Biology subjects

Riley, S.

Publications and source records attributed to Riley, S..

15 recordsLinked to original sources

bioRxiv2026

Distinct associations between multimodal brain measures and psychopathology domains predict adolescent functioning

Adolescent psychopathology is partly rooted in measurable disruptions across key neural networks, yet the field still lacks an integrated, multimodal understanding of these brain-behavior links. Here, we examined how structural, microstructural, and functional measures across corticostriatal, corticolimbic, and executive control networks relate to psychopathology domains and explored how these associations predicted future psychosocial functioning. We used data from the Adolescent Brain Cognitive DevelopmentSM Study (n=5,408) and ran a regularized canonical correlation analysis to identify distinct modes of covariation between multiple brain measures and psychopathology domains when youth were 13-14 years old. The resulting canonical brain and psychopathology scores were used to predict school-related impairment one year later. First, higher diffusivity and decreased activation during a reward task across all three networks as well as lower corticostriatal surface area were related to higher broad psychopathology. Second, lower corticolimbic diffusivity, executive control volume and surface area, and cortical thickness across all three networks as well as higher corticostriatal and corticolimbic volumes were related to higher anxiety but lower externalizing. For the first mode, higher psychopathology scores predicted more school-related impairment one year later. For the second mode, higher brain and higher psychopathology scores predicted less school-related impairment one year later. Identifying how specific neural measures align with psychopathology domains, as well as how both forecast reallilworld functioning, advances the conceptualization of adolescent mental health. This approach clarifies which levels of analysis provide distinct versus shared information about youth functioning and highlights potential mechanisms that may inform future targets for change.

neuroscience↗

bioRxiv2026

Modelling between-cell heterogeneity in within-host influenza virus infection

Cell tropism, or the preference of a virus for particular cell types, has major implications for viral transmission, pathogenesis, and evolution. An increase in viral fitness -- increased within-host replication, also leading to increased transmission between hosts -- can result from a virus changing its cell tropism. This is illustrated in the context of influenza, where adaptation to infect cells expressing 2-6 linked sialic acid receptors enhances human-to-human transmissibility. Target cell populations differ not only in abundance but also in intrinsic properties such as susceptibility, viral production, and interferon responses, rendering the relationship between tropism and viral fitness multi-faceted and complex. Understanding how different cell tropisms quantitatively change fitness remains an important open question in virology and quantitative biology. Here, we present a within-host mathematical model that incorporates distinct target cell types differing in key properties, and examine how cell tropism affects viral fitness, as measured by metrics such as peak viral load, infection duration, or total virus produced. Our analysis reveals that tradeoffs may arise when cell types differ by multiple characteristics. We further demonstrate that model parameters describing heterogeneity between cell types can be more accurately inferred when cell type proportions are measured alongside viral load. Our findings provide a framework for assessing the links between viral evolution, cell tropism, and within-host fitness, and motivate the design of experiments to collect quantitative data on between-cell heterogeneity.

microbiology↗

bioRxiv2025

Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotype

Popular methods for analyzing the brains functional connectome examine statistical associations between pairs of atlas-defined brain regions, viewing the strength of these links as independent values. However, edges within a standard connectivity matrix, i.e., correlations between individual regions or nodes, are not independent. They are part of an interconnected system. Here, we propose that consideration of both independent, linear relationships (as in standard approaches such as linear kernel ridge regression and connectome-based predictive modeling) as well as higher order statistical associations - such as tertiary interactions between matrix components and global features of the matrix space - will enhance identification of meaningful individual differences. To test this, we adopt a geometrically grounded measure of similarity that accounts for higher-order local statistical relationships and global interactions, the Wasserstein metric. Results indicate that considering connectivity matrices as representations of their associated Gaussian distributions significantly improves both identification of individuals based on their connectivity matrices (aka, fingerprinting) and prediction of individual differences in phenotypes such as fluid intelligence and openness to experience. Thus, both pairwise local and global brain connectivity properties encode for meaningful individual differences that relate to phenotypic expressions and should be considered in brain-behavior predictive models.

neuroscience↗

bioRxiv2025

TMT-based quantitative proteomic assessment of Vicia sativa induced neurotoxicity by β-cyano-L-alanine and γ-glutamyl-β-cyano-L-alanine in SH-SY5Y cells.

{beta}-Cyano-L-alanine (BCA) and {gamma}-glutamyl-{beta}-cyano-L-alanine (GBCA) are the primary antinutritional compounds present within the seeds of the high protein and drought tolerant orphan legume Vicia sativa. Evidence of neurotoxicity is limited to symptom analysis from animal feed trials whilst the molecular mechanisms underlying suspected neurotoxicity in monogastric animals are largely unknown. In this study, we first optimised an in vitro cell-based assay for rapid testing of BCA and GBCA toxicity in the retinoic acid differentiated SH-SY5Y human neuroblastoma cells. Using this system, we then performed proteomics analyses to determine dysregulated expression of proteins in BCA or GBCA treated of differentiated SH-SY5Y cells. Our findings indicate that BCA affects expression of proteins involved in DNA damage and translation whilst GBCA treatment causes dysregulation of those involved in mitosis and cell cycle. Following BCA treatment, we identified changes in many proteins previously suggested to have close association with neurodegenerative diseases including amyotrophic lateral sclerosis and Alzheimers disease as well as cancers. Following GBCA treatment, dysregulation of proteins involved in apoptosis pathways was observed. Finally, the lack of common dysregulated proteins and pathways in BCA or GBCA treated cells indicated that they most likely cause neurotoxicity via distinct mechanisms. SignificanceAntinutritional compounds or toxins limit the use of many potential grain crops including V. sativa (Common Vetch), and BCA and GBCA are the two principal toxic compounds in Common Vetch grain. Vetch grain toxicity is associated with neurotoxic symptoms in animals. Characterizing the proteome of BCA- and GBCA-treated neural cells gives insights into the excitotoxicity mechanism and identification of biomarkers for screening higher quality grain. Quantitative tandem mass tag (TMT) mass spectrometry-based proteome profiling of BCA treated neural cells characterized 6,827 proteins, of which 26 were significantly up-regulated and 73 significantly downregulated. However, TMT proteome analysis of GBCA treated cells identified 76 significantly up regulated and 86 downregulated proteins. These results provide insight into the toxicity of BCA and GBCA and enable future identification of putative direct protein targets. We describe for the first time BCA playing a role in modulating the expression DNA damage proteins and translation, and GBCA playing a role in modulating the expression of mitosis and cell cycle proteins, elucidating the mechanisms of plant-derived toxins in mammalian cell neurotoxicity HighlightsO_LIEstablished in vitro cell-based toxicity assay for {beta}-cyano-L-alanine and {gamma}-glutamyl-{beta}-cyano-L-alanine. C_LIO_LI{beta}-Cyano-L-alanine treatment dysregulated many proteins associated with DNA damage in retinoic acid differentiated SH-SY5Y cells. C_LIO_LI{gamma}-Glutamyl-{beta}-cyano-L-alanine treatment dysregulated proteins associated with cell cycle. C_LI

pharmacology and toxicology↗

bioRxiv2023

Pathogenic Leptospira are widespread in the urban wildlife of southern California

Leptospirosis is the most widespread zoonotic disease in the world, yet it is broadly understudied in multi-host wildlife systems. Knowledge gaps regarding Leptospira circulation in wildlife, particularly in densely populated areas, contribute to frequent misdiagnoses in humans and domestic animals. We assessed Leptospira prevalence levels and risk factors in five target wildlife species across the greater Los Angeles region: striped skunks (Mephitis mephitis), Northern raccoons (Procyon lotor), coyotes (Canis latrans), Virginia opossums (Didelphis virginiana), and fox squirrels (Sciurus niger). We sampled more than 960 individual animals, including over 700 from target species in the greater Los Angeles region, and an additional 260 sampled opportunistically from other regions and species. In the five target species, seroprevalences ranged from 5-60% and active infection prevalences ranged from 0.8-15.2% in all except fox squirrels (0%). Patterns of serologic reactivity suggest that mainland terrestrial wildlife, particularly mesocarnivores, could be the source of repeated observed introductions of Leptospira into local marine and island ecosystems. Overall, we found evidence of widespread Leptospira exposure in wildlife across Los Angeles and surrounding regions. This indicates exposure risk for humans and domestic animals and highlights that this pathogen can circulate endemically in many wildlife species even in densely populated urban areas.

ecology↗

bioRxiv2020

Stabilizing selection of seasonal influenza receptor binding in populations with partial immunity

Mutations that alter cellular receptor binding of influenza hemagglutinin (HA) have profound effects on immune escape. Despite its high mutation rate, it is not fully understood why human influenza HA displays limited antigenic diversity across circulating viruses. We applied phylogenetic analysis and phylodynamic modeling to understand the evolutionary and epidemiological effects of binding avidity adaptation in humans using net charge as a marker for receptor binding avidity. Using 686 human influenza A/H3N2 HA sequences, we found that HA net charge followed an age-specific pattern. Phylogenetic analysis suggested that many binding variants have reduced fitness. Next, we developed an individual-based disease dynamic model embedded with within-host receptor binding adaptation and immune escape in a population with varied partial immunity. The model showed that mean binding avidity was unable to adapt to values that maximized transmissibility due to competing selective forces between within- and between-host levels. Overall, we demonstrated stabilizing selection of virus binding in a population with increasing partial immunity. These findings have potential implications in understanding the evolutionary mechanisms that determine the intensity of seasonal influenza epidemics.

evolutionary biology↗

bioRxiv2020

Common Vetch: a drought tolerant, high protein neglected leguminous crop with potential as a sustainable food source.

Global demand for protein is predicted to increase by 50% by 2050. To meet the increasing demand whilst ensuring sustainability, protein sources that generate low-greenhouse gas emissions are required and protein-rich legume seeds have the potential to make a significant contribution. Legumes like common vetch (Vicia sativa) that grow in marginal cropping zones and are drought tolerant and resilient to changeable annual weather patterns, will be in high demand as the climate changes. In common vetch, the inability to eliminate the {gamma}-glutamyl-{beta}-cyano-alanine (GBCA) toxin present in the seed has hindered its utility as a human and animal food for many decades, leaving this highly resilient species an "orphan" legume. However, the availability of the vetch genome and transcriptome data together with the application of CRISPR-Cas genome editing technologies lay the foundations to eliminate the GBCA toxin constraint. In the near future, we anticipate that a zero-toxin vetch variety will become a significant contributor to global protein demand.

plant biology↗

bioRxiv2019

The nanophthalmos protein TMEM98 inhibits MYRF self-cleavage and is required for eye size specification

The precise control of eye size is essential for normal vision. TMEM98 is a highly conserved and widely expressed gene which appears to be involved in eye size regulation. Mutations in human TMEM98 are found in patients with nanophthalmos (very small eyes) and variants near the gene are associated in population studies with myopia and increased eye size. As complete loss of function mutations in mouse Tmem98 result in perinatal lethality, we produced mice deficient for Tmem98 in the retinal pigment epithelium (RPE), where Tmem98 is highly expressed. These mice have greatly enlarged eyes that are very fragile with very thin retinas. To gain insight into the mechanism of action we used a proximity labelling approach to discover interacting proteins and identified MYRF as an interacting partner. Mutations of MYRF are also associated with nanophthalmos. The protein is an endoplasmic reticulum-tethered transcription factor which undergoes autoproteolytic cleavage to liberate the N-terminal part which then translocates to the nucleus where it acts as a transcription factor. We find that TMEM98 inhibits the self-cleavage of MYRF, in a novel regulatory mechanism. In RPE lacking TMEM98, MYRF is ectopically activated and abnormally localised to the nuclei. Author summaryHaving the correct eye size is important, too large and you will be short-sighted and too small and you will be far-sighted. Nanophthalmos, literally very small eye from the Greek, is a condition where the eye is very small but structurally normal. In addition to being farsighted such eyes are prone to glaucoma which can lead to loss of vision. Here we studied a protein called TMEM98 which is found in the membranes of the cells which form a layer at the back of eye called the retinal pigment epithelium (RPE). Mutations in TMEM98 have been found in nanophthalmos patients. Patients have one normal copy of the gene and one carrying a mutation. We removed Tmem98 from the RPE of mice in order to ascertain its function. We found, surprisingly, that rather than having small eyes this led to the development of very large eyes that were structurally fragile. We went to on to identify protein partners of TMEM98 and found that it interacts with a protein called MYRF, mutations in which also causes nanophthalmos. This work demonstrates the importance of TMEM98 in eye size specification.

genetics↗

bioRxiv2019

Serosolver: an open source tool to infer epidemiological and immunological dynamics from serological data

We present a flexible, open source R package designed to obtain additional biological and epidemiological insights from commonly available serological datasets. Analysis of serological responses against pathogens with multiple strains such as influenza pose a specific statistical challenge because observed antibody responses measured in serological assays depend both on unobserved prior infections and the resulting cross-reactive antibody dynamics that these infections generate. We provide a general modelling framework to jointly infer these two typically confounded biological processes using antibody titres against current and historical strains. We do this by linking latent infection dynamics with a mechanistic model of antibody dynamics that generates expected antibody titres over time. This makes it possible to use observations of antibodies in serological assays to infer an individuals infection history as well as the parameters of the antibody process model. Our aim is to provide a flexible inference package that can be applied to a range of datasets studying different viruses over different timescales. We present two case studies to illustrate how our model can infer key immunological parameters, such as antibody titre boosting, waning and cross-reaction, and well as latent epidemiological processes such as attack rates and age-stratified infection risk.

immunology↗

bioRxiv2019

Social networks with strong spatial embedding generate non-standard epidemic dynamics driven by higher-order clustering

Some directly transmitted human pathogens such as influenza and measles generate sustained exponential growth in incidence, and have a high peak incidence consistent with the rapid depletion of susceptible individuals. Many do not. While a prolonged exponential phase typically arises in traditional disease-dynamic models, current quantitative descriptions of non-standard epidemic profiles are either abstract, phenomenological or rely on highly skewed offspring distributions in network models. Here, we create large socio-spatial networks to represent contact behaviour using human population density data, a previously developed fitting algorithm, and gravity-like mobility kernels. We define a basic reproductive number R0 for this system analogous to that used for compartmental models. Controlling for R0, we then explore networks with a household-workplace structure in which between-household contacts can be formed with varying degrees of spatial correlation, determined by a single parameter from the gravity-like kernel. By varying this single parameter and simulating epidemic spread, we are able to identify how more frequent local movement can lead to strong spatial correlation and thus induce sub-exponential outbreak dynamics with lower, later epidemic peaks. Also, the ratio of peak height to final size was much smaller when movement was highly spatially correlated. We investigate the topological properties of our networks via a generalized clustering coefficient that extends beyond immediate neighbourhoods, identifying very strong correlations between 4th order clustering and non-standard epidemic dynamics. Our results motivate the joint observation of incidence and socio-spatial human behaviour during epidemics that exhibit non-standard incidence patterns. Author SummaryEpidemics are typically described using a standard set of mathematical models that do not capture social interactions or the way those interactions are determined by geography. Here we propose a model that can reflect social networks influenced strongly by the way people travel and we show that they lead to very different epidemic profiles. This type of model will likely be useful for forecasting.

systems biology↗

bioRxiv2018

Characterising antibody kinetics from multiple influenza infection and vaccination events in ferrets

The strength and breadth of an individuals antibody repertoire are important predictors of their response to influenza infection or vaccination. Although progress has been made in understanding qualitatively how repeated exposures shape the antibody mediated immune response, quantitative understanding remains limited. We developed a set of mathematical models describing short-term antibody kinetics following influenza infection or vaccination and fit them to haemagglutination inhibition (HI) titres from 5 groups of ferrets which were exposed to different combinations of trivalent inactivated influenza vaccine (TIV with or without adjuvant), priming inoculation with A/H3N2 and post-vaccination inoculation with A/H1N1. Based on the parameter estimates of the best supported model, we describe a number of key immunological features. We found quantifiable differences in the degree of homologous and cross-reactive antibody boosting elicited by different exposure types. Infection and adjuvanted vaccination generally resulted in strong, broadly reactive responses whereas unadjuvanted vaccination resulted in a weak, narrow response. We found that the order of exposure mattered: priming with A/H3N2 improved subsequent vaccine response, and the second dose of adjuvanted vaccination resulted in substantially greater antibody boosting than the first. Although there was considerable uncertainty in our estimates of antibody waning parameters, our results suggest that both short and long term waning were present and would be identifiable with a larger set of experiments. These results highlight the potential use of repeat exposure animal models in revealing short-term, strain-specific immune dynamics of influenza.\n\nAuthor summaryDespite most individuals having some preexisting immunity from past influenza infections and vaccinations, a significant proportion of the human population is infected with influenza each year. Predicting how an individuals antibody profile will change following exposure is therefore useful for evaluating which populations are at greatest risk and how effective vaccination strategies might be. However, interpretation of antibody data from humans is complicated by immunological interactions between all previous, unobserved exposures in an individuals life. We developed a mathematical model to describe short-term antibody kinetics that are important in building an individuals immune profile but are difficult to observe in human populations. We validated this model using antibody data from ferrets with known, varied infection and vaccination histories. We were able to quantify the independent contributions of various exposures and immunological mechanisms in generating observed antibody titres. These results suggest that data from experimental systems may be included in models of human antibody dynamics, which may improve predictions of vaccination strategy effectiveness and how population susceptibility changes over time.

immunology↗

bioRxiv2018

National and Regional Influenza-Like-Illness Forecasts for the USA

Health planners use forecasts of key metrics associated with influenza-like-illness (ILI); near-term weekly incidence, week of season onset, week of peak, and intensity of peak. Here, we describe our participation in a weekly prospective ILI forecasting challenge for the United States for the 2016-17 season and subsequent evaluation of our performance. We implemented a metapopulation model framework with 32 model variants. Variants differed from each other in their assumptions about: the force-of-infection (FOI); use of uninformative priors; the use of discounted historical data for not-yet-observed time points; and the treatment of regions as either independent or coupled. Individual model variants were chosen subjectively as the basis for our weekly forecasts; however, a subset of coupled models were only available part way through the season. Most frequently, during the 2016-17 season, we chose; FOI variants with both school vacations and humidity terms; uninformative priors; the inclusion of discounted historical data for not-yet-observed time points; and coupled regions (when available). Our near-term weekly forecasts substantially over-estimated incidence early in the season when coupled models were not available. However, our forecast accuracy improved in absolute terms and relative to other teams once coupled solutions were available. In retrospective analysis, we found that the 2016-17 season was not typical: on average, coupled models performed better when fit without historically augmented data. Also, we tested a simple ensemble model for the 2016-17 season and found that it underperformed our subjective choice for all forecast targets. In this study, we were able to improve accuracy during a prospective forecasting exercise by coupling dynamics between regions. Although reduction of forecast subjectivity should be a long-term goal, some degree of human intervention is likely to improve forecast accuracy in the medium-term in parallel with the systematic consideration of more sophisticated ensemble approaches.\n\nAuthor summaryIt is estimated that there are between 3 and 5 million worldwide annual seasonal cases of severe influenza illness, and between 290 000 and 650 000 respiratory deaths [1]. Influenza-like-illness (ILI) describes a set of symptoms and is a practical way for health-care workers to easily estimate likely influenza cases. The Centers for Disease Control (CDC) collects and disseminates ILI information, and has, for the last several years, run a forecasting challenge (the CDC Flu Challenge) for modelers to predict near-term weekly incidence, week of season onset, week of peak, and intensity of peak. We have developed a modeling framework that accounts for a range of mechanisms thought to be important for influenza transmission, such as climatic conditions, school vacations, and coupling between different regions. In this study we describe our forecast procedure for the 2016-17 season and highlight which features of our models resulted in better or worse forecasts. Most notably, we found that when the dynamics of different regions are coupled together, the forecast accuracy improves. We also found that the most accurate forecasts required some level of forecaster interaction, that is, the procedure could not be completely automated without a reduction in accuracy.

epidemiology↗

bioRxiv2018

Differential human mobility and local variation in human infection attack rate

Infectious disease transmission in animals is an inherently spatial process in which a hosts home location and their social mixing patterns are important, with the mixing of infectious individuals often different to that of susceptible individuals. Although incidence data for humans have traditionally been aggregated into low-resolution data sets, modern representative surveillance systems such as electronic hospital records generate high volume case data with precise home locations. Here, we use a high resolution gridded spatial transmission model of arbitrary resolution to investigate the theoretical relationship between population density, differential population movement and local variability in incidence. We show analytically that uniform local attack rate is only possible for individual pixels in the grid if susceptible and infectious individuals move in the same way. Using a population in Guangdong, China, for which a robust quantitative description of movement is available (a movement kernel), and a natural history consistent with pandemic influenza; we show that for the estimated kernel, local cumulative incidence is positively correlated with population density when susceptible individuals are more connected in space than infectious individuals. Conversely, when infectious individuals are more connected, local cumulative incidence is negatively correlated with population density. The amplitude of correlation is substantial for the estimated kernel. However, the strength and direction of correlation changes sign for other kernel parameter values. These results describe a precise relationship between the spatio-social mixing of infectious and susceptible individuals and local variability in attack rates, and suggest a plausible mechanism for the counter-intuitive scenario in which local incidence is lower on average in less dense populations. Also, these results suggest that if spatial transmission models are implemented at high resolution to investigate local disease dynamics, including micro-tuning of interventions, the underlying detailed assumptions about the mechanisms of transmission become more important than when similar studies are conducted at larger spatial scales.\n\nAuthor SummaryWe know that some places have higher rates of infectious disease than others. However, at the moment, we usually only measure these differences for large towns and cities. With modern data, such as those we can get from mobile phones, we can measure rates of infection at much smaller scales. In this paper, we used a computer simulation of an epidemic to propose ways that rates of incidence in small local areas might be related to population density. We found that if infectious people are better connected than non-infectious people, perhaps because they receive visitors, then, on average, higher density areas would have lower rates of infection. If infectious people were less connected than non-infectious people then higher density areas would have higher rates of infection. As data get more accurate, this type of analysis will allow us to propose and test ways to optimize interventions such as the delivery of vaccines and antivirals during a pandemic.

epidemiology↗

bioRxiv2017

Timescales of influenza A/H3N2 antibody dynamics

Human immunity influences the evolution and impact of novel influenza strains. Because individuals are infected with multiple influenza strains during their lifetime and each virus can generate a cross-reactive antibody response, it is challenging to quantify the processes that shape observed immune responses, or to reliably detect recent infection from serological samples. Using a Bayesian model of antibody dynamics at multiple timescales, we explain complex cross-reactive antibody landscapes by inferring participants histories of infection with serological data from cross-sectional and longitudinal studies of influenza A/H3N2 in southern China and Vietnam. We show an individuals influenza antibody profile can be explained by a short-lived, broadly cross-reactive response that decays within a year to leave a smaller long-term response acting against a narrower range of strains. We also demonstrate that accounting for dynamic immune responses can provide a more accurate alternative to traditional definitions seroconversion for the estimation of infection attack rates. Our work provides a general model for explaining mechanisms of influenza immunity acting at multiple timescales based on contemporary serological data, and suggests a two-armed immune response to influenza infection consistent with competitive dynamics between B cell populations. This approach to analysing multiple timescales for antigenic responses could also be applied to other multi-strain pathogens such as dengue and related flaviviruses.

epidemiology↗

bioRxiv2017

Identifying factors that may improve mechanistic forecasting models for influenza

Influenza causes substantial morbidity and mortality and places strain on healthcare systems, some of which could be mitigated by accurate forecasting. Specific humidity and school vacations have both been shown independently to affect the transmission dynamics of influenza at large spatial scales. Here, we compare the ability of five compartmental transmission models, which include these two processes, to explain influenza-like-illness (ILI) incidence data for five United States counties for which school vacations and specific humidity data were available over a span of four seasons. We used the models in two different ways. First we fitted all available data at the same time and assessed model performance using standard measures of parsimony and goodness-of-fit. Then we conducted a retrospective forecasting study in which we attempted to predict incidence beyond a given week by fitting to data available up to that week. In general, when fitting the data using the whole season, we found that either specific humidity, school closures, or a combined model incorporating both effects captured the variability in incidence better than a fully constrained SIR-like model. Moreover, where these factors play a role, the timing of the variations suggests a causal relationship. When school vacations and specific humidity were important, the model-estimated parameters were broadly consistent. Retrospective forecasting simulations were consistent with the explanatory use of the models, with both specific humidity and school vacations giving more accurate forecasts than a simple SIR-like model in some populations and for some seasons. Our results suggest that influenza forecast models should test for the importance of different factors such as school vacations and specific humidity on a population-by-population and year-by-year basis.\n\nAuthor summaryUnderstanding the underlying factors that contribute to the transmission of influenza is crucial for developing models with predictive capabilities. In this study, we address two key effects: humidity and school vacations. We show that both can play an important role, depending on the location of the population as well as the timing of the school vacations. We then demonstrate how such mechanistic models can be used to forecast an influenza season as it unfolds, including estimates of the uncertainty of the predictions at each week of the forecast. To make better influenza forecasts, models need to test whether factors such as school vacations and specific humidity are important for a given season and population.

epidemiology↗