School of BioSciences - Research Publications

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    Post-outbreak surveillance strategies to support proof of freedom from foot-and-mouth disease
    Bradhurst, R ; Garner, G ; East, I ; Death, C ; Dodd, A ; Kompas, T ( 2021-04-28)
    Abstract Whilst emergency vaccination may help contain foot-and-mouth disease in a previously FMD-free country, its use complicates post-outbreak surveillance and the recovery of FMD-free status. A structured surveillance program is required that can distinguish between vaccinated and residually infected animals, and provide statistical confidence that the virus is no longer circulating in previously infected areas. Epidemiological models have been well-used to investigate the potential benefits of emergency vaccination during a control progam and when/where/whom to vaccinate in the face of finite supplies of vaccine and personnel. Less well studied are post-outbreak issues such as the management of vaccinated animals and the implications of having used vaccination during surveillance regimes to support proof-of-freedom. This paper presents enhancements to the Australian Animal Disease Model (AADIS) that allow comparisons of different post-outbreak surveillance sampling regimes for establishing proof-of-freedom from FMD. A case study is provided that compares a baseline surveillance sampling regime (derived from current OIE guidelines), with an alternative less intensive sampling regime. It was found that when vaccination was not part of the control program, a reduced sampling intensity significantly reduced the number of samples collected and the cost of the post-outbreak surveillance program, without increasing the risk of missing residual infected herds.
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    Estimating the true (population) infection rate for COVID-19: A Backcasting Approach with Monte Carlo Methods
    Phipps, S ; Grafton, Q ; Kompas, T ( 2020-05-18)

    ABSTRACT

    Differences in COVID-19 testing and tracing across countries, as well as changes in testing within each country overtime, make it difficult to estimate the true (population) infection rate based on the confirmed number of cases obtained through RNA viral testing. We applied a backcasting approach, coupled with Monte Carlo methods, to estimate a distribution for the true (population) cumulative number of infections (infected and recovered) for 15 countries where reliable data are available. We find a positive relationship between the testing rate per 1,000 people and the implied true detection rate of COVID-19, and a negative relationship between the proportion who test positive and the implied true detection rate. Our estimates suggest that the true number of people infected across our sample of 15 developed countries is 18.2 (5-95% CI: 11.9-39.0) times greater than the reported number of cases. In individual countries, the true number of cases exceeds the reported figure by factors that range from 1.7 (5-95% CI: 1.1-3.6) for Australia to 35.6 (5-95% CI: 23.2-76.3) for Belgium.
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    Health and Economic Costs of Early, Delayed and No Suppression of COVID-19: The Case of Australia
    Kompas, T ; Grafton, Q ; Che, TN ; Chu, L ; Camac, J (Cold Spring Harbor Laboratory, 2020-06-23)
    We compare the health and economic costs of early (actual), delayed and no suppression of COVID-19 infections in 2020 in Australia. Using a fit-for-purpose compartment model that we fitted from recorded data, a value of a statistical life year (VSLY) and an age-adjusted value of statistical life (A-VSL), we find: (1) the economic costs of no suppression are multiples more than for early suppression; (2) VSLY welfare losses of fatalities equivalent to GDP losses mean that for early suppression to not to be the preferred strategy requires that Australians prefer more than 12,500–30,000 deaths to the economy costs of early suppression, depending on the fatality rate; and (3) early rather than delayed suppression imposes much lower economy and health costs. We conclude that in high-income countries, like Australia, a ‘go early, go hard’ strategy to suppress COVID-19 results in the lowest estimated public health and economy costs.