Simulation analyses to evaluate alternative control strategies for the 2002 foot-and-mouth disease outbreak in the Republic of Korea.

Using the stochastic and spatial simulation model of between-farm spread of disease, InterSpread Plus, we evaluated the effect of alternative strategies for controlling the 2002 epidemic of foot-and-mouth disease (FMD) in the Republic of Korea. InterSpread Plus was parameterised to simulate epidemics of FMD in the population of farms containing susceptible animal species in the Korean counties of Yongin, Icheon, Pyongtaek, Anseong, Eumseong, Asan, Cheonan, and Jincheon. The starting point of our analyses was the simulation of a reference strategy, which approximated the real epidemic. The results of simulations of alternative epidemic-control strategies were compared with this reference strategy. Ring vaccination (when used with either limited or extended pre-emptive depopulation) reduced both the size and variability of the predicted number of infected farms. Reducing the time between disease incursion and commencement of controls had the greatest effect on reducing the predicted number of infected farms.

EpiMAN-FMD: a decision support system for managing epidemics of vesicular disease.

A comprehensive epidemiological information system (EpiMAN-FMD) has been developed to assist national disease control authorities contain and eradicate outbreaks of animal diseases as efficiently and cost-effectively as possible. The system was initially developed to control an incursion of foot and mouth disease (FMD) or any clinically indistinguishable vesicular disease, although it has since been progressively expanded to manage other exotic and endemic diseases. Design objectives for the information management elements of EpiMAN-FMD included the following: the need to manage the vast quantities of data that eradication procedures for an FMD epidemic would generate within a very short time the ability to innovatively apply epidemiological understanding of disease spread to the data processing tasks the reduction of some of the foreseen processing bottlenecks the provision of decision support tools for data entry personnel. Design objectives for the veterinary management elements of the system included the following: the presentation of up-to-date status reports in formats that facilitate decision-making at the national or regional level the ability to optimise manpower resource allocation the capacity to evaluate the relative merits of alternative technical decisions, each of which carry different implicit risks. The system combines a multi-user database management system, expert system elements, various computer simulation models of specific aspects of FMD epidemiology and a range of statistical analyses designed to monitor the state of the epidemic. Although designed in New Zealand, EpiMAN-FMD has been constructed in a flexible style which makes adoption of the system possible in other countries with broadly similar 'stamping-out' contingency plans.

Decision-support tools for foot and mouth disease control.

Recent experience with foot and mouth disease (FMD) has shown that large and very costly epidemics can occur in countries considered extremely unlikely to experience the disease. The consequences of an introduction are much more severe than in the past and effective control is more difficult to achieve. Few countries have developed effective risk management strategies and information-based response systems to respond to these developments. The authors describe the tools which can be employed to minimise the impact of a disease incursion, using the example of FMD. To make such systems effective, the development of a national farms database in advance, including geo-referencing, is highly desirable. This greatly enhances the power of the decision-support tools, which can then be applied as soon as a serious disease incursion has been detected. These tools include procedures to detect infected farms promptly, to protect as yet uninfected farms against exposure to virus and to manage control policies. Epidemiological evaluation and prediction tools have advanced particularly rapidly and can guide the choice of control policies during an outbreak. Integrated decision-support systems offer the best method of managing FMD outbreaks to minimise the cost and size of the epidemics.

EpiMAN-SF: a decision-support system for managing swine fever epidemics.

During an outbreak of classical swine fever (hog cholera) or African swine fever, decisions in emergency headquarters have to be made under conditions of time pressure, limited resources and uncertainty. The computer program EpiMAN-SF is designed to support decision-making in this situation by providing up-to-date information and a structured approach to choosing from among competing tasks by combining electronic data management with expert system components. EpiMAN-SF provides accurate and timely summaries of the epidemic, using both text and graphics. In addition, the program provides tools for the epidemiological analysis and forecasting of the emergency, and for assessing current and alternative control strategies. The expert system and simulation models integrated in EpiMAN-SF have been evaluated using sensitivity analysis, historic outbreak data and expert opinion. The results demonstrate that EpiMAN-SF is a valid alternative to traditional data management during exotic disease epidemics.

Predictive spatial modelling of alternative control strategies for the foot-and-mouth disease epidemic in Great Britain, 2001.

A spatial simulation model of foot-and-mouth disease was used in March and early April 2001 to evaluate alternative control policies for the 2001 epidemic in Great Britain. Control policies were those in operation from March 20, 2001, and comprised a ban on all animal movements from February 23, 2001, and a stamping-out policy. Each simulation commenced with the known population of infected farms on April 10, 2001, and ran for 200 days. For the control policy which best approximated that actually implemented from late March, the model predicted an epidemic of approximately 1800 to 1900 affected farms, and estimated that the epidemic would be eradicated between July and October 2001, with a low probability of continuing beyond October 2001. This policy included the slaughter-out of infected farms within 24 hours, slaughter of about 1.3 of the surrounding farms per infected farm within a further 48 hours, and minimal interfarm movements of susceptible animals. Delays in the slaughter of animals on infected farms beyond 24 hours after diagnosis slightly increased the epidemic size, and failure to achieve pre-emptive slaughter on an adequate number of at-risk farms substantially increased the expected size of the epidemic. Vaccination of up to three of the most outbreak-dense areas carried out in conjunction with the adopted control policy reduced the predicted size of the epidemic by less than 100 farms. Vaccination of buffer zones (designed to apply available vaccine and manpower as effectively as possible) carried out in place of the adopted control policy allowed the disease to spread out of control, producing an epidemic involving over 6000 farms by October 2001, with no prospect of immediate eradication.

InterSpread Plus: a spatial and stochastic simulation model of disease in animal populations.

We describe the spatially explicit, stochastic simulation model of disease spread, InterSpread Plus, in terms of its epidemiological framework, operation, and mode of use. The input data required by the model, the method for simulating contact and infection spread, and methods for simulating disease control measures are described. Data and parameters that are essential for disease simulation modelling using InterSpread Plus are distinguished from those that are non-essential, and it is suggested that a rational approach to simulating disease epidemics using this tool is to start with core data and parameters, adding additional layers of complexity if and when the specific requirements of the simulation exercise require it. We recommend that simulation models of disease are best developed as part of epidemic contingency planning so decision makers are familiar with model outputs and assumptions and are well-positioned to evaluate their strengths and weaknesses to make informed decisions in times of crisis.