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.

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.

A cost-benefit analysis of Vietnam's 2006-2010 foot-and-mouth disease control program.

Foot-and-mouth disease (FMD) is arguably the most damaging animal disease, affecting three-quarters of the global livestock population. This paper provides a cost-benefit analysis of the first five-year program that used vaccination to contain and control FMD in an endemic country, Vietnam. Our spatial and dynamic model to simulate FMD outbreaks fully considered the distance among livestock premises, their herd sizes, and composition, all of which significantly affect FMD transmission. Our program benefit was consistently estimated due to the Law of Large Number and the design of pairing the control and treatment scenarios which allowed capturing the true benefit of each outbreak realization. The data used to monetize the program benefit were largely drawn from Vietnam's context and statistics, thus obviating the need to make many potentially undue assumptions. Meanwhile, the program costs were actual spending and allocated budget. We found that the vaccination program is highly cost-effective for Vietnam, yielding a net present value of US$136 million (in 2006 prices) over five years and a benefit-cost ratio of 5.7. Our results were robust to different assumptions about the vaccine effectiveness of the livestock unit.

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.

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.

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.

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.