A generic framework for spatial quantitative risk assessments of infectious diseases: Lumpy skin disease case study.

The increase in availability of spatial data and the technological advances to handle such data allow for subsequent improvements in our ability to assess risk in a spatial setting. We provide a generic framework for quantitative risk assessments of disease introduction that capitalizes on these new data. It can be adopted across multiple spatial scales, for any pathogen, method of transmission or location. The framework incorporates the risk of initial infection in a previously uninfected location due to registered movement (e.g., trade) and unregistered movement (e.g., daily movements of wild animals). We discuss the steps of the framework and the data required to compute it. We then outline how this framework is applied for a single pathway using lumpy skin disease as a case study, a disease which had an outbreak in the Balkans in 2016. We calculate the risk of initial infection for the rest of Europe in 2016 due to trade. We perform the risk assessment on 3 spatial scales-countries, regions within countries and individual farms. We find that Croatia (assuming no vaccination occurred) has the highest mean probability of infection, with Italy, Hungary and Spain following. Including import detection of infected trade does reduce risk but this reduction is proportionally lower for countries with highest risk. The risk assessment results are consistent across the spatial scales, while in addition, at the finer spatial scales, it highlights specific areas or individual locations of countries on which to focus surveillance.

Effectiveness of simulated interventions in reducing the estimated prevalence of Salmonella in UK pig herds.

Salmonella spp are a major foodborne zoonotic cause of human illness. Consumption of pork products is believed to be a major source of human salmonellosis and Salmonella control throughout the food-chain is recommended. A number of on-farm interventions have been proposed, and some have been implemented in order to try to achieve Salmonella control. In this study we utilize previously developed models describing Salmonella dynamics to investigate the potential effects of a range of these on-farm interventions. As the models indicated that the number of bacteria shed in the faeces of an infectious animal was a key factor, interventions applied within a high-shedding scenario were also analysed. From simulation of the model, the probability of infection after Salmonella exposure was found to be a key driver of Salmonella transmission. The model also highlighted that minimising physiological stress can have a large effect but only when shedding levels are not excessive. When shedding was high, weekly cleaning and disinfection was not effective in Salmonella control. However it is possible that cleaning may have an effect if conducted more often. Furthermore, separating infectious animals, shedding bacteria at a high rate, from the rest of the population was found to be able to minimise the spread of Salmonella.

Semi-stochastic models for Salmonella infection within finishing pig units in the UK.

A multi-group semi-stochastic model is formulated to describe Salmonella dynamics on a pig herd within the UK and assess whether farm structure has any effect on the dynamics. The models include both direct transmission and indirect (via free-living infectious units in the environment and airborne infection). The basic reproduction number R0 is also investigated. The models estimate approximately 24.6% and 25.4% of pigs at slaughter weight will be infected with Salmonella within a slatted-floored and solid-floored unit respectively, which corresponds to values found in previous abattoir and farm studies, suggesting that the model has reasonable validity. Analysis of the models identified the shedding rate to be of particular importance in the control of Salmonella spread, a finding also evident in an increase in the R0 value.