Optimal allocation of resources among general and species-specific tools for plant pest biosecurity surveillance.

This paper proposes a surveillance model for plant pests that can optimally allocate resources among survey tools with varying properties. While some survey tools are highly specific for the detection of a single pest species, others are more generalized. There is considerable variation in the cost and sensitivity of these tools, but there are no guidelines or frameworks for identifying which tools are most cost-effective when used in surveillance programs that target the detection of newly invaded populations. To address this gap, we applied our model to design a trapping surveillance program in New Zealand for bark- and wood-boring insects, some of the most serious forest pests worldwide. Our findings show that exclusively utilizing generalized traps (GTs) proves to be highly cost-effective across a wide range of scenarios, particularly when they are capable of capturing all pest species. Implementing surveillance programs that only employ specialized traps (ST) is cost-effective only when these traps can detect highly damaging pests. However, even in such cases, they significantly lag in cost-effectiveness compared to GT-only programs due to their restricted coverage. When both GTs and STs are used in an integrated surveillance program, the total expected cost (TEC) generally diminishes when compared to programs relying on a single type of trap. However, this relative reduction in TEC is only marginally larger than that achieved with GT-only programs, as long as highly damaging species can be detected by GTs. The proportion of STs among the optimal required traps fluctuates based on several factors, including the relative pricing of GTs and STs, pest arrival rates, potential damage, and, more prominently, the coverage capacity of GTs. Our analysis suggests that deploying GTs extensively across landscapes appears to be more cost-effective in areas with either very high or very low levels of relative risk density, potential damage, and arrival rate. Finally, STs are less likely to be required when the pests that are detected by those tools have a higher likelihood of successful eradication because delaying detection becomes less costly for these species.

Epidemiological modelling of the health and economic effects of COVID-19 control in Australia's second wave.

Background: We investigated the public health and economy outcomes of different levels of social distancing to control a 'second wave' outbreak in Australia and identify implications for public health management of COVID-19. Methods: Individual-based and compartment models were used to simulate the effects of different social distancing and detection strategies on Australian COVID-19 infections and the economy from March to July 2020. These models were used to evaluate the effects of different social distancing levels and the early relaxation of suppression measures, in terms of public health and economy outcomes. Results: The models, fitted to observations up to July 2020, yielded projections consistent with subsequent cases and showed that better public health outcomes and lower economy costs occur when social distancing measures are more stringent, implemented earlier and implemented for a sufficiently long duration. Early relaxation of suppression results in worse public health outcomes and higher economy costs. Conclusions: Better public health outcomes (reduced COVID-19 fatalities) are positively associated with lower economy costs and higher levels of social distancing; achieving zero community transmission lowers both public health and economy costs compared to allowing community transmission to continue; and early relaxation of social distancing increases both public health and economy costs.

Estimating the transmission parameters of foot-and-mouth disease in Vietnam: A spatial-dynamic kernel-based model with outbreak and host data.

Foot-and-mouth disease (FMD) affects cloven-hoofed livestock and causes devastating damages to the world's economies. Being endemic in developing countries, FMD has imposed a significant threat to the FMD-freedom status in developed countries. The globally-concerted effort to eradicate FMD at its source has faced a substantial challenge of having little knowledge about how FMD spreads in developing countries. So far, FMD virus transmission parameters have been estimated based on only a dozen actual outbreak data, mostly in Europe. Meanwhile, the fundamental north-south differences in livestock production, trading, and quarantine systems have questioned the applicability of these estimates to developing countries. In this light, we aim to narrow the knowledge gap by estimating the FMD virus transmission parameters in an endemic country, Vietnam, the world's fifth- largest pork producer. We use the spatial-dynamic kernel-based approach combined with daily FMD incursion data and FMD-host census data. The estimation also considers livestock composition and livestock quantity by species, which can influence FMD transmission. In line with existing literature, we find that cattle and buffaloes have a larger influence on disease spread than pigs, and FMD transmission depends on the herd size and the distance between susceptible and infected premises. However, our findings show FMD virus can spread over a much more ample space in our case compared with those in existing literature (25 km and 50 km versus 10 km), and the kernels have much fatter tails. This difference is likely due to the weakness in biosecurity systems, poor implementation of surveillance and quarantine measures, and bad husbandry practices such as swill feeding, which are prevalent in developing countries. Thus, our estimated kernels will be helpful for Vietnam in developing suitable biosecurity measures to contain and eradicate the FMD virus. They are also highly relevant for other countries with livestock farming practices and climate conditions similar to those in Vietnam.

What vaccination rate(s) minimize total societal costs after 'opening up' to COVID-19? Age-structured SIRM results for the Delta variant in Australia (New South Wales, Victoria and Western Australia).

Using three age-structured, stochastic SIRM models, calibrated to Australian data post July 2021 with community transmission of the Delta variant, we projected possible public health outcomes (daily cases, hospitalisations, ICU beds, ventilators and fatalities) and economy costs for three states: New South Wales (NSW), Victoria (VIC) and Western Australia (WA). NSW and VIC have had on-going community transmission from July 2021 and were in 'lockdown' to suppress transmission. WA did not have on-going community transmission nor was it in lockdown at the model start date (October 11th 2021) but did maintain strict state border controls. We projected the public health outcomes and the economic costs of 'opening up' (relaxation of lockdowns in NSW and VIC or fully opening the state border for WA) at alternative vaccination rates (70%, 80% and 90%), compared peak patient demand for ICU beds and ventilators to staffed state-level bed capacity, and calculated a 'preferred' vaccination rate that minimizes societal costs and that varies by state. We found that the preferred vaccination rate for all states is at least 80% and that the preferred population vaccination rate is increasing with: (1) the effectiveness (infection, hospitalization and fatality) of the vaccine; (2) the lower is the daily lockdown cost; (3) the larger are the public health costs from COVID-19; (4) the higher is the rate of community transmission before opening up; and (5) the less effective are the public health measures after opening up.

Optimal surveillance against bioinvasions: a sample average approximation method applied to an agent-based spread model.

Trade-offs exist between the point of early detection and the future cost of controlling any invasive species. Finding optimal levels of early detection, with post-border active surveillance, where time, space and randomness are explicitly considered, is computationally challenging. We use a stochastic programming model to find the optimal level of surveillance and predict damages, easing the computational challenge by combining a sample average approximation (SAA) approach and parallel processing techniques. The model is applied to the case of Asian Papaya Fruit Fly (PFF), a highly destructive pest, in Queensland, Australia. To capture the non-linearity in PFF spread, we use an agent-based model (ABM), which is calibrated to a highly detailed land-use raster map (50 m × 50 m) and weather-related data, validated against a historical outbreak. The combination of SAA and ABM sets our work apart from the existing literature. Indeed, despite its increasing popularity as a powerful analytical tool, given its granularity and capability to model the system of interest adequately, the complexity of ABM limits its application in optimizing frameworks due to considerable uncertainty about solution quality. In this light, the use of SAA ensures quality in the optimal solution (with a measured optimality gap) while still being able to handle large-scale decision-making problems. With this combination, our application suggests that the optimal (economic) trap grid size for PFF in Queensland is ˜0.7 km, much smaller than the currently implemented level of 5 km. Although the current policy implies a much lower surveillance cost per year, compared with the $2.08 million under our optimal policy, the expected total cost of an outbreak is $23.92 million, much higher than the optimal policy of roughly $7.74 million.

Health and economic costs of early and delayed suppression and the unmitigated spread of COVID-19: The case of Australia.

We compare the health and economic costs of early and delayed mandated suppression and the unmitigated spread of 'first-wave' COVID-19 infections in Australia in 2020. Using a fit-for-purpose SIQRM-compartment model for susceptible, infected, quarantined, recovered and mortalities on active cases, 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 that the economic costs of unmitigated suppression are multiples more than for early mandated suppression. We also find that using an equivalent VSLY welfare loss from fatalities to estimated GDP losses, drawn from survey data and our own estimates of the impact of suppression measures on the economy, means that for early suppression not to be the preferred strategy requires that Australia would have to incur more than 12,500-30,000 deaths, depending on the fatality rate with unmitigated spread, to the economy costs of early mandated suppression. We also find that early rather than delayed mandated suppression imposes much lower economy and health costs and 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.

Rice land protection in a transitional economy: The case of Vietnam.

Agricultural land protection (ALP) is a standard policy response to a desire for food security. However, ALP may result in a misallocation of resources. Examining rice land policy in Vietnam, we determine the optimal level of rice land protected against other crops using a stochastic optimization model built on top of a general equilibrium framework, combined with sequential micro-simulations on household data. We find that converting part of protected rice land enhances economic efficiency. Nonetheless, the policy is relatively pro-rich, implying a trade-off between poverty reduction and economic efficiency, making some households in already poor areas worse off. Our approach can be applied to land-use planning generally, highlighting the relevant tradeoffs and the search for needed optimal land-use policies.

Approaches for estimating benefits and costs of interventions in plant biosecurity across invasion phases.

Nonnative plant pests cause billions of dollars in damages. It is critical to prevent or reduce these losses by intervening at various stages of the invasion process, including pathway risk management (to prevent pest arrival), surveillance and eradication (to counter establishment), and management of established pests (to limit damages). Quantifying benefits and costs of these interventions is important to justify and prioritize investments and to inform biosecurity policy. However, approaches for these estimations differ in (1) the assumed relationship between supply, demand, and prices, and (2) the ability to assess different types of direct and indirect costs at invasion stages, for a given arrival or establishment probability. Here we review economic approaches available to estimate benefits and costs of biosecurity interventions to inform the appropriate selection of approaches. In doing so, we complement previous studies and reviews on estimates of damages from invasive species by considering the influence of economic and methodological assumptions. Cost accounting is suitable for rapid decisions, specific impacts, and simple methodological assumptions but fails to account for feedbacks, such as market adjustments, and may overestimate long-term economic impacts. Partial equilibrium models consider changes in consumer and producer surplus due to pest impacts or interventions and can account for feedbacks in affected sectors but require specialized economic models, comprehensive data sets, and estimates of commodity supply and demand curves. More intensive computable general equilibrium models can account for feedbacks across entire economies, including capital and labor, and linkages among these. The two major considerations in choosing an approach are (1) the goals of the analysis (e.g., consideration of a single pest or intervention with a limited range of impacts vs. multiple interventions, pests or sectors), and (2) the resources available for analysis such as knowledge, budget and time.

Assessing biophysical and socio-economic impacts of climate change on regional avian biodiversity.

Climate change threatens biodiversity directly by influencing biophysical variables that drive species' geographic distributions and indirectly through socio-economic changes that influence land use patterns, driven by global consumption, production and climate. To date, no detailed analyses have been produced that assess the relative importance of, or interaction between, these direct and indirect climate change impacts on biodiversity at large scales. Here, we apply a new integrated modelling framework to quantify the relative influence of biophysical and socio-economically mediated impacts on avian species in Vietnam and Australia and we find that socio-economically mediated impacts on suitable ranges are largely outweighed by biophysical impacts. However, by translating economic futures and shocks into spatially explicit predictions of biodiversity change, we now have the power to analyse in a consistent way outcomes for nature and people of any change to policy, regulation, trading conditions or consumption trend at any scale from sub-national to global.

Optimal surveillance against foot-and-mouth disease: A sample average approximation approach.

Decisions surrounding the presence of infectious diseases are typically made in the face of considerable uncertainty. However, the development of models to guide these decisions has been substantially constrained by computational difficulty. This paper focuses on the case of finding the optimal level of surveillance against a highly infectious animal disease where time, space and randomness are fully considered. We apply the Sample Average Approximation approach to solve our problem, and to control model dimension, we propose the use of an infection tree model, in combination with sensible 'tree-pruning' and parallel processing techniques. Our proposed model and techniques are generally applicable to a number of disease types, but we demonstrate the approach by solving for optimal surveillance levels against foot-and-mouth disease using bulk milk testing as an active surveillance protocol, during an epidemic, among 42,279 farms, fully characterised by their location, livestock type and size, in the state of Victoria, Australia.

Robust estimates of the true (population) infection rate for COVID-19: a backcasting approach.

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.

Management strategies for vaccinated animals after an outbreak of foot-and-mouth disease and the impact on return to trade.

An incursion of Foot-and-mouth disease (FMD) in a previously FMD-free country can cause significant economic damage from immediate and prolonged closure of FMD-sensitive markets. Whilst emergency vaccination may help contain disease, the presence of vaccinated animals complicates post-outbreak management and the recovery of FMD-free status for return to trade. We present enhancements to the Australian Animal DISease (AADIS) model that allow comparisons of post-outbreak management strategies for vaccinated animals, for the purposes of securing the earliest possible return to trade. Two case studies are provided that compare the retention of vaccinated animals with removal for waste/salvage, and the impact on recovery of FMD-sensitive markets per OIE guidelines. It was found that a vaccinate-and-retain strategy was associated with lower post-outbreak management costs, however this advantage was outweighed by significantly higher trade losses. Under the assumptions of the study there was no cost advantage to salvaging the removed vaccinated animals.

Delimiting a species' geographic range using posterior sampling and computational geometry.

Accurate delimitation of the geographic range of a species is important for control of biological invasions, conservation of threatened species, and understanding species range dynamics under environmental change. However, estimating range boundaries is challenging because monitoring methods are imperfect, the area that might contain individuals is often incompletely surveyed, and species may have patchy distributions. In these circumstances, large areas can be surveyed without finding individuals despite occupancy extending beyond surveyed areas, resulting in underestimation of range limits. We developed a delimitation method that can be applied with imperfect survey data and patchy distributions. The approach is to construct polygons indicative of the geographic range of a species. Each polygon is associated with a specific probability such that each interior point of the polygon has at least that posterior probability of being interior to the true boundary according to a Bayesian model. The method uses the posterior distribution of latent quantities derived from an agent-based Bayesian model and calculates the posterior distribution of the range as a derived quantity from Markov chain Monte Carlo samples. An application of this method described here informed the Australian campaign to eradicate red imported fire ants (Solenopsis invicta).

Institutional impediments to conservation of freshwater dependent ecosystems.

When freshwater resources become scarce there is a trade-off between human resource demands and environmental sustainability. The cost of conserving freshwater ecosystems can potentially be reduced by implementing institutional reforms that endow environmental water managers with a permanent water entitlement and the capacity to store, trade and release water. Australia's Murray Darling Basin Plan (MDBP) includes one of the world's most ambitious programs to recover water for the environment, supported by institutional reforms that allow environmental water managers to operate in water markets. One of the anticipated benefits of the Plan is to improve the health of flood-dependent forests, which are among the most endangered ecosystems globally because of river regulation and land clearance. However, periodic flooding to conserve floodplain ecosystems in the MDB creates losses to riparian landowners such as damage to fencing and temporary loss of access to flooded land. To reduce these losses reservoir operators restrict daily water release volumes. Using a model of optimal water management in Australia's southern MDB we estimate that current reservoir operating restrictions will substantially reduce the ecological benefits of investments made to recover water for the environment. The reduction in benefits is largest if floodplain forests decline rapidly without periodic inundation. In the latter circumstances, ecological losses cannot significantly be reduced by allowing environmental water managers to operate in water markets. Our findings demonstrate that the recovery of large volumes of water for environmental purposes and water market reforms are insufficient for conserving flood-dependent ecosystems without coordination and cooperation among multiple stakeholders responsible for water and land management.

Early Decision Indicators for Foot-and-Mouth Disease Outbreaks in Non-Endemic Countries.

Disease managers face many challenges when deciding on the most effective control strategy to manage an outbreak of foot-and-mouth disease (FMD). Decisions have to be made under conditions of uncertainty and where the situation is continually evolving. In addition, resources for control are often limited. A modeling study was carried out to identify characteristics measurable during the early phase of a FMD outbreak that might be useful as predictors of the total number of infected places, outbreak duration, and the total area under control (AUC). The study involved two modeling platforms in two countries (Australia and New Zealand) and encompassed a large number of incursion scenarios. Linear regression, classification and regression tree, and boosted regression tree analyses were used to quantify the predictive value of a set of parameters on three outcome variables of interest: the total number of infected places, outbreak duration, and the total AUC. The number of infected premises (IPs), number of pending culls, AUC, estimated dissemination ratio, and cattle density around the index herd at days 7, 14, and 21 following first detection were associated with each of the outcome variables. Regression models for the size of the AUC had the highest predictive value (R2 = 0.51-0.9) followed by the number of IPs (R2 = 0.3-0.75) and outbreak duration (R2 = 0.28-0.57). Predictability improved at later time points in the outbreak. Predictive regression models using various cut-points at day 14 to define small and large outbreaks had positive predictive values of 0.85-0.98 and negative predictive values of 0.52-0.91, with 79-97% of outbreaks correctly classified. On the strict assumption that each of the simulation models used in this study provide a realistic indication of the spread of FMD in animal populations. Our conclusion is that relatively simple metrics available early in a control program can be used to indicate the likely magnitude of an FMD outbreak under Australian and New Zealand conditions.

A structural and stochastic optimal model for projections of LNG imports and exports in Asia-Pacific.

The Asia-Pacific region, the largest and fastest growing liquefied natural gas (LNG) market in the world, has been undergoing radical changes over the past few years. These changes include considerable additional supplies from North America and Australia, and a recent LNG price slump resulting from an oil-linked pricing mechanism and demand uncertainties. This paper develops an Asia-Pacific Gas Model (APGM), based on a structural, stochastic and optimising framework, providing a valuable tool for the projection of LNG trade in the Asia-Pacific region. With existing social-economic conditions, the model projects that Asia-Pacific LNG imports are expected to increase by 49.1 percent in 2020 and 95.7 percent in 2030, compared to 2013. Total LNG trade value is estimated to increase to US$127.2 billion in 2020 and US$199.0 billion in 2030. Future LNG trade expansion is mainly driven by emerging and large importers (i.e., China and India), and serviced, most importantly, by new supplies from Australia and the USA. The model's projected results are sensitive to changes in expected oil prices, pricing mechanisms, economic growth and energy policies, as well as unexpected geopolitical-economic events.

Strengthening tuberculosis control overseas: who benefits?

BACKGROUND: Although tuberculosis is a major cause of morbidity and mortality worldwide, available funding falls far short of that required for effective control. Economic and spillover consequences of investments in the treatment of tuberculosis are unclear, particularly when steep gradients in the disease and response are linked by population movements, such as that between Papua New Guinea (PNG) and the Australian cross-border region. OBJECTIVE: To undertake an economic evaluation of Australian support for the expansion of basic Directly Observed Treatment, Short Course in the PNG border area of the South Fly from the current level of 14% coverage. METHODS: Both cost-utility analysis and cost-benefit analysis were applied to models that allow for population movement across regions with different characteristics of tuberculosis burden, transmission, and access to treatment. Cost-benefit data were drawn primarily from estimates published by the World Health Organization, and disease transmission data were drawn from a previously published model. RESULTS: Investing $16 million to increase basic Directly Observed Treatment, Short Course coverage in the South Fly generates a net present value of roughly $74 million for Australia (discounted 2005 dollars). The cost per disability-adjusted life-year averted and quality-adjusted life-year saved for PNG is $7 and $4.6, respectively. CONCLUSIONS: Where regions with major disparities in tuberculosis burden and health system resourcing are connected through population movements, investments in tuberculosis control are of mutual benefit, resulting in net health and economic gains on both sides of the border. These findings are likely to inform the case for appropriate investment in tuberculosis control globally.

Marine reserves with ecological uncertainty.

To help manage the fluctuations inherent in fish populations scientists have argued for both an ecosystem approach to management and the greater use of marine reserves. Support for reserves includes empirical evidence that they can raise the spawning biomass and mean size of exploited populations, increase the abundance of species and, relative to reference sites, raise population density, biomass, fish size and diversity. By contrast, fishers often oppose the establishment and expansion of marine reserves and claim that reserves provide few, if any, economic payoffs. Using a stochastic optimal control model with two forms of ecological uncertainty we demonstrate that reserves create a resilience effect that allows for the population to recover faster, and can also raise the harvest immediately following a negative shock. The tradeoff of a larger reserve is a reduced harvest in the absence of a negative shock such that a reserve will never encompass the entire population if the goal is to maximize the economic returns from harvesting, and fishing is profitable. Under a wide range of parameter values with ecological uncertainty, and in the 'worst case' scenario for a reserve, we show that a marine reserve can increase the economic payoff to fishers even when the harvested population is not initially overexploited, harvesting is economically optimal and the population is persistent. Moreover, we show that the benefits of a reserve cannot be achieved by existing effort or output controls. Our results demonstrate that, in many cases, there is no tradeoff between the economic payoff of fishers and ecological benefits when a reserve is established at equal to, or less than, its optimum size.