Dataset Information

The performance of approximations of farm contiguity compared to contiguity defined using detailed geographical information in two sample areas in Scotland: implications for foot-and-mouth disease modelling.

ABSTRACT:

Background

When modelling infectious diseases, accurately capturing the pattern of dissemination through space is key to providing optimal recommendations for control. Mathematical models of disease spread in livestock, such as for foot-and-mouth disease (FMD), have done this by incorporating a transmission kernel which describes the decay in transmission rate with increasing Euclidean distance from an infected premises (IP). However, this assumes a homogenous landscape, and is based on the distance between point locations of farms. Indeed, underlying the spatial pattern of spread are the contact networks involved in transmission. Accordingly, area-weighted tessellation around farm point locations has been used to approximate field-contiguity and simulate the effect of contiguous premises (CP) culling for FMD. Here, geographic data were used to determine contiguity based on distance between premises' fields and presence of landscape features for two sample areas in Scotland. Sensitivity, positive predictive value, and the True Skill Statistic (TSS) were calculated to determine how point distance measures and area-weighted tessellation compared to the 'gold standard' of the map-based measures in identifying CPs. In addition, the mean degree and density of the different contact networks were calculated.

Results

Utilising point distances <1 km and <5 km as a measure for contiguity resulted in poor discrimination between map-based CPs/non-CPs (TSS 0.279-0.344 and 0.385-0.400, respectively). Point distance <1 km missed a high proportion of map-based CPs; <5 km point distance picked up a high proportion of map-based non-CPs as CPs. Area-weighted tessellation performed best, with reasonable discrimination between map-based CPs/non-CPs (TSS 0.617-0.737) and comparable mean degree and density. Landscape features altered network properties considerably when taken into account.

Conclusion

The farming landscape is not homogeneous. Basing contiguity on geographic locations of field boundaries and including landscape features known to affect transmission into FMD models are likely to improve individual farm-level accuracy of spatial predictions in the event of future outbreaks. If a substantial proportion of FMD transmission events are by contiguous spread, and CPs should be assigned an elevated relative transmission rate, the shape of the kernel could be significantly altered since ability to discriminate between map-based CPs and non-CPs is different over different Euclidean distances.

SUBMITTER: Flood JS

PROVIDER: S-EPMC4126065 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Publications

The performance of approximations of farm contiguity compared to contiguity defined using detailed geographical information in two sample areas in Scotland: implications for foot-and-mouth disease modelling.

Flood Jessica S JS Porphyre Thibaud T Tildesley Michael J MJ Woolhouse Mark E J ME

BMC veterinary research 20131008

<h4>Background</h4>When modelling infectious diseases, accurately capturing the pattern of dissemination through space is key to providing optimal recommendations for control. Mathematical models of disease spread in livestock, such as for foot-and-mouth disease (FMD), have done this by incorporating a transmission kernel which describes the decay in transmission rate with increasing Euclidean distance from an infected premises (IP). However, this assumes a homogenous landscape, and is based on ...[more]

PMID: 24099627

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Project description:UNLABELLED:Of the three foot-and-mouth-disease virus SAT serotypes mainly confined to sub-Saharan Africa, SAT 2 is the strain most often recorded in domestic animals and has caused outbreaks in North Africa and the Middle East six times in the last 25 years, with three apparently separate events occurring in 2012. This study updates the picture of SAT 2 phylogenetics by using all available sequences for the VP1 section of the genome available at the time of writing and uses phylogeographic methods to trace the origin of all outbreaks occurring north of the Sahara since 1990 and identify patterns of spread among countries of endemicity. Transitions between different host species are also enumerated. Outbreaks in North Africa appear to have origins in countries immediately south of the Sahara, whereas those in the Middle East are more often from East Africa. The results of the analysis of spread within sub-Saharan Africa are consistent with it being driven by relatively short-distance movements of animals across national borders, and the analysis of host species transitions supports the role of the African buffalo (Syncerus caffer) as an important natural reservoir. IMPORTANCE:Foot-and-mouth disease virus is a livestock pathogen of major economic importance, with seven distinct serotypes occurring globally. The SAT 2 serotype, endemic in sub-Saharan Africa, has caused a number of outbreaks in North Africa and the Middle East during the last decades, including three separate incidents in 2012. A comprehensive analysis of all available RNA sequences for SAT 2 has not been published for some years. In this work, we performed this analysis using all previously published sequences and 49 newly determined examples. We also used phylogenetic methods to infer the source country for all outbreaks occurring outside sub-Saharan Africa since 1990 and to reconstruct the spread of viral lineages between countries where it is endemic and movements between different host species.

Dataset Information

The performance of approximations of farm contiguity compared to contiguity defined using detailed geographical information in two sample areas in Scotland: implications for foot-and-mouth disease modelling.

Background

Results

Conclusion

Publications

The performance of approximations of farm contiguity compared to contiguity defined using detailed geographical information in two sample areas in Scotland: implications for foot-and-mouth disease modelling.

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