Abstract
Peste des petits ruminants (PPR) is a viral transboundary disease of
small ruminants that causes significant damage to agriculture. This
disease has not been previously registered in the Republic of Kazakhstan
(RK). This paper presents an assessment of the susceptibility of the
RK’s territory to the spread of the disease in the event of its
importation from infected countries. The Generalized Linear Negative
Binomial regression model that was trained on the PPR outbreaks in China
was used to rank municipal districts in the RK in terms of PPR spread
risk. The outbreaks count per administrative district was used as a risk
indicator, while a number of socio-economic, landscape and climatic
factors were considered as explanatory variables. Summary road length,
altitude, the density of small ruminants, the maximum green vegetation
fraction, cattle density and the Engel coefficient were the most
significant factors. The model demonstrated a good performance in
training data (R2 = 0.69) and was transferred to the
RK, suggesting a significantly lower susceptibility of this country to
the spread of PPR. Hot Spot analysis identified three clusters of
districts at the highest risk, located in the western, eastern and
southern parts of Kazakhstan. As part of the study, a countrywide survey
was conducted to collect data on the distribution of livestock
populations, which resulted in the compilation of a complete
geo-database of small ruminant holdings in the RK. The research results
may be used to formulate a national strategy for preventing the
importation and spread of PPR in Kazakhstan through targeted monitoring
in high-risk areas.
Keywords : Peste des Petits Ruminants, Republic of Kazakhstan,
People’s Republic of China, Generalized Linear Negative Binomial
Regression, Risk Factors, ArcGIS.
Introduction
The preservation of the sustainable epizootic welfare of the country’s
livestock in relation to threats from especially dangerous diseases,
such as Peste des petits ruminants (PPR), is the most important task of
veterinary science and practice, which is of paramount importance in
protecting the health and lives of people, providing the population with
high-grade and safe food products, and providing industry with
high-quality raw materials.
PPR is a highly contagious viral disease that affects small ruminants
with a 10% to 90% mortality rate among infected animals (EFSA AHAW
Panel, 2015; Peste des petits ruminants, 2021). Due to the significant
socio-economic damage and negative impact on food security in many
countries around the world, PPR is included on the list of priority
diseases of the Five-Year Plan of Action of the FAO / OIE World
Framework Program for the progressive control of transboundary animal
diseases which aims to eliminate PPR by 2030 (Global Strategy for the
Control and Eradication of PPR, 2015). The proliferation of this disease
in countries close to Kazakhstan makes it necessary to analyze the
threat of the importation and subsequent spread of PPR in this country
(Ahaduzzaman, 2020).
PPR is a typical transboundary disease: first reported in West Africa in
1942, this disease has steadily extended its range over the years. Thus,
during the period 2001 to 2011, this disease spread in 56 countries: 35
in Africa and 21 in Asia (Munir, 2015), and by 2016 it had been
registered in more than 70 countries and had become endemic in parts of
North, East, and West Africa, the Near and Middle East, South and
Central Asia, and Western Eurasia (Balamurugan et al., 2014; Bouchemla
et al., 2018; Zhuravlyova et al., 2020). The above African and Asian
regions are home to more than 80% of the world’s sheep and goats;
products such as goat’s milk, lamb, and wool play a huge role in the
welfare of many families (Robinson et al., 2011; Gilbert et al., 2018).
The FAO estimates that about 300 million small farming families
worldwide depend on small ruminants, as sheep and goats are critical
assets for poor rural households, providing them with protein, milk,
fertilizer, and wool, and often representing substantial social capital
and access to financial loans (Global Strategy for the Control and
Eradication of PPR, 2015).
According to official information provided by the OIE, the epizootic
situation with regard to PPR in the world remains tense (OIE WAHIS,
2021). Despite intense international, regional, and national efforts to
combat this disease, most developing countries around the world are not
free of PPR, thus constituting a constraint to free and liberal global
trade in animals and livestock products (Peste des petits ruminants,
2021).
The epizootic situation with regard to PPR in the Central Asian
countries neighboring Kazakhstan is ambiguous. Thus, in Armenia,
Azerbaijan, and Turkmenistan, outbreaks of PPR have not been previously
registered, although monitoring studies and preventive vaccinations of
33-70% of animals susceptible to PPR in risk zones are being carried
out (Koshemetov et al., 2014; Amirbekov et al., 2020). In Uzbekistan and
Kyrgyzstan, isolated outbreaks of this disease have been previously
recorded, and active monitoring and preventive vaccination are currently
being carried out (Yapici et al., 2014; Fine et al., 2020).
This disease leads to large economic losses annually. For example, a
series of epidemics in Kenya in 2006-2008 caused the deaths of 1.2
million small ruminants, resulting in losses of more than US $23.5
million, and milk production declining by 2.1 million liters. In
general, the annual damage from PPR is estimated at US $1.4-2.1 billion
(Kihu et al., 2015; Jones et al., 2016; Bardhan et al., 2017).
For the successful prevention of PPR, regional studies of the epizootic
process are important, which will allow the features of its
manifestation within a specific territory to be studied in specific
natural-geographical and socio-economic conditions, with subsequent
forecasting as a reliable foundation for managing the epizootic process
through the development and implementation of effective
counter-epizootic measures.
According to the official data of the State Veterinary Service of the
RK, PPR has never been registered in this country before, although some
publications indicated the isolation of the PPR pathogen from sick sheep
and goats in the RK in 2003 and 2014 (Lundervold et al., 2004; Kock et
al., 2015). The socio-economic, organizational, structural, and
geopolitical changes in Kazakhstan during the post-Soviet era, as well
as the expansion of international trade and economic and cultural ties
have led to greater risks of dangerous infectious disease pathogens
being imported onto its territory, including via cross-border areas.
The Republic of Kazakhstan has
been historically characterized by unique natural conditions that
preserve the activity of many known disease natural foci that can cause
a sudden aggravation of the epizootic situation in this region.
The purpose of this research is to assess the susceptibility of the RK’s
territory to the spread of PPR, also treated as the risk of PPR
spreading in the event of the pathogen being imported into this country.
Materials and methods
Study area
The area of interest for modeling the risk of the spread of PPR was the
entire territory of the Republic of Kazakhstan (RK, Kazakhstan). The RK
is a land-locked state in Central Asia, occupying an area of 2,725,000
km2 with a population of 18.28 million.
Administratively, the RK is divided into 14 units on the first level –
regions (“oblasts”). Each of these regions is sub-divided into
second-level administrative units – districts, whose area ranges from
283 to 138,663 km2 (mean 15,780
km2). In total, there are 173 districts in the RK
(Fig. 1).
Figure 1. Republic of Kazakhstan: first- and second-level
administrative divisions, small ruminants’ population density and
location of small ruminants’ farms
PPR outbreaks in the People’s Republic of China (PRC, China) were used
to train a regression model. The total area of China is 9,598,962
km2, while the population exceeds 1,404.328 million.
The second (prefectural) level of administrative divisions comprises 333
units with an area between 490 and 473,671 km2 (mean
27,670 km2).
In terms of area, China is the third largest country in the world, while
Kazakhstan is the ninth largest. Both countries share a land border of
more than 1,600 km.
We excluded from the model the two smallest Kazakhstan districts (Almaty
and Shymkent), which represent urban areas with a high population
density and no populations of small ruminants. For the sake of
consistency between the ranges of the explanatory variables for both
countries, we removed from the model Chinese prefectures with
geographical areas outside the range of Kazakhstan’s district areas.
Thus, the total number of analyzed administrative units was 311 in China
and 171 in Kazakhstan (Fig. 2).
Figure 2. The study region (Republic of Kazakhstan and People’s
Republic of China) and distribution of Peste des petits ruminants (PPR)
outbreaks in China, 2007 – 2020. Data source: FAO EMPRES-i.
Modeling method
Since the RK is currently free from PPR, no outbreaks were available to
validate an internally built model. Thus, to rank Kazakhstan’s districts
in terms of the risk of PPR spreading, a regression model trained on
outbreaks in China was applied. Second level administrative units
(districts in Kazakhstan, counties or prefectures in China) were chosen
as the units of analysis for building a regression model. For each unit,
the number of PPR outbreaks and explanatory factors were extracted (see
below). We considered the number of outbreaks’ per administrative
district throughout the entire study period to be a response variable.
As this count variable demonstrates significant overdispersion (a
variance of 2.60 and a mean of 0.87), we chose a Generalized Linear
Negative Binomial (GLNB) model to reveal the relationships between the
number of outbreaks per district and a set of predictors (Venables &
Ripley, 2002).
The following geographically distributed landscape, climate, and
socio-economic characteristics for each administrative unit were
selected as potential explanatory factors based on an analysis of
scientific publications on the spatial and temporal modeling of PPR
(Lembo et al., 2013; Ma et al., 2017, 2019; Mokhtari et al., 2017; Cao
et al., 2018; Gao et al., 2019; Ruget et al., 2019): 1) variables that
serve as a proxy of an intensity of regional economic activity that may
influence the disease spread by transport links - total road length;
road density; average population density and the Engel
coefficient11The Engel coefficient, known also as Engel’s
network density ratio, presents a density of the road network adjusted
for population density and is calculated as a total road length of a
region divided by the square root of its area and population number.
This parameter is used to evaluate the development of the
transportation network and its availability to population thus
providing an indirect metric for peoples’ movements intensity
(Mesjasz-Lech and Nowicka-Skowron, 2013; Golskay, 2019; Plotnikov et
al., 2019).; 2) average density of small ruminants and average cattle
density as indicators of susceptible population’s and other potential
host’s density; 3) most general landscape and climatic factors that may
shape suitable habitat for small ruminants and provide favorable
conditions for the virus spread - average elevation; annual mean
temperature; annual precipitation and maximum green vegetation fraction.
To provide environmental and socio-economic similarity between the study
regions in China and Kazakhstan, we ensured that each variable’s range
overlapped for both countries. The measurement units, data sources, and
range of variables are shown in Table 1. The distribution of variables
within the study area as well as their density plots for both countries
are presented in the Supplementary material (Figs. S1-S10). The only
variable that was significantly higher for all the Chinese prefectures
than for the Kazakhstan districts was population density, so we removed
this variable from the further analysis. To avoid the multicollinearity
of the model, we checked all the variables for correlation using a
Pearson test with a threshold of rs = 0.7.
<Table 1 about here>
The GLNB model was fitted to the China data by the backward stepwise
removal of the insignificant explanatory variables as evaluated by the
significance p-value (p≤0.05). The overall model significance was judged
based on Akaike’s Information Criterion (AIC) and a pseudo
R2, indicating a proportion of the response variables’
variance could be explained by regression.
The performance of the obtained model was tested by predicting for the
Chinese study region and comparing the observed and predicted number of
PPR outbreaks. The regression residuals were tested for spatial
autocorrelation using Moran’s I test (Mitchell, 2005). Moran’s Index
close to zero with a high p-value indicates that the observed residuals’
distribution is likely produced by a random spatial process and no
clustering patterns are detected.
Further, the successful model was used to predict the expected number of
outbreaks for the model region of Kazakhstan. To visualize spatial
clusters of districts that were most susceptible to the spread of PPR,
Hot Spot Analysis using Getis-Ord Gi* statistics was performed on the
predicted values to find aggregations of districts with statistically
significantly high predicted numbers of outbreaks at 90%, 95%, and
99% confidence levels (Getis and Ord, 1992; Mitchel, 2005).
Data sources
The data on the PPR outbreaks in China for the period 2007 to 2020 (as
of 30.08.2020) were obtained from the FAO EMPRES-I database
(http://empres-i.fao.org/eipws3g/ ). During this period, 289 PPR
outbreaks were registered in China, of which 273 fell within the
prefectures chosen for the analysis (Fig. 2). For 231 (85%) of these
outbreaks, the OIE is indicated as a source of data, while the remaining
15% are attributed to “national authorities”. Of these outbreaks, the
vast majority (245; 90%) were recorded in 2014. Within each outbreak, a
number of infected animals ranged between one and 3290 with a mean of
152. According to the data available, a mean prevalence was 0.49±0.31
(defined as the proportion of infected animals in the total number of
susceptible within each outbreak). In terms of small ruminants’
population, China holds a leading place in the world having more than
372 million head (FAOSTAT, 2021)
that provides an average density of 0 to 611 (mean 62)
head/km2.
Detailed data on the distribution of small ruminants in Kazakhstan were
obtained during a nationwide livestock survey undertaken by the research
team members from 2018 to 2019 (Schettino & Abdrakhmanov, 2021). This
survey included a series of expeditionary trips coordinated with
regional veterinary authorities. During this survey, complete
information was collected about livestock farms in the RK, including
geographical coordinates and the population, which allowed the livestock
population at any required level of spatial resolution to be mapped. A
total of 2,478 small ruminant holdings (farms) were georeferenced with
18 to 167,918 (mean 8,988) animals. The total population of small
ruminants in the RK thus adds up to 22,271,628 head, providing a
district-level density of between zero and 81 (mean 7)
head/km2. The density of the small ruminant population
at the district level along with the locations of the farms are
presented in Figure 1.
Software
The spatial data processing and visualization were conducted using
ArcMap Desktop 10.8.1 geographical information system with Spatial
Analyst (Esri, USA) extension. The regression was fitted in an R
software environment (R Core Team, 2020) with MASS package (Venables and
Ripley, 2002), while the correlation analysis and some of the data
processing were conducted using Microsoft Office Excel (Redmonds, WA,
USA).
Results and discussion
Variables selection and fitting the regression model
Independent variable correlation analysis demonstrated a significant
correlation between only two variables: temperature and precipitation (r
= 0.81, p < 0.05). Both variables, which were further
independently tested in the model, showed no significance and were thus
excluded. Fitting the Generalized Linear Negative Binomial regression
revealed the best combination of six independent variables that
demonstrated significance at p < 0.05 and provided the lowest
AIC: road length, altitude, density of small ruminants, MGVF, density of
cattle, and the Engel coefficient. The prediction for the Chinese study
region using the obtained model returned a satisfactory fit with
R2 of 0.69 (Fig. 3).
Figure 3. Observed vs. Predicted PPR outbreaks number as per
the model fit to the training prefectures in China
Testing the residuals using Moran’s I global autocorrelation tool
returned a Moran’s Index of 0.038 (p-value>0.1), which
suggests the absence of residual spatial clustering and thus allowing a
fairly good fit of the model.
Table 2 shows the regression model metrics. For each coefficient, a
standardized value is indicated, which allows a direct comparison
between the relative contribution of each variable on the same scale.
<Table 2 about here>
Analysis of the obtained coefficients allows conclusions to be made
about the largest contribution of the Engel coefficient that
demonstrated a negative relation with the dependent variable, so that
districts with less dense road networks per area and population were
found to be more vulnerable to the spread of PPR, which can obviously be
explained by the higher proportion of pastoral land in these districts.
The second important predictor was the maximum green vegetation fraction
to be positively associated with the number of PPR outbreaks. This
variable may be also related to the availability of pasturing lands and
greenery, thus providing food resources for small ruminants. The density
of small ruminants was also among those contributory factors that
demonstrated a positive relation with PPR, which may naturally be
thought of as an indicator of the contact rate between herds. The road
length showed a positive influence on the number of outbreaks, which may
be seen as a proxy of the regional geographic area. A positive
association of high altitudes with increased PPR numbers may be related
to poorer biosecurity in remote mountainous pastures. The least
important but still statistically significant predictor was cattle
density, which is negatively associated with the number of PPR
outbreaks. According to data in the literature, cattle may demonstrate
seropositivity to PPR, thus presenting a potential disease transmission
link (Lembo et al., 2013). Additionally, cattle density in China was
found to be low but positively correlated with the density of small
ruminants (r = 0.43), thus providing a natural idea of a similarly
positive effect on PPR outbreaks. A possible hypothesis explaining the
observed contradictory dependency comprises better surveillance and
biosecurity practices in those regions with a high density of cattle, as
cattle are a more resource-demanding species than small ruminants. Thus,
the presence of cattle in an agricultural area may suggest better
organization of livestock maintenance, thus providing a better defense
against potential diseases spread.
Extrapolation of the model to Kazakhstan districts
Using the obtained coefficients (Table 2), the GLNB model was applied to
the entire study area of Kazakhstan. Though the explanatory variables
demonstrate different density distributions (Fig. S1, S3, S5, S9, S10,
Supplementary material), their ranges for Kazakhstan fall within the
ranges for China thus allowing to avoid extrapolation. The resulting
predicted distribution of PPR outbreaks is shown in Fig. 4. In general,
the model suggests overall lower suitability to the spread of PPR in
Kazakhstan and demonstrates a heterogenous distribution of predicted PPR
outbreaks within the country. Hot Spot analysis allowed three clusters
of districts with increased predicted PPR outbreaks in the southern part
of Kazakhstan to be isolated (Turkistan oblast and parts of Zhambyl
oblast), the north-east part (eastern districts of East Kazakhstan
oblast), and the north-west part (western districts of West Kazakhstan
oblast) (Fig. 5).
These areas are characterized by a higher density of small ruminants
(Fig. 1). In particular, Turkistan and Zhambyl oblasts are historically
leading areas in terms of small ruminant breeding. In these areas, there
is also a high probability that the disease is being imported from the
border regions of Turkmenistan, Kyrgyzstan, Uzbekistan, and China, which
feature high density of small ruminants and demonstrated sporadic
outbreaks in the past (Yapici et al., 2014; Fine et al., 2020).
Figure 4. Predicted PPR outbreaks’ distribution in RK
Figure 5. Clusters of high-risk districts with regard to the
PPR spread in Kazakhstan
Model limitations
The constructed model demonstrates a satisfactory ability to explain the
variations in the input data, which can be partly explained by the need
to extrapolate the dependencies obtained for another country to the
territory of Kazakhstan, which was determined by the absence of PPR
outbreaks in the RK. The geographical and socio-economic risk factors
used in the model are the most general indicators and, perhaps, not
exhaustive for explaining the observed patterns of the epizootic
situation in China. Since the PPR spread is to lesser extent influenced
by environmental factors, the registration of outbreaks mainly depends
on the virus transmission on transport links, as well as interstage and
interfarm contacts, herd management practices, social and cultural
practices (Ruget et al., 2020), which could only be introduced into the
model indirectly through the geographical factors used. Other important
factors that may contribute into the observed spread of PPR in China
include animal movements data and detailed information of farms’
distribution, which were both not available to authors. Another model
limitation is a potential incompleteness of data on PPR outbreaks in
China due to the possible underreporting of PPR from less populated
prefectures in central and western parts of the country.
It should also be noted that the information on the small ruminant
population distribution used for modeling is the most accurate and
relevant for the Republic of Kazakhstan, as it was obtained by the
direct collection of the georeferenced data from 2018 to 2019, while for
China we used modelled data obtained by the dasymetric mapping based on
the 2010 national survey results.
In general, it can be noted that the created model demonstrates a
reasonable distribution of PPR spread risks across the RK districts,
which would be expected based on the information on the density of small
ruminant populations and the intensity of economic links, and can thus
be used by the national veterinary authorities as scientific support for
the national strategy for PPR prevention. The development of a more
accurate risk assessment study, as well as assessing the ways the
disease is possibly being imported, requires a more comprehensive model
to be built and more factors to be taken into account, both landscape
and socio-economic (in particular, building a network of animal
movements requires movement data that are not currently collected in the
RK on a regular basis), as well as knowledge of the current epizootic
situation and the results of monitoring studies on PPR in the countries
bordering the Republic of Kazakhstan.