Download 51. Network analysis of livestock movements to estimate potential silent spread of foot-and-mouth disease

The Global control of FMD - Tools, ideas and ideals – Erice, Italy 14-17 October 2008
Appendix 51
NETWORK ANALYSIS OF LIVESTOCK MOVEMENTS TO ESTIMATE POTENTIAL SILENT
SPREAD OF FOOT-AND-MOUTH DISEASE
C. Dubé1,*, C. Ribble 2, D. Kelton3, B. McNab 4, S. Javier1 and A. Rivera5
2
1
Canadian Food Inspection Agency, 59 Camelot, Ottawa, Ontario, K1A 0Y9.
Department of Ecosystem and Public Health Faculty of Veterinary Medicine, University of Calgary
3
Department of Population Medicine, Ontario Veterinary College, University of Guelph
4
Ontario Ministry of Agriculture, Food and Rural Affairs
5
Servicio Agrícola y Ganadero – SAG, Ministerio de Agricultura, Chile
ABSTRACT
Introduction
Recognizing the importance of livestock movements in the spread of contagious diseases, various
countries in the world have developed livestock movement databases. Social network analysis
techniques have recently been applied to study such databases as this technique has the
advantage of allowing the study of the interactions among all pairs of livestock holdings that are
formed following the movement of livestock. As a result, important holdings, which are central in
the flow of animals, may be identified. The objective of this paper was to show two examples of
uses of network analysis: (1) to estimate potential epidemic size following routine livestock
movements during the silent spread phase of a highly contagious disease, and (2) to generate
production types to specify the contact structure among livestock holdings in disease simulation
models.
Materials and methods
We used network analysis techniques using monthly networks of adult dairy cow movements
among dairy farms in Ontario (years 2004-2006) and beef cattle movements among all livestock
holdings in Region XI, Chile (year 2007). Potential epidemic size was calculated in Ontario using an
approach called “infection chain”. We used the degree distributions to classify beef farms into
production types required in Chile.
Results
The monthly networks of livestock movements were highly fragmented throughout the year in
Canada (mean=0.997, sd=0.001) and in Chile (mean=0.996, sd=0.002). The median monthly
maximal potential epidemic size in Ontario included 13-15 farms. Four production types were
created to simulate the spread of FMD in Chile: non-sellers, non-buyers, buyers-sellers and
markets.
Discussion
The infection chain provided a biologically plausible estimate of potential epidemic size as it
accounted for the direction of shipments and the time sequence of these shipments. Using the
degree distributions also allowed modellers to classify farms according to their movement patterns
and according to their management practices.
1. INTRODUCTION
One of the most important transmission pathways for the spread of highly infectious diseases like
foot-and-mouth disease (FMD) is the movement of animals among livestock holdings in a country
or region. This is particularly true during the silent spread phase, when the virus is spreading
undetected among these holdings. Various examples of such undetected spread have been
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reported in the literature; the most recent and memorable was the movement of infected sheep
through the United Kingdom in 2001, through the use of livestock markets (Mansley et al., 2003).
It is therefore recognized that there is a need to record these movements of animals to enable
tracing activities when a case of FMD is detected. As a result, several countries have implemented
livestock movement databases. Another indirect use of these data would be to describe the
frequency and patterns of livestock movements in order to inform contingency planning and
disease modelling. The large amounts of data and the fact that it is possible to link source and
recipient farms of animal movements have made it possible to study them by applying social
network analysis techniques (Kiss et al., 2006; Robinson et al., 2007).
In a population of livestock holdings, each holding represents a node in the network and the
movement of animals among nodes represent arcs, or directed links: from the source to the
recipient node. Following the movements of all animals during a period of time, a web of
connections among holdings emerges and it becomes possible to study the interactions among
pairs of holdings in the network. Whereas classical livestock movement studies focussed on
obtaining the frequency of movements to and from each holding (Sanson et al., 2005), it now
becomes possible to consider the relationships among all holdings in the network and to identify
those that might be central in the flow of animals in the network, or those that are highly
connected and therefore at risk of becoming infected or transmitting infection to a high number of
other holdings.
The objective of this paper was to show two useful applications of network analysis to study
livestock movements: (1) to provide plausible estimates of potential epidemic size at first detection
of FMD in a country like Canada, more specifically the Province of Ontario, and (2) to develop a
structured analytical approach to classify livestock holdings in Chile into production types according
to real movement data, required to simulate FMD in the North American Animal Disease Spread
Model (NAADSM; Harvey et al., 2007).
2. MATERIALS AND METHODS
2.1 Estimating potential epidemic size in Canada
Livestock movement information is scarce in Canada. One potential source of information is the
Dairy Herd Improvement (DHI) program which stores lactation information of adult milking cows
from member herds across the country. Milk testing occurs monthly in every herd at which time
herd inventories are obtained. When cows are sold among DHI herds, their lactation records are
also transferred to the recipient herd which enables the tracing of those cows.
We obtained all movement information for individual adult milking cows moving among DHI farms
in Ontario in 2004-2006. These movements were grouped into shipments, defined as the
movement of ≥1 cow, on a single day from a single source farm to a single recipient farm. Monthly
networks (n=36) of shipments among farms were created to represent the longest plausible
duration of undetected FMD spread in Ontario. The average number of shipments per farm, per
year was calculated by dividing the number of shipments by the total number of DHI farms enrolled
that year. Network fragmentation was obtained in UCINet (Borgatti et al., 1999). Fragmentation is
defined as the proportion of pairs of farms in a network that are unreachable, that is, the
proportion of farms that do not have direct or indirect links to join them (Wasserman and Faust,
1994).
In order to estimate potential epidemic size, we developed a network measure called the “infection
chain” which is a modified breadth-first search (Knuth, 1997). The breadth-first search, used in
graph theory, calculates the number of holdings that can be exposed by an infected holding either
directly, by receiving infected animals from that holding, or indirectly by receiving infected animals
from an intermediate holding. It accounts for the direction of the links among the farms in the
network and we modified it so that it would also account for the order of shipments in time.
Therefore if shipments took place from farm A to B to C, the shipment from A to B would have had
to occur prior to the shipment of B to C for it to be considered an infection chain of size 3. These
two features, time sequence and direction of shipments make the infection chain represent the
possible spread of an infectious agent. Stata 8 was used to obtained descriptive statistics in this
study (StataCorp. 2005; Stata Statiscal Software: Release 8.0; College Station, TX 77845, USA:
StataCorp LP).
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2.2 Creating production types based on movement patterns
As part of a large counter-terrorism and capacity building project (CTCB), the North American
Animal Disease Spread Model (NAADSM) is being applied in three countries in South America to
simulate the spread of FMD: Brazil, Chile and Colombia. In Chile, Region XI was selected as it
includes mandatory traceability of livestock movements (Figure 1). The database used in this study
included movements of all bovines for year 2007, originating in Region XI. All markets, abattoirs
and farms involved in movements, either as sources or destinations, were included in the study.
In order to simulate the spread of infectious agents in the NAADSM, the population at risk must be
specified. The NAADSM requires the following information: latitude and longitude of the holding,
the number of susceptible animals in the holding and the production type of holding. This last
characteristic, the type of holding, is defined by the user and can range from simply representing
species (cattle, sheep, swine) to more elaborate production systems (dairy, cow-calf, feedlot).
In most livestock holding databases the number of animals by species and age class are available.
Therefore rules must be developed to classify livestock holdings into types. We first identified
sheep only and cattle only farms. All units which declared swine also had sheep or cattle on
premises in majority and were classified as sheep or cattle. It is assumed that these swine animals
were used for personal consumption rather than being raised for commercial production. In the
case of herds where both sheep and cattle were present, if >50% of animals in the unit were
cattle, the unit would be classified as cattle and vice-versa. In the case of cattle herds, we decided
to use the movement patterns to classify these units into further production types.
We first extracted all movements among farms and markets to represent movements at risk of
transmitting FMD through direct movement of animals. The movements of individual animals were
grouped into shipments. We used the in- and out-degree values of each holding in the database to
classify units into the following types: non-seller, non-buyer, buyer-seller and market. The outdegree is defined as the number of individual recipients per seller in a defined time period while the
in-degree is defined as the number of individual sellers per buyer in a given time period
(Wasserman and Faust, 1994). Units were classified as non-seller if they had out-degree=0, units
were classified as non-buyer if they had an in-degree=0 and units were classified as buyer-sellers if
their out- and in-degree values were different than 0. Markets were identified as having very large
in- and out- degree values compared to the other units in the database.
In order to represent the contact structure in NAADSM, the proportion of movements from cattle
farms to markets was calculated. Production type combinations, required in NAADSM to specify
who can infect who, were then defined and entered into NAADSM. For example, if non-buyers sold
animals to markets 50% of the time, then they sold to non-sellers and buyer-sellers for the
remaining 50% of the time. Expert opinion was obtained to further describe the proportion that
would be sold to non-sellers and to buyer-sellers.
3. RESULTS
3.1 Estimating potential epidemic size in Canada
According to the 2006 census of Agriculture, the Province of Ontario has close to 33% of the dairy
cows in Canada. Approximately 50% of these farms are located in SW Ontario. A total of 77%
(4060/5282), 72% (3601/5013), and 76% (3583/4695) of Ontario dairy farms were enrolled on
DHI in 2004, 2005 and 2006, respectively. The mean farm size of DHI farms in Ontario was 61
cows (sd=43.3) over the three years. The number of shipments by month varied throughout the
year and across years. On average, a DHI farm had a 5.5% (sd=7%) chance of shipping a cow or
group of cows in a given month (Figure 1). The months of September to November and March were
associated with higher numbers of shipments, while summer months, June to August, were less
active. The size of shipment did not vary over the three years with an average of 1.39 cows
(sd=1.77) in 2004, 1.41 cows (sd=1.93) in 2005 and 1.43 cows (sd=1.82) in 2006.
The average fragmentation of monthly networks of farms linked by cow shipments was 0.997
(sd=0.001) in 2004-2006. This means that 99.7% of pairs of farms in the networks were
unreachable, indicating that the monthly networks were very highly fragmented. In year 2005, the
mean potential epidemic size, based on the average infection chain, was 0.1 farms (range 0-36
farms; Table 1).
3.2 Creating production types based on movement patterns
There were a total of 1,398 livestock premises with latitude and longitude information from the
traceability database of Region XI in Chile. Three of these were markets and the remaining
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included either cattle, ovine or porcine as shown in Table 2. Of the 727 mixed farms, 376 were
classified as ovine and 351 were classified as bovine. An example of the network observed on a
monthly basis is shown in Figure 2. The monthly networks were highly fragmented, on average,
99.6% of pairs in the network were unreachable. There was an average of 420 holdings involved in
shipments in the monthly networks of movements.
The out-degree and in-degree distributions of all farms sending animals either to other farms or to
market are shown in Figure 3. The final classification of farms into production types and daily
contact rate based on movement patterns are shown in Table 3. Close to 50% of cattle farms were
classified as non-buyers. These farms would typically sell throughout the year to only 1 other
holding, although a maximum of 5 holdings was reported. Another 33% of farms were classified as
non-sellers. Although these farms would purchase from one source only in a year up to 75% if the
time, one farm purchased animals from 12 different sources. Seventeen percent of farms were
classified as buyer-seller. Up to 99% of these farms sold animals to up to 6 recipients while one
farm sold to 10 different recipients. In the case of in-degree, up to 50% of these buyer-seller farms
would purchase from 1 farm, up to 99% of farms would purchase from 18 different sources and
one farm purchased from 45 different sources. The production type combinations used in
simulations in Chile are shown in Table 4.
4. DISCUSSION
The use of network analysis to study livestock movements can provide very useful information to
evaluate the impact of various livestock holdings in the spread of highly contagious diseases. The
infection chain procedure, used to study the DHI networks, provided plausible estimates of
epidemic size following direct movements of livestock as it accounted for the direction and the time
sequence of the shipments among farms. Other measures have been used for this purpose, to
estimate maximal potential epidemic size, however these estimates do not account for the time
sequence of shipments, only considering the network as cumulative with regards to the movement
of animals in a defined time period (Robinson et al., 2007).
The high fragmentation found in the DHI networks reflects how the networks were created: they
covered only a one month time period and only movements of adult milking cows among DHI
farms in the Province were used. Therefore we likely underestimated the potential epidemic size at
first detection. Considering movement networks of longer duration as well as adding movements of
other age classes of dairy cattle and movements involving non-DHI farms to the networks would
increase resulting epidemic size estimates and the level of fragmentation. These movements are
not presently tracked in any Canadian database. In addition, indirect contacts such as the
movement of people and fomites among herds and airborne spread, if applicable, could increase
the estimates of potential epidemic size of FMD at first detection. This study represented a first
step in trying to represent dairy cattle movements in Canada. As more information becomes
available, the infection chain approach will be used to determine the possible extent of spread of a
disease like FMD. The values obtained to date however can be used to parameterize disease spread
models.
The livestock movement database obtained in Chile included the movements of cattle of all age
classes, through all types of livestock holdings. Region XI is a beef and sheep region. For the first
time in NAADSM’s existence, we used network analysis measures, such as the in- and out-degree
values of holdings in the Region to characterize production types. Rather than trying to classify
holdings into feedlot or cow-calf based on the age structure of animals in each holding, we opted to
classify them according to their movement characteristics: were they sellers, buyers or both?
Unless there is a reason to differentiate cow-calf farms from feedlot farms, to represent varying
detection efficacy, or the biology of FMD in those farms for example, then only using movement
patterns can be a very useful and quick way to specify the contact structure in FMD disease
simulation models such as NAADSM or Interspread (Sanson, 1993).
The monthly networks of cattle movements in Region XI were highly fragmented. This can be
explained by the fact of low number of movements which are very well marked throughout the
year. The study of the out-degree and in-degree distributions provided a quick and efficient
analytical approach to identify the potential risk posed by the different farm types based on their
real movement patterns. For example, the majority of cattle farms would not represent a risk to
other farms or markets as 50% of them did not report selling animals in year 2007. However, a
few farms exhibited dealer-like behaviour by buying and selling to and from various different
holdings. This dealer-like behaviour, much like that of markets, places these operations at higher
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risk of becoming infected and of infecting a large number of recipients. These behaviours are very
important to capture when modelling the spread of infectious diseases.
Network analysis has only recently been used to study the movements of livestock. It has multiple
applications and provides various approaches to understand the behaviour of producers, dealers,
markets and abattoirs in the network. Making the maximal use of this information is critical for the
proper representation of the impact of the disease spread and its consequences in the population.
The approach developed in this study will help to produce input parameters for disease spread
models. The two examples in this study should encourage government organizations to develop
movement databases and make the data accessible to researchers for modelling and policy
decision making.
5. REFERENCES
[1] Borgatti, S.P., Everett, M.G. & Freeman, L.C. 1999. UCINET 6.0 Version 6.17. Natick:
Analytic Technologies.
[2] Garland, A.J.M. & Donaldson, A.I. 1990. Foot and Mouth Disease. Surveillance 17: 6-8.
[3] Harvey, N., Reeves A., Schoenbaum M.A., Zagmutt-Vergara, F.J., Dubé, C., Hill, A.E.,
Corso, B.A., McNab, W.B., Cartwright, C.I. & Salman, M.D. 2007. The North American Animal
Disease Spread Model: A simulation model to assist decision making in evaluating animal disease
incursions. Prev. Vet. Med. 82: 176-197.
[4] Kiss I.Z., Green D.M. & Kao R.R. 2006. The network of sheep movements within Great
Britain: network properties and their implications for infectious disease spread. J. R. Soc. Interface,
3: 669-677.
[5] Knuth, D.E. 1997: The art of computer programming Vol 1: Fundamental algorithms, 3rd
Edition. Reading, Massachussetts: Addison-Wesley.
[6] Mansley, L.M., Dunlop, P.J., Whiteside, S.M. & Smith, R.G.H. 2003. Early dissemination of
foot-and-mouth disease virus through sheep marketing in February 2001. Vet. Rec. 153: 43-50.
[7] Mclaws, M., Ribble, C., Martin, S.W. & Wilesmith, J. 2005. Factors associated with the
early detection of Foot-and-Mouth Disease during hte 2001 epidemic in the UK. Proc. Soc. Vet.
Epidemiol. Prev. Med., p.211-221.
[8] Robinson S.E., Everett M.G., Christley R.M. 2007. Recent network evolution increases the
potential for large epidemics in the British cattle population. J. R. Soc. Interface, 4(15), 587-762.
[9] Sanson, R.L., 1993: The development of a decision support system for an animal disease
emergency, Unpublished PhD thesis, Massey University, Palmerston North, New Zealand.
[10] Wasserman S., Faust K., 1994. Social network analysis: methods and applications.
Cambridge University Press, New York, NY.
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Table 1: Infection chain distributions by month in year 2005 based on the network of adult milking
cow movements in DHI member farms in Ontario. Similar results were observed in 2004-2006.
Infection chain
Average
25%
infection
chain
Month
January
February
March
April
May
June
July
August
September
October
November
December
0.1
0.1
0
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.2
0.1
0
0
0
0
0
0
0
0
0
0
0
0
50%
75%
95%
99%
Max
infection
chain
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
0
1
1
1
1
1
1
2
2
3
2
2
2
2
2
2
3
2
2
9
9
14
14
9
13
25
15
8
30
36
22
Table 2: Description of species by farm in Region XI, Chile.
ProductionType
Total
Bovine only
630
Ovine only
12
Bovine and ovine
580
Bovine and swine
24
Ovine and swine
2
Bovine + ovine + swine
147
Total
1,395
Table 3: Description of the production types and their contact rate (number of recipients / day)
created for FMD simulations in Region XI, Chile.
Production
type
Non-seller
Non-buyer
Buyer-seller
Market
Ovine
TOTAL
284
Frequency
72
699
234
3
390
1,398
Max
rate/year
0
28
20
78
28
Median
rate/year
0
1
3
36
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The Global control of FMD - Tools, ideas and ideals – Erice, Italy 14-17 October 2008
Table 4: Contact matrix showing the production type combinations used in FMD simulations in
Region XI, Chile. The proportion of movements sold directly to a market for cattle was obtained
from the livestock movement database. The remaining proportions were derived from expert
opinion.
Source
Markets
Cattle non-buyer
Recipients
Markets Cattle
seller
0
20%
61%
12%
Cattle buyer-sellers
Ovine
45%
10%
Cattle
sellers
70%
27%
17%
0
Year 2004
0.080
non-
buyer-
Ovine
10%
0%
39%
0%
Year 2005
0%
90%
Year 2006
0.070
Probability of shipment
0.060
0.050
0.040
0.030
0.020
0.010
December
October
November
September
July
August
May
June
April
March
January
February
December
October
November
September
July
August
May
June
April
March
January
February
December
October
November
September
July
August
May
June
April
March
January
February
0.000
Month of year
Figure 1: Probability of direct shipments by DHI farm in Ontario, by month in 2004-2006 (does not
include movements through markets).
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Figure 2: Representation of the Regions of Chile. Region XI was selected for NAADSM simualtions.
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The Global control of FMD - Tools, ideas and ideals – Erice, Italy 14-17 October 2008
InDegree
100
150
200
250
Figure 3: Network diagram of the movements of cattle among farms, markets and abattoirs in May
2007, Region XI, Chile. Circles represent cattle farms, triangles represent markets, squares
represent abattoirs. This network includes movements from all livestock holdings in Region XI to
farms, auctions or abattoirs located inside or outside of Region XI.
0
50
Markets
0
10
20
OutDegree
30
40
Figure 4: Scatterplot showing the in- and out-degree of all farms and markets in Region XI, Chile.
The three markets are represented by high in- and out-degree.
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288