Publications by authors named "James M McCaw"

76 Publications

Development of an influenza pandemic decision support tool linking situational analytics to national response policy.

Epidemics 2021 09 19;36:100478. Epub 2021 Jun 19.

Modelling and Simulation Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia; Peter Doherty Institute for Infection and Immunity, The Royal Melbourne Hospital and The University of Melbourne, Melbourne, Australia; School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia. Electronic address:

National influenza pandemic plans have evolved substantially over recent decades, as has the scientific research that underpins the advice contained within them. While the knowledge generated by many research activities has been directly incorporated into the current generation of pandemic plans, scientists and policymakers are yet to capitalise fully on the potential for near real-time analytics to formally contribute to epidemic decision-making. Theoretical studies demonstrate that it is now possible to make robust estimates of pandemic impact in the earliest stages of a pandemic using first few hundred household cohort (FFX) studies and algorithms designed specifically for analysing FFX data. Pandemic plans already recognise the importance of both situational awareness i.e., knowing pandemic impact and its key drivers, and the need for pandemic special studies and related analytic methods for estimating these drivers. An important next step is considering how information from these situational assessment activities can be integrated into the decision-making processes articulated in pandemic planning documents. Here we introduce a decision support tool that directly uses outputs from FFX algorithms to present recommendations on response options, including a quantification of uncertainty, to decision makers. We illustrate this approach using response information from within the Australian influenza pandemic plan.
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http://dx.doi.org/10.1016/j.epidem.2021.100478DOI Listing
September 2021

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

Viruses 2021 05 7;13(5). Epub 2021 May 7.

School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia.

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 use 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.
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http://dx.doi.org/10.3390/v13050850DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150684PMC
May 2021

Development and Validation of an Decision Tool To Guide Optimization of Intravenous Artesunate Dosing Regimens for Severe Falciparum Malaria Patients.

Antimicrob Agents Chemother 2021 05 18;65(6). Epub 2021 May 18.

Centre for Epidemiology & Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.

Most deaths from severe falciparum malaria occur within 24 h of presentation to a hospital. Intravenous (i.v.) artesunate is the first-line treatment for severe falciparum malaria, but its efficacy may be compromised by delayed parasitological responses. In patients with severe malaria, the life-saving benefit of the artemisinin derivatives is their ability to clear circulating parasites rapidly, before they can sequester and obstruct the microcirculation. To evaluate the dosing of i.v. artesunate for the treatment of artemisinin-sensitive and reduced ring stage sensitivity to artemisinin severe falciparum malaria infections, Bayesian pharmacokinetic-pharmacodynamic modeling of data from 94 patients with severe malaria (80 children from Africa and 14 adults from Southeast Asia) was performed. Assuming that delayed parasite clearance reflects a loss of ring stage sensitivity to artemisinin derivatives, the median (95% credible interval) percentage of patients clearing ≥99% of parasites within 24 h (PC24≥99%) for standard (2.4 mg/kg body weight i.v. artesunate at 0 and 12 h) and simplified (4 mg/kg i.v. artesunate at 0 h) regimens was 65% (52.5% to 74.5%) versus 44% (25% to 61.5%) for adults, 62% (51.5% to 74.5%) versus 39% (20.5% to 58.5%) for larger children (≥20 kg), and 60% (48.5% to 70%) versus 36% (20% to 53.5%) for smaller children (<20 kg). The upper limit of the credible intervals for all regimens was below a PC24≥99% of 80%, a threshold achieved on average in clinical studies of severe falciparum malaria infections. In severe falciparum malaria caused by parasites with reduced ring stage susceptibility to artemisinin, parasite clearance is predicted to be slower with both the currently recommended and proposed simplified i.v. artesunate dosing regimens.
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http://dx.doi.org/10.1128/AAC.02346-20DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8316083PMC
May 2021

Antibody Dynamics for Plasmodium vivax Malaria: A Mathematical Model.

Bull Math Biol 2021 01 2;83(1). Epub 2021 Jan 2.

School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia.

Malaria is a mosquito-borne disease that, despite intensive control and mitigation initiatives, continues to pose an enormous public health burden. Plasmodium vivax is one of the principal causes of malaria in humans. Antibodies, which play a fundamental role in the host response to P. vivax, are acquired through exposure to the parasite. Here, we introduce a stochastic, within-host model of antibody responses to P. vivax for an individual in a general transmission setting. We begin by developing an epidemiological framework accounting for P. vivax infections resulting from new mosquito bites (primary infections), as well as the activation of dormant-liver stages known as hypnozoites (relapses). By constructing an infinite server queue, we obtain analytic results for the distribution of relapses in a general transmission setting. We then consider a simple model of antibody kinetics, whereby antibodies are boosted with each infection, but are subject to decay over time. By embedding this model for antibody kinetics in the epidemiological framework using a generalised shot noise process, we derive analytic expressions governing the distribution of antibody levels for a single individual in a general transmission setting. Our work provides a means to explore exposure-dependent antibody dynamics for P. vivax, with the potential to address key questions in the context of serological surveillance and acquired immunity.
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http://dx.doi.org/10.1007/s11538-020-00837-5DOI Listing
January 2021

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

PLoS Comput Biol 2020 10 5;16(10):e1007838. Epub 2020 Oct 5.

School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia.

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 skin sores.
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http://dx.doi.org/10.1371/journal.pcbi.1007838DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7561265PMC
October 2020

Coronavirus Disease Model to Inform Transmission-Reducing Measures and Health System Preparedness, Australia.

Emerg Infect Dis 2020 12 28;26(12):2844-2853. Epub 2020 Sep 28.

The ability of health systems to cope with coronavirus disease (COVID-19) cases is of major concern. In preparation, we used clinical pathway models to estimate healthcare requirements for COVID-19 patients in the context of broader public health measures in Australia. An age- and risk-stratified transmission model of COVID-19 demonstrated that an unmitigated epidemic would dramatically exceed the capacity of the health system of Australia over a prolonged period. Case isolation and contact quarantine alone are insufficient to constrain healthcare needs within feasible levels of expansion of health sector capacity. Overlaid social restrictions must be applied over the course of the epidemic to ensure systems do not become overwhelmed and essential health sector functions, including care of COVID-19 patients, can be maintained. Attention to the full pathway of clinical care is needed, along with ongoing strengthening of capacity.
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http://dx.doi.org/10.3201/eid2612.202530DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7706956PMC
December 2020

Modelling within-host macrophage dynamics in influenza virus infection.

J Theor Biol 2021 01 20;508:110492. Epub 2020 Sep 20.

School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia.

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 load in influenza 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 load 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 the level of macrophage activation and viral load, 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.
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http://dx.doi.org/10.1016/j.jtbi.2020.110492DOI Listing
January 2021

Early analysis of the Australian COVID-19 epidemic.

Elife 2020 08 13;9. Epub 2020 Aug 13.

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.

As of 1 May 2020, there had been 6808 confirmed cases of COVID-19 in Australia. Of these, 98 had died from the disease. The epidemic had been in decline since mid-March, with 308 cases confirmed nationally since 14 April. This suggests that the collective actions of the Australian public and government authorities in response to COVID-19 were sufficiently early and assiduous to avert a public health crisis - for now. Analysing factors that contribute to individual country experiences of COVID-19, such as the intensity and timing of public health interventions, will assist in the next stage of response planning globally. We describe how the epidemic and public health response unfolded in Australia up to 13 April. We estimate that the effective reproduction number was likely below one in each Australian state since mid-March and forecast that clinical demand would remain below capacity thresholds over the forecast period (from mid-to-late April).
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http://dx.doi.org/10.7554/eLife.58785DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7449695PMC
August 2020

Key questions for modelling COVID-19 exit strategies.

Proc Biol Sci 2020 08 12;287(1932):20201405. Epub 2020 Aug 12.

School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.
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http://dx.doi.org/10.1098/rspb.2020.1405DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7575516PMC
August 2020

Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges.

Epidemics 2020 09 17;32:100393. Epub 2020 May 17.

Department of Zoology, The University of Oxford, Oxford, UK.

Modern data and computational resources, coupled with algorithmic and theoretical advances to exploit these, allow disease dynamic models to be parameterised with increasing detail and accuracy. While this enhances models' usefulness in prediction and policy, major challenges remain. In particular, lack of identifiability of a model's parameters may limit the usefulness of the model. While lack of parameter identifiability may be resolved through incorporation into an inference procedure of prior knowledge, formulating such knowledge is often difficult. Furthermore, there are practical challenges associated with acquiring data of sufficient quantity and quality. Here, we discuss recent progress on these issues.
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http://dx.doi.org/10.1016/j.epidem.2020.100393DOI Listing
September 2020

Coordinating the real-time use of global influenza activity data for better public health planning.

Influenza Other Respir Viruses 2020 03 3;14(2):105-110. Epub 2019 Dec 3.

WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong SAR, China.

Health planners from global to local levels must anticipate year-to-year and week-to-week variation in seasonal influenza activity when planning for and responding to epidemics to mitigate their impact. To help with this, countries routinely collect incidence of mild and severe respiratory illness and virologic data on circulating subtypes and use these data for situational awareness, burden of disease estimates and severity assessments. Advanced analytics and modelling are increasingly used to aid planning and response activities by describing key features of influenza activity for a given location and generating forecasts that can be translated to useful actions such as enhanced risk communications, and informing clinical supply chains. Here, we describe the formation of the Influenza Incidence Analytics Group (IIAG), a coordinated global effort to apply advanced analytics and modelling to public influenza data, both epidemiological and virologic, in real-time and thus provide additional insights to countries who provide routine surveillance data to WHO. Our objectives are to systematically increase the value of data to health planners by applying advanced analytics and forecasting and for results to be immediately reproducible and deployable using an open repository of data and code. We expect the resources we develop and the associated community to provide an attractive option for the open analysis of key epidemiological data during seasonal epidemics and the early stages of an influenza pandemic.
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http://dx.doi.org/10.1111/irv.12705DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7040973PMC
March 2020

An Activation-Clearance Model for Plasmodium vivax Malaria.

Bull Math Biol 2020 02 12;82(2):32. Epub 2020 Feb 12.

School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia.

Malaria is an infectious disease with an immense global health burden. Plasmodium vivax is the most geographically widespread species of malaria. Relapsing infections, caused by the activation of liver-stage parasites known as hypnozoites, are a critical feature of the epidemiology of Plasmodium vivax. Hypnozoites remain dormant in the liver for weeks or months after inoculation, but cause relapsing infections upon activation. Here, we introduce a dynamic probability model of the activation-clearance process governing both potential relapses and the size of the hypnozoite reservoir. We begin by modelling activation-clearance dynamics for a single hypnozoite using a continuous-time Markov chain. We then extend our analysis to consider activation-clearance dynamics for a single mosquito bite, which can simultaneously establish multiple hypnozoites, under the assumption of independent hypnozoite behaviour. We derive analytic expressions for the time to first relapse and the time to hypnozoite clearance for mosquito bites establishing variable numbers of hypnozoites, both of which are quantities of epidemiological significance. Our results extend those in the literature, which were limited due to an assumption of collective dormancy. Our within-host model can be embedded readily in multiscale models and epidemiological frameworks, with analytic solutions increasing the tractability of statistical inference and analysis. Our work therefore provides a foundation for further work on immune development and epidemiological-scale analysis, both of which are important for achieving the goal of malaria elimination.
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http://dx.doi.org/10.1007/s11538-020-00706-1DOI Listing
February 2020

Infectious disease pandemic planning and response: Incorporating decision analysis.

PLoS Med 2020 01 9;17(1):e1003018. Epub 2020 Jan 9.

Modelling and Simulation Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.

Freya Shearer and co-authors discuss the use of decision analysis in planning for infectious disease pandemics.
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http://dx.doi.org/10.1371/journal.pmed.1003018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952100PMC
January 2020

Modeling the dynamics of gametocytes in humans during malaria infection.

Elife 2019 10 29;8. Epub 2019 Oct 29.

School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.

Renewed efforts to eliminate malaria have highlighted the potential to interrupt human-to-mosquito transmission - a process mediated by gametocyte kinetics in human hosts. Here we study the in vivo dynamics of gametocytes by establishing a framework which incorporates improved measurements of parasitemia, a novel gametocyte dynamics model and model fitting using Bayesian hierarchical inference. We found that the model provides an excellent fit to the clinical data from 17 volunteers infected with (3D7 strain) and reliably predicts observed gametocytemia. We estimated the sexual commitment rate and gametocyte sequestration time to be 0.54% (95% credible interval: 0.30-1.00%) per asexual replication cycle and 8.39 (6.54-10.59) days respectively. We used the data-calibrated model to investigate human-to-mosquito transmissibility, providing a method to link within-human host infection kinetics to epidemiological-scale infection and transmission patterns.
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http://dx.doi.org/10.7554/eLife.49058DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6819085PMC
October 2019

Anatomy of a seasonal influenza epidemic forecast

Commun Dis Intell (2018) 2019 Mar 15;43. Epub 2019 Mar 15.

Murdoch Childrens Research Institute, Victoria.

Bayesian methods have been used to predict the timing of infectious disease epidemics in various settings and for many infectious diseases, including seasonal influenza. But integrating these techniques into public health practice remains an ongoing challenge, and requires close collaboration between modellers, epidemiologists, and public health staff. During the 2016 and 2017 Australian influenza seasons, weekly seasonal influenza forecasts were produced for cities in the three states with the largest populations: Victoria, New South Wales, and Queensland. Forecast results were presented to Health Department disease surveillance units in these jurisdictions, who provided feedback about the plausibility and public health utility of these predictions. In earlier studies we found that delays in reporting and processing of surveillance data substantially limited forecast performance, and that incorporating climatic effects on transmission improved forecast performance. In this study of the 2016 and 2017 seasons, we sought to refine the forecasting method to account for delays in receiving the data, and used meteorological data from past years to modulate the force of infection. We demonstrate how these refinements improved the forecast’s predictive capacity, and use the 2017 influenza season to highlight challenges in accounting for population and clinician behaviour changes in response to a severe season.
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March 2019

Infection-acquired versus vaccine-acquired immunity in an SIRWS model.

Infect Dis Model 2018 15;3:118-135. Epub 2018 Jun 15.

School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria 3010, Australia.

In some disease systems, the process of waning immunity can be subtle, involving a complex relationship between the duration of immunity-acquired either through natural infection or vaccination-and subsequent boosting of immunity through asymptomatic re-exposure. We present and analyse a model of infectious disease transmission where primary and secondary infections are distinguished to examine the interplay between infection and immunity. Additionally we allow the duration of infection-acquired immunity to differ from that of vaccine-acquired immunity to explore the impact on long-term disease patterns and prevalence of infection in the presence of immune boosting. Our model demonstrates that vaccination may induce cyclic behaviour, and the ability of vaccinations to reduce primary infections may not lead to decreased transmission. Where the boosting of vaccine-acquired immunity delays a primary infection, the driver of transmission largely remains primary infections. In contrast, if the immune boosting bypasses a primary infection, secondary infections become the main driver of transmission under a sufficiently long duration of immunity. Our results show that the epidemiological patterns of an infectious disease may change considerably when the duration of vaccine-acquired immunity differs from that of infection-acquired immunity. Our study highlights that for any particular disease and associated vaccine, a detailed understanding of the waning and boosting of immunity and how the duration of protection is influenced by infection prevalence are important as we seek to optimise vaccination strategies.
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http://dx.doi.org/10.1016/j.idm.2018.06.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6326260PMC
June 2018

Sequential infection experiments for quantifying innate and adaptive immunity during influenza infection.

PLoS Comput Biol 2019 01 17;15(1):e1006568. Epub 2019 Jan 17.

School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria, Australia.

Laboratory models are often used to understand the interaction of related pathogens via host immunity. For example, recent experiments where ferrets were exposed to two influenza strains within a short period of time have shown how the effects of cross-immunity vary with the time between exposures and the specific strains used. On the other hand, studies of the workings of different arms of the immune response, and their relative importance, typically use experiments involving a single infection. However, inferring the relative importance of different immune components from this type of data is challenging. Using simulations and mathematical modelling, here we investigate whether the sequential infection experiment design can be used not only to determine immune components contributing to cross-protection, but also to gain insight into the immune response during a single infection. We show that virological data from sequential infection experiments can be used to accurately extract the timing and extent of cross-protection. Moreover, the broad immune components responsible for such cross-protection can be determined. Such data can also be used to infer the timing and strength of some immune components in controlling a primary infection, even in the absence of serological data. By contrast, single infection data cannot be used to reliably recover this information. Hence, sequential infection data enhances our understanding of the mechanisms underlying the control and resolution of infection, and generates new insight into how previous exposure influences the time course of a subsequent infection.
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http://dx.doi.org/10.1371/journal.pcbi.1006568DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6353225PMC
January 2019

Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts.

Trop Med Infect Dis 2019 Jan 11;4(1). Epub 2019 Jan 11.

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville 3052, Australia.

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., 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 reverse transcription polymerase chain reaction (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. These methods are also applicable beyond the Australian context, as similar community-level surveillance systems operate in other countries.
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http://dx.doi.org/10.3390/tropicalmed4010012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6473244PMC
January 2019

Predicting the Outcomes of New Short-Course Regimens for Multidrug-Resistant Tuberculosis Using Intrahost and Pharmacokinetic-Pharmacodynamic Modeling.

Antimicrob Agents Chemother 2018 12 26;62(12). Epub 2018 Nov 26.

Department of Medicine at The Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia.

Short-course regimens for multidrug-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 6 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 modeling 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 modeling suggests that BDQ could reduce MDR-TB treatment duration to just 18 weeks (4 months) while still maintaining a very high treatment success rate (100% for daily BDQ for 2 weeks, or 95% for daily BDQ for 1 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.
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http://dx.doi.org/10.1128/AAC.01487-18DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6256788PMC
December 2018

Investigation of the Decline in Clinical Efficacy of Artemisinin Combination Therapies Due to Increasing Artemisinin and Partner Drug Resistance.

Antimicrob Agents Chemother 2018 12 26;62(12). Epub 2018 Nov 26.

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.

Antimalarial treatment currently relies on an artemisinin derivative and a longer-acting partner drug. With the emergence of resistance to the artemisinin derivatives and the potential pressure this exerts on the partner drugs, the impact of resistance to each drug on efficacy needs to be investigated. An exploration of dihydroartemisinin-piperaquine and mefloquine-artesunate, two artemisinin-based combination therapies that are commonly used in Southeast Asia, was performed. The percentage of treatment failures was simulated from a within-host pharmacokinetic-pharmacodynamic (PKPD) model, assuming that parasites developed increasing levels of (i) artemisinin derivative resistance or (ii) concomitant resistance to both the artemisinin derivative and the partner drug. Because the exact nature of how resistant parasites respond to treatment is unknown, we examined the impact on treatment failure rates of artemisinin resistance that (i) reduced the maximal killing rate, (ii) increased the concentration of drug required for 50% killing, or (iii) shortened the window of parasite stages that were susceptible to artemisinin derivatives until the drugs had no effect on the ring stages. The loss of the ring-stage activity of the artemisinin derivative caused the greatest increase in the treatment failure rate, and this result held irrespective of whether partner drug resistance was assumed to be present or not. To capture the uncertainty regarding how artemisinin derivative and partner drug resistance affects the assumed concentration-killing effect relationship, a variety of changes to this relationship should be considered when using within-host PKPD models to simulate clinical outcomes to guide treatment strategies for resistant infections.
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http://dx.doi.org/10.1128/AAC.01292-18DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6256805PMC
December 2018

Investigating the Efficacy of Triple Artemisinin-Based Combination Therapies for Treating Plasmodium falciparum Malaria Patients Using Mathematical Modeling.

Antimicrob Agents Chemother 2018 11 24;62(11). Epub 2018 Oct 24.

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia

The first line treatment for uncomplicated falciparum malaria is artemisinin-based combination therapy (ACT), which consists of an artemisinin derivative coadministered with a longer-acting partner drug. However, the spread of resistant to both artemisinin and its partner drugs poses a major global threat to malaria control activities. Novel strategies are needed to retard and reverse the spread of these resistant parasites. One such strategy is triple artemisinin-based combination therapy (TACT). We developed a mechanistic within-host mathematical model to investigate the efficacy of a TACT (dihydroartemisinin-piperaquine-mefloquine [DHA-PPQ-MQ]) for use in South-East Asia, where DHA and PPQ resistance are now increasingly prevalent. Comprehensive model simulations were used to explore the degree to which the underlying resistance influences the parasitological outcomes. The effect of MQ dosing on the efficacy of TACT was quantified at various degrees of DHA and PPQ resistance. To incorporate interactions between drugs, a novel model is presented for the combined effect of DHA-PPQ-MQ, which illustrates how the interactions can influence treatment efficacy. When combined with a standard regimen of DHA and PPQ, the administration of three 6.7-mg/kg doses of MQ was sufficient to achieve parasitological efficacy greater than that currently recommended by World Health Organization (WHO) guidelines. As a result, three 8.3-mg/kg doses of MQ, the current WHO-recommended dosing regimen for MQ, combined with DHA-PPQ, has the potential to produce high cure rates in regions where resistance to DHA-PPQ has emerged.
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http://dx.doi.org/10.1128/AAC.01068-18DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6201091PMC
November 2018

Within-host modeling of blood-stage malaria.

Immunol Rev 2018 09;285(1):168-193

Kirby Institute, UNSW Sydney, Sydney, NSW, Australia.

Malaria infection continues to be a major health problem worldwide and drug resistance in the major human parasite species, Plasmodium falciparum, is increasing in South East Asia. Control measures including novel drugs and vaccines are in development, and contributions to the rational design and optimal usage of these interventions are urgently needed. Infection involves the complex interaction of parasite dynamics, host immunity, and drug effects. The long life cycle (48 hours in the common human species) and synchronized replication cycle of the parasite population present significant challenges to modeling the dynamics of Plasmodium infection. Coupled with these, variation in immune recognition and drug action at different life cycle stages leads to further complexity. We review the development and progress of "within-host" models of Plasmodium infection, and how these have been applied to understanding and interpreting human infection and animal models of infection.
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http://dx.doi.org/10.1111/imr.12697DOI Listing
September 2018

The distribution of the time taken for an epidemic to spread between two communities.

Math Biosci 2018 09 7;303:139-147. Epub 2018 Jul 7.

IBM Research Australia, Melbourne, VIC 3006, Australia. Electronic address:

Assessing the risk of disease spread between communities is important in our highly connected modern world. However, the impact of disease- and population-specific factors on the time taken for an epidemic to spread between communities, as well as the impact of stochastic disease dynamics on this spreading time, are not well understood. In this study, we model the spread of an acute infection between two communities ('patches') using a susceptible-infectious-removed (SIR) metapopulation model. We develop approximations to efficiently evaluate the probability of a major outbreak in a second patch given disease introduction in a source patch, and the distribution of the time taken for this to occur. We use these approximations to assess how interventions, which either control disease spread within a patch or decrease the travel rate between patches, change the spreading probability and median spreading time. We find that decreasing the basic reproduction number in the source patch is the most effective way of both decreasing the spreading probability, and delaying epidemic spread to the second patch should this occur. Moreover, we show that the qualitative effects of interventions are the same regardless of the approximations used to evaluate the spreading time distribution, but for some regions in parameter space, quantitative findings depend upon the approximations used. Importantly, if we neglect the possibility that an intervention prevents a large outbreak in the source patch altogether, then intervention effectiveness is not estimated accurately.
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http://dx.doi.org/10.1016/j.mbs.2018.07.004DOI Listing
September 2018

Clonally diverse CD38HLA-DRCD8 T cells persist during fatal H7N9 disease.

Nat Commun 2018 02 26;9(1):824. Epub 2018 Feb 26.

Shanghai Public Health Clinical Center & Institutes of Biomedical Sciences, Key Laboratory of Medical Molecular Virology of Ministry of Education/Health, Shanghai Medical College, Fudan University, 201508, Shangai, China.

Severe influenza A virus (IAV) infection is associated with immune dysfunction. Here, we show circulating CD8 T-cell profiles from patients hospitalized with avian H7N9, seasonal IAV, and influenza vaccinees. Patient survival reflects an early, transient prevalence of highly activated CD38HLA-DRPD-1 CD8 T cells, whereas the prolonged persistence of this set is found in ultimately fatal cases. Single-cell T cell receptor (TCR)-αβ analyses of activated CD38HLA-DRCD8 T cells show similar TCRαβ diversity but differential clonal expansion kinetics in surviving and fatal H7N9 patients. Delayed clonal expansion associated with an early dichotomy at a transcriptome level (as detected by single-cell RNAseq) is found in CD38HLA-DRCD8 T cells from patients who succumbed to the disease, suggesting a divergent differentiation pathway of CD38HLA-DRCD8 T cells from the outset during fatal disease. Our study proposes that effective expansion of cross-reactive influenza-specific TCRαβ clonotypes with appropriate transcriptome signatures is needed for early protection against severe influenza disease.
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http://dx.doi.org/10.1038/s41467-018-03243-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5827521PMC
February 2018

Evidence for Viral Interference and Cross-reactive Protective Immunity Between Influenza B Virus Lineages.

J Infect Dis 2018 01;217(4):548-559

WHO Collaborating Centre for Reference and Research on Influenza, Victorian Infectious Diseases Reference Laboratory, Melbourne, Australia.

Background: Two influenza B virus lineages, B/Victoria and B/Yamagata, cocirculate in the human population. While the lineages are serologically distinct, cross-reactive responses to both lineages have been detected. Viral interference describes the situation whereby infection with one virus limits infection and replication of a second virus. We investigated the potential for viral interference between the influenza B virus lineages.

Methods: Ferrets were infected and then challenged 3, 10, or 28 days later with pairs of influenza B/Victoria and B/Yamagata viruses.

Results: Viral interference occurred at challenge intervals of 3 and 10 days and occasionally at 28 days. At the longer interval, shedding of challenge virus was reduced, and this correlated with cross-reactive interferon γ responses from lymph nodes from virus-infected animals. Viruses from both lineages could prevent or significantly limit subsequent infection with a virus from the other lineage. Coinfections were rare, indicating the potential for reassortment between lineages is limited.

Conclusions: These data suggest that innate and cross-reactive immunity mediate viral interference and that this may contribute to the dominance of a specific influenza B virus lineage in any given influenza season. Furthermore, infection with one influenza B virus lineage may be beneficial in protecting against subsequent infection with either influenza B virus lineage.
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http://dx.doi.org/10.1093/infdis/jix509DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5853430PMC
January 2018

Epidemic forecasts as a tool for public health: interpretation and (re)calibration.

Aust N Z J Public Health 2018 Feb 27;42(1):69-76. Epub 2017 Dec 27.

Modelling and Simulation Unit, Melbourne School of Population and Global Health, The University of Melbourne, Victoria.

Objective: Recent studies have used Bayesian methods to predict timing of influenza epidemics many weeks in advance, but there is no documented evaluation of how such forecasts might support the day-to-day operations of public health staff.

Methods: During the 2015 influenza season in Melbourne, Australia, weekly forecasts were presented at Health Department surveillance unit meetings, where they were evaluated and updated in light of expert opinion to improve their accuracy and usefulness.

Results: Predictive capacity of the model was substantially limited by delays in reporting and processing arising from an unprecedented number of notifications, disproportionate to seasonal intensity. Adjustment of the predictive algorithm to account for these delays and increased reporting propensity improved both current situational awareness and forecasting accuracy.

Conclusions: Collaborative engagement with public health practitioners in model development improved understanding of the context and limitations of emerging surveillance data. Incorporation of these insights in a quantitative model resulted in more robust estimates of disease activity for public health use. Implications for public health: In addition to predicting future disease trends, forecasting methods can quantify the impact of delays in data availability and variable reporting practice on the accuracy of current epidemic assessment. Such evidence supports investment in systems capacity.
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http://dx.doi.org/10.1111/1753-6405.12750DOI Listing
February 2018

A Dynamic Stress Model Explains the Delayed Drug Effect in Artemisinin Treatment of Plasmodium falciparum.

Antimicrob Agents Chemother 2017 12 22;61(12). Epub 2017 Nov 22.

School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia

Artemisinin resistance constitutes a major threat to the continued success of control programs for malaria, particularly in light of developing resistance to partner drugs. Improving our understanding of how artemisinin-based drugs act and how resistance manifests is essential for the optimization of dosing regimens and the development of strategies to prolong the life span of current first-line treatment options. Recent short-drug-pulse experiments have shown that the parasite killing rate depends not only on drug concentration but also the exposure time, challenging the standard pharmacokinetic-pharmacodynamic (PK-PD) paradigm in which the killing rate depends only on drug concentration. Here, we introduce a dynamic stress model of parasite killing and show through application to 3D7 laboratory strain viability data that the inclusion of a time-dependent parasite stress response dramatically improves the model's explanatory power compared to that of a traditional PK-PD model. Our model demonstrates that the previously reported hypersensitivity of early-ring-stage parasites of the 3D7 strain to dihydroartemisinin compared to other parasite stages is due primarily to a faster development of stress rather than a higher maximum achievable killing rate. We also perform simulations using the dynamic stress model and demonstrate that the complex temporal features of artemisinin action observed have a significant impact on predictions for parasite clearance. Given the important role that PK-PD models play in the design of clinical trials for the evaluation of alternative drug dosing regimens, our novel model will contribute to the further development and improvement of antimalarial therapies.
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http://dx.doi.org/10.1128/AAC.00618-17DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5700357PMC
December 2017

The Mechanisms for Within-Host Influenza Virus Control Affect Model-Based Assessment and Prediction of Antiviral Treatment.

Viruses 2017 07 26;9(8). Epub 2017 Jul 26.

School of Mathematics and Statistics, The University of Melbourne, Melbourne, Victoria 3010, Australia.

Models of within-host influenza viral dynamics have contributed to an improved understanding of viral dynamics and antiviral effects over the past decade. Existing models can be classified into two broad types based on the mechanism of viral control: models utilising target cell depletion to limit the progress of infection and models which rely on timely activation of innate and adaptive immune responses to control the infection. In this paper, we compare how two exemplar models based on these different mechanisms behave and investigate how the mechanistic difference affects the assessment and prediction of antiviral treatment. We find that the assumed mechanism for viral control strongly influences the predicted outcomes of treatment. Furthermore, we observe that for the target cell-limited model the assumed drug efficacy strongly influences the predicted treatment outcomes. The area under the viral load curve is identified as the most reliable predictor of drug efficacy, and is robust to model selection. Moreover, with support from previous clinical studies, we suggest that the target cell-limited model is more suitable for modelling in vitro assays or infection in some immunocompromised/immunosuppressed patients while the immune response model is preferred for predicting the infection/antiviral effect in immunocompetent animals/patients.
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http://dx.doi.org/10.3390/v9080197DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5580454PMC
July 2017

A mechanistic model quantifies artemisinin-induced parasite growth retardation in blood-stage Plasmodium falciparum infection.

J Theor Biol 2017 10 18;430:117-127. Epub 2017 Jul 18.

School of Mathematics and Statistics, The University of Melbourne, Melbourne, Victoria, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia; Modelling and Simulation, Infection and Immunity Theme, Murdoch Childrens Research Institute, The Royal Children's Hospital, Parkville, Victoria, Australia. Electronic address:

Falciparum malaria is a major parasitic disease causing widespread morbidity and mortality globally. Artemisinin derivatives-the most effective and widely-used antimalarials that have helped reduce the burden of malaria by 60% in some areas over the past decade-have recently been found to induce growth retardation of blood-stage Plasmodium falciparum when applied at clinically relevant concentrations. To date, no model has been designed to quantify the growth retardation effect and to predict the influence of this property on in vivo parasite killing. Here we introduce a mechanistic model of parasite growth from the ring to trophozoite stage of the parasite's life cycle, and by modelling the level of staining with an RNA-binding dye, we demonstrate that the model is able to reproduce fluorescence distribution data from in vitro experiments using the laboratory 3D7 strain. We quantify the dependence of growth retardation on drug concentration and identify the concentration threshold above which growth retardation is evident. We estimate that the parasite life cycle is prolonged by up to 10 hours. We illustrate that even such a relatively short delay in growth may significantly influence in vivo parasite dynamics, demonstrating the importance of considering growth retardation in the design of optimal artemisinin-based dosing regimens.
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http://dx.doi.org/10.1016/j.jtbi.2017.07.017DOI Listing
October 2017

Corrigendum to ''Modelling cross-reactivity and memory in the cellular adaptive immune response to influenza infection in the host'' [J.Theor. Biol. 413 (2017) 34-49].

J Theor Biol 2017 04;419:394

School of Mathematics and Statistics, University of Melbourne, Parkville, VIC 3010, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC 3010, Australia; Modelling and Simulation, Infection and Immunity Theme, Murdoch Children's Research Institute, Parkville, VIC 3052, Australia. Electronic address:

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http://dx.doi.org/10.1016/j.jtbi.2017.03.015DOI Listing
April 2017
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