School of Mathematics and Statistics - Research Publications
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ItemNo Preview AvailableEstimation of the probability of epidemic fade-out from multiple outbreak data(ELSEVIER, 2022-03-01)Deterministic epidemic models 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. Epidemic dynamics are, however, influenced by stochastic effects, particularly when the prevalence is low. At the beginning of an epidemic, stochastic die-out is possible and well characterised through use of a branching process approximation. 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, 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 precise estimates of the probability of fade-out than is possible if considering each epidemic in isolation. Our methods may be applied 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.
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ItemRapid assessment of the risk of SARS-CoV-2 importation: case study and lessons learned(ELSEVIER, 2022-03-01)During the early stages of an emerging disease outbreak, governments are required to make critical decisions on how to respond, despite limited data being available to inform these decisions. Analytical risk assessment is a valuable approach to guide decision-making on travel restrictions and border measures during the early phase of an outbreak. Here we describe a rapid risk assessment framework that was developed in February 2020 to support time-critical decisions on the risk of SARS-CoV-2 importation into Australia. We briefly describe the context in which our framework was developed, the framework itself, and provide an example of the type of decision support provided to the Australian government. We then report a critical evaluation of the modelling choices made in February 2020, assessing the impact of our assumptions on estimated rates of importation, and provide a summary of "lessons learned". The framework presented and evaluated here provides a flexible approach to rapid assessment of importation risk, of relevance to current and future pandemic scenarios.
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ItemCOVID-19 in low-tolerance border quarantine systems: Impact of the Delta variant of SARS-CoV-2(AMER ASSOC ADVANCEMENT SCIENCE, 2022-04-01)In controlling transmission of coronavirus disease 2019 (COVID-19), the effectiveness of border quarantine strategies is a key concern for jurisdictions in which the local prevalence of disease and immunity is low. In settings like this such as China, Australia, and New Zealand, rare outbreak events can lead to escalating epidemics and trigger the imposition of large-scale lockdown policies. Here, we develop and apply an individual-based model of COVID-19 to simulate case importation from managed quarantine under various vaccination scenarios. We then use the output of the individual-based model as input to a branching process model to assess community transmission risk. For parameters corresponding to the Delta variant, our results demonstrate that vaccination effectively counteracts the pathogen's increased infectiousness. To prevent outbreaks, heightened vaccination in border quarantine systems must be combined with mass vaccination. The ultimate success of these programs will depend sensitively on the efficacy of vaccines against viral transmission.
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ItemFrom Climate Change to Pandemics: Decision Science Can Help Scientists Have Impact(FRONTIERS MEDIA SA, 2022-02-14)Scientific knowledge and advances are a cornerstone of modern society. They improve our understanding of the world we live in and help us navigate global challenges including emerging infectious diseases, climate change and the biodiversity crisis. However, there is a perpetual challenge in translating scientific insight into policy. Many articles explain how to better bridge the gap through improved communication and engagement, but we believe that communication and engagement are only one part of the puzzle. There is a fundamental tension between science and policy because scientific endeavors are rightfully grounded in discovery, but policymakers formulate problems in terms of objectives, actions and outcomes. Decision science provides a solution by framing scientific questions in a way that is beneficial to policy development, facilitating scientists’ contribution to public discussion and policy. At its core, decision science is a field that aims to pinpoint evidence-based management strategies by focussing on those objectives, actions, and outcomes defined through the policy process. The importance of scientific discovery here is in linking actions to outcomes, helping decision-makers determine which actions best meet their objectives. In this paper we explain how problems can be formulated through the structured decision-making process. We give our vision for what decision science may grow to be, describing current gaps in methodology and application. By better understanding and engaging with the decision-making processes, scientists can have greater impact and make stronger contributions to important societal problems.
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ItemAnatomy of a seasonal influenza epidemic forecast(Department of Health, Australian Government, 2019-03-15)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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ItemTurnover of Village Chickens Undermines Vaccine Coverage to Control HPAI H5N1(WILEY, 2017-02-01)Highly pathogenic avian influenza (HPAI) subtype H5N1 remains an enzootic disease of village chickens in Indonesia, posing ongoing risk at the animal-human interface. Previous modelling showed that the fast natural turnover of chicken populations might undermine herd immunity after vaccination, although actual details of how this effect applies to Indonesia's village chicken population have not been determined. We explored the turnover effect in Indonesia's scavenging and mixed populations of village chickens using an extended Leslie matrix model parameterized with data collected from village chicken flocks in Java region, Indonesia. Population dynamics were simulated for 208 weeks; the turnover effect was simulated for 16 weeks after vaccination in two 'best case' scenarios, where the whole population (scenario 1), or birds aged over 14 days (scenario 2), were vaccinated. We found that the scavenging and mixed populations have different productive traits. When steady-state dynamics are reached, both populations are dominated by females (54.5%), and 'growers' and 'chicks' represent the most abundant age stages with 39% and 38% in the scavenging, and 60% and 25% in the mixed population, respectively. Simulations showed that the population turnover might reduce the herd immunity below the critical threshold that prevents the re-emergence of HPAI H5N1 4-8 weeks (scavenging) and 6-9 weeks (mixed population) after vaccination in scenario 1, and 2-6 weeks (scavenging) and 4-7 weeks (mixed population) after vaccination in scenario 2. In conclusion, we found that Indonesia's village chicken population does not have a unique underlying population dynamic and therefore, different turnover effects on herd immunity may be expected after vaccination; nonetheless, our simulations carried out in best case scenarios highlight the limitations of current vaccine technologies to control HPAI H5N1. This suggests that the improvements and complementary strategies are necessary and must be explored.
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ItemNo Preview AvailableDevelopment and Validation of an In Silico Decision Tool To Guide Optimization of Intravenous Artesunate Dosing Regimens for Severe Falciparum Malaria Patients(AMER SOC MICROBIOLOGY, 2021-06-01)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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ItemWithin-host modeling of blood-stage malaria(WILEY, 2018-09-01)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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ItemOn the extinction probability in models of within-host infection: the role of latency and immunity(SPRINGER HEIDELBERG, 2016-10-01)Not every exposure to virus establishes infection in the host; instead, the small amount of initial virus could become extinct due to stochastic events. Different diseases and routes of transmission have a different average number of exposures required to establish an infection. Furthermore, the host immune response and antiviral treatment affect not only the time course of the viral load provided infection occurs, but can prevent infection altogether by increasing the extinction probability. We show that the extinction probability when there is a time-dependent immune response depends on the chosen form of the model-specifically, on the presence or absence of a delay between infection of a cell and production of virus, and the distribution of latent and infectious periods of an infected cell. We hypothesise that experimentally measuring the extinction probability when the virus is introduced at different stages of the immune response, alongside the viral load which is usually measured, will improve parameter estimates and determine the most suitable mathematical form of the model.
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ItemNo Preview AvailableDevelopment of an influenza pandemic decision support tool linking situational analytics to national response policy.(Elsevier, 2021-06-19)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.