Compound processes as models for
clumped parasite data
Dominik Heinzmann1,2∗, A.D. Barbour1, and Paul R. Torgerson2,3
1Institute of Mathematics, University of Zurich, Switzerland
2Institute of Parasitology, University of Zurich, Switzerland
3School of Veterinary Medicine, Ross University, West Indies
Compound processes are proposed as models for the acquisition of hy-
datid cysts in sheep, caused by the parasite Echinococcus granulosus. The
hypothesis of a clumped infection process against single ingestions is tested
and it is shown that the clump-based approach provides a more accurate
description of the two data sets investigated. Models with simple and mixed
Poisson incidence processes and different clump size distributions are com-
pared. A mixed Poisson incidence process with a zero-truncated negative
binomial distribution for the clump sizes is shown to give an adequate
description, suggesting that the acquisition of hydatid cysts in the sheep
population is heterogeneous, and that the clump sizes are aggregated. The
estimates of the parameters derived from the data take plausible values.
∗Corresponding author. Address: Winterthurerstrasse 190, 8057 Zurich. Tel. +41-44-635
58 92. Email address: email@example.com
The average infection rate and the clump size distribution are comparable
in both data sets. Goodness-of-fit measures indicate that the model fits
the data reasonably well.
Keywords: Compound processes, clumped infection, mixed Poisson, parasite
Parasitic disease data often consist of counts of a parasite (or an intermediate
stage) in an animal, together with the animal’s age. The data typically exhibit
two well-known features, a substantial proportion of zeros and skewed positive
counts [1, 2, 3], meaning that some hosts harbor many parasites while most have
just a few. To analyze such aggregated parasite data, the fitting of the negative
binomial distribution is a common method, as in  to model the abundance of
the fluke Diplostomum spathaceum in fish, in  for European red mite on apple
leaves, in  for the tapeworms Echinococcus granulosus and multilocularis in
dogs, in  for the nematode Trichinella spiralis in rabbits and in  for the
larval stage of the mites Allothrombium pulvinum Ewing in lice. However, these
models do not take into account the age of the hosts, which is known to influence
the parasite pattern [9, 10, 11]. To incorporate age, negative binomial regres-
sion can be used, as in modeling the age-dependent frequency of the nematode
Wuchereria bancrofti in humans , or of the nematodes Ostertagia gruehneri
and Marshallagia marshalli in reindeer . The approaches in both studies allow
one to model (exponentially) increasing or decreasing mean parasite burdens as
a function of age, in the latter study with a rather complicated relation between
the over-dispersion parameter and mean of the negative binomial distribution
and the covariate age. However, they do not provide any biological reason as to
why this should occur.
While the negative binomial model takes aggregation into account, it may
not adequately deal with high numbers of parasite-free hosts. For that purpose,
zero-inflated (ZI) models [14, 15, 16] and two-part conditional (TPC) models
[17, 18] can be used. These have been shown to outperform the negative binomial
regression  for applications with an excess of zeros. These models introduce
a state A in which the only counts are zeros, and a state B, in which the counts
could be either zeros or positive values (ZI), or only positive values (TPC). The
model parameters are pA, the probability to be in state A, and the parameters
of the conditional distribution given state B. The parameters (or combinations
thereof) can be allowed to depend on covariates. In , a ZI negative binomial
regression was applied to model egg counts of different gastrointestinal nematodes
in fecal samples from young cattle by parametrizing pA and the mean of the
negative binomial distribution as functions of age. A TPC was used in  for
modeling the density of the nematode Wuchereria bancrofti in mosquitoes. They
argued that a zero count of microfilariae in the blood sampled by a mosquito
can arise either because the human bitten is uninfected or because the blood
taken from an infected human happened to contain no microfilariae. They fitted
a negative binomial TPC to the aggregated data, but did not attempt to fit the
underlying age-dependent model that they envisaged, because of its prohibitive
Alternatively, mechanistic models are used to understand the mechanisms
leading to aggregation in the parasite distribution in hosts. A vital source of
such aggregation is the infection of hosts by parasite clumps rather than single
parasite ingestions [22, 23].  and  used infinite compartmentalisation of
hosts, according to their burdens of 0,1,2,3,... parasites per host, to model the
transmission of Schistosomiasis between the definitive hosts, humans, and the
intermediate hosts, water snails, by assuming clumped infections. The interme-
diate host is not explicitly modelled and they assume that there is no superin-
fection in humans.  used moment closure equations to describe the immuno-
epidemiology of trochostrongylid nematodes in wild ruminant populations. The
infection of hosts is modelled by an (inhomogeneous) compound Poisson pro-
cess to account for clumped infections, and they consider nonlinear effects such
as immunity and parasite-induced host mortality. Their model contains many
parameters; some were fixed based on values from other studies, and the remain-
der were estimated from the model. However, their model describes the mean
parasite burden, but not the prevalence of infection in animals.  modelled
the transmission dynamics between hosts and free-living larvae with a infinite
system of differential equations based on clumped infections, allowing for super-
infection. Then they assumed that parasites are distributed in hosts according to
a negative binomial distribution, leading to a simplified four-dimensional system,
whose qualitative behavior they discussed. However, it is difficult to estimate
the model parameters for such diseases, as for example the rate at which larvae
are produced by adult parasites, since appropriate data sets are in general not
available.  used a model that allows several parasite stages, clumped infec-
tions and between-host heterogeneity, to describe macroparasitic transmissions
involving a free-living parasite stage. As before, estimation of the parameters is
difficult since this requires the knowledge of the distribution of the numbers of
parasite larvae and mature parasites in hosts and of the life distribution since
In this paper, biologically interpretable mechanistic models for hydatid cysts
in sheep, caused by the parasite Echinococcus granulosus (E.g.) [1, 28], are
discussed. E.g. causes echinococcosis, a (re-)emerging hydatid disease in many
parts of the world and, in particular, in Eastern Europe and the former Soviet
Union [29, 30, 31]. E.g. is also potentially dangerous for humans. For this disease,
it can be assumed that the cysts survive their hosts but do not replicate, and that
there is no parasite-induced mortality and no acquired immunity in sheep [1, 32].
This implies a simpler infection dynamics than for example that encountered
by  and . Compound processes ([33, p.49], [34, p.25], [35, p.22]) are
used to investigate the biological hypotheses that clumped (super)infections and
heterogeneity in the acquisition of infection in the host population can explain
the substantial proportion of zeros and thus the prevalence of infection, and the
skewed positive counts of E.g. cysts in sheep.
The processes explicitly describe the underlying infection process and thus
allow a natural modeling of aggregation and excess of zeros of the parasite dis-
tribution in the hosts. The prevalence and intensity is described simultaneously.
The parameters can be estimated based on (standard) field data containing age
and cyst counts of sheep. Goodness-of-fit measures are introduced to assess the
performance of the model.
Based on two data sets from Kazakhstan  and Jordan , it is shown that
clumped acquisition of infection by biologically heterogeneous hosts, where the
clump sizes are aggregated, provides a satisfactory fit. Heterogeneity of acqui-
sition of clumped infections may result from behavioral differences of sheep on
pasture, or from differences in the immune system of sheep. Aggregation of clump
sizes are reasonable given the highly aggregated adult parasite distribution in the
definitive host, the dog . Fitting the models yields parameter estimates which
take biologically reasonable values. Goodness-of-fit measures indicate the reason-
able performance of the model.
2Data sets and models
2.1 Empirical data
The data sets used in this paper are from Kazakhstan  and Jordan . The
Kazakhstan sample contains 2505 individual reports of the variables age and hy-
datid cyst burden in sheep, caused by the parasite Echinococcus granulosus (E.g.)
. The Jordan sample counts 832 individual reports of the same variables.
Hydatid cysts develop conditional on ingestion of infective biomass by sheep
(intermediate host) from contaminated environment. Contamination is caused
by dogs (definitive host), which harbor adult E.g. worms in the intestine and
release infective eggs in the feces. Hydatid cysts form in organs such as the liver
(60 − 70%), lungs and brain and develop over a period of years in the sheep.
Cysts do not proliferate inside their hosts, but protoscoleces are produced inside
the cysts which play a role in the infection of the definitive host . It can be
assumed that cysts survive their hosts, that there is no parasite-induced mortality
and no acquired immunity in sheep [1, 32].
The records were obtained at necropsy in abattoirs with examination of the
viscera of the sheep, including the lungs and liver, for the presence of hydatid
cysts. The ages of the sheep were estimated from the stage of dentition and by
questioning the owners of the animals. Small immature cysts were not recorded,
as resources were not available for the systematic slicing of organs. A more
detailed discussion of the applied sampling frame can be found in  and .
In the Kazakhstan sample, the mean and median ages are 2.037 and 2 years
respectively. The interquartile range is 1 − 3 years and the maximum age is 8
years. The prevalence in sheep is 0.363 (0.344,0.382). Conditional on infection,
a proportion of 0.774 (0.745,0.800) harbors 1 − 10 cysts, 0.186 (0.161,0.213)
11 − 30 cysts and the remaining 0.041 (0.029,0.056) have more than 30 cysts.
The maximal cyst burden is 64. In the Jordan sample, the mean and median
ages are 2.267 years and 1 year respectively. The interquartile range is 0.5 − 4
years and the maximum age is 10 years. The prevalence is 0.293 (0.263,0.325).
Conditional on infection, a proportion of 0.672 (0.609,0.730) have 1 − 10 cysts,
0.234 (0.183,0.293) 11 − 30 and 0.094 (0.062,0.140) harbor more than 30 cysts.
The maximal burden is 80 cysts. The observations in both samples agree with
other study areas in Central Asia .
2.2Compound Poisson process
The positive cyst burdens of Echinococcus granulosus in sheep are in general in
the range of 1−80 cysts per sheep [1, 3, 36]; the majority of cyst counts in sheep
in both our data sets are rather low, with a large proportion of zeros. Since there
is no acquired immunity in hosts [37, 38], and cysts survive for the lifetime of
the sheep, the observations suggest a low infection rate and clumped ingestions
of infective eggs. Sheep potentially make many random contacts with infective
dog feces on pasture, but only a small proportion of the contacts lead to an
infection. Thus the resulting infection process can be viewed as a thinning of the
point process at which contacts with potential infective dog feces are made. A
reasonable assumption for E.g. is that the transmission system of the parasite is
in a steady state [2, 28, 36], so that the ingested clumps can be supposed to be
identically distributed and the low incidence rate can be supposed to be constant.
Additionally, we assume that clumps are independent since infected dogs spread
their feces widely, so that consecutive infections of a sheep are likely to be due to
feces from different dogs. Possible clustering due to reinfection of a sheep with
the same feces can be neglected since clumps in the environment have a relatively
short survival time and the incidence rate is low.
The above assumptions make compound processes [33, 34, 35] a suitable choice
for modeling the cyst burdens in sheep. Let the random variable Ytdenote the
total number of cysts established in an individual up to time t. Then
where (Nt)t≥0is a Poisson process with constant rate µ describing the number of
clumps ingested by an individual sheep during the time interval [0,t] and Sj(j =
1,2,...) are i.i.d. random variables with distribution Q on the positive integers
?, independent of Nt, which describe the numbers of successfully established
cysts per ingested clump. The distribution of Ytis given by
where Q∗kis the kth convolution of Q. In particular,
?(Yt= 0) = e−µt.
The expectation and the variance of Ytare
?(Nt)(Var(S1) + [?(S1)]2).
2.3 Compound mixed Poisson process
To account for possible heterogeneity in the rate of acquisition of clumped in-
fections within the sheep population, for example caused by differential immune
response between sheep, the Poisson process (Nt)t≥0 with fixed rate µ can be
replaced by a mixed Poisson process (˜ Nt)t≥0, where the infection rate is a non-
negative random variable M.
It follows that
?(˜ Nt= n) =
where H(µ) =
?(M ≤ µ) and H(0) = 0. The distribution function H of M is
also referred to as the structure distribution of the mixed Poisson process .
A special case is the simple Poisson process where the random variable M is
degenerate at some µ > 0. Mixed Poisson processes are particular examples of
Cox processes or doubly stochastic Poisson processes [35, p.7].
An appropriate choice of H in (3) should provide a reasonably close approx-
imation to the true distribution, should be easy to fit and should yield a useful
interpretation of the parameters. The two-parameter gamma distributions offer a
flexible and tractable family, with parameters conveniently identified as measures
of skewness and scale. Let H be the distribution function of a gamma distributed
random variable with shape and scale parameters ψ,ξ > 0 such that
where Γ is the gamma function. Then
?(˜ Nt= n) =
0zne−azdz = n!a−n−1,
?(˜ Nt= n) =Γ(ψ + n)
tξ + 1
tξ + 1
Equation (5) describes a negative binomial distribution, with Var(˜ Nt) >
?(˜ Nt) = ψξt =: at and Var(˜ Nt) = (ψξt)(1 + ξt) =: at + bt2.
Using (5) in (1), the distribution of Ytbecomes
Γ(ψ + k)
tξ + 1
tξ + 1
˜ p0(t) :=˜
?(Yt= 0) =
tξ + 1
? is the probability measure under˜ Ntas counting process. Setting ξ =
µ/ψ, for fixed n, t and µ, (5) becomes
?(˜ Nt= n) = µψ + n − 1
tµ + ψ
µψ + n − 2
tµ + ψ
tµ + ψ
− − − →e−µt(µt)n
where the exponential term in the limit is based on Euler’s formula exp(x) =
limN→∞(1 + (x/N))N, for any real x. The limit is thus a Poisson distribution.
3Decompounding and estimation
Decompounding  defines the procedure of obtaining the base distribution Q
and the Poisson rate parameter µ based on a sample of the compound process
(Pt)t≥0. Given a parametric form of the discrete distribution Q, the convolution
Q∗kcan easily be computed and (1) respectively (7) can be fitted to the data by
the maximum likelihood estimation method. This approach is easy to implement
and provides reasonable computational performance, since cyst burdens in sheep
are mostly rather low, the maximal burdens being of magnitude 80. Since Q is
defined on the positive integers, Q∗kneeds only be computed for small k’s. In
addition, simulation from the fitted model is computationally fast (we will use
the fitted model in a subsequent paper).
A nonparametric alternative to estimate the distribution Q is presented in
. Using an empirical estimator for the distribution of Ytfor t fixed, an estima-
tor for the distribution of the Si’s is obtained by a suitable inversion of the Panjer
recursions  of the distribution of Yt. As shown in , the procedure requires
an accurate empirical estimation of the distribution of Ytfor each t. Since the
sheep in our sample are of many different ages and the loads are heavily skewed,
it is difficult to obtain an appropriate empirical estimate of the distribution of Yt
for the nonparametric procedure.
Suppose that Q is the zero-truncated Po(η) distribution. Then the following
result  is useful.
Theorem 3.1. Let Sj (1 ≤ j ≤ n) be i.i.d. zero-truncated Po(η) distributed
random variables, so that
?(Sj= s) = ηs/(s!(eη− 1)) for s ∈ N. Then
Sj= z) =
k=0(−1)k(n − k)z?n
if n ≤ z ∈
To take into account aggregation of the clump size distribution, let Q be the
zero-truncated negative binomial distribution, so that for s ∈ N,
?(Sj= s) =Γ(θ + s)
(1 + ζ)θ− 1,
where θ is the shape and ζ is the scale parameter of the negative binomial distri-
bution. Then the following results  applies.
Theorem 3.2. Let Sj (1 ≤ j ≤ n) be i.i.d. zero-truncated negative binomial
distributed random variables specified by (10). Then for z ∈
Sj= z) =
if n ≤ z
?Ωbe the probability measure corresponding to the compound Poisson
process if Ω = µ and to the compound mixed Poisson process if Ω = (ψ,ξ); let
Ntdenote the corresponding incidence process. Then,
?(Yt|Nt= n) = n ?(S1)
and Var(Yt|Nt= n) = nVar(S1). Hence for the a zero-truncated Poisson clump
?(Yt|Nt= n) =
1 − e−η, Var(Yt|Nt= n) =
1 − e−η
and for a zero-truncated negative binomial clump distribution,
?(Yt|Nt= n) =
1 − (1/(ζ + 1))θ
Var(Yt|Nt= n) = n
θζ(1 + ζ + θζ)
1 − (1/(ζ + 1))θ−
1 − (1/(ζ + 1))θ
Expressions (1) and (7) can be used with Theorems 3.1 and 3.2 to compute the
unconditional distribution of Yt,
?Ω(Yt= j) =
if j = 0
if j ≥ 1.
Given independent realizations yi(1 ≤ i ≤ n) of Ytat time points ti, the log-
likelihood function is
where I is the indicator function. The log-likelihood function for the case of a
single ingestion mechanism, with clump size fixed to be 1, is thus
Let us introduce the following model notation for the rest of the paper. The
single ingestion models with Poisson and mixed Poisson incidence process are
denoted by P/1 and MP/1 respectively. The compound process (Yt)t≥0(13) with
(Nt)t≥0 a Poisson process and with the clump size distribution Q specified to
be the zero-truncated Poisson distribution is denoted by P/ztP, and if (Nt)t≥0is
a mixed Poisson process, then the model is denoted by MP/ztP. Analogously, if
the clump size distribution is specified to be the zero-truncated negative binomial
distribution, we denote the resulting models by P/ztnb and MP/ztnb, depending
on the incidence process.
Parameter estimates for the models of interest are obtained from the two data
sets of Kazakhstan and Jordan (Section 2.1). We test single against clumped
infection, heterogeneity of the Poisson rate parameter of the incidence process,
and aggregation of the clump size distribution. Then we compare the best fitting
models for the two data sets and assess the goodness-of-fit.
4.1 Clumped infection
First, we compare the single ingestion models P/1 and MP/1 to the compound
processes P/ztP and MP/ztP respectively using a standard likelihood ratio test
based on (14) and (15) with 1 degree of freedom. The log-likelihood values are
reported in Table 1. Testing the P/1 against the P/ztP results in p-values of
< 0.001 for Kazakhstan and Jordan. Similarly, testing the MP/1 against the
MP/ztP also results in p-values of < 0.001 for Kazakhstan and Jordan. Hence
there is strong evidence for a clumped infection process in both samples.
[Table 1 about here.]
4.2Heterogeneity in acquisition and aggregated clump sizes
In (9), we have seen that, if ξ = µ/ψ with µ fixed and ψ → ∞, then the MP/ztP
model converges to the P/ztP model. To test if the acquisition of hydatid cysts
of sheep is heterogeneous, we have to test the null hypothesis H0: ξ = 0 against
ξ > 0. Analogously, to test if the clump size distribution is aggregated, we note
that if ζ = η/θ with η fixed and θ → ∞, then the P/ztnb model converges to
the P/ztP model, and thus we need to test H0: ζ = 0 against ζ > 0. Clearly,
the MP/ztP and the P/ztnb models are also nested within the MP/ztnb model,
which allows heterogeneity in the acquisition of cysts together with an aggregated
clump size distribution. For the tests with H0: ξ = 0 and H0: ζ = 0, we test
a parameter which is on the boundary of the parameter space under H0. 
showed that the asymptotic distribution of the likelihood ratio test statistic in
the presence of a parameter that is on the boundary of the null hypothesis is
1, a 50 : 50 mixture of χ2
1distributions. Given the observed test
statistic ¯ χ, the p-value is given by (
0> ¯ χ) +
1> ¯ χ))/2.
Applying the likelihood ratio test with the above asymptotic χ2mixture dis-
tribution to the reported log-likelihood values in Table 1 implies that the P/ztnb
and the MP/ztP model both fit the Kazakhstan and Jordan sample significantly
better than the P/ztP (all p-values smaller than 0.001). In addition, the MP/ztnb
fits the two samples significantly better than the P/ztnb (p-values for Kazakhstan
< 0.001 and Jordan 0.027) and the MP/ztP models (p-values for Kazakhstan and
Jordan < 0.001).
To verify the asymptotic distribution of the test statistic under H0, we apply
a Monte Carlo method and simulate data under H0(simpler model), then fit both
the simpler and more complex model to the generated data sets and compute the
likelihood test statistic. For the generation of the data sets, starting with the
original ages tk(1 ≤ k ≤ n) of the n sheep in the sample, a new cyst burden is
attributed to each of them as a realization of the simpler model with t = tk, with
the model parameters fixed at their estimated values given in Table 2. Repeating
this procedure 2000 times yields an approximating reference distribution of the
test statistic under H0. Testing the P/ztnb model against the MP/ztnb model for
the Jordan sample implies a p-value of 0.035, which is slightly larger than the p-
value of 0.027 obtained by using the asymptotic reference distribution. The other
p-values computed with the simulated reference distribution also differ slightly
from the ones obtained with the asymptotic reference distribution, however they
are also smaller or equal to 0.002. It appears that our samples are too small to
be able to rely completely on asymptotics. However, the test results with the
simulated reference distribution also imply that the MP/ztnb model significantly
better fits the data sets from Kazakhstan and Jordan than the other models. We
conclude that there is evidence in the data that the acquisition of hydatid cysts
of Echinococcus granulosus by sheep is heterogeneous, and that the clump size
distribution is aggregated.
[Table 2 about here.]
Table 2 shows the estimates of the MP/ztnb model for the parameters a = ψξ
and b = ψξ2of the incidence process Ntdefined in (6) and for the mean c :=
?(Yt|Nt= 1) and variance d := Var(Yt|Nt= 1) of the clump size distribution
defined in (11) and (12). The parameter a is not significantly different in the
samples from Kazakhstan and Jordan, suggesting that a sheep gets infected on
average every third year. The parameter b is significant larger in the Kazakhstan
sample, so that the variance of the infection rates Var(Nt) = at + bt2is larger
for this sample. The difference of the variance of the infection rate in the two
samples is especially pronounced in older sheep since Var(Nt) ∼ bt2. The result-
ing gamma mixture distributions (4) of the infection rate for the two samples are
plotted in Figure 1, indicating that in the Kazakhstan sample, the infection rates
are more heterogeneous than in the Jordan sample. Table 2 also indicates that
the estimated mean and variance for the clump size distribution are not signif-
icantly different in the two samples, suggesting that the number of successfully
established cysts per infection is similar in the two samples. Thus on average, an
infective clump leads to about 4 − 5 established cysts in the sheep.
[Figure 1 about here.]
The fitted MP/ztnb model provides estimates for the prevalence of infection
as well as for the probability mass function (pmf) of the positive loads. Fig-
ure 2 shows the estimated prevalence of infection for the MP/ztnb model for
the Kazakhstan and Jordan samples together with the observed prevalences. In
both samples, the estimated prevalence of the MP/ztnb explains the observations
[Figure 2 about here.]
The estimated pmf of the MP/ztnb model for the age classes reported in
Figure 2 are displayed in Figure 3 for the Kazakhstan and in Figure 4 for the
Jordan sample. Given an age class, the fitted pmf are computed as mixture of
the pmf’s corresponding to the different ages within the class. The fitted pmf are
reasonable in both samples, taking into account the small number of observed
positive loads in some of the age classes, especially in the Jordan sample.
[Figure 3 about here.]
[Figure 4 about here.]
The goodness-of-fit of the MP/ztnb model is evaluated as follows. Divide the
sheep into age classes, and treat the observations in the different classes as i.i.d.
data. The classes are specified as in Figure 2. The observed and estimated dis-
tributions of cysts are then compared within each age class using an appropriate
statistic. Note that, as before, the resulting pmf for an age class is a mixture of
the pmf’s corresponding to the different ages within that class.
With the age classes as before, let ni1 ≤ i ≤ 6 be the number of animals in age
class i, and stratify them with respect to load into cistrata. Then two possible
goodness-of-fit measures for the distribution of the numbers of cysts within any
given age class i are
where Mikis a random variable describing the numbers of animals of age class
i having cyst counts in stratum k (1 ≤ k ≤ ci), and mikis the (corresponding)
The number of strata cifor age class i is chosen to be the maximal number
such that the expected number of counts in each stratum is at least 10. The
strata in the age classes are computed for the model with parameters fixed by
their estimates in Table 2. To generate the reference distribution of χ2and L, a
Monte Carlo approach is used, where data sets are generated under the MP/ztnb
model. Given the original ages tk(1 ≤ k ≤ n) of the n sheep in the sample, a
new cyst burden is attributed to each of them as a realization of the MP/ztnb
model with t = tkand the parameters fixed by their estimates given in Table 2.
We then fit the MP/ztnb model to this new data set, and compute with the new
estimates the test statistics for each of these sets. We use the same stratification
of the age classes as before. The observed values of the two test statistics can
then be compared to the reference distributions for each age class i.
Figures 5 and 6 display the results for the samples from Kazakhstan and
Jordan for 1000 simulations. For the Kazakhstan sample, the observed values of
the test statistics χ2and L (indicated by a solid line) although consistently large,
are in reasonable agreement with the simulated distributions for all age strata.
For the Jordan sample, the solid line lies well outside the simulated distribution
in age class (0,1]. This is for two reasons. First, the observed prevalence in that
age class is overestimated by the model (see Figure 2). Secondly, there are only 12
positive loads in that class, which are not well described by the model. However,
the results in the other age classes suggest that the model fit is reasonable.
The model seems to have some tendency to underestimate the zero load stra-
tum and to overestimate the numbers of high cyst counts in the first age class.
The opposite tendency can be observed in the age strata 4 − 6. Since only 4
parameters are used in the model, to fit the distributions of prevalence and cyst
burden observed in 6 different age classes, a perfect fit can hardly be expected.
[Figure 5 about here.]
[Figure 6 about here.]
In this paper, different mechanistic models are used to explain the acquisition
of hydatid cysts in sheep, caused by the parasite Echinococcus granulosus. The
models allow one to test the biologically interesting hypotheses of clumped infec-
tions, host heterogeneity with respect to infection and aggregation of clump sizes.
The experimentally supported assumptions of Echinococcus granulosus cysts in-
fections in sheep such as life-long survival of cysts in the host, no replication inside
the host, no parasite-induced mortality and no acquired immunity in sheep imply
simpler infection dynamics than for example those encountered by  and ,
as discussed in the introduction to this paper. Hence our models are straight-
forward to fit to the most commonly available data sets, which only contain the
ages and cyst burdens of the sheep. The models provide age-dependent estimates
for the prevalence of infection and for the probability mass functions of positive
cyst burdens in sheep.
The application of the models to two data sets from Kazakhstan and Jordan
supports a clumped infection process, with a rate of acquisition of infection which
is heterogeneous within the population, and with clump sizes which are aggre-
gated. The infection process is described by a compound mixed Poisson process
with a zero-truncated negative binomial distribution for the number of cysts per
ingested clump. The goodness-of-fit measures indicate that the chosen model
describes the given data reasonably well, but not perfectly. The estimates sug-
gest a mean infection rate of about 0.315 infections per year and a mean clump
size of about 4.5 cysts, suggesting that on average every third year, a sheep will
ingest an infectious clump, each clump leading to approximately 4−5 established
hydatid cysts in the sheep. The results indicate that the observed aggregation
in the distribution of cysts among sheep may be the result both of differences
between sheep and also of clumped infections.
Our model can be used to investigate how changes in the underlying param-
eters may affect the parasite distribution, and thus may be useful in assessing
control programs for Echinococcus granulosus. In particular, it can be used as
sub-process for describing infections in the sheep population in a fully stochastic
model for the complete life-cycle of Echinococcus granulosus.
Achnowledgements The authors gratefully acknowledge the comments and
suggestions of two referees and the handling editor, that greatly improved the
presentation. This work was supported by the Schweizerischer Nationalfonds
(SNF), project no. 107726.
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Figure 1: Estimated gamma density function (4) of the infection rate in the inci-
dence process for the samples from Kazakhstan (solid line) and Jordan (dashed
Figure 2: Fitted prevalence curves ˆ q(t) = 1 − 1/(tˆξ + 1)ˆψ(withˆψ andˆξ given in
Table 2) for the MP/ztnb model for the samples from Kazakhstan and Jordan,
together with the observed prevalences and their 95% confidence intervals. The
observed prevalences are computed for the age classes (0,1], (1,2], (2,3], (3,4],
(4,5], 5+, where 5+ summarizes all sheep older than 5 years. For the age classes
1 − 4, the majority of the observed ages coincide with the end points of the
interval. The prevalences are plotted at the means of the ages of the animals in
the corresponding classes.
13579 121518 2124 2730
147 11 15 19 23 27 31 35 39 43
147 1115 192327 313539
147 11 15 19 23 27 31 35 39 43
147 11 15 19 23 27 31 35 39 43
1 3 5 7 9 1215 18 21 2427 30
Figure 3: Estimated probability mass functions of the MP/ztnb model for the
positive loads of the Kazakhstan sample for the age classes (a) (0,1], (b) (1,2], (c)
(2,3], (d) (3,4], (e) (4,5], (f) 5+, together with a histogram of the corresponding
observed quantities. The class sizes are 185, 315, 282, 84, 29 and 15. For a better
presentation of the results, the following points are not plotted in the histograms:
64 (with corresponding mass 0.003) in age class (1,2], 47 and 57 (mass 0.119 each)
in age class (3,4] and 56 (mass 0.034) in age class (4,5].
13579 12 15 182124 2730
147 10 14 18222630 34 38
147 111519 2327 313539
147 11 15 19 23 27 31 35 39 43
14711 15 19 23 27 31 35 39 43
Figure 4: Estimated probability mass functions of the MP/ztnb model for the
positive loads of the Jordan sample for the age classes (a) (0,1], (b) (1,2], (c)
(2,3], (d) (3,4], (e) (4,5], (f) 5+, together with a histogram of the corresponding
observed quantities. The class sizes are 12, 14, 23, 29, 47 and 119. For an better
presentation of the results, the following points are not plotted in the histograms:
52 and 63 (mass 0.021 each) in age class (4,5], and 57 (mass 0.008) and two loads
of 80 (combined mass 0.016) in age class 5+.
10 15 20 25
0 102030 40
Figure 5: Goodness-of-fit of the MP/ztnb model in the Kazakhstan sample. The
observed values of the test statistics (solid lines) χ2((a1)-(a8)) and L ((b1)-(b8))
are plotted with the corresponding simulated distributions under the MP/ztnb
model with parameters fixed with its estimates in Table 2 for the age classes (x1)
(0,1], (x2) (1,2], (x3) (2,3] (x4) (3,4], (x5) (4,5] and (x6) 5+, with x=a,b.
0.00 0.04 0.08 0.12
02468 10 14
0 10 20304050
Figure 6: Goodness-of-fit of the MP/ztnb model in the Jordan sample, analo-
gously to Figure 5.
Table 1: Log-likelihood values for the models fitted to the Kazakhstan and Jordan
samples, together with the number of parameters in the models.
Table 2: Maximum likelihood estimates for the parameters and key quantities Download full-text
of the MP/ztnb model for the Kazakhstan and Jordan samples, together with
95% confidence intervals computed by the bootstrap percentile method. The
parameters a = ψξ and b = ψξ2of the incidence process Ntare defined in (6),
variance d := Var(Yt|Nt= 1) of the clump size distribution are defined in (11)
and (12) respectively.
?(Nt) = at and Var(Nt) = at + bt2. The mean c :=
?(Yt|Nt= 1) and