidemiologic characteristics in the UK under different sample size and testing practice scenarios, we assessed the model performance in estimating RSV disease burden as the annual RSV positive proportion in lower respiratory tract infection (LRTI) cases across three respiratory seasons (August 2021 to July 2024) in four adult age groups: 18 to 49 years, 50 to 64 years, 65 to 74 years and over 75 years. Results: We demonstrated that model performance increased substantially with increased sample size, achieving over 80% in accuracy at a sample size of 30,000 tests and 95% in accuracy at a sample size of 60,000 tests; by contrast, smaller sample size could lead to severe over-estimation of the RSV disease burden. In comparison with the existing approaches, both the naive model and the multiplier model systematically under-estimated the RSV disease burden regardless of sample size. The Bayesian model yielded more accurate estimates when the sample size reached 30,000 tests or more; its advantage over the other two models was even more pronounced if the number of testing approaches reduced to 3. Conclusion: The findings above suggest that the proposed Bayesian model provides a robust framework for estimating RSV burden by integrating complex, individual-level testing data when fitting with sufficient input data, offering a critical tool for generating more accurate RSV disease burden estimates to inform national immunisation policies.

BackgroundThe COVID-19 pandemic was strongly shaped by the interaction between population behaviour and transmission dynamics. Standard mathematical models do not account for this interaction, however. Objectivewe tested whether adding a mechanistic representation of population behavioural dynamics improves the ability of a mathematical model to explain and predict COVID-19 pandemic waves. MethodsWe compared a standard Susceptible-Infected-Recovered (SIR) model to a variant (SIRx) with a mechanistic representation of behavioural processes, including two-way coupling between behaviour and transmission dynamics. We used approximate Bayesian computation to parameterise the models with SARS-CoV-2 case incidence and the Oxford stringency index from 13 European countries. Models were fitted to the Spring 2020 wave, and their out-of-sample prediction for the Summer/Fall 2020 wave was tested. Outcome measures included the Akaike Information Criterion (AICc), the area between empirical and model epidemic curves, and predicted timing/magnitude of the second wave. ResultsThe average AICc for the SIRx model across all 13 countries was lower (-2638{+/-}345 versus - 2295{+/-}212 for SIR), meaning that the SIRx model explains the data more parsimoniously. The average area-between-curves was also lower (0.072{+/-}0.071 versus 0.16{+/-}0.11). The predicted peak magnitude for the SIRx model (0.0015{+/-}0.0014) was closer to the data (0.0006{+/-}0.0005) than the SIR prediction (0.0083{+/-}0.0090). The average day-of-peak for the SIRx model (283{+/-}19 days from first data point) was also closer to the data (278{+/-}25), than the SIR prediction (253{+/-}31), although the 95% credible intervals for individual countries were very large. ConclusionCoupling behavioural and disease dynamics improves the ability of mathematical models to explain and predict crucial features of pandemic waves. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSMost mathematical models of infectious disease transmission do not explicitly account for behaviour, but the COVID-19 pandemic clarified the role of behavioural processes in determining the trajectory of infectious diseases in populations. On the other hand, many theoretical models of coupled behaviour-disease processes exist, although relatively few attempt to validate these models against data. We searched Google Scholar using the terms COVID-19 model, and behavio*-disease or behavio* epidem* from March 1, 2020 to October 8, 2025. We did not find any papers that compared retrospective out-of-sample model predictions of COVID-19 pandemic waves of a non-behavioural transmission model to the predictions of a coupled behaviour-disease model, in multiple populations. Added value of this studyWe carried out such a comparison for 13 European countries, by fitting models to the first COVID-19 wave in Spring 2020 and testing how well they would have predicted the second wave. We found that the coupled behaviour-disease model predicted the second wave better than the non-behavioural model, and was also more parsimonious, despite having more parameters. This study shows that feedback between disease dynamics and behavioural dynamics is a significant factor for determining the timing and magnitude of pandemic waves caused by an acute respiratory infection. It also shows that integrating population behaviour dynamics into transmission models is feasible, and can better explain observed temporal patterns in case incidence. Implications of all the available evidenceMathematical models that endogenously include the feedback between infectious disease dynamics and behavioural dynamics can add a unique and complementary tool to the public health modelling toolbox during a pandemic. Such models could help design public health interventions by improving our ability to anticipate population responses to both the interventions themselves, and a rapidly evolving epidemiological landscape.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continuously evolved since its emergence in the human population in 2019. As of 1st August 2025, more than 1,700 Omicron subvariants have been designated by the Pango nomenclature system. The Pango nomenclature system designates a new lineage based on genetic and epidemiological information of SARS-CoV-2 strains. However, there is a possibility that strains that have similar genetic backgrounds and the same phenotype are given different Pango lineage names. In this paper, we propose a new algorithm, called FindPart-w, which can identify groups of viral lineages that share the same relative effective reproduction numbers. We introduced a new lineage replacement model, called the constrained RelRe model, which constrains groups of lineages to have the same relative effective reproduction numbers. The FindPart-w algorithm searches the equality constraints that minimise the Akaike Information Criterion of constrained RelRe models. Using hypothetical observation count data created by simulation, we found that the FindPart-w algorithm can identify groups of lineages having the same relative effective reproduction number in a practical computational time. Applying FindPart-w to actual real-world data of time-stamped lineage counts from the United States, we found that the Pango lineage nomenclature system may have given different lineage names to SARS-CoV-2 strains even if they have the same relative effective reproduction number and similar genetic backgrounds. In conclusion, this study showed that viruses that had the same relative effective reproduction number were identifiable from temporal count data of viral sequences. These findings will contribute to the future development of lineage designation systems that consider both genetic backgrounds and transmissibilities of lineages.

AbstractsO_ST_ABSBackgroundC_ST_ABSAccurate dengue forecasting is vital for public health preparedness. Despite a surge in forecasting approaches, a quantitative ranking of the relative performance and practical utility of dengue forecasting is lacking. MethodsA systematic review and Network Meta-Analysis (NMA) of studies comparing dengue forecasting methods (2014-2024) was conducted. Models were categorised into five groups: Time Series, Deep Learning (DL), Machine Learning (excluding DL), Hybrid, and Ensembles. NMA was applied to the logarithm of the most common forecast error metric to rank relative performance--an "Implementability Score" quantified analyst and data requirements, and computational costs. Results59 studies were included. NMA of Root Mean Squared Error identified k-Nearest Neighbour (k-NN) models as achieving the highest predictive accuracy, followed closely by Vector Autoregression, Kalman Filtering, Generalised Linear Model and Autoregressive Neural Network (ARNN). While DL models showed high potential, they scored lowest in implementability due to poor interpretability and high data requirements. Most studies utilised meteorological covariates, with significant gaps in the use of socio-economic and entomological predictors. ConclusionsAlthough there was some trade-off between accuracy and implementability, traditional statistical models were often comparable in accuracy to machine learning approaches, with advantages in interpretability and data needs. Under-explored areas for future research include the use of ensemble models and the use of socio-economic and entomological data. RegistrationPROSPERO CRD420251016662. Author SummaryDengue is a critical global health threat affecting the worlds population. While many forecasting models exist to help officials prepare for outbreaks, there has been no standardised way to compare their performance. This leaves health experts in resource-limited areas uncertain about which tools are truly reliable or easy to use under their specific local conditions. We conducted a network meta-analysis of studies comparing dengue forecasting methods accuracy, grouping them into five categories: Machine Learning, Deep Learning, Time Series, Ensemble, and Hybrid. Beyond ranking their accuracy, we developed an "Implementability Score" to evaluate the practical feasibility of each model, accounting for technical complexity, data requirements, and software accessibility. Our analysis identified the top-performing models. Notably, traditional statistical models often performed as well as complex Deep Learning algorithms. While advanced models show potential, they are often difficult to implement or explain to decision-makers. There is no "one-size-fits-all" solution; the best model depends on capacity and data in each setting. This study provides a roadmap for public health officials to select tools that are both accurate and feasible.

Dengue is a mosquito-transmitted viral disease that has long defied control but now has a growing list of promising interventions becoming available. Major investments in these interventions are being made without a clear picture of the long-term implications of those choices. We used a mathematical model to project the impacts of alternative suites of interventions across 1,634 cities to the year 2050. We found that recently developed interventions have the potential for significant reductions in dengue hospitalisations over a period of a few years. Beyond that timeframe, our model predicts a buildup of susceptibility that diminishes intervention effectiveness over time. Routine vaccination leads to only modest reductions when layered on top of other interventions. The most effective and sustainable strategy combines short-term investments in new interventions with long-lasting changes to remove mosquito habitat in urban environments, resulting in > 90% reductions in disease burden in all cities over a 25-year period.

Scrub typhus remains a persistent public health concern with strong spatial and temporal variability. This study analyses the spatio-temporal distribution, clustering patterns, and forecasting of scrub typhus across five districts, Chittoor, Ranipet, Tirupattur, Vellore, and Tiruvannamalai, using long-term surveillance data from May 2005 to May 2024. We applied spatio-temporal exploratory analysis to identify trends, seasonal behaviour, and inter-district heterogeneity in disease incidence. Hotspot analysis was conducted using the Getis-Ord Gi* statistics to detect statistically significant hotspots and coldspot clusters and examine their evolution over time. To support decision-making, we developed statistical, machine learning (ML), and deep learning (DL) based forecasting models using monthly scrub typhus and climatic features. Root mean square error (RMSE), and R-square error (R2) evaluation metrics are used to compare the performance of the prediction model. Scrub typhus shows clear and recurring seasonal peaks across all five districts, and incidence increases are associated with precipitation, dew point, relative humidity, and vegetation cover. Temperature shows a strong negative correlation, while relative humidity and normalized difference vegetation index (NDVI) show strong positive correlations in all districts. Hotspot analysis identifies Vellore and Chittoor as persistent core transmission zones, with weaker clustering in surrounding districts. Forecasting results indicate that model performance varies by location. The results reveal persistent hotspots, clear seasonal signals, and short-term forecasts across districts. This integrated spatiotemporal and forecasting framework provides actionable insights for targeted surveillance and timely intervention strategies to control scrub typhus.