Methods
Part One of the study re-analysed the dataset of 10,000 simulated
outbreaks of FMD in New Zealand generated during the study by Garner and
colleagues (2016), to calculate the third quartile values for the
numbers of IPs and the observed EDR values at days 14, 21, 28 and 35 of
the response (post first-detection). The purpose was to use these values
to define a time-varying series of triggers that operated within
specified time periods within the ISP modelling platform. These time
periods were defined as response days (post first detection) 11-14,
15-21, 22-28 and 29-35 inclusive.
In Part Two of the study, the threshold values for the numbers of IPs
and EDR for each time period were specified as a complex EDI trigger
within the ISP platform, and the New Zealand Standard Model of FMD was
initialized to simulate a further set of FMD incursions into New
Zealand. The underlying farm denominator dataset was based on a
September 2015 extract of AgriBase (Sanson 2005), a national farms
database in New Zealand, owned and operated by AsureQuality Limited, a
state-owned enterprise. The model was set up to randomly introduce FMD
into farms in the upper North Island, within an area termed the
“Auckland Mega-region”, which was created by combining the Northland,
Auckland, Waikato and Bay of Plenty regions.
Before each introduction, several other variables were randomly varied
(see Table 1). These included whether the FMD virus was of a type that
could be transmitted by the wind, the number of personnel of various
roles that were available for response duties, the number of direct
and/or indirect contacts that a tracer could process per shift, and the
number of farms that a surveillance veterinarian could visit per day.
Once detected by passive surveillance, each outbreak was controlled by
standard stamping-out (SO) measures, including tracing of movements,
quarantine and depopulation of IPs, movement controls and active
surveillance by patrol veterinarians. Each simulated outbreak continued
until eradication or to a maximum of 365 days if not eradicated.
Data generated during each simulated outbreak was stored in a Sqlite3
database. The main outputs of the model for each iteration were whether
the EDI trigger fired and if so when, the number of farms infected each
day, the number of IPs detected each day, the number of farms
depopulated per day, and the number of personnel used in response duties
per day by role type. From these, further outputs were derived,
including the farm type of the primary case, the day of first detection,
the total number of IPs detected, and the duration of each outbreak (day
of last detection – day of first detection + 1). In addition, there
were some variables that were able to be measured such as the farm and
livestock densities around the primary and index cases (see Table 2).
For the purposes of evaluating the performance of the EDI trigger
prospectively, ‘large’ outbreaks were defined as the final number of IPs
being in the upper quartile (i.e. > 75thpercentile) of all outbreaks in the Part Two simulations, and ‘long’
outbreaks were classified as having duration within the upper quartile
(> 75th percentile) of epidemic lengths
for the Part Two simulations. Performance was evaluated by calculating
the sensitivity (the proportion of large / long outbreaks during which
the trigger fired [Se]), specificity (the proportion of small /
short outbreaks during which the trigger did not fire [Sp]),
positive predictive value (the proportion of trigger firings which
resulted in large / long outbreaks [PPV]) and negative predictive
value (the proportion of outbreaks for which the EDI trigger did not
fire which ended up as small / short outbreaks [NPV]) against both
IPs and duration using 2x2 contingency tables. Sensitivity analysis of
these performance measures was conducted by re-classifying the outbreaks
into large or long using the 70th and
80th percentiles.
Statistical analysis included logistic regression of the factors that
were associated with the trigger firing, with the independent variables
being cattle, sheep, pig and farm densities within a 5x5 km square
centred on the primary case, whether airborne spread could occur or not,
the numbers of personnel available by role and the time of first
detection. Fitting the model was by backwards, stepwise elimination of
non-significant variables (p > 0.05) based on the Wald
test. Logistic regression modelling was conducted on the largeand long variables to see if the trigger firing was associated
with large or long outbreaks. All analyses were conducted using R
v3.5.3.