This document describes adapting the Cameron model to estimate the probability of freedom from Peste des Petits Ruminants (PPR) and Sheep and Goat Pox (SGP) in the Thrace regions of Bulgaria and Greece over time based on surveillance data collected. The original Cameron model was modified by updating input parameters and surveillance components for PPR and SGP based on each country's surveillance designs, literature, and expert opinions. The adapted models output the cumulative probabilities of freedom from the diseases in each country monthly. Results showed probabilities of freedom above 97% for Bulgaria and suggested the current surveillance systems have successfully provided evidence of freedom and early detection of potential outbreaks.

1. MODELING OF FREEDOM FROM PESTE DES PETITS RUMINANTS (PPR) AND
SHEEP AND GOAT POX (SGP) IN THRACE
Introduction
• Transboundary high-risk area coordinated epidemio-surveillance program (Thrace) was
initiated in 2013 at the shared borders of Bulgaria, Greece and Turkey (Figure 1)
• The main goal was to provide continuous evidence of freedom from foot-and-mouth
disease (FMD) in this region & to early detect any possible incursions of FMD
• To analyse the data collected through Thrace, ‘Cameron Model’ was developed in
2013, in MS Excel
• Cameron Model uses cluster-level surveillance data collected from multiple
surveillance components to calculate the progressive probability of freedom from FMD
over time
• Since 2016, surveillance activities in this region have been extended to other important
transboundary animal diseases, including:
• Peste des petit ruminants (PPR)
• Sheep & goat pox (SGP)
• Lumpy skin disease (LSD)
• There has been a clear need for similar models to analyse the data collected through
the integrated surveillance activities for the above diseases
• Objective
To adapt the original Cameron model to estimate the probability of freedom from
PPR & SGP in the Thrace regions of Bulgaria & Greece over time
Omid Nekouei1*, Maria De la Puente-Arevalo2, Enrico Mezzacapo2, Tsvyatko Alexandrov3, Aleksandra Miteva3
1. Department of Infectious Diseases and Public Health, City University of Hong Kong, Hong Kong
2. European Commission for the Control of Food and Mouth Disease (EuFMD), FAO, Rome, Italy
3. Bulgarian Food Safety Agency, Sofia, Bulgaria
*Corresponding author’s email: omid.nekouei@cityu.edu.hk
Methods
• The original Cameron model was generated as a deterministic model to avoid
complications associated with probability distributions and to be as simple and
practical as possible
• The model was adapted to PPR & SGP in the context of each country by updating the
input parameters and surveillance components
• Using the original surveillance designs for PPR & SGP in place in Bulgaria and
Greece, available literature, and the expert opinion of focal points
• Three major components under PPR surveillance:
o Passive farmer reporting (covers all population; low sensitivity)
o Clinical examinations (sampling is assumed at random; medium sensitivity)
o Laboratory testing/sero-surveillance (sampling is assumed at random; high
sensitivity)
• Two major components under SGP surveillance:
o Passive farmer reporting (low-medium sensitivity)
o Clinical examinations (sampling assumed at random; medium-high sensitivity)
• The only significant monthly variation in the annual probability of introduction of PPR
& SGP in the region was assumed to be a result of Qurban Festival (3× increase in
risk)
• Wherever the input parameters were opinion-based, reasonable assumptions were
made and the most conservative estimates were adopted
• To partially account for the uncertainty associated with the opinion-based input
parameters, a simple ‘sensitivity analysis’ was conducted, considering two extreme
scenarios:
• A ‘best-case scenario’, including the most reasonable estimates for the
controversial parameters (probability of introduction, prior probability of
freedom, and sensitivity of the surveillance components)
• A ‘worst-case scenario’, including the most conservative estimate/boundary
(yet in a plausible range) for the controversial parameters
• Data analysis was carried out on a monthly basis per epi-unit for each country
(Bulgaria-village & Greece-holding)
• Table 1 summarizes the input variables used in the final models for Bulgaria (as an
example)
Figure 1. Map of ‘Thrace’ in Southeast Europe. This region extends
across the borders between Bulgaria, Greece and Turkey.
Taken from ‘Motta et al. 2019; https://doi.org/10.1111/tbed.13143’
Results
• The outputs of the adapted models were the cumulative probabilities of freedom from
PPR & SGP in Bulgaria and Greece over time
• For Bulgaria, it was agreed to start the analyses from ‘January 2019’, due to a number
of PPR outbreaks in the summer of 2018
• For Greece, analysis was started from 2016, when initially the surveillance data became
available
• As an example, detailed results only for Bulgaria are presented (Figure 2)
• As seen in Figure 2, probability of freedom from both diseases in Bulgaria has been
above 97% for the last months of study, under the best-case scenario
• Under the most conservative assumptions (worst-case scenario), this probability has
still been above 95% over the same period of time
• The differences in the probabilities of freedom from both diseases between the best
and worst case scenarios were quite small (see the very narrow gaps between the
green and red lines in Figure 2 for Bulgaria)
Parameter PPR SGP
Best Worst Best Worst
Design Prevalence (cluster-level) 5% 5% 5% 5%
Design Prevalence (animal-level) 10% 10% 10% 10%
Probability of introduction (annual) 10% 20% 10% 20%
Prior probability of freedom (2019) 50% 50% 90% 75%
Surveillance components:
1. Passive farmer reporting (Se*) 40% 20% 40% 20%
2. Clinical examination (Se) 50% 30% 50% 30%
3. Laboratory testing (Se) ELISA 80% ELISA 80% - -
PCR 80% PCR 80% - -
Table 1. input parameters used in the final adapted models for Bulgaria. Two scenarios were considered for opinion-based
variables (best and worst) in the sensitivity analysis. *Se: diagnostic sensitivity – very rough estimates for the components
0
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PPR
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Prior
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SGP
Figure 2. Probability of freedom from PPR (on the left) and SGP (on the right), estimated using the adapted
Cameron models for Bulgaria (Jan 2019-March 2020). Under the two defined scenarios.
Main conclusions
• The model outputs (probability of freedom) were quite robust against changes in
controversial ‘input parameters’
• The high probability of freedom, achieved in a short time period, mainly appeared to be
due to the full coverage of the passive reporting component
• Re-emphasizing the importance of passive reporting as a cost-effective component in
different surveillance systems
• Both countries have already achieved > 97% confidence in freedom from PPR & SGP
• Current surveillance systems for PPR & SGP in Thrace have proven successful in
providing cumulative evidence of freedom, as well as in early detecting potential
outbreaks; e.g., PPR outbreaks in Summer 2018 in Bulgaria