Model-based estimation of the impact on rotavirus disease of RV3-BB vaccine administered in a neonatal or infant schedule.

Rotavirus infection is a common cause of severe diarrheal disease and a major cause of deaths and hospitalizations among young children. Incidence of rotavirus has declined globally with increasing vaccine coverage. However, it remains a significant cause of morbidity and mortality in low-income countries where vaccine access is limited and efficacy is lower. The oral human neonatal vaccine RV3-BB can be safely administered earlier than other vaccines, and recent trials in Indonesia have demonstrated high efficacy. In this study, we use a stochastic individual-based model of rotavirus transmission and disease to estimate the anticipated population-level impact of RV3-BB following delivery according to either an infant (2, 4, 6 months) and neonatal (0, 2, 4 months) schedule. Using our model, which incorporated an age- and household-structured population and estimates of vaccine efficacy derived from trial data, we found both delivery schedules to be effective at reducing infection and disease. We estimated 95-96% reductions in infection and disease in children under 12 months of age when vaccine coverage is 85%. We also estimate high levels of indirect protection from vaccination, including 78% reductions in infection in adults over 17 years of age. Even for lower vaccine coverage of 55%, we estimate reductions of 84% in infection and disease in children under 12 months of age. While open questions remain about the drivers of observed lower efficacy in low-income settings, our model suggests RV3-BB could be effective at reducing infection and preventing disease in young infants at the population level.

The economic benefits of targeted response strategies against foot-and-mouth disease in Australia.

Recent developments in control of highly infectious diseases attempt to improve emergency response efforts by more clearly focusing or targeting response tools according to risk. For example, advances in surveillance testing and sampling deliver their results by more accurately and precisely targeting the population of interest. In this work, targeted implementation of trading zones and vaccination were examined for simulated outbreaks of foot-and-mouth disease (FMD) in Australia. Trading zones allowing unaffected Australian states to resume exports following an outbreak of FMD were assessed using multiple tools. A Victorian incursion scenario with traditional stamping out and vaccination as control options, was simulated using the AADIS model Version 2.47, to characterise the geographic extent of potential outbreaks, the number of animals infected, and the date of last cull indicating duration of the outbreak. Information on disease spread from the AADIS simulations was then used to identify the boundaries of trading zones for the incursion scenario, in which vaccination with trading zones was found to further reduce disease impacts relative to stamping out alone with trading zones. The number of animals culled due to disease provided supply shocks for stamping out alone and vaccinate-to-retain, while the number of vaccinated animals was added to the number of animals culled due to disease for the supply shock of vaccinate-to-remove. The day of last cull was combined with historical FMD trade recovery and Australian export data to estimate the share of Australian exports that would be embargoed under trading zones. The market impacts - changes in equilibrium quantities and prices - of the supply shock, trading zones, and consumer reactions - were simulated within ABARES' AgEmissions partial equilibrium model of Australian agriculture. For this simulated large outbreak, where vaccinate-to-remove was utilised along with trading zones, producer losses were reduced by AUD 4 billion in present value terms over 10 years estimated at a 7% discount rate (PV10,7%) compared to an outbreak where stamping out alone is applied with trading zones. Introducing FMD virus risk mitigation measures for wool to further target trading zones reduced the economic impacts by an additional AUD 3.6 billion (PV10,7%). Outbreak response cost savings and additional potential costs under vaccinate-to-retain with trading zones were also compared to the vaccinate-to-remove control with trading zones. Results emphasised the importance of outbreak characteristics in determining trading zones and targeting of vaccination. Economic analyses identified how additional investments in targeting outbreak response is of value to producers.

Development of a transboundary model of livestock disease in Europe.

Epidemiological models of notifiable livestock disease are typically framed at a national level and targeted for specific diseases. There are inherent difficulties in extending models beyond national borders as details of the livestock population, production systems and marketing systems of neighbouring countries are not always readily available. It can also be a challenge to capture heterogeneities in production systems, control policies, and response resourcing across multiple countries, in a single transboundary model. In this paper, we describe EuFMDiS, a continental-scale modelling framework for transboundary animal disease, specifically designed to support emergency animal disease planning in Europe. EuFMDiS simulates the spread of livestock disease within and between countries and allows control policies to be enacted and resourced on a per-country basis. It provides a sophisticated decision support tool that can be used to look at the risk of disease introduction, establishment and spread; control approaches in terms of effectiveness and costs; resource management; and post-outbreak management issues.

A Simulation Study of the Use of Vaccination to Control Foot-and-Mouth Disease Outbreaks Across Australia.

This study examines the potential for foot-and-mouth disease (FMD) control strategies that incorporate vaccination to manage FMD spread for a range of incursion scenarios across Australia. Stakeholder consultation was used to formulate control strategies and incursion scenarios to ensure relevance to the diverse range of Australian livestock production regions and management systems. The Australian Animal Disease Spread model (AADIS) was used to compare nine control strategies for 13 incursion scenarios, including seven control strategies incorporating vaccination. The control strategies with vaccination differed in terms of their approaches for targeting areas and species. These strategies are compared with two benchmark strategies based on stamping out only. Outbreak size and duration were compared in terms of the total number of infected premises, the duration of the control stage of an FMD outbreak, and the number of vaccinated animals. The three key findings from this analysis are as follows: (1) smaller outbreaks can be effectively managed by stamping out without vaccination, (2) the size and duration of larger outbreaks can be significantly reduced when vaccination is used, and (3) different vaccination strategies produced similar reductions in the size and duration of an outbreak, but the number of animals vaccinated varied. Under current international standards for regaining FMD-free status, vaccinated animals need to be removed from the population at the end of the outbreak to minimize trade impacts. We have shown that selective, targeted vaccination strategies could achieve effective FMD control while significantly reducing the number of animals vaccinated.

Comparing surveillance approaches to support regaining free status after a foot-and-mouth disease outbreak.

Following an FMD eradication program, surveillance will be required to demonstrate that the program has been successful. The World Organization for Animal Health (OIE) provides guidelines including waiting periods and appropriate surveillance to support regaining FMD-free status. Serological surveillance is the recommended method for demonstrating freedom but is time consuming and expensive. New technologies such as real-time reverse transcription polymerase chain reaction (RT-qPCR) tests and sampling techniques such as bulk milk testing (BMT) of dairy cattle, oral swabs, and saliva collection with rope tethers in piggeries could enable surveillance to be done more efficiently. Epidemiological modelling was used to simulate FMD outbreaks, with and without emergency vaccination as part of the response, in Australia. Baseline post-outbreak surveillance approaches for unvaccinated and vaccinated animals based on the European FMD directive were compared with alternative approaches in which the sampling regime, sampling approaches and/or the diagnostic tests used were varied. The approaches were compared in terms of the resources required, time taken, cost, and effectiveness i.e., ability of the surveillance regime to correctly identify the infection status of herds. In the non-vaccination scenarios, the alternative approach took less time to complete and cost less, with the greatest benefits seen with larger outbreaks. In vaccinated populations, the alternative surveillance approaches significantly reduced the number of herds sampled, the total number of tests done and costs of the post-outbreak surveillance. There was no reduction in effectiveness using the alternative approaches, with one of the benefits being a reduction in the number of false positive herds. Alternative approaches to FMD surveillance based on non-invasive sampling methods and RT-qPCR tests have the potential to enable post outbreak surveillance substantiating FMD freedom to be done more quickly and less expensively than traditional approaches based on serological surveys.

Transmission network reconstruction for foot-and-mouth disease outbreaks incorporating farm-level covariates.

Transmission network modelling to infer 'who infected whom' in infectious disease outbreaks is a highly active area of research. Outbreaks of foot-and-mouth disease have been a key focus of transmission network models that integrate genomic and epidemiological data. The aim of this study was to extend Lau's systematic Bayesian inference framework to incorporate additional parameters representing predominant species and numbers of animals held on a farm. Lau's Bayesian Markov chain Monte Carlo algorithm was reformulated, verified and pseudo-validated on 100 simulated outbreaks populated with demographic data Japan and Australia. The modified model was then implemented on genomic and epidemiological data from the 2010 outbreak of foot-and-mouth disease in Japan, and outputs compared to those from the SCOTTI model implemented in BEAST2. The modified model achieved improvements in overall accuracy when tested on the simulated outbreaks. When implemented on the actual outbreak data from Japan, infected farms that held predominantly pigs were estimated to have five times the transmissibility of infected cattle farms and be 49% less susceptible. The farm-level incubation period was 1 day shorter than the latent period, the timing of the seeding of the outbreak in Japan was inferred, as were key linkages between clusters and features of farms involved in widespread dissemination of this outbreak. To improve accessibility the modified model has been implemented as the R package 'BORIS' for use in future outbreaks.

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.

Reconstructing foot-and-mouth disease outbreaks: a methods comparison of transmission network models.

A number of transmission network models are available that combine genomic and epidemiological data to reconstruct networks of who infected whom during infectious disease outbreaks. For such models to reliably inform decision-making they must be transparently validated, robust, and capable of producing accurate predictions within the short data collection and inference timeframes typical of outbreak responses. A lack of transparent multi-model comparisons reduces confidence in the accuracy of transmission network model outputs, negatively impacting on their more widespread use as decision-support tools. We undertook a formal comparison of the performance of nine published transmission network models based on a set of foot-and-mouth disease outbreaks simulated in a previously free country, with corresponding simulated phylogenies and genomic samples from animals on infected premises. Of the transmission network models tested, Lau's systematic Bayesian integration framework was found to be the most accurate for inferring the transmission network and timing of exposures, correctly identifying the source of 73% of the infected premises (with 91% accuracy for sources with model support >0.80). The Structured COalescent Transmission Tree Inference provided the most accurate inference of molecular clock rates. This validation study points to which models might be reliably used to reconstruct similar future outbreaks and how to interpret the outputs to inform control. Further research could involve extending the best-performing models to explicitly represent within-host diversity so they can handle next-generation sequencing data, incorporating additional animal and farm-level covariates and combining predictions using Ensemble methods and other approaches.

Does Size Matter to Models? Exploring the Effect of Herd Size on Outputs of a Herd-Level Disease Spread Simulator.

Disease spread modeling is widely used by veterinary authorities to predict the impact of emergency animal disease outbreaks in livestock and to evaluate the cost-effectiveness of different management interventions. Such models require knowledge of basic disease epidemiology as well as information about the population of animals at risk. Essential demographic information includes the production system, animal numbers, and their spatial locations yet many countries with significant livestock industries do not have publically available and accurate animal population information at the farm level that can be used in these models. The impact of inaccuracies in data on model outputs and the decisions based on these outputs is seldom discussed. In this analysis, we used the Australian Animal Disease model to simulate the spread of foot-and-mouth disease seeded into high-risk herds in six different farming regions in New Zealand. We used three different susceptible animal population datasets: (1) a gold standard dataset comprising known herd sizes, (2) a dataset where herd size was simulated from a beta-pert distribution for each herd production type, and (3) a dataset where herd size was simplified to the median herd size for each herd production type. We analyzed the model outputs to compare (i) the extent of disease spread, (ii) the length of the outbreaks, and (iii) the possible impacts on decisions made for simulated outbreaks in different regions. Model outputs using the different datasets showed statistically significant differences, which could have serious implications for decision making by a competent authority. Outbreak duration, number of infected properties, and vaccine doses used during the outbreak were all significantly smaller for the gold standard dataset when compared with the median herd size dataset. Initial outbreak location and disease control strategy also significantly influenced the duration of the outbreak and number of infected premises. The study findings demonstrate the importance of having accurate national-level population datasets to ensure effective decisions are made before and during disease outbreaks, reducing the damage and cost.

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.