The collection, assembled as a Research Topic titled Advances in Mathematical Modelling for Infectious Disease Control and Prevention, brings together eight studies that span an extraordinary range of biological scales and methodological traditions. Edited by Fahad Al Basir of Asansol Girls’ College in India and Khalid Hattaf of Hassan II University of Casablanca in Morocco, the Research Topic pursues three interrelated goals: understanding the mechanisms underlying infectious diseases, predicting their dynamics, and ensuring effective control and prevention. According to the editors, recent advances in the field have produced models that account for biological delays, coinfection, disease progression, vector-host interaction, and the constraints of healthcare systems, while machine learning, artificial intelligence, and time series analysis now offer powerful tools for analyzing large-scale epidemiological datasets.

At the mechanistic end of the spectrum, Elaiw and colleagues investigated the within-host infection dynamics of a simultaneous HIV and HBV infection using a nonlinear delayed system model. The inclusion of delay parameters, which capture the lagged nature of viral dynamics, makes the system considerably more realistic than instantaneous formulations. By analyzing the stability and long-term behavior of the modeled system, the study demonstrates how delay differential equations can reveal whether a coinfection settles into a persistent state or is driven toward clearance, a question of direct clinical relevance for patients carrying both viruses.

Mathematics can also connect infection dynamics to their long-term consequences, as shown by Amanso and coauthors, who built a model for human papillomavirus infections and cervical cancer prevention. Their framework incorporates disease progression and interventions including vaccination, awareness campaigns, screening, and treatment. Using sensitivity analysis and an optimal control approach, they explored the impact of intervention measures, identifying strategies that minimize both infection and cancer incidence at acceptable cost. The work is a striking example of how mathematical models can bridge the gap between a viral infection today and a malignancy decades later, giving policymakers a quantitative basis for investing in prevention.

The scope of the Research Topic extends beyond human medicine into agriculture. Collins and colleagues extended infectious disease modeling to plant pathology with a study of African cassava mosaic virus and East African cassava mosaic Cameroon virus, two pathogens that devastate a staple crop across much of the developing world. Their model considers disease dynamics and control in the presence of both infected plants and the whiteflies that transmit the viruses, making the study directly relevant to the agricultural and food industries. Because cassava feeds hundreds of millions of people, plant-virus modeling of this kind carries implications for food security that are every bit as serious as those of human epidemic models.

A second cluster of studies turns to the interaction between disease spread and the resilience of healthcare systems. Shaale and coauthors examined the effects of healthcare breakdown on nosocomial infections, constructing a model that explores how reduced healthcare capacity can contribute to rising hospital-acquired infections through weakened infection control measures and the persistence of pathogens in the hospital environment. The importance of this work is especially pronounced because infection control cannot be viewed separately from the healthcare environment itself. Models that explicitly incorporate the capacity of the healthcare system, the editors note, can yield additional insight into infection control problems that capacity-blind models miss entirely, a lesson underlined by the strains placed on hospitals during recent global emergencies.

While mechanistic models focus on the process of transmission, forecasting models aim to predict future epidemic behavior, and the Research Topic features several computational contributions in this vein. Demongeot and colleagues studied epidemic peak forecasting for COVID-19 and dengue by relying on the index of dispersion of new cases, a fairly simple statistical measure. Their research underscores the capabilities of parsimonious approaches while pointing to the importance of surveillance and reporting systems, since even the simplest forecasting statistic is only as good as the case data feeding it.

More sophisticated signal-processing and machine-learning techniques appear in the work of Feng and coauthors, who developed an approach to long-term forecasting of influenza epidemics using spectral filtering and a frequency-aligned loss function. By aligning the training objective with the frequency content of epidemic time series, the authors showed the effectiveness of applying tools from signal processing to traditional epidemiological forecasting, capturing seasonal patterns that standard loss functions can obscure. In a related contribution, Wang and colleagues compared time-series foundation models, namely TimesFM and Moirai, with ARIMA and LSTM networks in their ability to forecast hand, foot and mouth disease. The comparison drew on data from Korea, Singapore, and Chongqing and involved various forecasting horizons, and its findings show that the effectiveness of forecasting depends on the model, the horizon, and the specifics of the data, with no single architecture dominating across all settings.

The final contribution addresses a problem that is sometimes neglected in mathematical modeling: making a model sustainable for public health practice. Thakur and coauthors reflected on lessons learned from the development of the California Communicable Diseases Assessment Tool, known as CalCAT, a public health dashboard built on mathematical modeling. CalCAT illustrates that the usefulness of an infectious disease model goes far beyond its mathematical structure. Proper implementation, the authors argue, involves a reliable data pipeline, transparency of uncertainty estimates, validation against observed outcomes, and sustained interaction with users. If a model is not sustainable and not capable of being used by working epidemiologists, its usefulness is questionable, however elegant its equations may be.

Taken together, the eight studies reveal two complementary trends shaping the future of the field. The first is increasing biological and mathematical complexity, with models incorporating coinfections, time delays, disease progression, vector-host dynamics, and healthcare system limitations. The second is computational convergence, as machine learning, foundation models, time series analysis, and decision-support platforms are folded into the modeling workflow. The editors suggest that future infectious disease modeling will benefit most from the combined use of both traditions: mechanistic models contribute biological plausibility and interpretability, while data-driven models help discover patterns in large datasets. Yet uncertainty remains essential, because epidemiological measurements are subject to incomplete surveillance, reporting delays, changes in behavior and policy, and pathogen adaptation. The central theme of the collection is clear: mathematical models perform at their best when theory and data are combined in the context of real-world public health issues, and continued progress will depend on state-of-the-art techniques, high-quality data, realistic assumptions, proper validation, interdisciplinary collaboration, and clear communication with the practitioners who rely on these models for preparedness and prevention.

Subject of Research: Infectious Disease Modelling

Article Title: Editorial: Advances in mathematical modelling for infectious disease control and prevention

Article References: Al Basir, F., & Hattaf, K. (2026). Editorial: Advances in mathematical modelling for infectious disease control and prevention. Frontiers in Applied Mathematics and Statistics, 12, Article 2016937. https://doi.org/10.3389/fams.2026.2016937

Image Credits: AI Generated

DOI: 10.3389/fams.2026.2016937

Keywords: mathematical modelling, infectious disease, epidemiology, machine learning, public health, forecasting, Editorial, Advances, mathematical, modelling, infectious, disease

Senior author Dr. Chadi Saad-Roy, an assistant professor in UBC’s Department of Mathematics and Department of Microbiology and Immunology, said the models indicate that even modest post-infection costs can tip the scales and make the case for vaccine-driven elimination. The central insight of the work is that most existing frameworks for evaluating infectious disease management strategies focus almost exclusively on the acute phase of infection, quantifying hospitalizations, deaths, and transmission during the active illness while treating recovery as the end of the story. When a substantial fraction of recovered individuals go on to experience lingering sequelae, that accounting error can fundamentally distort the cost-benefit calculus that determines how aggressively a population should be vaccinated.

To address this gap, the team developed a system of mathematical equations designed to examine the interactions between interventions and post-infection effects. From the perspective of society, Dr. Saad-Roy explained, there is some cost to producing vaccines, and there is a large benefit to reducing infections and their downstream consequences, so the essential question is where the optimum lies. For a genuinely benign virus, it may not be worth vaccinating at all. For other infections, it may be best to vaccinate enough people to eliminate the disease entirely. In many realistic cases, the ideal point falls somewhere between those extremes, and the new modelling framework is intended to help answer precisely those questions in a principled way.

The researchers began with an epidemiological model of the classic susceptible-infectious-recovered type, which tracks the fraction of a population in each disease state over time. This compartmental structure allowed them to evaluate non-pharmaceutical interventions that reduce transmission, such as masking, social distancing, and air filtration, as complements to vaccination rather than substitutes for it. On long timescales, the team found that these non-pharmaceutical measures substantially decreased the portion of people who became infected and then recovered, and who were therefore vulnerable to post-infection effects. That result suggests that transmission-reducing measures can meaningfully reduce the long-term disease burden even when their most visible impact is on acute case counts.

The next step was to couple the epidemiological model to an economic one, allowing the researchers to assess how the costs associated with post-recovery syndromes alter the optimal level of vaccine coverage. To build a tool capable of pinpointing that sweet spot, they wrote equations that account for the societal cost of both acute and long-term infection, including expenses such as hospitalization and the loss of labor force productivity, alongside the cost of vaccination in terms of development, production, distribution, and recruitment. This dual accounting makes explicit what has often been implicit in immunization policy debates: the true price of an infection extends well beyond the acute episode, and the true cost of a vaccine campaign depends on how easily coverage can be expanded.

A key innovation of the study lies in how it treats vaccine costs. The team examined three different scenarios describing how the marginal cost of vaccination changes as the number of doses administered increases. In the accelerating scenario, there is a first wave of easy-to-reach vaccine adopters, and it becomes progressively harder to vaccinate additional people because of hesitancy or logistical barriers. In the decelerating scenario, initial resistance to vaccination gives way to peer influence that makes each additional individual easier to reach. The third scenario alternates between acceleration and deceleration, capturing settings where uptake dynamics shift back and forth as campaigns mature.

Across all three cost scenarios, the researchers found that the ideal amount of vaccine coverage was highly sensitive to post-acute effects. Even small post-infection costs were sufficient to shift the optimal coverage to higher rates, and in some cases to push the optimum all the way to elimination levels. This sensitivity means that diseases once considered marginal candidates for elimination campaigns, on the basis of acute severity alone, may become clear candidates once the cumulative burden of post-infection morbidity is factored in. Conversely, the result underscores how fragile optimal policy can be when the long-term consequences of infection are dismissed or left unmeasured.

Dr. Saad-Roy emphasized that the phenomenon needs to be tackled rigorously rather than dismissing individuals’ experiences as anecdotal. To more systematically monitor and document post-infection effects, he suggested that researchers conduct large, long-term cohort studies that survey participants on a broad constellation of symptoms. Such studies would supply the empirical cost estimates that models like these require, replacing scattered clinical impressions with population-level data on incidence, duration, and severity of post-acute conditions. Because these interventions reduce acute infection, he noted, they in turn mitigate post-acute burden and ultimately lead to a healthier world.

The practical implications extend to how vaccination campaigns are designed and communicated. It is critical, the authors argue, to understand which vaccine cost scenario, accelerating or decelerating, is actually at play in a specific setting in order to determine the optimal coverage level. That determination will require socioeconomic studies exploring vaccine decision-making dynamics in a given time and place, which may themselves be shaped by outreach efforts. Prakhar Jaiswal, a graduate student in the UBC Department of Mathematics and first author of the study, observed that educating people about the benefits of vaccination and reducing hesitancy can lower the cost of vaccination and tip the balance in favor of vaccinating to eliminate the disease. In other words, public engagement is not merely a soft complement to immunization programs but a direct lever on the economics that determine whether elimination is the rational target.

The interdisciplinary team behind the study includes co-authors Daniel Coombs of UBC Mathematics, Caroline Wagner of McGill Bioengineering, and Troy Day of Queen’s Mathematics and Statistics. The research was supported by the Natural Sciences and Engineering Research Council of Canada and the Canadian Institutes of Health Research. As health systems worldwide continue to grapple with the legacy of long COVID and prepare for future viral threats, the study offers a quantitative argument that the era of evaluating vaccines by acute outcomes alone is drawing to a close, and that the arithmetic of elimination looks far more favorable once the full lifetime cost of infection is on the table.

Subject of Research: Mathematical modelling of how post-infection morbidity costs influence optimal vaccine coverage levels

Article Title: Even modest post-infection complications can make expanded vaccine coverage cost effective: New modelling

Article References: Even modest post-infection complications can make expanded vaccine coverage cost effective: New modelling. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: vaccine coverage, post-infection complications, long COVID, mathematical modelling, cost-effectiveness, epidemiology, non-pharmaceutical interventions, vaccine hesitancy, disease elimination, public health, University of British Columbia, PNAS

The rationale behind the launch lies in a long-standing imbalance in how science rewards its outputs. Mathematical models and experimental or observational data play a fundamental role in assuring and improving food supply, quality and safety in a globalised and rapidly changing world, prompting food scientists, agronomists and computer scientists to form strategic cross-disciplinary alliances. Yet the rapid development of new methods, software and hardware has produced an ever-growing flood of data, models and tools, often described as a data deluge. While scholarly tradition credits researchers primarily through conventional articles and citation counts, many invaluable products of the research cycle, so-called research objects such as data, software scripts, visualisation routines and models, remain undervalued, underused and uncredited despite their importance for scientific progress.

The consequences of this neglect are tangible. Scientists frequently repeat work already done by others instead of building on it as a stepping stone for further analysis, and reproducibility or reusability of published results can become impossible or restricted. Worse still, a vast share of precious data, software tools and other research outcomes has been lost outright, for example tools hosted on academic web pages that quietly go inactive over time, because there is no opportunity to publish them within a research article and no long-term preservation infrastructure exists to keep them alive. For industry and other stakeholders, these intermediate products are often of primary interest, which makes their disappearance from the scientific record all the more costly.

The Open Science movement has been steadily correcting this picture. What began as Open Access, a barrier-free route to research articles championed by initiatives such as the Budapest Open Access Initiative, evolved into Open Data, extending openness to the data and data-processing procedures underpinning research results. Following the natural logic of the research cycle, other valuable products, including software, methods, protocols and models, then emerged as discrete, openly publishable research objects linked to each other via repositories or within the research article itself, ensuring reproducibility and reusability. The idea has since influenced every stage of the research process, including scholarly publishing, and has been materialised in funder, governmental and European Union policy documents.

The Food Modelling Journal was created to help research scientists, modellers and software developers in the food domain share their valuable research objects more efficiently than conventional articles or information repositories allow. Its stated aim is to provide the much-needed scientific record and credit for dedicated models, data, data analytics workflows and software, thereby fostering the overall reproducibility and reusability of research. The journal emerged after intensive consultations with communities participating in the AGINFRA+ project and beyond, and enjoys particular support from the Risk Assessment Modelling and Knowledge Integration Platform (RAKIP) community, established by three European institutions specialising in food safety risk assessment: the French agency ANSES, the German Federal Institute for Risk Assessment (BfR) and Denmark’s National Food Institute (DTU Food).

RAKIP was created to provide infrastructure for hosting, saving and preserving a variety of scientific knowledge, including publications, experimental data and mathematical models, alongside resources such as databases and software tools for model generation and application, all based on harmonised data formats and consistent rules for knowledge annotation. The new journal is expected to support this and many other community activities in food modelling by adding a scholarly publishing infrastructure that facilitates the exchange of digital assets at community- and industry-accepted standards. Its scope covers information objects and digital resources related to food safety, food quality, food control, food defence and food design, documenting data, models, software and services, data analytics pipelines, visualisation methods and related digital resources.

Several key features distinguish the journal from conventional outlets. The first is a philosophy of quality rather than quantity: FMJ focuses on important outcomes of the research cycle other than research papers, which nonetheless meet rigorous scientific standards, with subject editors drawn from among world-leading international experts overseeing peer review. A cross-disciplinary approach from the outset aims to bridge basic and applied research through non-conventional article types such as model descriptions, data analytics papers, software descriptions and design concepts. The journal also enforces a strict open data and software code policy: authors must make all data, software or models described in their articles compliant with the FAIR principles, ensuring maximum transparency and reproducibility.

Speed and machine-readability are further hallmarks. Thanks to an innovative, entirely XML-based publishing workflow provided by Pensoft, accepted manuscripts can be published within two to three days of acceptance. All articles appear in semantically enhanced HTML, JATS XML and PDF formats, multiplying opportunities for content dissemination, data and text mining, discovery through automated and targeted searches, and barrier-free data sharing. Behind the scenes, the ARPHA-XML workflow supports every stage of publication, from manuscript writing through peer review, publication, distribution and archiving, within a single online collaborative working space.

The ARPHA Writing Tool at the heart of this workflow offers collaborative manuscript authoring with pre-defined but flexible templates covering most types of research outcomes. Co-authors can work together on a manuscript and invite external contributors, such as mentors, pre-submission reviewers, linguistic and copy editors or colleagues, who may correct and comment before submission without being listed as co-authors. Rich functionalities allow searching and importing literature and data references, cross-referencing in-text citations, importing tables, uploading images and multimedia, and building image plates. An automated technical validation step checks manuscripts for consistency, complemented by human pre-submission validation from the editorial office, while optional pre-submission external peer reviews can be submitted alongside the manuscript to speed up post-submission evaluation. The collaborative peer-review process supports change tracking, comments and replies, and automated, customisable email and social network notifications, with reviews automatically consolidated into a single online file for editors.

Published papers remain living objects. Readers can comment through an inbuilt commenting tool and an integrated hypothes.is plugin, and articles can undergo open post-publication peer review. Authors can convert published papers back into editing mode at the click of a button, revise them and republish them as new versions under different DOIs linked to previous versions via CrossMark, realising the concept of a living article. In a particularly practical touch, users of food model repositories can automatically convert FSK-ML, the Food Safety Knowledge Markup Language, metadata describing their models into manuscripts in the ARPHA Writing Tool, which can then be elaborated, submitted, peer reviewed and published in the journal.

The journal is also tightly woven into the AGINFRA+ Virtual Research Environment, which follows a system-of-systems approach, aggregating resources from domain-agnostic service providers such as D4Science, EGI and OpenAIRE as well as community-specific ones including AgroDataCube, AGROVOC and the RAKIP model repository. Through a web gateway offering a single access point, researchers gain a shared workspace for storing and sharing research artefacts, a social networking area for collaborative discussion, semantic data management solutions built on ontologies, thesauri and vocabularies, and a data analytics workbench integrated with RStudio, Jupyter notebook and Galaxy workflow environments. Data visualisation and publishing services range from a graphs management workbench producing interactive charts, from generic spline and scatter plots to highly specific plant-height-over-time graphs with values and images, to a catalogue-based publishing platform aligned with the FAIR principles. By relying on this platform, authors can mix the narrative of a traditional paper with links that give effective access to the digital versions of their research products, closing the loop between publication and the working tools of science.

The editorial team behind the Food Modelling Journal is convinced that the venture fills an obvious gap in the publishing landscape by bringing to light research outputs of exceptional value to their authors and to the food science community at large. By turning models, data and code into first-class, citable publications, the journal invites researchers to share their passion for Open Science and to spread the word throughout the community, so that the next breakthrough in food safety or quality can begin not from scratch, but from a well-documented, preserved and credited foundation left by those who came before.

Subject of Research: Open-access scholarly publishing of food science models, data and software as citable research objects

Article Title: Open Science meets Food Modelling: Introducing the Food Modelling Journal (FMJ)

Article References: Filter, M., Candela, L., Guillier, L., Nauta, M., Georgiev, T., Stoev, P., & Penev, L. (2019). Open Science meets Food Modelling: Introducing the Food Modelling Journal (FMJ). Food Modelling Journal, 1, Article e46561. https://doi.org/10.3897/fmj.1.46561

Image Credits: AI Generated

DOI: 10.3897/fmj.1.46561

Keywords: Food Modelling Journal, Open Science, food safety, food quality, mathematical modelling, FAIR principles, AGINFRA+, Virtual Research Environment, ARPHA Publishing Platform, peer review, research objects, FSK-ML

The research team, working with incipient colonies collected at the Federal University of São Carlos and experimented on at the Luiz de Queiroz College of Agriculture in Piracicaba, São Paulo, set out to quantify a behaviour that is easy to observe but hard to weigh. They housed miniaturised colonies in plaster-lined Petri dishes, each containing half a gram of symbiotic fungal garden, one larva, one pupa, and ten medium-sized ants, and let them acclimate for 48 hours. Three treatments followed: a Tween 80 control, a suspension of the entomopathogenic fungus Metarhizium anisopliae, and a suspension of Escovopsis phialicopiosa, the notorious garden parasite. Suspensions were adjusted to 10^8 conidia per millilitre and applied with an airbrush spray. Cameras recorded four hours of behaviour per replicate at 30-minute intervals, with five-minute focal sampling analysed in BORIS software, allowing the researchers to measure, in seconds, how much time workers spent on self-grooming versus allogrooming.

The behavioural data revealed a striking asymmetry. Under the control treatment, maximum allogrooming reached 1701 seconds, with a mean of 330 seconds. When Metarhizium anisopliae was present, those figures collapsed to a maximum of 169 seconds and a mean of just 54 seconds, suggesting that workers facing a virulent pathogen pull back from social contact, likely to avoid horizontal spore transmission between nestmates. Escovopsis phialicopiosa, by contrast, produced allogrooming values close to the control, with a maximum of 1639 seconds and a mean of 82 seconds. Self-grooming, meanwhile, remained relatively stable across treatments, with means hovering between 12 and 14.4 seconds. The largest difference among treatments appeared between the maximum and mean allogrooming values, hinting that social hygiene is the more behaviourally plastic of the two defences.

To translate these seconds into colony-scale consequences, the team built on a mathematical framework originally developed by Kang and colleagues in 2011 to describe biomass dynamics in Acromyrmex ants, a genus that typically cultivates its fungus in a single chamber. Two coupled differential equations track ant biomass and fungal biomass over time: ant growth depends on fungal biomass consumed through a Holling Type I functional response, while fungal growth depends on the labour ants allocate to harvesting and processing leaves, modelled with a Type III numerical response and a half-saturation constant. Mortality rates for both partners, plus a conversion rate of fungus into ant biomass, close the loop. The new study extended this single-chamber model into a two-chamber system, because Atta sexdens cultivates its symbiotic fungus across multiple underground chambers rather than one.

The extension introduced two crucial innovations. First, grooming was written directly into the mortality terms: pathogen-induced mortality for ants and fungus is scaled by the factor one minus the grooming time divided by its maximum, so the longer workers spend grooming, the lower the pathogen’s lethal intensity. The same structure was applied to allogrooming by substituting the allogrooming time. Second, a one-way transfer rule was added between chambers: when fungal biomass in chamber A exceeds its carrying capacity, a density-dependent flux moves ants and fungus to chamber B, mimicking how real Atta workers relocate brood and fungal biomass to newly excavated chambers as the colony expands. This directional flow reflects the colony’s developmental trajectory, in which older chambers act as sources of biomass for new compartments, and it embodies the organisational immunity that compartmentalised nests provide by limiting pathogen dissemination.

Simulations solved with the deSolve package in R, using the lsoda numerical method, produced sigmoidal growth curves for both partners, consistent with classical models of obligate mutualism. When the one-way transfer parameter was set to 0.1 per day, biomass decreased in the source chamber while increasing in the receiving chamber, and inflection points in the growth curves, identified from the second derivative of the biomass trajectories, were delayed. Those delayed inflection points mark a critical growth shift: the moment when accelerating growth gives way to deceleration. Under pathogen pressure, the timing of these shifts became diagnostic. In the Metarhizium treatment, ant and fungal biomasses dropped significantly compared with the control, and inflection points moved to higher values, producing a time-delayed effect. In the Escovopsis treatment, curves closely resembled the control, with only minor shifts in inflection values.

The most sobering result concerned Metarhizium anisopliae’s resilience to grooming. Even when the model was fed the maximum measured self-grooming and allogrooming durations, the pathogen still significantly reduced the biomasses of both ants and symbiotic fungus. The reason lies in the pathogen’s direct virulence to workers: Metarhizium infects insects through conidia that adhere to the cuticle, germinate, and penetrate the integument, so self-grooming alone may be insufficient to prevent infection even though it reduces spore quantity and viability. Infection may also impair worker activity, and empirical studies have shown that Metarhizium can disrupt essential tasks such as foraging, destabilising the social homeostasis that keeps a colony functioning. Escovopsis phialicopiosa, an opportunistic pathogen that attacks the cultivated fungus rather than the ants themselves, had minimal impact unless fungal mortality exceeded a critical threshold, roughly above 0.5 per day in the simulations, suggesting the strain’s lower virulence fails to trigger strong defensive responses.

Bifurcation diagrams, which map how equilibrium states change as parameters vary, exposed the system’s stability landscape. The proportion of workers allocated to fungus cultivation, denoted q, proved decisive: when q approached zero, too few workers tended the garden and biomass collapsed; when q approached one, workers neglected other essential tasks to the detriment of colony function. Intermediate values between 0.4 and 0.6 maximised stability by balancing competing demands. Ant and fungal mortality rates produced non-linear declines in most simulations, with a pronounced threshold near 0.5 per day for ant mortality, beyond which worker losses exceeded the colony’s replacement capacity and fungal biomass accelerated its decline. Self-grooming proved more effective than allogrooming in mitigating Metarhizium’s effects, likely reflecting the prioritisation of individual defences under high pathogen pressure, and ant biomass associated with self-grooming was significantly higher than biomass associated with allogrooming, although fungal biomass did not follow this pattern.

These findings carry practical weight well beyond ecology. Atta species are dominant herbivores in Neotropical biomes, capable of consuming up to 17 percent of available foliar biomass, and they inflict substantial damage on Eucalyptus and Pinus plantations as well as crops such as cassava and coffee. Biological control with entomopathogenic fungi has long struggled against these ants precisely because their multi-chambered nests and layered behavioural defences hinder uniform application of control agents. By identifying the behavioural and structural factors that limit or enhance pathogen effectiveness, the model suggests that targeted interventions could exploit critical mortality thresholds and worker allocation dynamics, minimising environmental impacts while optimising population control. The framework also provides a useful tool for understanding epidemiological dynamics in natural nests, integrating behavioural defences and spatial strategies to safeguard a mutualism that has persisted for 66 million years, and pointing toward future studies that could extend the two-chamber model into interconnected multi-chamber networks to reveal how full nest topology shapes ecological stability.

Subject of Research: Grooming behaviour and nest architecture regulating biomass growth of leafcutter ants and their symbiotic fungus under entomopathogenic fungal infection

Article Title: The role of grooming in regulating biomass growth of ants and symbiotic fungi under entomopathogenic fungal infection: experiments and mathematical modelling

Article References: Bueno, I., Toledo, J. V. M. F. D., Canuto, L. S., & Godoy, W. A. C. (2025). The role of grooming in regulating biomass growth of ants and symbiotic fungi under entomopathogenic fungal infection: experiments and mathematical modelling. BioRisk, 23, 17-43. https://doi.org/10.3897/biorisk.23.151538

Image Credits: AI Generated

DOI: 10.3897/biorisk.23.151538

Keywords: leafcutter ants, Atta sexdens, symbiotic fungus, grooming, Metarhizium anisopliae, Escovopsis phialicopiosa, entomopathogenic fungi, mathematical modelling, bifurcation analysis, mutualism, social immunity, biological control

The new work, led by Martin Lubich of University Hospital Sofiamed together with Krassimir Atanassov, Peter Vassilev, Vassia Atanassova, Chavdar Slavov, Elenko Popov and Lyudmila Todorova, uses a modelling framework known as generalized nets. These structures, an extension of Petri nets developed by Atanassov and colleagues, are designed to represent parallel, concurrent and interdependent processes. That makes them unusually well suited to physiology, where material flows such as blood and urine run simultaneously with informational flows such as the signals exchanged between the bladder, the nervous system and the muscles. The model is conceptually aligned with network physiology, the perspective that views the body as interacting subsystems rather than isolated organs.

The physiological basis of the model is drawn from established urological literature. The kidneys, two bean-shaped organs, receive roughly twenty percent of cardiac output through the renal arteries, which branch down to the glomerular capillary plexus. Each kidney contains on the order of one to one and a half million nephrons, the functional units where filtration begins. Blood enters the glomerulus through the afferent arteriole, and hydrostatic pressure drives water and small solutes across the filtration barrier, which consists of the fenestrated capillary endothelium, the glomerular basement membrane and the filtration slits between podocyte foot processes. Blood cells and most plasma proteins are held back and leave through the efferent arteriole to rejoin the bloodstream.

The numbers involved are striking. In a healthy young adult male, the glomerular filtration rate is approximately 180 liters per 24 hours, or about 125 milliliters per minute. With a typical blood volume of around five liters and a plasma volume near three liters, renal blood flow runs at about 1.1 liters per minute. Of the roughly 605 milliliters of plasma entering the glomeruli each minute, about 125 milliliters, nearly a fifth, is filtered, while the remaining 480 milliliters continues through the efferent arterioles. The filtration rate depends on the water permeability of the filtration membrane, the available filtration surface area and the net filtration pressure, the algebraic sum of hydrostatic and oncotic pressures. In many diseases, the reduction in filtration rate stems not from pressure changes but from the loss of functioning nephrons, which shrinks the total glomerular filtration surface.

In the generalized net model, these nephron-level processes, including glomerular filtration, tubular reabsorption, tubular secretion and urine concentration, are aggregated within each kidney transition. The model contains eight transitions, nineteen places and seventeen types of tokens. The transitions represent the cardiovascular system, the left and right kidneys, the bladder, the nervous system, the prostate gland, the muscle system and the urethra. Tokens carry the material and informational content: blood tokens flow from the cardiovascular system to each kidney and return as purified blood, while urine tokens travel toward the bladder. Status tokens monitor and report the current state of each organ, including the quantity of urine in the bladder and, in the pathological case, the residual urine left after incomplete emptying.

The essence of the model can be described in a few sentences. Blood from the cardiovascular system enters the left and right kidneys and, after being purified, returns to the cardiovascular system, while the substances excreted through the ureters go to the bladder. When the bladder is full, a signal is sent from it through the nervous system to the bladder muscles, which causes urine to be pushed out of the bladder into the urethra. When the prostate gland is functioning normally, urine passes through the prostatic urethra without significant obstruction. In the pathological branch, prostate-related bladder outlet obstruction reduces urinary outflow and may result in incomplete bladder emptying and residual urine.

The prostate itself is the pivot of the pathological branch. The gland, part of the male reproductive system that produces seminal fluid, typically has a volume of about 20 milliliters in young adult men. It sits beneath the bladder and in front of the rectum, surrounding the proximal portion of the urethra. With advancing age, particularly in association with benign prostatic hyperplasia, the gland may enlarge, and a volume greater than approximately 30 milliliters is commonly used as a clinical threshold for prostatic enlargement. An enlarged prostate compresses the urethra and impedes the normal flow of urine, producing a weak urinary stream, difficulty initiating urination, nocturia, a sensation of incomplete emptying, double voiding and the need to apply intra-abdominal pressure during urination. These symptoms are collectively known as lower urinary tract symptoms.

What makes the model technically interesting is how it encodes the difference between normal and pathological states. In the bladder transition, when there is enough urine, a token splits and a signal token enters the nervous system with one of two characteristics: a signal from stretch receptors if there is no problem with the prostate, or an increased frequency of bladder emptying impulses if there is. That detail mirrors a real clinical phenomenon: obstructed bladders tend to send more frequent, more urgent signals, which is why men with prostate obstruction often experience nocturia and frequency. The nervous system then relays a contraction signal to the muscle system, the detrusor contracts, and urine leaves the bladder. In the pathological case, the residual quantity of urine is reflected in the updated characteristic of the bladder-status token, which remains in place, capturing the clinical reality of incomplete emptying.

The construction was deliberately analytical. No patient-level dataset, automated model-generation tool or proprietary software was used; the anatomical entities, physiological processes and interactions were identified on the basis of the cited medical literature and the authors’ clinical expertise. The formal specification was examined for internal consistency, checking the correspondence between input and output places, the continuity of token pathways, the use of declared token types, the consistency of transition predicates and the symmetry of the left- and right-kidney representations. The authors note that the model extends earlier generalized net research on the human body and complements more focused models concerning nephrological diseases, prostate cancer development and the evaluation of lower urinary tract symptoms.

The limitations are acknowledged candidly. The model is intentionally qualitative and operates at an aggregated organ-system level; it does not represent detailed intrarenal, urodynamic, neurological or muscular mechanisms, nor does it distinguish among different degrees of obstruction. No clinical or data-driven validation has been performed, and such validation would require temporally aligned measurements of renal function, bladder filling and emptying, neural signalling, detrusor activity, obstruction and urine flow in individual patients. Despite this, the framework provides a formal basis for the qualitative comparison of normal and pathological states, and future development may include clinically defined degrees of obstruction, enabling scenario-based simulation and comparison with patient observations. The work was supported by the Bulgarian National Science Fund under a grant on prognostic modelling in prostate cancer patients. For a field where benign prostatic hyperplasia affects a large share of aging men, a formal, extensible map of the urinary relay, with the prostate explicitly in the path of flow, offers a foundation that educators, modellers and, eventually, clinical decision-support developers can build upon.

Subject of Research: Mathematical modelling of the male urinary system with prostate-related outflow obstruction

Article Title: Generalized net modelling of the male urinary system with prostate-related outflow obstruction

Article References: Lubich, M., Atanassov, K., Vassilev, P., Atanassova, V., Slavov, C., Popov, E., & Todorova, L. (2026). Generalized net modelling of the male urinary system with prostate-related outflow obstruction. Frontiers in Network Physiology, 6, Article 1888801. https://doi.org/10.3389/fnetp.2026.1888801

Image Credits: AI Generated

DOI: 10.3389/fnetp.2026.1888801

Keywords: generalized nets, mathematical modelling, prostate gland, benign prostatic hyperplasia, urinary system, network physiology, Generalized, modelling, male, urinary, system, prostate-related

The study addresses what the authors describe as an urgent need in teacher education: preparing graduates with the knowledge, skills, and ethical dispositions required to confront pressing global challenges, particularly climate change. Teacher education programmes have traditionally treated sustainability as the domain of geography, biology, or environmental science courses, leaving mathematics largely untouched. But mathematics is deeply implicated in how climate information is produced, communicated, and contested. Rising temperature curves, carbon budget calculations, probability estimates of extreme weather events, and statistical summaries of biodiversity loss are all mathematical artefacts. A teacher who cannot help students read these artefacts critically leaves them ill-equipped to navigate public discourse about the climate.

To probe this gap, the researchers designed media-based learning activities within the undergraduate programme. Prospective teachers of mathematics engaged with climate-related media and then participated in structured reflection and dialogue focused on pedagogical approaches for integrating climate literacy into mathematics teaching. The method is deceptively simple: rather than lecturing future teachers about sustainability, the programme asked them to encounter media representations of the climate emergency, reflect in writing on what they saw, and discuss in groups how such material might be turned into mathematics lessons. The researchers then applied discourse analysis to the prospective teachers’ written reflections and group discussions, examining how the participants made sense of the climate emergency and of their roles as future educators.

The findings point to three powerful psychological and social mechanisms at work. First, the media-based reflections fostered shared attention. When a group of future teachers engages with the same piece of climate media, they develop a common object of focus, which makes collective discussion possible in a way that abstract lectures rarely achieve. Second, the activities produced emotional engagement. Climate media, by its nature, tends to evoke affective responses, and the study found that these responses were not obstacles to learning but resources for it, animating the prospective teachers’ sense that the issue mattered to them personally. Third, and perhaps most significantly, the reflections cultivated a sense of collective responsibility. Participants came to see responding to the climate emergency not as someone else’s job but as part of what it means to be a mathematics educator.

Beyond these mechanisms, the analysis revealed that participants articulated genuine shifts in perspective and envisaged concrete changes to their future mathematics teaching practices. The researchers organised these envisioned changes into three thematic areas, each of which maps onto a distinct strand of mathematical capability. The first theme is raising awareness of climate change through cultivating data literacy. Climate science rests on data: time series of global temperatures, measurements of atmospheric carbon dioxide concentrations, records of sea level rise, and inventories of greenhouse gas emissions. A mathematics teacher who equips students to interpret graphs, understand rates of change, and evaluate the reliability of data sources is, in effect, teaching climate awareness through the mathematics curriculum itself.

The second theme involves teaching environmental sustainability through mathematical modelling. Modelling is a cornerstone of both school mathematics reform and climate science. When students build a simple model of population growth, resource consumption, or carbon accumulation, they experience firsthand how mathematical assumptions translate into projections about the future. The prospective teachers in the study came to see modelling not merely as an abstract technique for satisfying curriculum requirements but as a vehicle through which students can explore sustainability questions, test scenarios, and appreciate the consequences of different policy and behavioural choices. This reframing turns the mathematics classroom into a space where environmental futures are not just described but actively explored.

The third theme centres on fostering critical evaluation of quantitative information and representations. This may be the most distinctive contribution of mathematics education to climate literacy. Public debates about climate change are saturated with numbers, charts, and statistics, and these can be manipulated, cherry-picked, or misleadingly scaled. A line graph with a truncated axis can make a steep warming trend look gentle; a percentage change without an absolute baseline can exaggerate or minimise an effect. The prospective teachers recognised that teaching students to interrogate such representations, to ask who produced a figure, how it was calculated, and what it leaves out, is a form of civic education that mathematics is uniquely positioned to deliver. Critical data literacy, in this view, is a defence against both misinformation and despair.

What makes the study methodologically interesting is its use of discourse analysis as the primary analytical lens. Rather than measuring climate literacy with a before-and-after test, the researchers examined the language prospective teachers used in their written reflections and group discussions, tracking how their sense-making evolved. Discourse analysis is well suited to capturing the subtle ways in which people negotiate identity and responsibility. When a future teacher shifts from describing climate change as a global problem to describing what she would do in her own classroom, that linguistic shift marks a genuine reorientation of professional identity, something a standardised questionnaire might miss entirely.

The broader significance of the work lies in its argument about pedagogy. Media-based reflective pedagogies, the authors suggest, can support the development of climate literacy by strengthening prospective teachers’ capacity for critical reflection on sustainability issues and their pedagogical implications. This positions media not as a distraction from rigorous mathematics teaching but as a catalyst for it. News reports, documentary footage, and data visualisations provide authentic contexts that give mathematical content meaning, while the structured reflection ensures that emotional responses are channelled into professional planning rather than left as free-floating anxiety. The approach also aligns with international calls to embed education for sustainable development across teacher preparation, rather than confining it to specialist courses.

The study was conducted in accordance with the ethical principles of the Declaration of Helsinki, with ethical approval granted by the Chinese University of Hong Kong’s Survey and Behavioural Research Ethics Committee, and it was fully supported by the Worldwide Universities Network Research Development Fund. Its implications extend well beyond Hong Kong. Mathematics teacher educators around the world face the same challenge: a generation of students who will live through intensifying climate impacts, taught by teachers who may feel that climate change belongs to someone else’s subject. This research offers a concrete, replicable pathway forward, one in which the humble tools of the mathematics classroom, graphs, models, and critical questions about numbers, become instruments of climate understanding. As the climate emergency deepens, the study suggests, the question is no longer whether mathematics teachers should engage with it, but how quickly teacher education programmes can prepare them to do so well.

Subject of Research: Media-based reflection activities for developing climate literacy among prospective mathematics teachers

Article Title: Media-based reflections to foster climate literacy in mathematics teacher education

Article References: Ng, O.-L., Wan, S. W.-Y., & Coles, A. (2026). Media-based reflections to foster climate literacy in mathematics teacher education. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09162-z

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09162-z

Keywords: climate literacy, mathematics education, teacher education, media-based reflection, prospective teachers, data literacy, mathematical modelling, sustainability education, discourse analysis, critical evaluation, Hong Kong, environmental sustainability

The study, led by Bamidele Nyemike Ogunro of the University of Ibadan together with colleagues from Corona Management Systems in Abuja, the Federal University of Technology Akure, and Nigeria’s National Malaria Elimination Programme, emerged from the Nigeria Malaria Modelling Fellowship, an initiative funded by the Bill & Melinda Gates Foundation. The work addresses a persistent gap in malaria strategy: while bed nets and preventive drugs act on adult mosquitoes or on the parasite once infection has occurred, larva source management attacks the vector at its aquatic stages, killing larvae in ponds, puddles, irrigation channels and other standing water before they can mature into biting adults.

At the heart of the analysis is a deterministic SEIR-based compartmental model, a mathematical framework that divides the human population into susceptible, exposed, infectious and recovered classes, and couples these to the mosquito population across its developmental stages, from eggs and larvae to pupae and adults. This human-vector coupling is what allows the model to capture the feedback loop that drives malaria transmission: more infected people mean more parasites available to mosquitoes, and more adult mosquitoes mean more infectious bites. Seasonality, a defining feature of malaria in the Sahelian zone where Zamfara sits, was incorporated through a Gaussian function that modulates vector dynamics across the year, producing the characteristic seasonal surges that strain health facilities during and after the rainy season.

To ground the model in reality, the researchers calibrated it against historical malaria incidence data from 2020 to 2024, supplied by the National Malaria Elimination Programme. Calibration is the process by which model parameters, such as biting rates, development times and recovery rates, are adjusted until simulated case numbers track observed surveillance data. The team then computed the basic reproduction number, R0, using the next-generation matrix method, a standard technique in mathematical epidemiology that identifies how many secondary infections a single infectious individual generates in a fully susceptible population. For Zamfara under the current intervention mix, R0 came out at 1.18, a value indicating persistent but not explosive transmission, consistent with a setting where control measures are holding the epidemic back but not breaking it.

Because models are only as trustworthy as their sensitivity to assumptions, the team performed a one-at-a-time local sensitivity analysis, varying each parameter individually by plus or minus ten percent while holding all others at baseline values. The results were unambiguous: parameters governing adult mosquito survival and abundance exerted the strongest influence on malaria incidence, which is precisely the life stage that larva source management ultimately reduces by throttling the supply of new adults. This finding aligns with the broader logic of integrated vector management, which holds that reducing vector density complements, rather than replaces, interventions that target the parasite or protect individuals.

A critical test of any predictive model is whether it can forecast data it has never seen. The researchers therefore carried out out-of-sample validation, simulating the calibrated model against independent empirical malaria incidence data from 2025. The model accurately synchronized with the timing of the 2025 seasonal peak, a demanding test given how sharply transmission rises and falls across the year in Zamfara. It did, however, underpredict absolute case volumes during the peak months, a discrepancy the authors attribute to single-year transmission volatility, the inherent randomness of real-world epidemics that a deterministic model smooths over. The validation nonetheless supports the model’s use as a strategic planning tool for comparing intervention scenarios.

The headline result concerns what happens when larva source management is layered onto existing interventions. Simulations showed that adding LSM significantly reduced malaria incidence, with up to 19.3 percent of cases averted when 50 percent of breeding sites were covered. But the relationship between coverage and impact proved strikingly non-linear. Diminishing returns set in beyond 20 percent implementation, meaning each additional percentage point of coverage bought progressively less epidemiological benefit. The sweet spot, according to the analysis, lies at around 30 percent implementation, a threshold the authors identify as balancing epidemiological gain against efficiency loss, with an estimated 132,000 malaria cases averted annually at that level. For resource-constrained health systems, this kind of efficiency analysis is often more actionable than a simple maximum-impact figure, because it identifies where the marginal naira of programme spending stops delivering proportional returns.

Timing emerged as another crucial practical consideration. The impact of larva source management did not appear immediately: measurable reductions in incidence became apparent only after roughly five months of continuous implementation. This lag reflects the biology of the intervention, which works by progressively depleting the adult mosquito population rather than instantly killing infected vectors or curing patients. For programme planners, the implication is that LSM must be initiated well before the transmission season peaks, and that expectations must be managed during the early months of a campaign when case counts may not yet show improvement.

The model also revealed an important distributional dimension of the intervention’s benefits. Larva source management produced higher case reductions among children, the group most vulnerable to severe malaria and malaria-related mortality. Because severe disease in children drives hospital admissions, school absenteeism and long-term developmental harm, an intervention that disproportionately protects this age group delivers benefits beyond raw case counts. This finding strengthens the case for LSM as a child-focused equity measure, complementing seasonal malaria chemoprevention, which already targets children under five with preventive antimalarial drugs during peak transmission months.

The authors conclude that malaria transmission in Zamfara State remains persistent under the current intervention mix, and that incorporating larva source management into the existing strategy would be beneficial for control. The study was conducted under ethical approval from Nigeria’s National Health Research Ethics Committee and in accordance with the Code of Professional Ethics for Simulationists of the Society for Modeling and Simulation International, using only aggregated secondary data for calibration. As Nigerian health authorities weigh how to push toward elimination goals in high-burden northwestern states, this analysis offers a quantified, locally calibrated argument that the fight against malaria should begin where the mosquito does: in the water.

Subject of Research: Mathematical modelling of larva source management as a supplementary malaria control intervention in Zamfara State, Nigeria

Article Title: A compartmental modelling approach for incorporating larva source management into malaria control strategy in Zamfara State, Nigeria

Article References: Ogunro, B. N., Adegbite, G., Obi, C., Eche-George, A., Eshikhena, G., Ojielo, N., Akoma, D., Gayawan, E., Nelson, E., Ogbulafor, N. C., Ntadom, G., & Kaduru, C. (2026). A compartmental modelling approach for incorporating larva source management into malaria control strategy in Zamfara State, Nigeria. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14536-4

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14536-4

Keywords: malaria, larva source management, mathematical modelling, SEIR model, vector control, Zamfara State, Nigeria, basic reproduction number, sensitivity analysis, seasonal malaria chemoprevention, integrated vector management, BMC Infectious Diseases

The research team, led by Chisom Chimbundum Adim of the Africa Center of Excellence for Public Health and Toxicological Research, together with Aminanyanaba Onari Asimiea and Ugochi Adaku Okengwu, constructed a deterministic compartmental model of the SEIR-SEI type. In this framework, human hosts move through susceptible, exposed, infectious and recovered states, while mosquito vectors pass through susceptible, exposed and infectious stages. This dual structure captures the essential biology of malaria: the parasite must complete incubation in both a human and an Anopheles mosquito before transmission can continue, and the model tracks those coupled populations simultaneously over time.

Biological parameters governing mosquito longevity, biting behaviour, parasite development rates and recovery times were drawn from the published literature. Crucially, the team anchored their model to reality by calibrating transmission intensity against parasite prevalence among under-five children reported for the study area in the 2021 Nigeria Malaria Indicator Survey. All data cleaning and analysis were performed in the R programming environment, and because the study relied on secondary, anonymised information, the Ethics Research Committee of the University of Port Harcourt approved the work without requiring individual patient consent.

The centrepiece of the analysis is the effective reproduction number, a quantity epidemiologists use to gauge whether an outbreak is growing or shrinking. Derived here using the next-generation-matrix method, the reproduction number represents the average number of secondary infections generated by a single infectious individual in a population where some control measures are already in place. When this number exceeds one, each infection sparks more than one new infection and transmission persists; when it falls below one, the epidemic chain gradually breaks apart.

Under current intervention coverage in Rivers State, the model estimated an effective reproduction number of approximately 2.6, well above the elimination threshold. In other words, even with insecticide-treated nets and artemisinin-based combination therapy deployed at existing levels, every childhood malaria infection is still generating more than two subsequent infections on average. The counterfactual scenario is even starker: without any interventions at all, the reproduction number would climb to roughly 8.5, meaning current programmes are already preventing an enormous volume of transmission, just not enough to turn the tide.

To understand which levers matter most, the researchers performed a sensitivity analysis, systematically varying each model parameter and measuring its influence on transmission. The results were instructive. The mosquito biting rate exerted the strongest positive influence, adding about 1.0 to the reproduction number, followed by the transmission probabilities and the vector-to-host ratio, each contributing roughly 0.5. On the protective side, insecticide-treated-net coverage emerged as the single most powerful brake, subtracting about 1.08, with ITN-induced mosquito mortality and natural mosquito mortality contributing further reductions of 0.42 and 0.35 respectively. The message is clear: anything that reduces how often mosquitoes bite humans, or how long mosquitoes live, delivers outsized epidemiological returns.

Scenario analysis then translated these insights into projections for control policy. At present coverage levels, the combined effect of nets and treatment roughly halves the endemic infection prevalence among under-fives, from an estimated 15.8 percent in the absence of control to about 7.5 percent. That is a substantial public health gain, translating into fewer clinical episodes, fewer hospital admissions and fewer deaths among a group whose immature immune systems leave them uniquely vulnerable to severe malaria. Yet the model shows this gain is epidemiologically fragile: with the reproduction number still near 2.6, prevalence rebounds quickly whenever coverage slips.

The pivotal finding concerns what happens when both interventions are scaled up together. The model projects that raising insecticide-treated-net use to at least 80 percent coverage, combined with expanded effective treatment using artemisinin-based combination therapies, drives the reproduction number down to approximately 0.8, below the critical value of one. At that level, each infection generates fewer than one secondary case, and transmission chains begin to collapse, projecting genuine interruption of malaria transmission in the study population. Importantly, neither intervention alone at currently attainable coverage could achieve this; the synergy arises because nets suppress the vector population and biting rate while effective therapy shortens how long infected children remain infectious, attacking the transmission cycle from both directions simultaneously.

These results carry direct implications for malaria control strategy in Rivers State and, by extension, other highly endemic settings in Nigeria. Rather than pursuing nets and drugs as parallel, independently managed programmes, the findings argue for integrated planning in which coverage targets are set jointly and progress is monitored against the reproduction number rather than prevalence alone. The sensitivity analysis offers a practical prioritisation: investments that push net coverage toward and beyond the 80 percent mark, sustain the insecticidal effect that raises mosquito mortality, and ensure that suspected cases receive prompt, effective artemisinin-based therapy will yield the greatest epidemiological payoff. Conversely, complacency about biting-rate drivers, such as unresolved breeding sites and gaps in vector suppression, risks undermining gains made elsewhere.

The study also demonstrates the value of data-driven modelling as a decision-support tool for public health authorities working with limited resources. By calibrating a mechanistic model to national survey data and then testing intervention scenarios computationally, the researchers could compare futures that would be impossible to test experimentally in the field, estimating in advance which combinations of coverage levels are likely to achieve elimination and which will merely blunt the epidemic. As Nigeria pursues its national malaria elimination ambitions, work of this kind suggests that the path forward in Rivers State runs not through any single silver bullet, but through the disciplined, simultaneous scale-up of the two interventions already in the toolbox, guided by models that can tell programme managers precisely how close they are to the tipping point.

Subject of Research: Mathematical modelling of malaria transmission dynamics and intervention strategies in children under five in Rivers State, Nigeria

Article Title: Data-driven modelling of malaria transmission among under-five children to inform targeted control strategies in Rivers State, Nigeria

Article References: Adim, C. C., Asimiea, A. O., & Okengwu, U. A. (2026). Data-driven modelling of malaria transmission among under-five children to inform targeted control strategies in Rivers State, Nigeria. BMC Public Health. https://doi.org/10.1186/s12889-026-29781-0

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29781-0

Keywords: malaria, mathematical modelling, SEIR-SEI model, effective reproduction number, insecticide-treated nets, artemisinin-based combination therapy, under-five children, Rivers State, Nigeria, transmission dynamics, public health, sensitivity analysis

The research team, led by Abbie Evans and William S. Hart of the University of Warwick together with collaborators including Eunok Jung of Konkuk University and Robin N. Thompson of the University of Oxford, developed an age-structured mathematical model designed to capture two intertwined processes. The first is the demographic transition itself: the shifting distribution of ages within a population as fertility declines and longevity increases. The second is behavioural adaptation, the ways in which individuals and institutions respond to demographic change, most notably through extended workforce participation as societies age and older people remain economically active for longer. By coupling these processes within a single framework, the model moves beyond static snapshots of contact patterns and instead tracks how outbreak risk evolves across decades of structural change.

The central quantity the model estimates is the probability of a major outbreak, defined as the likelihood that a single pathogen introduction into a population leads to sustained transmission rather than rapid extinction. This probability is a cornerstone of outbreak risk assessment, yet it is often estimated using contact data from a single point in time. The new study argues that such snapshots can be misleading when the underlying population is changing. Contact patterns, which describe how often individuals of different ages interact, are strongly age-dependent: children mix intensively at school, working-age adults encounter large numbers of colleagues and commuters, and older adults typically have fewer daily contacts. Susceptibility and infectiousness, meanwhile, can also vary with age depending on the pathogen in question.

To ground the model in realistic data, the authors drew on demographic projections for the Republic of Korea, a country whose fertility rate has fallen among the lowest in the world and whose age structure is transforming faster than almost any other. Korea’s trajectory makes it a natural laboratory for studying the epidemiological consequences of population ageing, since the changes it will experience over the coming decades are ones that many other nations, including China, Italy and Japan, are likely to follow. The researchers combined these projections with age-specific contact matrices, which quantify the rates at which individuals in each age band interact with those in every other band, and with pathogen-specific assumptions about how susceptibility and infectiousness scale with age.

The headline finding is that population ageing may, in many scenarios, reduce the probability that an imported infection triggers a major outbreak. The mechanism is intuitive: as the share of older individuals in the population grows and the share of children and young adults shrinks, the average number of contacts per person declines, since older individuals tend to have fewer social interactions than younger age groups. Fewer contacts mean fewer opportunities for transmission, which lowers the effective reproduction number and makes sustained chains of spread less likely. For pathogens whose transmissibility does not increase sharply with age, an older population therefore acts as a partial buffer against epidemic take-off.

However, the study shows that this protective effect is far from universal, and the authors identify two main factors that can attenuate or even reverse it. The first is the pathogen’s own age profile. For infections whose susceptibility or infectiousness increases with age, the demographic advantage of an older population shrinks, because the growing fraction of older individuals is precisely the group most likely to acquire and transmit infection. In such cases, ageing does not simply reduce the number of transmission opportunities; it shifts those opportunities toward the individuals for whom each contact carries the greatest epidemiological weight. The model demonstrates that the direction and magnitude of the age gradient in susceptibility and infectiousness can therefore determine whether demographic change raises or lowers outbreak risk.

The second attenuating factor is behavioural. As societies age, labour force participation among older adults is expected to rise, whether through later retirement ages, pension reforms or the need to sustain shrinking workforces. When older individuals remain in employment, their daily contact rates increase, eroding the contact advantage that retirement confers. The study specifically models extended workforce participation as a behavioural response to demographic change and finds that it increases the number of contacts older individuals have, thereby offsetting the reduction in outbreak probability that ageing alone would produce. This coupling between demography and behaviour is the study’s key conceptual contribution: the two cannot be assessed in isolation, because demographic transitions induce behavioural responses that feed back into transmission dynamics.

The implications for public health planning are significant. Outbreak risk assessments are typically built on contact surveys and demographic data from the recent past, and models calibrated to current conditions may project inaccurate risks for future decades. If a country’s age structure is changing rapidly, the probability that a given pathogen, say a novel respiratory virus or a re-emerging vaccine-preventable infection, establishes sustained transmission may shift substantially over a planning horizon of twenty to thirty years. The study suggests that governments preparing pandemic preparedness strategies should incorporate demographic projections and anticipated behavioural changes, such as rising retirement ages, into their risk models rather than assuming a stationary population with fixed mixing patterns.

The work also highlights the value of computational approaches that integrate multiple drivers of epidemic risk. Age-structured models of this kind allow researchers to disentangle the separate contributions of contact rates, susceptibility, infectiousness and population composition, and to ask counterfactual questions: how would outbreak risk change if retirement ages rose by five years, or if a pathogen’s age gradient in severity were steeper? By varying these components systematically, the authors show that the net effect of demographic change on outbreak vulnerability is the product of competing forces, some of which reduce risk and others of which increase it. This decomposition provides a template for applying the same analysis to other countries and other pathogens.

More broadly, the study is a reminder that infectious disease risk is embedded in social structure. The probability that a pathogen introduction becomes an epidemic is not a fixed biological property of the pathogen alone; it emerges from the interaction between the pathogen’s characteristics and the age distribution, contact behaviour and economic organisation of the host population. As demographic transitions reshape societies worldwide, the authors argue that accounting for demographic and socio-behavioural context will be essential for accurately assessing future outbreak risks and for designing interventions, from vaccination targeting to workplace policies, that remain effective as populations age.

Subject of Research: Age-structured modelling of how demographic change and behavioural responses alter the probability of major infectious disease outbreaks

Article Title: Demographic changes and behavioural responses shape vulnerability to infectious disease outbreaks

Article References: Evans, A., Hart, W. S., Jung, E., Nah, K., Bonic-Babic, K., Jung, S.-M., & Thompson, R. N. (2026). Demographic changes and behavioural responses shape vulnerability to infectious disease outbreaks. PLOS Computational Biology, 22(10), e1014319. https://doi.org/10.1371/journal.pcbi.1014319

Image Credits: AI Generated

DOI: 10.1371/journal.pcbi.1014319

Keywords: population ageing, infectious disease outbreaks, mathematical modelling, age-structured model, contact patterns, South Korea, workforce participation, outbreak probability, epidemiology, PLOS Computational Biology, demographic transition, behavioural adaptation

KRAS is one of the most frequently mutated oncogenes in human cancer, acting as a molecular switch that locks cells into a growth-promoting state. In most colorectal cancers, the same handful of canonical KRAS variants appear again and again, particularly substitutions at codons 12 and 13 that interfere with the protein’s ability to switch itself off. These canonical mutations are potent drivers, and their recurrence has traditionally been read as evidence of strong positive selection. Yet in hypermutant cancers, tumours burdened with extraordinarily high mutation loads, a strikingly different picture emerges. Atypical KRAS variants, mutations at unusual positions or of unusual types, appear far more often than their oncogenic strength would seem to justify.

This paradox has troubled cancer evolutionary biologists for years. If a weak driver confers less of a growth advantage than a canonical one, and if both are available to a tumour, conventional evolutionary logic predicts that the stronger driver should prevail and outcompete the weaker alternative. The persistence of weak drivers in hypermutant tumours therefore demanded an explanation. One intuitive hypothesis was that these tumours might somehow favour weak drivers through differential selection, perhaps because the cellular context of a hypermutant cancer changes what mutations are useful. Nicholson and Tomlinson set out to test whether such an appeal to altered selection was actually necessary.

Their approach was to build mathematical models of tumour evolution that track the appearance and expansion of driver mutations over the course of a cancer’s development. The models incorporate the basic currency of cancer genetics: cell divisions, mutation rates, the probability that a given division produces a driver mutation, and the selective growth advantage each driver confers. By varying these parameters, the researchers could ask under what conditions weak drivers would be expected to dominate the final tumour population. The answer they found was deceptively simple. When the mutation rate per cell division is elevated, further driver events occur so rapidly that the dynamics of tumour evolution are fundamentally altered, and weak drivers can rise to dominance even in direct competition with stronger alternatives.

The intuition behind this result is worth unpacking. In a tumour with a normal mutation rate, driver events are rare. A cell that acquires a strong driver mutation has time to expand into a large clone before the next driver arises, so the strongest available driver tends to sweep through the population. But in a hypermutant tumour, driver events arrive in quick succession. A cell carrying a weak driver may acquire additional driver mutations before any stronger competitor has had the chance to establish itself. The weak driver effectively gets a head start, and the sheer speed of subsequent mutation means that the tumour’s evolutionary trajectory is shaped more by the order and timing of events than by the fine ranking of selective advantages. Selection still matters, but the race is run so fast that the starting position can matter more than the finishing speed.

The researchers also found that this effect can be amplified by mutational biases, systematic tendencies of a mutational process to produce certain nucleotide changes in certain sequence contexts rather than others. Mutational processes are not random. Ultraviolet light, tobacco smoke, defective DNA repair enzymes and defective proofreading polymerases each leave characteristic fingerprints in the genome, preferentially mutating particular bases in particular neighbourhoods. If a hypermutational process happens to target the sequence contexts required for atypical KRAS variants, while rarely generating the contexts needed for canonical ones, then the supply of candidate weak drivers is inflated relative to the supply of strong ones. Combined with an elevated mutation rate, this bias can be sufficient to make atypical drivers the dominant species in the tumour.

To test whether this theoretical framework matched reality, the team focused on a specific and clinically important example: colorectal cancers with mutations in POLE, the gene encoding the proofreading domain of DNA polymerase epsilon. When this proofreading function fails, the enzyme makes copying errors at a greatly elevated rate, producing some of the highest mutation burdens seen in any human cancer. Nicholson and Tomlinson quantified the mutation bias of the POLE-mutant process for a range of KRAS driver variants, comparing the sequence contexts generated under POLE deficiency with those generated under non-hypermutant mutational processes. They found that the combination of the POLE-specific bias and the elevated mutation rate was sufficient to explain the observed enrichment of atypical KRAS drivers in these tumours. No special selective regime needed to be invoked.

The model’s predictive power extended beyond POLE-mutant cancers. Mismatch repair deficient colorectal cancers, another hypermutant class arising from a different DNA repair defect, also show an elevated prevalence of atypical KRAS drivers. When the researchers applied their model to this setting, its predictions were consistent with the observed variant frequencies. This is a notable result because POLE deficiency and mismatch repair deficiency generate mutations through different chemical mechanisms with different sequence biases, yet both produce the same qualitative outcome: weak, atypical drivers rising to prominence. The convergence suggests that the underlying principle, hypermutation reshaping the balance between mutation supply and selection, is general rather than specific to one repair pathway.

The broader implication of the study is a clarification of how mutation and selection interact during tumorigenesis. Textbook accounts of cancer evolution often treat driver mutations as the winners of a pure selection contest, with the fittest mutations sweeping to fixation. This work shows that in hypermutant tumours, the mutational process itself can act as a filter that determines which drivers are even available in meaningful numbers, and that the tempo of mutation can override fine-grained differences in selective advantage. Differential selection across cancer types need not be invoked to explain why different tumour classes carry different driver repertoires. The variation may simply be a consequence of how fast each tumour mutates and what kinds of mutations its particular defect produces.

There are also practical consequences for precision oncology. Atypical KRAS variants have historically been poorly characterised compared with their canonical counterparts, and their functional consequences are often uncertain. If many of these variants arise as passengers or weak drivers propelled by hypermutation rather than as potent oncogenic signals, this has implications for how they are interpreted in clinical sequencing reports and for which variants become targets of drug development. More broadly, the study adds to a growing appreciation that mutation rate is not merely a background parameter of cancer evolution but an active force that shapes which evolutionary paths tumours take. Understanding the interplay between mutational processes and selection, the authors conclude, is essential for explaining the diversity of driver mutations observed across human cancers, and mathematical modelling of the kind employed here offers a way to disentangle these forces that raw genomic data alone cannot provide.

Subject of Research: Evolutionary dynamics of weak KRAS driver mutations in hypermutant colorectal cancers

Article Title: Hyper-mutational processes provide a head-start for weak cancer drivers: Explaining atypical KRAS variants

Article References: Nicholson, M. D., & Tomlinson, I. (2026). Hyper-mutational processes provide a head-start for weak cancer drivers: Explaining atypical KRAS variants. PLOS Computational Biology, 22(10), e1014858. https://doi.org/10.1371/journal.pcbi.1014858

Image Credits: AI Generated

DOI: 10.1371/journal.pcbi.1014858

Keywords: KRAS, hypermutation, POLE, colorectal cancer, mathematical modelling, cancer evolution, driver mutations, mismatch repair deficiency, mutational bias, PLOS Computational Biology, tumorigenesis, selection