School of BioSciences - Research Publications
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ItemEarly Decision Indicators for Foot-and-Mouth Disease Outbreaks in Non-Endemic Countries(Frontiers Media, 2016)This Research Topic presents valuable studies presenting different aspects and implementations of mathematical modeling for disease spread and control in the veterinary field.
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ItemComment 2 on 'Natural resource management' by Pannell, Doole and Cheung(WILEY-BLACKWELL, 2016-10)
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ItemBudgeting and portfolio allocation for biosecurity measures(WILEY, 2019-07)This paper presents a practical model for optimally allocating a budget across different biosecurity threats and measures (e.g. prevention or border quarantine, active surveillance for early detection, and containment and eradication measures) to ensure the highest rate of return. Our portfolio model differs from the common principle, which ranks alternative projects by their benefit cost ratios and picks the one that generates the highest average benefit cost ratio. The model we propose, instead, aims to allocate shares of the budget to the species where it is most cost‐effective, and consequently determine the optimal scale of the control program for each threat under varying budget constraints. The cost‐effectiveness of each block of budget spent on a threat is determined by minimising its expected total cost, including the damages it inflicts, and the control expenditures incurred in preventing or mitigating damages. As an illustration, the model is applied to the optimal allocation of a budget across four of Australia's most dangerous pests and diseases: red imported fire ants; foot‐and‐mouth disease; papaya fruit fly; and orange hawkweed. The model can readily be extended to consider more species and activities, and more complex settings including cases where detailed spatial and temporal information needs to be considered.
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ItemLook before you treat: increasing the cost effectiveness of eradication programs with aerial surveillance(SPRINGER, 2017-02)Most successful invasive species eradication programs were applied to invasions confined to a small area. Invasions occupying large areas at a low density can potentially be eradicated if individual infestations can be found at affordable cost. The development of low cost aerial surveillance methods allows for larger areas to be monitored but such methods often have lower sensitivity than conventional surveillance methods, making their cost-effectiveness uncertain. Here, we consider the cost-effectiveness of including a new aerial monitoring method in Australia’s largest eradication program, the campaign to eradicate red imported fire ants (Solenopsis invicta). The program previously relied on higher sensitivity ground surveillance and broadcast treatment. The high cost of those methods restricted the total area that could be managed with available resources below the level required to prevent ongoing expansion of the invasion. By increasing the area that can be monitored and thereby improving the targeting of treatment and ground surveillance, we estimate that remote sensing could substantially reduce eradication costs despite the method’s low sensitivity. The development of low cost monitoring methods could potentially lead to substantially improved management of invasive species.
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ItemEstimating the true (population) infection rate for COVID-19: A Backcasting Approach with Monte Carlo Methods( 2020)
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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ItemRobust estimates of the true (population) infection rate for COVID-19: a backcasting approach(ROYAL SOC, 2020-11-18)Differences in COVID-19 testing and tracing across countries, as well as changes in testing within each country over time, 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 to estimate a distribution for the true (population) cumulative number of infections (infected and recovered) for 15 developed countries. Our sample comprised countries with similar levels of medical care and with populations that have similar age distributions. Monte Carlo methods were used to robustly sample parameter uncertainty. We found a strong and statistically significant negative relationship between the proportion of the population who test positive and the implied true detection rate. Despite an overall improvement in detection rates as the pandemic has progressed, our estimates showed that, as at 31 August 2020, the true number of people to have been infected across our sample of 15 countries was 6.2 (95% CI: 4.3–10.9) times greater than the reported number of cases. In individual countries, the true number of cases exceeded the reported figure by factors that range from 2.6 (95% CI: 1.8–4.5) for South Korea to 17.5 (95% CI: 12.2–30.7) for Italy.
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ItemHealth and Economic Costs of Early, Delayed and No Suppression of COVID-19: The Case of Australia(Cold Spring Harbor Laboratory, 2020)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.