Infectious Disease Modelling

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.

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.

Sylvatic Yellow Fever (YF) is an infectious mosquito-borne disease with significant epidemiological relevance due to its widespread distribution and high lethality for human and non-human primates, particularly in tropical regions of the planet such as in Brazil. Identifying regions and periods of high environmental suitability for the occurrence of YF is essential for preventing or mitigating its burden, as it enables the efficient allocation of surveillance efforts, prevention, and implementation of control measures. Environmental modeling of YF occurrence has proven to be an effective approach toward this goal; however, its effectiveness strongly depends on the modeling framework's capabilities as well as the spatial and temporal precision of all associated data. We propose a fine-scale geospatial modeling of YF environmental suitability that is based on a generative machine-learning ensemble method built on a large set of high-resolution environmental covariates. First, we take the spatiotemporal statistical description of the environment of each of the 545 YF cases from 2019--2024 up to 30 m/monthly resolution at three buffer scales: 100 m, 500 m, and 1000 m ratios. Then, we perform a feature selection and train hundreds of One-Class Support Vector Machine submodels to form a robust ensemble model, whose predictions are projected to a 1x1 km resolution grid of Brazil under several metrics, exceeding seven million ensemble evaluations. The predictions ranked the Southern Brazil region with the highest mean suitability for YF, with a level of 0.64; Southeast comes next with 0.46, followed closely by Central-West region (0.44), North (0.39), and finally Northeast (0.28). The model exhibited high uncertainty for the North region, indicating that data collection efforts are much needed in this region. As for the environmental covariates, a feature analysis pointed out that Land use and cover accounts for the largest influence in the model output.

Infectious disease surveillance systems in tropical countries show that respiratory disease incidence generally manifests as year-round activity with weak fluctuations and irregular seasonality. Previously, using a ten-year time series of influenza-like illness (ILI) collected from outpatient clinics in Ho Chi Minh City (HCMC), Vietnam, we found a combination of nonannual and annual signals driving these dynamics, but with unknown mechanisms. In this study, we use seven stochastic dynamical models incorporating humidity, temperature, and school term to investigate plausible mechanisms behind these annual and nonannual incidence trends. We use iterated filtering to fit the models and evaluate the models by comparing how well they replicate the combination of annual and nonannual signals. We find that a model including specific humidity, temperature, and school term best fits our observed data from HCMC and partially reproduces the irregular seasonality. The estimated effects from specific humidity and temperature on transmission are nonlinearly negative but weak. School dismissal is associated with decreased transmission, but also with low magnitude. Under these weak external drivers, we hypothesize that stochasticity makes a strong sub-annual cycle more likely to be observed in ILI disease dynamics. Our study shows a possible mechanism for respiratory disease dynamics in the tropics. When the external drivers are weak, the seasonality of respiratory disease dynamics is prone to the influence of stochasticity.

Entomological surveillance plays a crucial role in areas where malaria remains endemic, yet gathering data on mosquito populations is often expensive and complicated, particularly in remote locations with challenging logistics and inconsistent sampling schedules. Access to extensive time series data on mosquito species at specific sites would greatly enhance insights into seasonal trends and the biting habits of vectors of malaria parasites. Gaps in mosquito count records pose a significant challenge for researchers and public health officials seeking to establish early warning systems and effective vector control programs. In this study, we apply quantitative machine learning techniques to address missing data in estimates of mosquito abundance collected from 2009 to 2016 in Bolivar State, Venezuela. We evaluated Linear Regression, Stochastic Linear Regression, K Nearest-Neighbor, and Gradient Boosting methods for imputing missing counts of Anopheles mosquitoes, employing a leave-one-out cross-validation strategy. Additionally, we developed a predictive malaria transmission model incorporating mosquito abundance and climate variables (El Nino 3.4 Index, rainfall, and mean air temperature) as covariates. Our generalized time series model forecasts malaria incidence of Plasmodium vivax and Plasmodium falciparum based on climate dynamics and imputed mosquito data. Model performance was assessed using root mean square error, mean absolute error, and mean absolute percentage error. The final results demonstrated that machine learning imputation significantly improved the accuracy and reliability of P. vivax malaria incidence predictions but failed to predict P. falciparum incidence. The study demonstrates that method choice significantly influences the reconstruction of seasonal abundance patterns and the performance of malaria incidence models. Nevertheless, the proposed models strengthen the foundation for targeted interventions and surveillance in endemic regions. Despite limitations in data continuity and coverage, the findings highlight the value of combining multiyear entomological data sets with robust imputation and sensitivity analyses to improve predictive modeling in resource-constrained, malaria-endemic settings.

The basic reproduction number of an infectious disease is known to depend on the structure of contacts between individuals in a population. This relationship has been explored mathematically through two well-known models: one which depends on a matrix of contact rates between different demographic groups, and another which depends on the variability of contact rates over the population. Here we introduce a model that combines and generalises these two approaches. We derive a formula for the basic reproduction number and validate it through comparisons to simulated outbreaks. Applying this method to contact survey data collected in Belgium between 2020 and 2022, we find that our model produces higher estimates of the basic reproduction number and larger relative changes over periods when social contact behaviour was changing during the COVID-19 pandemic. Our analysis suggests some practical considerations when using contact data in models of infectious disease transmission.

Background Use of oral cholera vaccine (OCV) is globally recommended as a public health response to cholera outbreaks, alongside water, sanitation and hygiene (WASH) interventions. Estimating vaccine effectiveness during emergencies in low-and middle-income countries is challenging because vaccination campaigns are often implemented over short time frames, while individual-level data are frequently incomplete due to constraints in infrastructure, resources and data systems. There is a need for pragmatic approaches that can generate timely, policy-relevant evidence using routinely collected data. Methods We analysed routine surveillance data from a large 2022-2023 cholera outbreak in Blantyre District, Malawi. The EpiEstim framework was used to generate estimates of the time-varying reproduction number (Rt) from line-listed case data. We modelled changes in Rt as a function of cumulative OCV coverage using a log-linear framework and propagated uncertainty through posterior sampling. Lagged WASH exposure variables were incorporated in the model to generate adjusted vaccine effectiveness estimates and to explore potential interaction effects. Sensitivity analyses assessed robustness to alternative lag structures. Findings The Blantyre outbreak was characterised by an initial period of low-level transmission followed by a sharp increase in cases from late November 2022, after which transmission declined steadily through April 2023. This decline coincided with the implementation of a reactive OCV campaign. The majority of the cases were among middle-aged men living in urban Blantyre. The unadjusted vaccine-associated reduction in transmission was estimated at 53.52% (95% credible interval (CrI):42.5-64.1%). After adjusting for a 7-day rolling average WASH activity, total vaccine effectiveness increased to 62.1% (95% CrI: 49.3-74.9%). Sensitivity analyses using alternative lag structures for WASH exposure produced comparable adjusted estimates. Interpretation Implementation of OCV contributed to a substantial reduction in cholera transmission during the outbreak. This study demonstrates a feasible approach for estimating vaccine-attributable impact whilst accounting for public health and social measures, such as WASH interventions. The methods described will be useful in outbreaks where classical observational designs are not possible, providing actionable evidence to policy makers for outbreak response in resource-limited settings.

Plasmodium vivax transmission from humans to mosquitoes depends on the density of gametocytes that in turn depends on asexual parasite replication and gametocyte commitment. These processes are often analyzed separately, despite being biologically linked and measured with substantial uncertainty. We used a joint Bayesian latent-variable model to simultaneously analyze parasite density, gametocyte density, and mosquito infectivity while accounting for measurement error and propagating uncertainty across linked processes. The model was applied to individual-level data from three P. vivax transmission studies conducted in Ethiopia (n = 455). A tenfold increase in gametocyte density was associated with more than a twofold increase in the odds of mosquito infection (odds ratio [OR] = 2.32, 95% credible interval [CrI]: 2.12-2.54). Asexual parasite density was also positively associated with infectivity after accounting for gametocyte density (OR = 1.74, 95% CrI: 1.60-1.90), and inclusion of parasite density improved predictive performance. Pathway decomposition within the joint model indicated that approximately 41% of the parasite-infectivity association operated through gametocyte density. Increasing age was associated with lower asexual parasite density but higher gametocyte density, resulting in minimal overall association with infectivity. Predicted infection probability increased sigmoidally with gametocyte density, remaining low at lower densities before increasing sharply and approaching a plateau at higher densities. Gametocyte density produced the largest predicted changes in the proportion of infected mosquitoes, while asexual parasite density added predictive information not fully captured by measured gametocyte density alone. This approach links molecular parasite measurements with mosquito infection risk while accounting for measurement uncertainty and provides an interpretable framework for studying the P. vivax infectious reservoir. Author SummaryMalaria transmission occurs when mosquitoes ingest sexual-stage parasites, called gametocytes, during a blood meal. In Plasmodium vivax infections, human-to-mosquito transmission depends on linked biological stages, including asexual parasite replication, gametocyte production, and mosquito infection. These processes are closely connected and often measured with uncertainty, making them difficult to study using standard approaches that analyze them separately. In this study, we applied a joint Bayesian model that analyzes parasite density, gametocyte density, and mosquito infectivity together while accounting for uncertainty in laboratory measurements. Using data from three studies in Ethiopia, we quantified how parasite density, gametocyte density, and host characteristics relate to mosquito infection. The analysis showed that measured gametocyte density alone did not fully explain variation in infectivity, and that asexual parasite density provided additional predictive information. We also found that age was associated differently with asexual parasite and gametocyte densities, resulting in little overall association with infectivity. This approach helps link molecular parasite measurements with transmission outcomes and improves understanding of the P. vivax infectious reservoir in endemic settings.

Dengue transmission is strongly influenced by climatic conditions that affect mosquito population dynamics and virus circulation. In Southern Brazil, where dengue historically occurred at low levels, recent climatic anomalies may be contributing to the expansion of Aedes vectors and an increase in local dengue incidence. This study investigated the spatiotemporal association between climatic variables, Aedes mosquito infestation and dengue cases in Porto Alegre (Southern Brazil, 2018 to 2025). Entomological, surveillance and climatic data were analyzed using Morans I and LISA for spatial association, Kendall correlation, polynomial regression and LASSO to identify relevant drivers and develop predictive models of mosquito infestation and dengue incidence. A strong spatial association between Aedes aegypti and Aedes albopictus was observed, with persistent local clusters detected across all years. Annual climatic variables were associated with mosquito abundance in several districts. Overall, rainfall frequency had a stronger effect on Aedes aegypti abundance than accumulated rainfall. Temperature and lagged infestation indices showed strong association with both species and dengue incidence, with effects observed up to four weeks prior. Predictive models demonstrated good agreement between observed and predicted values, particularly within low to moderate infestation levels. Lagged variables were consistently retained in both mosquito infestation abundance and dengue incidence models, highlighting the importance of temporal predictors for anticipating vector dynamics and dengue risk. This approach is generally applicable for predicting Aedes infestation and disease incidence and emphasizes the importance of integrating entomological and climatic surveillance data to improve anticipation and detection of dengue risk periods and support more effective public health interventions.

Emerging respiratory disease outbreaks pose a major threat to food production systems. Agricultural workers live in larger, more crowded households than the general population, amplifying their potential exposure to respiratory pathogens, yet the consequences for worker health and food production remain poorly understood. We developed a household-structured susceptible-infectious-recovered (SIR) transmission model to compare disease dynamics between agricultural workers and the general U.S. population across six regions. We simulated outbreaks across a range of epidemiological scenarios and assessed productivity losses in California for three labor-intensive crops (oranges, iceberg lettuce, strawberries) with different harvest seasonalities. For a baseline reproduction number of R0 = 1.5, peak disease prevalence among agricultural workers was 1.23-1.45 times higher than that of the general population across regions, and final outbreak sizes were 1.15-1.28 times higher. Peak productivity losses ranged from 0.50%-0.62% across crops, translating to millions in lost revenue. At higher transmissibility and severity (R0 = 3 and assuming all infections are symptomatic), losses were over 2.5 times higher. Household crowding may lead to disproportionate respiratory disease burden among agricultural workers, highlighting the need for targeted outbreak preparedness and mitigation strategies in the agricultural sector to maintain food system resilience and support public health in these communities.

Scorpionism is a public health concern in warm regions, particularly affecting children under 10 years old. Timely treatment with antivenom, provided free by the Brazilian Unified Health System, at strategic care points (PEs) is crucial to prevent avoidable deaths. Our study focused on the Sao Paulo state (SP), which has the largest population in Brazil. The objectives were to adapt a network analysis method suited to SPs context; to assess the efficiency of the SP PE network coverage, considering the 90-minute response time; and to determine the ideal number of vials to be stored at each PE. After adapting the healthcare network analysis, we applied spatial coverage models to evaluate the adequacy of PE response times. We also estimated the demand for antivenom vials at each PE based on Notifiable Diseases Information System data from 2021 to 2023, which is currently limited to the state level. We identified 12 areas lacking coverage, of which only one was suitable for a new PE. The estimated serum requirements aligned with SP's current distributions. However, the estimation carried out according to the PEs has the advantage of reducing the risk of antivenom shortages, especially in emergencies, thus ensuring timely care to prevent avoidable deaths. Our adapted method and PE serum estimates can enhance the scorpion sting care system by supporting geographic planning and optimizing resource allocation. Moreover, these findings and methodologies have potential applicability to other Brazilian regions and warm countries facing similar challenges, contributing to improved access and outcomes for scorpionism victims.

Against the backdrop of global climate change and accelerating population mobility in 2025, chikungunya fever (CHIKF) exhibited a trend of worldwide spread, significantly increasing the difficulty of controlling tropical mosquito-borne diseases. To enhance the precision of intervention strategies, this study developed an age- and sex-structured human-mosquito interaction dynamic model based on data from the largest CHIKF outbreak ever recorded in China, and conducted a targeted analysis of prevention and control strategies. By decomposing the basic reproduction number and examining population heterogeneity, asymptomatic males aged 15-59 years were identified as the core transmission group. Optimal control analysis revealed that the synergistic implementation of three measures-- reducing the effective human-to-mosquito transmission rate, reducing the effective mosquito-to-human transmission rate, and suppressing mosquito population density--could reduce the overall infection rate by 95.7586%. Among these, mosquito population suppression should be prioritized as a universal core strategy; however, its protective effect on females aged 60 years and above was relatively weak, warranting particular attention. The study further demonstrated that asymmetric intensity combinations targeting these three intervention pathways--such as intensity profiles of "10%, 90%, 90%" or "60%, 80%, 90%"--could achieve effective outbreak control. This research elucidates population-specific transmission patterns and key pathways for intervention intensity, providing a theoretical and strategic foundation for the precise control of mosquito-borne diseases. It also provides actionable operational insights to support rapid response and strategy optimization for future emerging outbreaks. Author summaryCHIKF is a mosquito-borne viral disease that is gradually spreading from tropical regions to other areas. To achieve more precise control of this disease, we developed an age- and sex-structured analytical model based on the largest CHIKF outbreak in China, aiming to provide a scientific basis for responding to potential future outbreaks with inherent uncertainties. The study found that asymptomatic males aged 15-59 years were the primary drivers of transmission and should be prioritized as a key population for reducing viral spread in prevention efforts. When evaluating the effectiveness of different intervention strategies, females aged 60 years and above were the least affected by the implemented measures, indicating that this group should strengthen personal protection to lower their infection risk. Among all control measures, mosquito suppression was the most effective, suggesting that vector control strategies should be prioritized in future outbreak responses.

Despite the evolution of epidemiological analysis and modeling tools, difficulties still remain, especially in developing countries, regarding the availability and use of these tools. Often expensive, requiring high technical expertise, demanding constant connectivity of several or sometimes even significant resources, these tools, although efficient, present a major gap with the operational realities of health districts. It is in this context that we introduce Episia, an open-source Python library designed and conceived to provide a framework to facilitate epidemiological analysis and modeling. It integrates a suite of compartmental epidemic models (SIR, SEIR, SEIRD) with a sensitivity analysis using the Monte Carlo method, a complete biostatistics suite validated against the OpenEpi reference standard, as well as a native DHIS2 client for automated data ingestion. Developed in Burkina Faso, it is optimized and aims not only to address these health challenges encountered in Africa but also remains a versatile tool for global health informatics.

BackgroundTo control leishmaniasis, chemotherapy drugs are currently under development. However, these drugs often exhibit poor efficacy and are associated with toxicity, adverse effects, and drug resistance. At present, no specific drug is available for the treatment of leishmaniasis. Meanwhile, vaccine research is ongoing. Recent studies have analysed some experimental vaccines using mathematical models. AimIn previous work, drug targeting was focused on the entire human body rather than specifically addressing infected macrophages and parasites. In our current approach, we aim to eliminate infected macrophages and parasites through nano-drug design. Specifically, we utilise two types of nanoparticles: iron oxide and citric acid-coated iron oxide. Moving forward, we plan to advance this strategy using mathematical modelling of macrophage-parasite interactions. MethodsWe design PDE-based models of macrophages and parasites, incorporating cytokine dynamics, to support nano-drug development. Drug efficacy is estimated using posterior distributions to analyse phenotypic fluctuations of macrophages and parasites during the design phase. We investigate implicit and semi-implicit treatment schemes, focusing on energy decay properties. To model drug flow during treatment, we introduce a three-phase moving boundary problem. Comparative analyses are conducted to evaluate macrophage and parasite behaviour with and without treatment. Finally, the entire framework is implemented within a virtual lab environment. ResultsThe results show that the nano-drug exhibits better efficacy compared to combined drug doses. We analysed and compared two types of nano-drug particles: iron oxide and citric acid-coated iron oxide. We discuss how the drug effectively targets and eliminates infected macrophages and parasites. ConclusionOur models results and simulations will support researchers conducting further studies in nano-drug design for leishmaniasis. These simulations are performed within a virtual lab environment.

Justification: Accidental lab-acquired infections (LAIs) with potential pandemic pathogens (PPPs) in high-biosafety research facilities risk causing a pandemic. Routine testing of lab workers for LAIs coupled with isolation of infected workers could reduce the risk, but the impact of such an intervention may depend on pathogens' epidemiological characteristics. Objective: This study aims to understand how the epidemiological characteristics of PPPs moderate the efficacy of a routine testing and isolation intervention in preventing larger outbreaks after an LAI. Methods: We employed a discrete-time stochastic network infectious disease model to run 625,000 epidemic simulations encompassing 625 unique combinations of five parameters of interest: test frequency, pathogen transmissibility, the self-isolation rate for symptomatic cases, the percentage of cases that are asymptomatic, and the percentage of infectious time that is spent in the pre-symptomatic state among those who show symptoms. To summarize the Monte Carlo simulations, we paired visual analysis with logistic regression for formal hypothesis testing, with an emphasis on the interaction terms that capture the moderating effect of epidemiological parameters on the impact of test frequency. Main Results: There were four main findings. First, the relative reductions in risk of outbreak that were caused by increased test frequency were inversely correlated with pathogen transmissibility. Second, the effect of test frequency was magnified at higher asymptomatic shares when the symptomatic self-isolation rate was high, but minimally when the self-isolation rate is low. Third, the direction of how the symptomatic self-isolation rate moderated the effect of increased test frequency depended on the asymptomatic share. Fourth, as the pre-symptomatic share of infectious time increased, the effect of test frequency on the probability of an outbreak was strongly magnified largely independent of symptomatic self-isolation rates. Conclusions: Routine testing and isolation could significantly mitigate the risk of catastrophic PPP escapes, with the intervention's success varying based on pathogen characteristics. High shares of asymptomatic and pre-symptomatic transmission notably increased the relative risk reductions achieved by the intervention. These findings suggest prioritizing testing interventions for pathogens with high asymptomatic and pre-symptomatic transmission and highlight the symptomatic self-isolation rate as a policy intervention target.

There is an ongoing Oropouche Fever (OF) outbreak in Brazil since 2024. There are dengue and chikungunya prediction models available, but none to help discriminate dengue, chikungunya, and OF. Objective: This study aims to develop and validate clinical prediction models for dengue, chikungunya, OF. Methods: This study uses surveillance data from Espirito Santo state / Brazil, from 2023-2025. Epidemiological investigations and biological samples were used to conclude cases as either (a) clinical-epidemiologically confirmed, (b) laboratory confirmed, or (c) discarded. The predictors were all data related to signs, symptoms, and comorbidities available in the notification forms. The analysis was performed using random forest regression models, one for each outcome, in development and validation datasets. Results: A total of 465,280 observations were analyzed, 261,691 dengue cases (56.6%), 18,676 chikungunya cases (4.0%), 12,174 OF cases (2.6%), and 179,115 discarded cases (38.6%). All three models had good discrimination and moderate to good calibration after scaling prediction. The models retained from 26 to 16 predictors each. Leukopenia and vomiting were the most discriminatory predictors for dengue, arthritis, arthralgia, and rash were the most discriminatory for chikungunya, and epidemiological features were the most relevant for OF. The dengue, chikungunya, and OF models had ROC AUC of 0.726, 0.851, and 0.896 in the validation set, respectively. Conclusion: This research identified predictors most discriminative between dengue, chikungunya, and OF. We developed and validated predictive models, one for each condition, with moderate to very good performance available at https://pedrobrasil.shinyapps.io/INDWELL/. One may use them in diagnostic work-up and arbovirus surveillance.

BackgroundThe World Health Organization (WHO) has raised concerns over increasing Pfhrp2/3 deletions, undermining the sensitivity of Pfhrp2-based rapid diagnostic tests (RDTs). Close monitoring of the population and a change in diagnostic methods are recommended if the prevalence of parasites with Pfhrp2/3 deletions exceeds 5%. In high transmission settings, accurate estimates are hampered by the frequent occurrence of mixed-clone infections (multiplicity of infection; MOI). Objective and MethodsIf parasites with and without deletions are present in an infection, standard molecular assays cannot detect the presence of the former. To accurately estimate frequencies of haplotypes with Pfhrp2/3 deletions in the presence of mixed infections, a novel statistical model that combines genetic/molecular information from Pfhrp2/3 with that from neutral markers is introduced. Maximum-likelihood estimates (MLEs) are obtained for haplotype frequencies characterized by markers at Phrp2/3 loci and loci for neutral markers. The expectation-maximization algorithm is used to derive the MLEs. The adequacy of the method (precision and accuracy) is assessed by numerical simulations. ResultsThe method was applied to an active surveillance study conducted in a tribal community in Jagdalpur, India, which enrolled febrile community members (n = 432) between October and November 2021. Four markers each at Pfhrp2 and Pfhrp3 are combined with one marker each at Pfmsp1 (which encodes P. falciparum merozoite surface protein 1) and Pfmsp2. Data from a total of 117 patients who had both P. falciparum infections and genetic information for the molecular markers underwent further analysis with the novel statistical method. ConclusionResults indicate that this novel method has promising statistical properties (asymptotic and in finite samples) and can be readily applied to real-world situations. A stable implementation of the method in R is provided. This novel approach enables accurate estimation of Pfhrp2/3 deletion frequencies in complex P. falciparum infections, addressing a key limitation of current molecular surveillance methods. Author summaryPlasmodium falciparum (Pf) causes the most severe form of human malaria, accounting for over 90% of cases. Rapid diagnostic tests (RDTs) have become a cornerstone of malaria control. These RDTs detect Pf-specific antigens in a blood drop. HRP2/3 emerged as the best antigen for such tests because it is Pf-specific and expressed in abundance. However, some parasites lack the genes that code for HRP2/3 proteins. If parasites in an infection have such gene deletions, RDT results can be false negative. The WHO considers the containment of such deletions a public health priority and recommends monitoring their prevalence. The detection of HRP deletions is challenging if parasites with and without deletions co-occur in infections because standard molecular assays cannot detect deletions in this situation. To overcome this challenge, we introduce a novel statistical method to estimate the frequency distribution of parasite variants with deletions. The method combines information from neutral molecular markers and from HRP-related markers to correct for unobservable information. Here we provide a derivation of the statistical model, a stable implementation, and test its statistical properties with synthetic and real data, thereby showing that our method is well-suited for the underlying problem.

Background To anticipate the impact of new pneumococcal vaccines and guide future updates, accurate forecasts of changes in non-vaccine serotypes (NVTs) are needed. We developed and evaluated three models that incorporated different assumptions about the way in which NVTs will increase and generated ensemble predictions for the frequency of NVTs in different post- pneumococcal conjugate vaccines (PCV) periods. Methods We analyzed age- and serotype-specific invasive pneumococcal disease (IPD) cases from the United States CDCs Active Bacterial Core surveillance during the pre-PCV (1998-1999), early post-PCV7 (2000-2004), late post-PCV7/pre-PCV13 (2005-2009), early post-PCV13 (2010-2014), and late post-PCV13 (2015-2019) periods. These data were augmented with IPD cases from several countries and combined with serotype-specific invasiveness to infer serotype-specific carriage prevalence. Three models (Ranking, Proportionate, NFDS-lite) generated independent predictions of post-PCV IPD frequencies, which were integrated using an accuracy-weighted ensemble. Model performance was evaluated using the normalized root mean square error (NRMSE). Results A total of 23,959 non-PCV7 and 15,580 non-PCV13 cases were analyzed. NVT cases increased from the pre-PCV7 to the late post-PCV7 and post-PCV13 periods. The accuracy of predictions across age groups and models was consistent and high during the post-PCV13 periods but varied during the post-PCV7 periods. The Proportionate model (NRMSE=0.70-3.95) outperformed the NFDS-lite (NRMSE=0.93-8.91) and Ranking (NRMSE=1.51-5.37) models during the early-post-PCV7 period, whereas the NFDS-lite model (NRMSE=1.55-9.82) was superior to the Proportionate (NRMSE=1.45-10.22) and Ranking (NRMSE=1.86-11.35) models during the late post-PCV7 period. The Ensemble model improved on these individual models. Conclusions The Ensemble model offers a tool for forecasting serotype patterns to inform pneumococcal vaccines impact and future pneumococcal vaccine formulation.

Lightweight epidemic calculators are widely used for teaching and rapid scenario exploration, yet many omit the methodological detail needed for scientific reuse. We present a browser-native SIR calculator that exposes forward Euler and classical fourth-order Runge--Kutta (RK4) integration alongside epidemiologically interpretable outputs and a population-conservation diagnostic. The implementation is anchored to analytical properties of the deterministic SIR system, including the epidemic threshold, the peak condition, and the final-size relation. Benchmark experiments show that RK4 is essentially step-size invariant over practical discretizations, whereas Euler at a coarse one-day step overestimates peak prevalence by 3.97% and final size by 0.66% relative to a fine-step RK4 reference. These results demonstrate that browser-based tools can support publication-quality computational narratives when solver choice, diagnostics, and assumptions are treated as first-class outputs.

Despite the remarkable progress of artificial intelligence represented by large language models, how AI technologies can contribute to the construction of evidence in evidence-based medicine (EBM) remains an overlooked issue. Now, we need an AI that can be compatible with EBM. In the present paper, we aim to propose an example analysis that may contribute to this approach using variable Vision Transformer.