Análisis competitivo Modelo de las cinco fuerzas de Porter El poder colectivo de las cinco fuerzas del Michael Porter (Ver figura 1) determina la

Cowled et al. (2009) carried out a descriptive and cluster analysis on the 2007 EI outbreak. They found that the outbreak consisted of three key phases. The first was a dispersion phase where there was substantial spatial scattering of a few infected horses. This is why most of the final area affected by EI was determined by a few cases in the days before detection. The dispersion of the disease rapidly decreased and had mainly ceased by 1st September after the implementation of movement bans on 25th August. The second phase was characterised by local spread. In this phase minimal dispersion occurred and instead there was a large increase in the number of IPs. The movement bans restricted the long range dispersion, but

transmission to neighbouring farms 1 continued. It was during this phase that vaccination

control methods were implemented. The third phase was the disease fade out stage. In their cluster analysis Cowled et al. (2009) found 37 epidemiologically linked premise clusters. They found that urban clusters generally had a longer epidemic duration and shorter distances of disease spread. However, surprisingly they also found little difference in the incidence rates, cumulative incidence and reproduction rates between rural and urban regions.

Firestone et al. (2011) carried out a case control study of 200 horse premises to investigate in- trinsic premise factors and biosecurity compliance factors. Intrinsic factors remain unchanged once the epidemic begins, such as descriptions of the locations and types of premises in which horses were most at risk. Biosecurity compliance factors relate to methods used to reduce the risk of contamination of a premise. They found that the most significant intrinsic factor was the proximity of a premise to its nearest IP. They found an increase likelihood of new cases

1

We use the word ‘farm’ throughout this chapter as short hand for a premises with horses. There are, of course, farms without horses, and premises with horses that would not usually be described as a farm.

occurring within a 5km radius of IPs. Their analysis also found that the 10km buffer zones used were appropriate for local spread containment. With the biosecurity compliance factors, two were found significant and used in the final model. These were the presence of a footbath and daily monitoring for clinical signs. The results of their study suggest the compliance with on-farm biosecurity controls prevented the spread of EI onto a premise in high risk areas.

8.5

Data

8.5.1 Infected premises data

Information on disease control activities within Australia is recorded via the Animal Emer- gency Management Information Systems (ANEMIS) software. With the EI outbreak, all premises involved within NSW were given a unique ANEMIS record that contained informa- tion on the location, disease status, horse population, laboratory test results, notes on clinical signs, dates of visits, dates on status changes and the dates of first clinical signs (Cowled et al., 2008).

The majority of information that makes up the IP database was created by running progressive enquires of ANEMIS throughout the epidemic. This was then cleaned to remove duplicates and incorrect entries; the resulting dataset contained 5944 IPs. This was then combined with information on identified suspect premises (SPs) that were likely to be infected. Any SPs that were tested and came back with negative laboratory results were deleted. A total of 160 IPs were added to the IP database bringing the total to 6104 IPs. An additional 212 IPs were added to the database after laboratory results determined these premises to be historically serotype positive: antibodies suggest there was a previous infection even though clinical signs are no longer present. These farms were classed as resolved rather then infected as the active infection had passed. Altogether this brings the total number of identified IPs in the final dataset to 6316 (Cowled et al., 2008).

A limitation of this dataset outlined by Cowled et al. (2008) is that the list of IPs may be under estimated, as once movement restrictions were lifted any farms with sero-positive laboratory results and a status date after this were not included. This was because the authors took a conservative approach where if the origin of the horse, during active infection, was unknown the event was not included.

8.5.2 Horse population data

When evaluating an outbreak it is important to know where the susceptible farms (i.e. premises with horses) are located. Before the outbreak there was no official government record of horse location and population within Australia. During the outbreak the NSW lo- cal disease control centre compiled a horse population database. They pooled resources from the Rural Lands Protection Board (RLPB) database, the Australian Horse Industry Council Database (AHIC), the Equine Influenza Registration Database (EIRDB), the ANEMIS data base, and other smaller databases such as travelling horse statements and the yellow pages. This pooling of resources produced a dataset with 102,000 records. This was later cleaned and reduced down to 51,615 records. Cleaning the data involved the removal of duplicates and incomplete records (for example when there was insufficient information to geocode the data) (Cowled et al., 2008).

Despite the efforts that went in to compiling this dataset, its creators concede that it almost certainly does not capture the entire horse population (Cowled et al., 2008).