Doherty Institute - Research Publications

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    Infectious disease pandemic planning and response: Incorporating decision analysis
    Shearer, FM ; Moss, R ; McVernon, J ; Ross, JV ; McCaw, JM (PUBLIC LIBRARY SCIENCE, 2020-01-01)
    Freya Shearer and co-authors discuss the use of decision analysis in planning for infectious disease pandemics.
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    Early analysis of the Australian COVID-19 epidemic
    Price, DJ ; Shearer, FM ; Meehan, MT ; McBryde, E ; Moss, R ; Golding, N ; Conway, EJ ; Dawson, P ; Cromer, D ; Wood, J ; Abbott, S ; McVernon, J ; McCaw, JM (eLIFE SCIENCES PUBL LTD, 2020-08-13)
    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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    Diagnosis and Antiviral Intervention Strategies for Mitigating an Influenza Epidemic
    Moss, R ; McCaw, JM ; McVernon, J ; Davis, CT (PUBLIC LIBRARY SCIENCE, 2011-02-04)
    BACKGROUND: Many countries have amassed antiviral stockpiles for pandemic preparedness. Despite extensive trial data and modelling studies, it remains unclear how to make optimal use of antiviral stockpiles within the constraints of healthcare infrastructure. Modelling studies informed recommendations for liberal antiviral distribution in the pandemic phase, primarily to prevent infection, but failed to account for logistical constraints clearly evident during the 2009 H1N1 outbreaks. Here we identify optimal delivery strategies for antiviral interventions accounting for logistical constraints, and so determine how to improve a strategy's impact. METHODS AND FINDINGS: We extend an existing SEIR model to incorporate finite diagnostic and antiviral distribution capacities. We evaluate the impact of using different diagnostic strategies to decide to whom antivirals are delivered. We then determine what additional capacity is required to achieve optimal impact. We identify the importance of sensitive and specific case ascertainment in the early phase of a pandemic response, when the proportion of false-positive presentations may be high. Once a substantial percentage of ILI presentations are caused by the pandemic strain, identification of cases for treatment on syndromic grounds alone results in a greater potential impact than a laboratory-dependent strategy. Our findings reinforce the need for a decentralised system capable of providing timely prophylaxis. CONCLUSIONS: We address specific real-world issues that must be considered in order to improve pandemic preparedness policy in a practical and methodologically sound way. Provision of antivirals on the scale proposed for an effective response is infeasible using traditional public health outbreak management and contact tracing approaches. The results indicate to change the transmission dynamics of an influenza epidemic with an antiviral intervention, a decentralised system is required for contact identification and prophylaxis delivery, utilising a range of existing services and infrastructure in a "whole of society" response.
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    Likely effectiveness of pharmaceutical and non-pharmaceutical interventions for mitigating influenza virus transmission in Mongolia
    Bolton, KJ ; McCaw, JM ; Moss, R ; Morris, RS ; Wang, S ; Burma, A ; Darma, B ; Narangerel, D ; Nymadawa, P ; McVernon, J (WORLD HEALTH ORGANIZATION, 2012-04-01)
    OBJECTIVE: To assess the likely benefit of the interventions under consideration for use in Mongolia during future influenza pandemics. METHODS: A stochastic, compartmental patch model of susceptibility, exposure, infection and recovery was constructed to capture the key effects of several interventions--travel restrictions, school closure, generalized social distancing, quarantining of close contacts, treatment of cases with antivirals and prophylaxis of contacts--on the dynamics of influenza epidemics. The likely benefit and optimal timing and duration of each of these interventions were assessed using Latin-hypercube sampling techniques, averaging across many possible transmission and social mixing parameters. FINDINGS: Timely interventions could substantially alter the time-course and reduce the severity of pandemic influenza in Mongolia. In a moderate pandemic scenario, early social distancing measures decreased the mean attack rate from around 10% to 7-8%. Similarly, in a severe pandemic scenario such measures cut the mean attack rate from approximately 23% to 21%. In both moderate and severe pandemic scenarios, a suite of non-pharmaceutical interventions proved as effective as the targeted use of antivirals. Targeted antiviral campaigns generally appeared more effective in severe pandemic scenarios than in moderate pandemic scenarios. CONCLUSION: A mathematical model of pandemic influenza transmission in Mongolia indicated that, to be successful, interventions to prevent transmission must be triggered when the first cases are detected in border regions. If social distancing measures are introduced at this stage and implemented over several weeks, they may have a notable mitigating impact. In low-income regions such as Mongolia, social distancing may be more effective than the large-scale use of antivirals.
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    Reducing disease burden in an influenza pandemic by targeted delivery of neuraminidase inhibitors: mathematical models in the Australian context
    Moss, R ; McCaw, JM ; Cheng, AC ; Hurt, AC ; McVernon, J (BMC, 2016-10-10)
    BACKGROUND: Many nations maintain stockpiles of neuraminidase inhibitor (NAI) antiviral agents for use in influenza pandemics to reduce transmission and mitigate the course of clinical infection. Pandemic preparedness plans include the use of these stockpiles to deliver proportionate responses, informed by emerging evidence of clinical impact. Recent uncertainty about the effectiveness of NAIs has prompted these nations to reconsider the role of NAIs in pandemic response, with implications for pandemic planning and for NAI stockpile size. METHODS: We combined a dynamic model of influenza epidemiology with a model of the clinical care pathways in the Australian health care system to identify effective NAI strategies for reducing morbidity and mortality in pandemic events, and the stockpile requirements for these strategies. The models were informed by a 2015 assessment of NAI effectiveness against susceptibility, pathogenicity, and transmission of influenza. RESULTS: Liberal distribution of NAIs for early treatment in outpatient settings yielded the greatest benefits in all of the considered scenarios. Restriction of community-based treatment to risk groups was effective in those groups, but failed to prevent the large proportion of cases arising from lower risk individuals who comprise the majority of the population. CONCLUSIONS: These targeted strategies are only effective if they can be deployed within the constraints of existing health care infrastructure. This finding highlights the critical importance of identifying optimal models of care delivery for effective emergency health care response.
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    Epidemic forecasts as a tool for public health: interpretation and (re)calibration
    Moss, R ; Fielding, JE ; Franklin, LJ ; Stephens, N ; McVernon, J ; Dawson, P ; McCaw, JM (WILEY, 2018-02-01)
    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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    Model-Informed Risk Assessment and Decision Making for an Emerging Infectious Disease in the Asia-Pacific Region
    Moss, R ; Hickson, RI ; McVernon, J ; McCaw, JM ; Hort, K ; Black, J ; Madden, JR ; Tran, NH ; McBryde, ES ; Geard, N ; Liang, S (PUBLIC LIBRARY SCIENCE, 2016-09-01)
    BACKGROUND: Effective response to emerging infectious disease (EID) threats relies on health care systems that can detect and contain localised outbreaks before they reach a national or international scale. The Asia-Pacific region contains low and middle income countries in which the risk of EID outbreaks is elevated and whose health care systems may require international support to effectively detect and respond to such events. The absence of comprehensive data on populations, health care systems and disease characteristics in this region makes risk assessment and decisions about the provision of such support challenging. METHODOLOGY/PRINCIPAL FINDINGS: We describe a mathematical modelling framework that can inform this process by integrating available data sources, systematically explore the effects of uncertainty, and provide estimates of outbreak risk under a range of intervention scenarios. We illustrate the use of this framework in the context of a potential importation of Ebola Virus Disease into the Asia-Pacific region. Results suggest that, across a wide range of plausible scenarios, preemptive interventions supporting the timely detection of early cases provide substantially greater reductions in the probability of large outbreaks than interventions that support health care system capacity after an outbreak has commenced. CONCLUSIONS/SIGNIFICANCE: Our study demonstrates how, in the presence of substantial uncertainty about health care system infrastructure and other relevant aspects of disease control, mathematical models can be used to assess the constraints that limited resources place upon the ability of local health care systems to detect and respond to EID outbreaks in a timely and effective fashion. Our framework can help evaluate the relative impact of these constraints to identify resourcing priorities for health care system support, in order to inform principled and quantifiable decision making.