Biology subjects

McCaw, J. M.

Publications and source records attributed to McCaw, J. M..

11 recordsLinked to original sources

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

Viral kinetics of H5N1 infections in dairy cattle

Since early-2024 unprecedented outbreaks of highly pathogenic avian influenza H5N1 clade 2.3.4.4b have been ongoing in dairy cattle in the United States with significant consequences for the dairy industry and public health. Estimation of key epidemiological parameters is required to support outbreak response, including predicting the likely effectiveness of interventions and testing strategies. Here we pool limited publicly available data from three studies of naturally and experimentally infected dairy cattle. We quantify Ct value trajectories of infected dairy cattle and the relationship between Ct value and the log-titre of infectious virus, {beta}2 a proxy for infectiousness. We estimate that following infection peak Ct values are rapidly reached within 1-2 days with a population mean Ct value of 16.9 (13.2, 20.5). We identify a critical threshold Ct value of 21.5 (20.1, 23.6), with values of Ct value above this threshold representing little-to-no infectious viral load. Finally, we estimate the distribution of the duration of infectiousness for dairy cattle (i.e. the duration their Ct value remains above the critical threshold) with a population median of 6.2 (2.8, 13.1) days.

systems biology↗

bioRxiv2022

Enhanced viral infectivity and reduced interferon production are associated with high pathogenicity for influenza viruses

Epidemiological and clinical evidence indicates that humans infected with the 1918 pandemic influenza virus and highly pathogenic avian H5N1 influenza viruses often displayed severe lung pathology. High viral load and extensive infiltration of macrophages are the hallmarks of highly pathogenic (HP) influenza viral infections. However, it remains unclear what biological mechanisms primarily determine the observed difference in the kinetics of viral load and macrophages between HP and low pathogenic (LP) viral infections, and how the mechanistic differences are associated with viral pathogenicity. In this study, we develop a mathematical model of viral dynamics that includes the dynamics of different macrophage populations and interferon. We fit the model to in vivo kinetic data of viral load and macrophage level from BALB/c mice infected with an HP or LP strain of H1N1/H5N1 virus using Bayesian inference. Our primary finding is that HP viruses has a higher viral infection rate, a lower interferon production rate and a lower macrophage recruitment rate compared to LP viruses, which are strongly associated with more severe tissue damage (quantified by a higher percentage of epithelial cell loss). We also quantify the relative contribution of macrophages to viral clearance and find that macrophages do not play a dominant role in direct clearance of free virus although their role in mediating immune responses such as interferon production is crucial. Our work provides new insight into the mechanisms that convey the observed difference in viral and macrophage kinetics between HP and LP infections and establishes an improved model fitting framework to enhance the analysis of new data on viral pathogenicity. Author SummaryInfections with highly pathogenic (HP) influenza virus (e.g., the 1918 pandemic virus) often lead to serious morbidity and mortality. HP influenza virus infection is characterised by rapid viral growth rate, high viral load and excessive infiltration of macrophages to the lungs. Despite extensive study, we do not yet fully understand what biological processes leading to the observed viral and macrophage dynamics and therefore viral pathogenicity. Experimental studies have previously suggested that bot viral factors (e.g., viral proteins) and host factors (e.g., the host immune response) play a role to enhance viral pathogenicity. Here, we utilise in vivo kinetic data of viral load and macrophages and fit a viral dynamic model the data. Our model allow us to explore the biological mechanisms that contribute to the difference viral and macrophage dynamics between HP and LP infections. This study improves our understanding of the role of interferon on distinguishing immunodynamics between HP and LP infections. Our findings may contribute to the development of next-generation treatment which rely upon an understanding of the host different immunological response to HP influenza viruses.

immunology↗

bioRxiv2021

Estimation of the probability of epidemic fade-out from multiple outbreak data

Deterministic epidemic models, such as the SIRS model or an SIR model with demography, that allow for replenishment of susceptibles typically display damped oscillatory behaviour. If the population is initially fully susceptible, once an epidemic takes off a distinct trough will exist between the first and second waves of infection where the number of infectious individuals falls to a low level. Epidemic dynamics are, however, influenced by stochastic effects, particularly when the number of infectives is low. At the beginning of an epidemic, stochastic die-out is possible and well characterised through use of a branching process approximation to the full non-linear stochastic dynamics. Conditional on an epidemic taking off, stochastic extinction is highly unlikely during the first epidemic wave, but the probability of extinction increases again as the wave declines. Extinction during this period, prior to a potential second wave of infection, is defined as epidemic fade-out. We consider a set of observed epidemics, each distinct and having evolved independently, in which some display fade-out and some do not. While fade-out is necessarily a stochastic phenomenon, in general the probability of fade-out will depend on the model parameters associated with each epidemic. Accordingly, we ask whether time-series data for the epidemics contain sufficient information to identify the key driver(s) of different outcomes--fade-out or otherwise--across the sub-populations supporting each epidemic. We apply a Bayesian hierarchical modelling framework to synthetic data from an SIRS model of epidemic dynamics and demonstrate that we can 1) identify when the sub-population specific model parameters supporting each epidemic have significant variability and 2) estimate the probability of epidemic fade-out for each sub-population. We demonstrate that a hierarchical analysis can provide more accurate and precise estimates of the probability of fade-out than is possible if considering each epidemic in isolation. Our methods may be applied more generally, to both epidemiological and other biological data to identify where differences in outcome--fade-out or recurrent infection/waves are purely due to chance or driven by underlying changes in the parameters driving the dynamics.

systems biology↗

bioRxiv2021

Modelling The Effect of MUC1 on Influenza Virus Infection Kinetics and Macrophage Dynamics

MUC1 belongs to the family of cell surface (cs-) mucins. Experimental evidence indicates that its presence reduces in vivo influenza viral infection severity. However, the mechanisms by which MUC1 influences viral dynamics and the host immune response are not yet well understood, limiting our ability to predict the efficacy of potential treatments that target MUC1. To address this limitation, we utilize available in vivo kinetic data for both virus and macrophage populations in wildtype and MUC1 knockout mice. We apply two mathematical models of within-host influenza dynamics to this data. The models differ in how they categorise the mechanisms of viral control. Both models provide evidence that MUC1 reduces the susceptibility of epithelial cells to influenza virus and regulates macrophage recruitment. Furthermore, we predict and compare some key infection-related quantities between the two mice groups. We find that MUC1 significantly reduces the basic reproduction number of viral replication as well as the number of cumulative macrophages but has little impact on the cumulative viral load. Our analyses suggest that the viral replication rate in the early stages of infection influences the kinetics of the host immune response, with consequences for infection outcomes, such as severity. We also show that MUC1 plays a strong anti-inflammatory role in the regulation of the host immune response. This study improves our understanding of the dynamic role of MUC1 against influenza infection and may support the development of novel antiviral treatments and immunomodulators that target MUC1.

immunology↗

bioRxiv2020

Modelling within-host macrophage dynamics in influenza virus infection

Human respiratory disease associated with influenza virus infection is of significant public health concern. Macrophages, as part of the front line of host innate cellular defence, have been shown to play an important role in controlling viral replication. However, fatal outcomes of infection, as evidenced in patients infected with highly pathogenic viral strains, are often associated with prompt activation and excessive accumulation of macrophages. Activated macrophages can produce a large amount of pro-inflammatory cytokines, which leads to severe symptoms and at times death. However, the mechanism for rapid activation and excessive accumulation of macrophages during infection remains unclear. It has been suggested that the phenomena may arise from complex interactions between macrophages and influenza virus. In this work, we develop a novel mathematical model to study the relationship between the level of macrophage activation and the level of viral shedding in influenza virus infection. Our model combines a dynamic model of viral infection, a dynamic model of macrophages and the essential interactions between the virus and macrophages. Our model predicts that the level of macrophage activation can be negatively correlated with the level of viral shedding when viral infectivity is sufficiently high. We further identify that temporary depletion of resting macrophages in response to viral infection is a major driver in our model for the negative relationship between macrophage activation and viral shedding, providing new insight into the mechanisms that regulate macrophage activation. Our model serves as a framework to study the complex dynamics of virus-macrophage interactions and provides a mechanistic explanation for existing experimental observations, contributing to an enhanced understanding of the role of macrophages in influenza viral infection.

immunology↗

bioRxiv2019

A sub-exponential branching process to study early epidemic dynamics with application to Ebola

Exponential growth is a mathematically convenient model for the early stages of an outbreak of an infectious disease. However, for many pathogens (such as Ebola virus) the initial rate of transmission may be sub-exponential, even before transmission is affected by depletion of susceptible individuals.\n\nWe present a stochastic multi-scale model capable of representing sub-exponential transmission: an in-homogeneous branching process extending the generalised growth model. To validate the model, we fit it to data from the Ebola epidemic in West Africa (2014-2016). We demonstrate how a branching process can be fit to both time series of confirmed cases and chains of infection derived from contact tracing. Our estimates of the parameters suggest transmission of Ebola virus was sub-exponential during this epidemic. Both the time series data and the chains of infections lead to consistent parameter estimates. Differences in the data sets meant consistent estimates were not a foregone conclusion. Finally, we use a simulation study to investigate the properties of our methodology. In particular, we examine the extent to which the estimates obtained from time series data and those obtained from chains of infection data agree.\n\nOur method, based on a simple branching process, is well suited to real-time analysis of data collected during contact tracing. Identifying the characteristic early growth dynamics (exponential or sub-exponential), including an estimate of uncertainty, during the first phase of an epidemic should prove a useful tool for preliminary outbreak investigations.\n\nAuthor SummaryEpidemic forecasts have the potential to support public health decision making in outbreak scenarios for diseases such as Ebola and influenza. In particular, reliable predictions of future incidence data may guide surveillance and intervention responses. Existing methods for producing forecasts, based upon mechanistic transmission models, often make an implicit assumption that growth is exponential, at least while susceptible depletion remains negligible. However, empirical studies suggest that many infectious disease outbreaks display sub-exponential growth early in the epidemic. Here we introduce a mechanistic model of early epidemic growth that allows for sub-exponential growth in incidence. We demonstrate how the model can be applied to the types of data that are typically available in (near) real-time, including time series data on incidence as well as individual-level case series and chains of transmission data. We apply our methods to publically available data from the 2014-2016 West Africa Ebola epidemic and demonstrate that early epidemic growth was sub-exponential. We also investigate the statistical properties of our model through a simulation re-estimation study to identify it performance characteristics and avenues for further methodological research.

bioinformatics↗

bioRxiv2019

Estimation of the force of infection and infectious period of skin sores in remote Australian communities using interval-censored data

Prevalence of impetigo (skin sores) remains high in remote Australian Aboriginal communities, Fiji, and other areas of socio-economic disadvantage. Skin sore infections, driven primarily in these settings by Group A Streptococcus (GAS) contribute substantially to the disease burden in these areas. Despite this, estimates for the force of infection, infectious period and basic reproductive ratio -- all necessary for the construction of dynamic transmission models -- have not been obtained. By utilising three datasets each containing longitudinal infection information on individuals, we estimate each of these epidemiologically important parameters. With an eye to future study design, we also quantify the optimal sampling intervals for obtaining information about these parameters. We verify the estimation method through a simulation estimation study, and test each dataset to ensure suitability to the estimation method. We find that the force of infection differs by population prevalence, and the infectious period is estimated to be between 12 and 20 days. We also find that optimal sampling interval depends on setting, with an optimal sampling interval between 9 and 11 days in a high prevalence setting, and 21 and 27 days for a lower prevalence setting. These estimates unlock future model-based investigations on the transmission dynamics of GAS and skin sores.

epidemiology↗

bioRxiv2019

Modelling the population dynamics of Plasmodium falciparum gametocytes in humans during malaria infection

Every year over two hundred million people are infected with the malaria parasite. Renewed efforts to eliminate malaria has highlighted the potential to interrupt transmission from humans to mosquitoes which is mediated through the gametocytes. Reliable prediction of transmission requires an improved understanding of in vivo kinetics of gametocytes. Here we study the population dynamics of Plasmodium falciparum gametocytes in human hosts by establishing a framework which incorporates improved measurements of parasitaemia in humans, a novel mathematical model of gametocyte dynamics, and model validation using a Bayesian hierarchical inference method. We found that the novel mathematical model provides an excellent fit to the available clinical data from 17 volunteers infected with P. falciparum, and reliably predicts observed gametocyte levels. We estimated the P. falciparums sexual commitment rate and gametocyte sequestration time in humans to be 0.54% (95% credible interval: 0.30-1.00) per life cycle and 8.39 (6.54-10.59) days respectively. Furthermore, we used the data-calibrated model to predict the effects of those gametocyte dynamics parameters on human-to-mosquito transmissibility, providing a method to link within-human host kinetics of malaria infection to epidemiological-scale infection and transmission patterns.

bioinformatics↗

bioRxiv2018

Accounting for healthcare-seeking behaviours and testing practices in real-time influenza forecasts

For diseases such as influenza, where the majority of infected persons experience mild (if any) symptoms, surveillance systems are sensitive to changes in healthcare-seeking and clinical decision-making behaviours. This presents a challenge when trying to interpret surveillance data in near-real-time (e.g., in order to provide public health decision-support). Australia experienced a particularly large and severe influenza season in 2017, perhaps in part due to (a) mild cases being more likely to seek healthcare; and (b) clinicians being more likely to collect specimens for RT-PCR influenza tests. In this study we used weekly Flutracking surveillance data to estimate the probability that a person with influenza-like illness (ILI) would seek healthcare and have a specimen collected. We then used this estimated probability to calibrate near-real-time seasonal influenza forecasts at each week of the 2017 season, to see whether predictive skill could be improved. While the number of self-reported influenza tests in the weekly surveys are typically very low, we were able to detect a substantial change in healthcare seeking behaviour and clinician testing behaviour prior to the high epidemic peak. Adjusting for these changes in behaviour in the forecasting framework improved predictive skill. Our analysis demonstrates a unique value of community-level surveillance systems, such as Flutracking, when interpreting traditional surveillance data.

epidemiology↗

bioRxiv2018

Predicting the Outcomes of New Short-Course Regimens for Multi-Drug Resistant Tuberculosis Using Intrahost and Pharmacokinetic-Pharmacodynamic Modelling

Short-course regimens for multi-drug resistant tuberculosis (MDR-TB) are urgently needed. Limited data suggest that the new drug, bedaquiline (BDQ), may have the potential to shorten MDR-TB treatment to less than six months when used in conjunction with standard anti-TB drugs. However, the feasibility of BDQ in shortening MDR-TB treatment duration remains to be established. Mathematical modelling provides a platform to investigate different treatment regimens and predict their efficacy. We developed a mathematical model to capture the immune response to TB inside a human host environment. This model was then combined with a pharmacokinetic-pharmacodynamic model to simulate various short-course BDQ-containing regimens. Our modelling suggests that BDQ could reduce MDR-TB treatment duration to just 18 weeks (four months) while still maintaining a very high treatment success rate (100% for daily BDQ for two weeks, or 95% for daily BDQ for one week during the intensive phase). The estimated time to bacterial clearance of these regimens ranges from 27 to 33 days. Our findings provide the justification for empirical evaluation of short-course BDQ-containing regimens. If short-course BDQ-containing regimens are found to improve outcomes then we anticipate clear cost-savings and a subsequent improvement in the efficiency of national TB programs.

systems biology↗