Discussion
The time period selected for the operation of the complex EDI trigger
was from Days 11 – 35 of each response, with the response beginning
once the first IP (index case) was discovered. Day 11 was both the
earliest day that a 5-day EDR could be calculated (excluding Day 1 of
the response) and the earliest possible date by which a vaccination
programme could be initiated, given the requirement to confirm the
strain type of FMD virus, receive sufficient vaccine doses from the
international vaccine bank and arrange teams of personnel to administer
the vaccine. Day 35 represented 5 weeks into the response, and by this
time there should be a reasonably clear picture of the spatio-temporal
progression of the disease, but still be at an early enough stage that
vaccination could assist with outbreak control.
The thresholds for the numbers of IPs used in the trigger were based on
the third quartiles of cumulative IPs detected as at days 14, 21, 28 and
35 within the 10,000 outbreaks simulated for the study by Garner and
colleagues (2016). Our premise was that should these values be exceeded
prior to or equal to those time points, then it would signify a large
outbreak was developing (i.e. within the largest 25% of outbreak
sizes). Whilst this was somewhat arbitrary, it was felt that it was not
unreasonable.
The trigger mostly fired between Days 11 – 21, with the largest
proportion of firings on Day 11 (see Figure 1). Overall, the trigger had
very high sensitivities for predicting large and long epidemics.
Conversely, the trigger had poor specificity and PPV, but very high NPV.
What this means is that the trigger missed very few outbreaks that
subsequently turned out to be large, but not all of the outbreaks
predicted to be large or long turned out to be so. Putting it another
way, if the trigger did not fire during Days 11 – 35, the subsequent
outbreak size was most likely to be small or short. Sensitivity analysis
of the thresholds for classifying outbreaks as large or long showed that
the performance characteristics of the trigger did not change much, with
slight increases in sensitivities as the threshold was raised from the
70th percentile to the 80thpercentile, with corresponding small decreases in specificities. The
ability to identify most of the large and long epidemics during the
early stage of simulated responses showed the utility of the trigger in
providing inputs that could assist the decision-makers responsible for
making response decisions based on epidemic situation, including the
need for urgent implementation of vaccination to strengthen the
eradication efforts.
An assessment of the relative performances of the IP and EDR components
showed that they were both of similar high sensitivity, but the EDR
component had lower specificity and PPV measures (see Tables 8 & 11).
Note that the IP and EDR components were not strictly independent of
each other, as they operated in parallel and the threshold that fired
first was the one that was recorded. Further, the trigger could only
fire once for each outbreak, so this did not mean that thresholds were
not exceeded later, it’s just that there were relatively few first
firings beyond Day 21. It appears that the lower specificities and PPVs
demonstrated by the EDR component were because the 5-day EDR values were
less stable and could exhibit short-term spikes. Nevertheless, the
overall results suggest that the complex EDI used could identify large
and long outbreaks during the early stages of simulated outbreaks.
There are several options for trying to improve the overall performance
of the EDI trigger. Given the high number of firings that occurred on
the first day that the trigger started operating (Day 11), the trigger
could be adapted to operate from Day 8 onwards by using a 4-day EDR
together with cumulative IP number thresholds appropriate to the time
frame, then switch to a 5-day EDR on Day 10 and then to a 6-day EDR on
Day 12 of the response. It must be acknowledged however, that it is very
difficult to get accurate assessments of disease dynamics very early in
the response phase, as new IPs would comprise a combination of cases
that were infected during the ‘Silent Phase’ but still be in the process
of being discovered, as well as newly infected cases. To try and improve
the specificity of the trigger, particularly of the EDR component,
longer baseline time periods for the EDR calculations would reduce the
likelihood of short-term spikes that are not truly indicative of
sustained transmission. Further, EDR thresholds could be raised
slightly, or it could be made a requirement that the EDR thresholds had
to be exceeded on two separate occasions for the trigger to fire.
Extrinsic factors that influenced the trigger firing were farm type of
the primary case, time to first detection and farm density in the
vicinity of the primary case. Time to first detection has been
associated with larger outbreaks as reported by other researchers
(McLaws & Ribble, 2007; East et al. 2015). Type of primary case
farm would affect movement frequencies, and increasing farm densities
would allow for higher levels of local spread.
The two logistic regression models that explored factors associated with
large and long outbreaks (see Tables 12 and 13 respectively) showed that
trigger firing was by far the most influential factor: the odds of a
large or long outbreak were many times higher when the trigger fired.
The other significant factors were related to resourcing issues –
affecting active surveillance and depopulation capabilities.
Veterinarians (vet variable) have roles in surveillance within
the 3 km patrol zones around IPs and in traced farms as well as in
depopulation, and Field Technicians (ft variable) have a role in
depopulation. The survfpd variable indicates the number of farms
that a veterinarian can visit per day, so increasing numbers of farms
that could be visited per day by veterinarians had an effect of reducing
the odds of a large and long outbreak.
In conclusion, the study showed that an EDI using a combination of
cumulative IP numbers and EDR values indicating sustained spread are
highly predictive of the eventual size of the outbreak. Further
evaluation and improvement of the complex EDI may lead to a valuable
tool to predict the eventual epidemic size based on the observed IP
numbers and rates of spread during the early stages of outbreaks.
Linking temporal changes in observed dissemination rates to the
implementation of response measures can provide insights into the
effectiveness of the controls already in place and, if required,
highlight the need for additional response measures to deliver more
effective control of an FMD outbreak.