Integrating serological and genetic data to quantifycross-species transmission: brucellosis as a case study

MAFALDA VIANA1, GABRIEL M. SHIRIMA2, KUNDA S. JOHN3,JULIE FITZPATRICK4, RUDOVICK R. KAZWALA5, JORAM J. BUZA2,SARAH CLEAVELAND1, DANIEL T. HAYDON1* and JO E. B. HALLIDAY1

1Boyd Orr Centre for Population and Ecosystem Health, Institute of Biodiversity, Animal Health and ComparativeMedicine, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK2Nelson Mandela African Institution of Science and Technology, School of Life Sciences and Bioengineering, Arusha,Tanzania3National Institute of Medical Research, PO Box 9653, 11101 Dar es Salaam, Tanzania4Moredun Research Institute, Pentlands Science Park. Penicuik, Midlothian EH26 0PZ, UK5Department of Veterinary Medicine and Public Health, Sokoine University of Agriculture, P.O. Box 3021, Morogoro,Tanzania

(Received 30 September 2015; revised 8 December 2015; accepted 8 December 2015)

SUMMARY

Epidemiological data are often fragmented, partial, and/or ambiguous and unable to yield the desired level of understand-ing of infectious disease dynamics to adequately inform control measures. Here, we show how the information contained inwidely available serology data can be enhanced by integration with less common type-specific data, to improve the under-standing of the transmission dynamics of complex multi-species pathogens and host communities. Using brucellosis innorthern Tanzania as a case study, we developed a latent process model based on serology data obtained from the field,to reconstruct Brucella transmission dynamics. We were able to identify sheep and goats as a more likely source ofhuman and animal infection than cattle; however, the highly cross-reactive nature of Brucella spp. meant that it wasnot possible to determine which Brucella species (B. abortus or B. melitensis) is responsible for human infection. Weextended our model to integrate simulated serology and typing data, and show that although serology alone can identifythe host source of human infection under certain restrictive conditions, the integration of even small amounts (5%) oftyping data can improve understanding of complex epidemiological dynamics. We show that data integration will oftenbe essential when more than one pathogen is present and when the distinction between exposed and infectious individualsis not clear from serology data. With increasing epidemiological complexity, serology data become less informative.However, we show how this weakness can be mitigated by integrating such data with typing data, thereby enhancingthe inference from these data and improving understanding of the underlying dynamics.

Key words: data integration, serology, brucellosis, genetics, epidemiological modelling, Bayesian modelling, state-spacemodels.

INTRODUCTION

It is a regrettable but ubiquitous state of affairs thatwe are unable to directly observe those aspects of theecology of a pathogen that are most informative of itsepidemiological dynamics. For example, we may beable to observe how the prevalence of a diseasechanges over space and time, but not the underlyingtransmission processes that give rise to thesechanges. Wemay also be able to observe the presenceof clinical disease, but not when a host became infec-tious. Similarly, the identification of antibodiesthrough serological assays can reveal past exposureof a host to a pathogen but inferring precisely

when this exposure occurred and what it impliesabout infectiousness can be challenging.There is an increasing availability of large-scale,

cross-sectional and longitudinal serology datasetsfrom human, livestock and wildlife systems aroundthe world, as the technological requirements neces-sary to generate serological data are low and evidenceof exposure can persist. The analysis of serologicaldata is a primary methodology for investigating theprevalence and transmission dynamics of infectiousdiseases. However, in addition to inferring thetiming of exposure, there are several factors that com-plicate the epidemiological insight that can be gainedfrom it. For example, serological data are generatedby tests with imperfect sensitivity and specificity. Inaddition, the antibodies detected may also have beengenerated in response to a number of closely relatedpathogens that may be circulating independently ofeach other (known as cross-reactivity). Cut-offthresholds for distinguishing between exposed and

* Corresponding author: Daniel T. Haydon, Boyd OrrCentre for Population and Ecosystem Health, Instituteof Biodiversity, Animal Health and ComparativeMedicine, College of Medical Veterinary and LifeSciences, University of Glasgow, Glasgow G12 8QQ,UK. E-mail: Daniel.Haydon@glasgow.ac.uk

1SPECIAL ISSUE ARTICLE

Parasitology, Page 1 of 14. © Cambridge University Press 2016. This is an Open Access article, distributed under the terms of theCreative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution,and reproduction in any medium, provided the original work is properly cited.doi:10.1017/S0031182016000044

unexposed individuals are often ambiguous, and anti-bodies may decline with time after the peak immuneresponse that follows exposure (Gilbert et al. 2013).These factors all create challenges for the interpret-ation of, and inference from, serological data.How should epidemiologists proceed when con-

fronted by data that are only weakly informative ofthe dynamics that we so urgently need to under-stand? We could simply reject such ‘weak’ data andorganize the collection of more informative data.However, this is not always possible and is likely tobe time consuming, expensive, wasteful, and has po-tential ethical and welfare implications in terms ofunnecessary animal handling and sampling.Furthermore, as a reflection of the immunesystems ‘memory’ of historical exposure, serologicaldata are information rich. Rather than reject theseimperfect data we should instead develop moresophisticated analyses to interrogate the data wehave and extract the information they contain(Jones et al. 2009, 2012; Norris et al. 2009).The most effective response to these analytical

challenges will involve a pragmatic combination ofapproaches, including the development of techni-ques that allow better use of serological datathrough integration with additional data relating toother observable aspects of the system. While eachof these data types may be only weakly informativeon its own, considered together they can strengtheneach other (Strelioff et al. 2013; Viana et al. 2014).The effective synthesis of available data is an intui-tive and increasingly recognized approach in manyscientific disciplines (Searls, 2005). However, inte-gration of multiple different types of data toconduct robust analysis is a challenge in itself thatrequires significant methodological development.Current Bayesian statistical methods facilitate this

approach (Gelman, 2004) and are becoming increas-ingly popular among epidemiologists (Basanez et al.2004; Broemeling, 2014). For example, networks of‘who-infected-who’, known as transmission trees(Morelli et al. 2012; Jombart et al. 2014; Mollentzeet al. 2014), have been used to integrate epidemio-logical, genetic and transmission data from the out-break of foot-and-mouth disease virus in the UKin 2001, and greatly enhanced understanding of thespread of the epidemic. By simultaneously analysingthe time that farms were reported as infected, theirgeographic locations and the age of the oldestlesions found on livestock on each infected farm, itwas possible to estimate a transmission tree(Haydon et al. 2003). The further inclusion ofwhole genome sequence data of the virus isolatedfrom each farm substantially reduced the numberof potentially plausible transmission trees (Cottamet al. 2008). Similarly, state-space models(Patterson et al. 2008; Holdo et al. 2009; Hookeret al. 2011; Viana et al. 2015) also known as hiddenor latent process models, can enable inference of

the unobserved (or hidden) epidemiological processbehind the generation of observations. These haverecently been used to integrate data on the age ofthe host, and the timing of vaccination campaignswith serology data to reconstruct the dynamics ofCanine Distemper Virus outbreaks in populationsof lions and dogs in and around the SerengetiNational Park (Viana et al. 2015).The overall aim of this paper is to explore how

widely available serology data can be enhanced byintegration with less common type-specific data, toimprove the understanding of the transmission dy-namics of complex multi-species pathogens andhost communities. We focus on brucellosis as anexample of such a system, but note that there areseveral important infectious diseases for which ser-ology data are relatively common and type-specificdata much rarer, for example, leptospirosis, foot-and-mouth disease and blue tongue.

Case study: brucellosis in northern Tanzania

Brucellosis is a bacterial zoonosis that has a world-wide distribution, but human disease incidence ishigher in low- and middle-income countries (Deanet al. 2012b). In humans, it causes non-specificfebrile illness, debilitating symptoms, includingjoint and muscle pain, as well as more severe compli-cations such as endocarditis and neurological symp-toms (Dean et al. 2012a). Multiple animal species,including livestock, are affected by brucellosiswhich impacts on productivity through abortion,reduced reproductive efficiency and decreased milkproduction. Human infections are typically acquiredthrough contact, ingestion or inhalation of bacteriashed by infected animals.Brucellosis can be caused by one of several bac-

teria of the genus Brucella. The two species of great-est relevance for human and livestock health innorthern Tanzania are Brucella abortus, which istypically thought to be associated with cattle, andBrucella melitensis, which is often reported in sheepand goat populations (hereafter combined and re-ferred to as ‘caprids’) (World Health Organizationet al. 2006). The main risk factor identified as thedriver of animal and human infection is largeanimal population size (Kadohira et al. 1997;McDermott and Arimi, 2002). However, theknown capacity for B. abortus and B. melitensis tobe transmitted between cattle and caprids compli-cates the interpretation of the roles played bydifferent host populations in the maintenance andtransmission of this disease.In sub-Saharan Africa, current efforts to develop

control strategies are constrained by limited under-standing of two fundamental features of brucellosisepidemiology: (1) which animal species are affectedand act as the source of human infections; and (2)which Brucella species are maintained in which

2Mafalda Viana and others

animal hosts? The distinction among epidemiologic-al scenarios is crucial because vaccination of animalsis currently the most effective method to controlbrucellosis (Godfroid et al. 2011), but vaccines arepathogen species and host-specific; i.e. forB. abortus vaccine for cattle and B. melitensisvaccine for caprids.Most epidemiological data on brucellosis in sub-

Saharan Africa are obtained through serologicalsurveys. Brucellosis seroprevalence data are com-pound representations of the occurrence of the twoBrucella species that cross-react with the testantigen (McGiven, 2013). A seropositive status istherefore an indication of exposure to B. melitensisand/or B. abortus. Genetic detection allows identifi-cation of the infecting species of Brucella, but this islikely to be successful only if sample collectionoccurs within the short-time window around eitheracute illness in people, or the time of parturition orabortion in animals. The considerable investmentrequired to obtain large numbers of these samplesmakes such data extremely scarce.Figure 1 depicts three of several possible scenarios

of brucellosis transmission in northern Tanzania,which we will use to illustrate challenges and pos-sible solutions to better understanding thesecomplex epidemiological systems. The power to dis-tinguish between these transmission scenarios likelydepends on how the different hosts co-occur. Forexample, cattle and caprids can be highly positivelycorrelated (e.g. sampled households with morecattle have more caprids), clearly segregated (e.g.sampled households contain mostly one hostspecies) or weakly correlated. These possible popu-lation structures are demonstrated in Fig. 2.The aim of this paper is to answer two fundamen-

tal questions:

(1) When can analysis of serology alone identify thesource of human Brucella infection?

(2) When does the integration of realistically sparsegenetic-typing data allow identification of theappropriate source of infection when serologyalone does not?

To address these questions, we first develop a methodto determine the animal source of humanBrucella spp.infection and quantify cross-species transmissionbetween cattle and caprids using field serology datafromourBrucella case study.Second,weuse simulateddata to explore howdifferent epidemiological scenariosand population structures might impact on the powerof serological data to reveal similar transmission pat-terns to those observed in our data. Third, we simulategenetic type-specific data to augment the serologicaldata and explore the circumstances under which thisintegration enhances serology data and providesenough analytical power to distinguish between thedifferent transmission scenarios.

MATERIALS AND METHODS

Field serological survey

Data collection. The serological data on brucel-losis were collected through a cross-sectional fieldstudy conducted in the Arusha and Manyararegions of northern Tanzania in 2002–2003.Within five districts in these regions, a multi-stagecluster sampling strategy was used to identify andrandomize the selection of villages, sub-villages,ten cell units and livestock keeping households.Data collection at the 86 selected householdsincluded blood sample collection from randomlyselected sheep and goats (combined as caprids),cattle and humans. The number of cattle and/orcaprids sampled at each household was based onthe number required to estimate the within-herdprevalence given the size of the household herd.This was calculated for an expected prevalence of

Fig. 1. Plausible alternative epidemiological scenarios for inter-species transmission and sources of human Brucellainfection. Arrows indicate the direction and magnitude of transmission. Question marks indicate that transmission occurswith an unknown magnitude (which will be estimated by our models). In Scenario 1, humans can be infected by capridswith B. melitensis and cattle with B. abortus; in Scenario 2 caprids with B. melitensis can transmit to humans and cattle butonly caprids can transmit infection to humans; and in Scenario 3 both caprids and cattle with B. melitensis can infecthumans.

3Integrating data to quantify cross-species transmission

5%, with 80% power and 95% confidence (Martinet al. 1987). All humans present at the householdwere invited to participate in the study. Serumsamples from all species were tested for antibodiesagainst Brucella species at the Animal and PlantHealth Agency (APHA) using a competitiveELISA (McGiven et al. 2003; Perrett et al. 2010).A cut-off of 60% inhibition was used to definesample status for all species tested. This dataset is asubset of data described previously (Shirima, 2005;Kunda, 2006). Figure 2c shows the number ofcattle and caprids sampled in each household andFig. 3a shows the seroprevalence of Brucella sp. inhumans, cattle and caprids in this dataset. Nogenetic-typing data of the Brucella species wereavailable in this field study.

Generalized linear model (GLM) analysis. GLMswere used to examine the relationship between theseroprevalence of Brucella exposure in humans andthe (crudely) estimated total numbers of seropositivecattle and caprids present at each household in theTanzanian field dataset. A GLM was used instead ofa GLMM because the inclusion of random effects(e.g. household and village) did not make a differenceto the results, both in terms of significance and good-ness-of-fit of the model (i.e. AIC difference was lessthan 2; Burnham and Anderson, 2002). The responsevariablewas theproportionofhumansat thehouseholdthat tested seropositive, weighted by the number ofhumans sampled at the household.The covariates con-sidered were estimates for the total numbers of sero-positive cattle and caprids present at each household,which were calculated by multiplying the proportionseropositive in the sample by the total population sizeat the household for each animal group.

Bayesian serology model. A latent model wasdeveloped to quantify the contribution of different

animal hosts to human infection probability fromserology data. A detailed description of this modelis provided in Supplementary information (SI). Inessence, the model is composed of five interlinkedbinomial processes: the first two binomials estimatethe cattle and caprid probability of being seroposi-tive in the sampled population, the third andfourth binomials take these inferences from thesample to estimate the number of seropositivecattle and caprids in the whole population, and thefifth binomial process uses the population inferencesto estimate the contribution of seropositive animalsto the probability of human infection. One of themain advantages of this modelling approach is thatit allows propagation of uncertainties from the infer-ences of the animal samples, to those of the wholeanimal population and finally to humans. We notethat this is not a one-way propagation as the fivebinomials are estimated simultaneously and informeach other.For each host [i.e. cattle c, caprids (sheep and

goats) s and humans h], a binomial model (equiva-lent to a Bernoulli process) is implemented to de-scribe the observation process of each individualserology test result. The likelihood of the datafrom an individual being classified as seropositivewas based on the serological test data and a probabil-ity of misclassification. This probability of misclas-sification (i.e. whether the serology assay generatesfalse positives or negatives (further details in theSI) typically incorporates pre-existing informationabout the performance of the test being used. Forsimplicity and because there is no misclassificationin the simulated data, this was always set to0. These binomials are ultimately defined by a prob-ability of an individual testing seropositive in ahousehold. At the household level, these are linear-ized through a logit transformation and describedthrough a linear predictor (lnSer). For humans,

Fig. 2. Population structures used in simulations. In population structure 1 there is a positive correlation between cattleand caprid numbers in each household (HH). In population structure 2 there is clear segregation and each household hasmostly cattle or mostly caprids. Population structure 3 shows an intermediate relationship with weak correlation of cattleand caprid numbers and represents the structure of the real sampled population from northern Tanzania.

4Mafalda Viana and others

this linear predictor is described below. For cattle(lnSerc) and caprids (lnSers) these predictors aredescribed as a function of the number of cattle[Ncattle(i)] and caprids [Ncaprids(i)] in each house-hold (i):

lnSercðiÞ ¼ β0;c þ β1;cNcattleðiÞ þ β2;cNcapridsðiÞð1Þ

lnSersðiÞ ¼ β0;s þ β1;sNcattleðiÞ þ β2;sNcapridsðiÞð2Þ

where β0,c and β0,s correspond to the intercepts, β1,cand β2,c, and β1,s and β2,s, correspond to the coeffi-cients governing the effect of the number of cattleand caprids on the probability of infection in cattleand caprids, respectively. The unobserved totalnumbers of infected cattle and caprids per household[Yc(i) and Ys(i)] are then estimated through aBinomial process using the infection probabilityestimated from the sampled population (pSer,which corresponds to the inverse logit of the predict-or lnSer) and data on the total herd/flock size at eachhousehold:

YcðiÞ ∼ binomialðpSercðiÞ;NcattleðiÞÞ ð3Þ

YsðiÞ ∼ binomialðpSersðiÞ;NcapridsðiÞÞ ð4Þ

The estimated total numbers of positive animals[Yc(i) and Ys(i)] are then used as covariates in thelogit linear predictor [lnSerh(i)] of the probabilityof human infection:

lnSerhðiÞ ¼ β0;h þ β1;hYcðiÞ þ β2;hYsðiÞ ð5Þ

where β0,h corresponds to the intercept, β1,h and β2,hcorrespond to the coefficients governing the effect ofthe number of infected cattle and caprids on theprobability of human infection.Further details of the model, including the prior

distributions allocated to each coefficient and themodel code for implementation in JAGS, are pro-vided in the SI.This model only used serology data and was

implemented with the field survey data to estimatethe human source of infection in the northernTanzanian setting. This serology only model wasalso implemented with simulated datasets (see thesubsequent section) to address our specific aim 1and explore how different epidemiological scenariosand population structures might impact on the infer-ences made from serological data.

Simulation study

Simulation of serology & genetic typing data. Wesimulated datasets to illustrate the three alternativeepidemiological scenarios of Brucella transmission

(Fig. 1) within three distinct population structures(Fig. 2). In epidemiological scenario 1, cattle wereinfected with B. abortus only and caprids withB. melitensis only. Humans could acquire Brucellainfection from both livestock populations, but thecontribution of B. melitensis-positive caprids tohuman infection probability was twice as large asthat of B. abortus-positive cattle. See Table S3 forsimulation coefficient values. In this case, genetic-typing data were simulated only for B. abortus-posi-tive cattle and B. melitensis-positive caprids (seebelow for details of the sampling mechanisms usedfor simulations). In epidemiological scenario 2,only B. melitensis was present. Here, cattle couldbe exposed to infection from caprids and seroconvertbut humans could only acquire infection fromcaprids. This illustrates a situation where cattle donot shed the pathogen and no genetic-typing dataare available. In epidemiological scenario 3, onlyB. melitensis was present but both caprids andcattle were infected. Genetic-typing data were simu-lated for B. melitensis-positive cattle and caprids andthe probability of obtaining typing data from sero-positive individuals in these two groups was equal.In this scenario, humans acquire infection fromboth hosts, but the contribution of B. melitensis-positive caprids to human infection probability istwice as large as the contribution of B. melitensis-positive cattle. We note that there is no genetic-typing data for humans, and that in all simulationsthe only data available for humans are serologicaltest results. Scenarios where only B. abortus ispresent were not included but these would be func-tionally equivalent to the B. melitensis only scenariosexplored in scenarios 2 and 3.Each of the epidemiological scenarios was simu-

lated for the three distinct population structures inFig. 2. The simulated population structuresincluded 86 households and 428 humans (identicalto the household and human numbers in the fielddataset). The total size of the livestock populationin each household (cattle + caprids) was also asobserved in the field study, but the ratio of cattle:caprids was altered for each population structure.In population structure 1, the total animal popula-tion was divided between cattle and caprids (with aprobability of 0·5 of being a cow vs caprid), leadingto a strong positive correlation (measured by thePearson’s correlation coefficient, r) between cattleand caprid numbers in each household (r = 0·975).In population structure 2, there is clear segregationof the cattle and caprid populations such that eachhousehold had either 90% cattle or 90% caprids(r = 0·08). In population structure 3, the number(and ratio) of cattle and caprids in each householdwas as observed in the field dataset (r = 0·656).The models and parameters used to simulate the

alternative datasets are based on the serologymodel presented above, and its extension that

5Integrating data to quantify cross-species transmission

includes genetic-typing data presented below. Inbrief, animal infection probability was simulated asfunction of the size of the cattle and caprid popula-tions at the household level. Human infection prob-ability was simulated as a function of the number ofanimals in each of four possible infectious popula-tions: B. abortus infected cattle, B. abortus infectedcaprids, B. melitensis-infected cattle and B. meliten-sis-infected caprids. The parameter values used forthese simulations differed for the different epidemio-logical scenarios and the details of the values used aregiven in Table S3. The plausibility of the valuesused for the simulated datasets was ensured bykeeping the number of households, the number ofhuman and animals per household (explainedabove), and the prevalence values generated withinrealistic values (i.e. similar to those in the fielddataset). Simulated prevalence values were below10% for cattle and caprid populations and 16% forhumans.To simulate both genetic-typing and serological

data, the Brucella species-specific (B. melitensis orB. abortus) infection status (infected or not) wassimulated at the individual animal level. For simpli-city, all genetically positive individuals were clas-sified as seropositive, analogous to assuming thatall animal infections would lead to a detectable andlasting antibody response. This is an oversimplifica-tion that assumes that there is no misclassification inserostatus and that only seropositive animals can begenetically positive. We deal with the latter issue inthe model analyses by considering such samples(genetically typed positives from seronegative indi-viduals) as missing data. This simulation strategyalso meant that all seropositive animals were positivefor B. melitensis and/or B. abortus, corresponding toan assumption that the Brucella species responsiblefor all seropositivity could be determined in allcases. The exception to this assumption is thegenetic-typing data simulated for cattle in epidemio-logical scenario 2. Here, all cattle test negative forB. melitensis as they constitute a dead-end host andwould not shed Brucella.As complete sampling of populations is rarely

achievable in field studies, we sampled individualsfrom these simulated datasets using a rationaleanalogous to the sampling strategy used in the ori-ginal field study; i.e. the sample size required to es-timate prevalence of 5% with 95% confidence and90% precision (Dohoo et al. 2003). Figure 3 illus-trates the number of animals sampled from therange of population sizes. Only the data obtainedfrom these sampled animals were considered in themodel analyses.While the determination of the serostatus of an in-

dividual is relatively straightforward, the apparentlyintermittent or temporally variable nature ofBrucella shedding by the infected animals (WorldHealth Organization et al. 2006; Ebrahimi et al.

2014), the potential for animals to seroconvert andrecover (to a genetically negative but seropositivestatus) and relatively low diagnostic sensitivity ofPCR-based techniques for the direct detection ofBrucella, all ensure that the genetic identity of theinfecting Brucella species will only ever be obtain-able for a small subset of previously infectedanimals. To represent the sparseness of availablegenetic-typing data and explore the amount oftyping data required to effectively identify the hostand pathogen species that pose the greatest threatto human populations, we used different proportionsof the genetic-typing data available for the seroposi-tive animals sampled, i.e. 50, 10 and 5% (e.g. for the5% situation, genetic-typing data were only availablefor 5% of the seropositive animals in the sampledpopulation).

Model extension to integrate serology with genetics. Inorder to integrate genetic-typing data, we extendedthe serology model described above to include theinfluence of different pathogen and animal host com-binations upon the probability of human infection.In this extended model, we replace lnSerh(i) bylnTypeh(i) and define this linear predictor as:

lnTypehðiÞ ¼ α0;h þ α1;hYc;aðiÞ þ α2;hYs;aðiÞþ α3;hYc;mðiÞ þ α4;hYs;mðiÞ

ð6Þ

where α0,h corresponds to the intercept, α1,h and α2,hcorrespond to the coefficients governing the effect ofthe number of B. abortus infected cattle and caprids(Yc,a andYs,a), respectively, and α3,h and α4,h corres-pond to the coefficients governing the effect of thenumber of B. melitensis infected cattle and caprids

Fig. 3. Serology sampling strategy. The continuous boldline shows the relationship between the number of animalspresent at each household and the number sampled. Thedotted line shows the number of animals that would besampled if all animals present were sampled.

6Mafalda Viana and others

(Yc,m and Ys,m), respectively, on the probability ofhuman infection. The main difference between theextended model and the serology only model is inthe way the ‘true’ number of infected cattle andcaprids in each household are estimated [i.e. fourY’s from equation (6) compared with the two Y’sfrom equation (5)]. In this extended model, thenumber of B. abortus and B. melitensis-infectedcattle and caprids were estimated from Binomialprocesses in which the probability of being infectedwith a pathogen/host combination (pType) isweighted by the probability of the host being sero-positive (pSer), which was estimated from the ser-ology only model. It is in this step that theintegration between serology and genetic-typingdata occurs:

Yc;aðiÞ ∼ binomialðpTypec;aðiÞ× pSercðiÞ;NcattleðiÞÞ ð7Þ

Yc;mðiÞ ∼ binomialðpTypec;mðiÞ× pSercðiÞ;NcattleðiÞÞ ð8Þ

Ys;aðiÞ ∼ binomialðpTypes;aðiÞ× pSersðiÞ;NcapridsðiÞÞ ð9Þ

Ys;mðiÞ ∼ binomialðpTypes;mðiÞ× pSersðiÞ;NcapridsðiÞÞ ð10Þ

The remaining components of this model areequivalent to those of the serology only model.However, the probability of being genetically posi-tive (pType) comes from the binomial likelihood ofthe data [e.g. for B. abortus in cattle ∼binomial(pTypec,a(i), Ncattle(i))], rather than being aBernoulli trial, as we assume that the misclassifica-tion associated with genetic typing is negligible(e.g. there are no incorrectly typed individuals byPCR-based diagnostics). This probability pType isfinally linearized (lnType) through a logit trans-formation and described as a function of thenumber of animals in the household. For cattlewith B. abortus [lnTypec,a(i)]:

lnTypec;aðiÞ ¼ α0;c;a þ α1;c;aNcattleðiÞþ α2;c;aNcapridsðiÞ ð11Þ

The parameter α0,c,a corresponds to the intercept,α1,c,a and α2,c,a correspond to the coefficients govern-ing the effect of the number of cattle and caprids inthe household. Further details of the model, includ-ing the full linear predictors for the remainingsources of infection (e.g. B. abortus infectedcaprids, B. melitensis infected cattle and B. meliten-sis-infected caprids), are given in the SI.Although the model is a stochastic one, the model

formulation matches the process of data simulation.

Consequently, it should allow us to retrieve thecoefficient values used to generate the contributionsof the different infected animal populations to theprobability of human infection. In addition, to veri-fying that we could retrieve the coefficient valuesused for simulations, we further determine the good-ness-of-fit by evaluating its fit to the data, confi-rming that we can recover the generated animalpopulation sizes and prevalence values, and bychecking convergence of the model. Further detailsare given in the SI.

RESULTS

Brucella field survey: GLM analysis

The GLM analysis revealed no statistically signifi-cant relationships between the human seropreva-lence and the predicted number of seropositivecaprids or cattle at the household. The coefficientestimates and standard errors (S.E.) obtained in themodel are given in Table 1.

Serology model applied to the field data

The results of the serology model analysis of the fieldstudy data suggest that the model was appropriate todescribe animal and human Brucella infection. Thesimilarity of the mean seroprevalence values perhousehold of each host population calculated fromthe data (Fig. 4 black bars) and those estimatedfrom our serology model (Fig. 4 blue bars) is an in-dication of good model fit. Later we also show thatwe can retrieve all the coefficients used in the simu-lations with our model. Although the mean sero-prevalence estimated for humans was somewhatunderestimated by the model, the estimated credibleintervals fall within the standard deviation (S.D.) ofthe data, which has a large variability.Caprids were identified by our model as the main

source of human infection in northern Tanzania fielddata. This is shown in Fig. 4 (right panel). The pos-terior distribution of the coefficient governing thecontribution of Brucella seropositive caprids tohuman infection probability [blue, i.e. β2,h in equa-tion (5)] is 95% above zero. In comparison the distri-bution for seropositive cattle [red, i.e. β1,h inequation (5)] is below or close to zero (Fig. 4, rightpanel).Our results showed increased infection probability

in larger livestock populations. All the coefficientsgoverning the contribution of cattle (Fig. 5, red)and caprid (Fig. 5, blue) household population sizeto the probability of infection in cattle [β’s fromequation (1); Fig. 5 left panel] and caprids [β’sfrom equation (2); Fig. 5 right panel] were positive(i.e. at least 95% of the credible interval of each pos-terior distribution is greater than zero). However,the caprid population size (blue) seems to have a

7Integrating data to quantify cross-species transmission

greater positive influence on both caprid and cattleinfection probability than cattle population size(red).

Simulation study

The results of the serology only analysis of the simu-lated data (Fig. 6) show that the ability of our modelto accurately quantify the contribution of differentlivestock populations to human infection probabilitydepends on the epidemiological scenario and the

population structure of the animal hosts involved.Figure 6 shows the posterior distributions of the coeffi-cients governing the influence of seropositive cattle(red) and caprids (blue) upon the probability ofhuman infection [β’s in equation (5)] from each com-bination of the different epidemiological scenarios(rows) and population structures (columns). If thecoefficient value used in the simulations (indicated asa small vertical bar on the x-axis) falls within the re-spective posterior distribution, and the 95% credibleintervals are above zero, it suggests that the model

Table 1. Summary of the GLM analysis examining the relationship between human Brucella seroprevalenceand the seropositive population size of caprids and cattle at each household in the Tanzanian field dataset

Variable Coeff. S.E. P Odds ratio 95% CI

Predicted no. positive caprids 0·016 0·017 0·35 1·02 0·98–1·051Predicted no. positive cattle −0·018 0·029 0·54 0·98 0·93–1·039

Fig. 4. Results from the serology model on the Brucella field survey data. The left panel shows the raw meanseroprevalence per household, per species (with associated S.D.; black) and the equivalent model estimated means(with associated 95% credible intervals; blue). The right panel shows the posterior distributions of the coefficientsgoverning the contribution of cattle (β1,h in red) and caprids (β2,h in blue) to the probability of human infection in northernTanzania.

Fig. 5. Model estimates for the influence of animal population size on infection probability of cattle (β1,c & β2,c, left panel)and caprids (β1,s & β2,s, right panel).

8Mafalda Viana and others

accurately quantified the contribution to human in-fection probability. However, if the 95% credibleintervals cross zero, it suggests that the host popula-tion associated with that coefficient was not found tohave a significant contribution to human infection.Nonetheless, only when the posterior is flat (typicallynot visible in our plots), or 95% below zero can we becertain that the host does not contribute to humaninfection.Figure 6 suggests that in positively correlated host

populations (e.g. Population structure 1) it isdifficult to identify the main host source of human in-fection from serology alone, as the posteriors for bothcaprids and cattle are similar, highly variable and their95% credible intervals crosses zero. In epidemiologic-al scenario 2 the contribution of caprids was accurate-ly quantified but cattle are still (wrongly) implicatedin human infection in some cases. In contrast, in thepresence of an uncorrelated host population(Population structure 2), serology data alone seem tobe sufficient to identify the host sources and quantifytheir contribution to human infection, in all epi-demiological scenarios investigated. For moderatelycorrelated host populations (Population structure 3)our results show that the main source of human infec-tion (i.e. caprids in all cases) is accurately identifiedand its contribution to human infection is quantifiedreasonably well (although sometimes the mean isslightly overestimated). However, the contribution

of cattle to human infection is only accurately quan-tified in epidemiological scenario 1. In epidemiologic-al scenarios 2 and 3, the 95% credible intervals of theposterior distributions governing the effect of cattleon human infection (red) cross zero, but their largepercentage above zero are sufficient to consider themas potential sources. However, while cattle are cor-rectly identified as a potential contributor to humaninfection in epidemiological scenario 3 (but with thewrongmagnitude of contribution), in epidemiologicalscenario 2 cattle should not have been identified at all.The results of the extended model implemented

with 50, 10 or 5% of genetic-typing data are shownin Fig. 7. Given the low resolution achieved by theserology data alone for population 1, results for thispopulation are not included in the figure. The poster-ior distributions in Fig. 7 correspond to the estimatedcoefficients governing the influence of B. abortus andB. melitensis-positive cattle (grey and red, respective-ly) andB. melitensis in caprids (green) upon the prob-ability of human infection [i.e. α’s in equation (6)] foreach combination of epidemiological scenario (rows)and population structure (columns). We note thatB. abortus in caprids is never visible as this patho-gen–host combination never occurs, and hence itsposterior distribution is null. This figure shows thateven a small amount of genetic-typing data (e.g. 5%)is enough to empower serology data to distinguishthe pathogen species being transmitted by the

Fig. 6. Posterior distributions of the coefficients governing the effect of Brucella-seropositive cattle (β1,h in red) andcaprids (β2,h in blue) on the probability of human infection. These posteriors were obtained from the serology onlymodel applied to each combination of the epidemiological scenarios and population structures used for simulations. Smallvertical lines on the x-axes correspond to the coefficient values used for simulation.

9Integrating data to quantify cross-species transmission

different hosts (see change in colour of the posteriordistributions from Figs 6 and 7).The integration of genetic-typing data with ser-

ology data also enables effective discriminationbetween infectious and exposed hosts. This isvisible in the results of the extended model for

epidemiological scenario 2, population structure 3,where the posterior distribution governing the ap-parent contribution of positive cattle to human infec-tion probability (simulated with an effectively zerocoefficient value) is no longer visible (as it was forthe serology model, see red in Fig. 6).

Fig. 7. Posterior distributions for the coefficients describing the contributions of different infected animal populations tothe probability of human infection from the model integrating genetic and serology data (α1,h in green, α3,h in red and α4,hin grey), with decreasing levels of genetic-typing data (50% in top row, 10% in middle row and 5% in bottom row withineach epidemiological scenario). Small vertical lines on the x-axes correspond to the coefficient values used for simulation.

10Mafalda Viana and others

The estimated host contributions from theextended model with 50% genetic-typing data aresimilar to those from the serology only model.However, as we decrease the amount of data to 10and 5% the uncertainty in the coefficient estimatesincreases (as seen by the increasingly wide posteriordistributions in Fig. 7). In examples such as epi-demiological scenario 3 and population structure 3,we may lose completely the ability to identify a par-ticular source of infection (see flattening of red pos-terior distribution for 5% of genetic-typing data).The amount of genetic-typing data necessary toadd value to serology data depends on the populationstructure and epidemiological scenario. However,our results suggest that a sample of genetic-typingdata from 5 to 10% of the serology samples may besufficient to achieve the epidemiological insightsthat can be gained from the integration of thesetypes of data.We note that our model sometimes overestimates

the coefficient value governing the influence ofcaprids orB. melitensis-positive caprids in human in-fection (see median of posterior distributions com-pared with simulated values). Nonetheless, thesesimulated values fall within the posterior distribu-tion, which indicates that the model accuratelydescribes the data but may require further informa-tion to reduce uncertainty.

DISCUSSION

To effectively control infectious diseases, we mustclearly identify the infecting pathogen(s) and distin-guish between multiple possible epidemiologicaltransmission scenarios. This need provided the mo-tivation to explore the circumstances under whichformal integration of data can improve our under-standing of the disease dynamics. We use brucellosisin northern Tanzania as a case study, to develop amodelling framework that enables the accurate iden-tification of the host and pathogen-specific source(s)of human infection, and show that integration ofmultiple types of data is a powerful technique. Thevalue of this approach is most clear under two cir-cumstances: (1) when more than one pathogen ispresent and serology data give a compound re-presentation of the presence of multiple pathogens;and (2) the distinction between exposed and infec-tious individuals is not clear from serology data.Brucellosis is transmitted to humans by animals.

Animal vaccines exist but are host and pathogenspecies-specific, and bothB. abortus andB. melitensisspecies have been isolated in Tanzania (Bouley et al.2012; Mathew et al. 2015). This makes clear under-standing of the multi-host infection dynamics critic-al, and without using the data integration approachesdeveloped here we risk selecting inappropriatecontrol options for brucellosis. The results of thisstudy show that the animal population that

constitutes the source of human Brucella infectioncan often be accurately identified with serologydata alone. However, integration of genetic-typingdata is essential to distinguish which pathogen gen-erates the exposures observed. For example, theresults shown in Fig. 7 (but not Fig. 6) for epidemio-logical scenarios 1 and 3 have quite different implica-tions for the control measures that might be appliedin these two situations. In both cases, caprids andcattle contribute with similar magnitudes to humaninfection. However, integration of genetic-typingdata enables distinction of the contribution ofB. abortus-positive cattle in scenario 1 from theB. melitensis-positive cattle in scenario 3. Second,in epidemiological scenario 2, cattle can be exposedto B. melitensis transmitted from caprids, but arenot infectious to humans. In contrast, in epidemio-logical scenario 3 both caprids and cattle cantransmit infection to humans. The difference in theroles played by the cattle in these two scenarios isfundamental, yet it is very difficult to discern usingserological data only and the model can only dothis in the more segregated populations. Despitethe precision of the estimated coefficients decreasingwith decreasing amounts of typing data, integrationof even a small amount of genetic-typing data (e.g.5%) enables the model to distinguish these two situa-tions, so that it does not estimate a contribution ofcattle to human infection probability in any of thescenario 2 datasets.The improvement brought by the integration and

the amount of genetic-typing data is influenced byboth population structure and epidemiological scen-ario. The analyses of the simulated datasets indicatethat when two host populations are clearly segre-gated, the analysis of serology data alone issufficient to accurately identify the host source(s)of human Brucella infection under all of the epi-demiological scenarios considered. However, withincreasing levels of correlation between the twohost populations it is increasingly difficult to identifythe source of human infection from serology alone.Integration of genetic-typing data becomes morevaluable in these more complex scenarios. It is alsopossible that with higher forces of infection (e.g.larger coefficients), some of the effects would beeasier to capture. However, we have deliberatelyused coefficients of biologically plausible magnitudefor the Brucella case study.The specific results of the case study, i.e. from the

brucellosis field serology survey; are consistent withthe transmission processes illustrated in the epi-demiological scenario 2. For northern Tanzania,caprids were estimated to be the most likelysource of human Brucella exposure and the size ofthe household caprid population is a key driver ofinfection probability in both animal hosts.Current understanding of the host–pathogen pre-ferences of Brucella suggests that B. melitensis is

11Integrating data to quantify cross-species transmission

the most likely pathogen to be transmitted fromcaprids to humans (World Health Organizationet al. 2006). However, only integration of genetic-typing data can confirm this hypothesis. The nega-tive coefficient estimated for the contribution ofBrucella-positive cattle to human infection couldindicate some kind of protective effect of owning in-creasing numbers of cattle upon human probabilityof Brucella exposure. Cattle ownership is linked tosocioeconomic status in the study area, which maylead to indirect impacts upon Brucella exposureprobability. A preference for consumption of cowmilk could also mean that consumption of milkfrom caprids would become increasingly unlikelyin households with more cattle. However, thereal-world plausibility of these explanationsrequires further investigation. We also cannot ruleout the possibility that the estimation of this appar-ent protective effect may arise from an influence ofpopulation structure (e.g. posterior for epidemio-logical scenario 3 & population structure 3 peaksat positive values but a large portion is seen also atnegative values).All seroprevalence values estimated by the model

for the field dataset are well within the S.D. seen inthe data. However, we note the slight underestimateof the mean seroprevalence per household inhumans from the model estimate as compared withthe observed data. The credible interval of themodel estimate is also considerably narrower thanthe equivalent S.D. in the observed data. This under-estimation of both prevalence and uncertainty couldsuggest that in addition to the risk posed by the pres-ence of infected animals within the household envir-onment, human infection could also be influenced,to a greater extent than is true for cattle and capridsthemselves, by factors external to the household en-vironment that are currently not included in themodel. Plausible factors would include for examplethe consumption of milk from animals from otherhouseholds within the same village, district or region.The results implicating caprids as the main source

of human Brucella infection were not revealed by astandard GLM analysis. This is likely due to amore appropriate handling of the uncertainty struc-ture in the Bayesian model as compared with theGLM. This may be particularly important for theestimation of the main risk factor, i.e. seropositiveanimal population size. The Bayesian approachallows propagation of uncertainty from individualanimal samples to human inferences in the wholepopulation. While intuitively this should just leadto a higher uncertainty in the final estimates,because the processes defined for animals andhumans are interlinked and their inference aremade simultaneously, they can inform each otherand empower individual inferences.In addition to identifying sources of infection,

the modelling framework implemented here can

highlight other aspects of the underlying disease dy-namics. Strong associations between human andanimal Brucella seropositivity at the householdlevel have been recorded previously in East Africanpopulations (Osoro et al. 2015). However, otherstudies have found no associations (Zolzaya et al.2014) or positive correlations are identified butonly at larger spatial scales (Bonfoh et al. 2012).Increased correlation between human and capridseroprevalences is also seen when our dataset isaggregated at the village and district levels as com-pared with the correlation seen at the householdlevel (Shirima, 2005). The question of the optimalspatial unit to consider is likely to vary between set-tings and depend to a large extent on livestock man-agement and animal–human interaction practices.The degree to which household herds are managedas closed units or mixed with herds from neighbour-ing households or villages, and the degree to whichhumans interact with their own animals only or thewider population, either directly or indirectly (e.g.through milk consumption), will impact on thescales at which transmission occurs. Althoughdifferent patterns of correlation will be seen indifferent settings, questions about the linksbetween infection in different hosts and the spatialscale at which any links occur are consistentthemes in the recent brucellosis literature and callshave been made for greater efforts to understandthe complexities of brucellosis transmission at theanimal–human interface at different scales (Zolzayaet al. 2014).In this study, we used household as the spatial

scale for the analysis but the models could beapplied at different spatial scales, or alternativelyextended to include more complex spatial structur-ing providing flexibility for application to a rangeof settings. For example, we may wish to add arandom effect of village to the linear predictors to es-timate how much variability in the mean prevalenceis driven by a sample being collected in a specificvillage (or village-associated factors) instead of ahousehold. Other options, such as a full nested hier-archical model (e.g. household within villages,within regions) may also be desirable and arestraightforward extensions to our model. Theunderestimation of the variance in the human sero-prevalence at household level may reflect theabsence of extra-household influences (associatedwith spatial effects mentioned above) on human in-fection probability in our model. As the network ofthe trade of dairy products become increasinglycomplex and involves more actors across greaterspatial scales, the spatial scales that are importantfor understanding human brucellosis infection riskmay increasingly become distinct from the scales atwhich transmission between cattle and capridsoccurs. Other useful extensions to this modelling ap-proach would be the inclusion of a formal distinction

12Mafalda Viana and others

between the various forms of missing values (e.g.non-typed animals vs typed but undetected patho-gen, or sensitivity/specificity in the typing data)and improvements of the observational process toinclude estimation of the timing of exposure (Vianaet al. 2015).Here we focused on a case study of Brucella in

northern Tanzania; however, our findings and chal-lenges are applicable to many systems, in whichthere is considerable need to clearly identify theinfecting pathogen(s) that impact on human health(as well as livestock productivity) and to distinguishbetween a single or multi-pathogen transmissionepidemiological scenario. Our results show that ourmethod for integrating multiple types of data ispowerful and that important enhancements tounderstanding of underlying infection dynamicscan be made through the integration of just smallamounts of genetic typing data.

SUPPLEMENTARY MATERIAL

To view supplementary material for this article, pleasevisit http://dx.doi.org/10.1017/S0031182016000044

ACKNOWLEDGEMENTS

We would like to thank Adrian Whatmore, LorrainePerrett, Alistair Macmillan and others at the Animal &Plant Health Agency, UK (previously Animal Health &Veterinary Laboratories Agency) for diagnostic testingsupport.

FINANCIAL SUPPORT

This work was supported by the Biotechnology andBiological Sciences Research Council (UK Zoonoses andEmerging Livestock Systems) (grant number BB/L018845/1) and a Leverhulme-Royal Society AfricaAward (AA130131). SC, DTH and JEBH also receivedfunding support from the Biotechnology and BiologicalSciences Research Council (grant number BB/J010367/1)and JB and RK also received support from the BBSRCZELS grant BB/BB/L018926/1. The collection and sero-logical analysis of the Tanzanian field samples was sup-ported through a grant from the DfID Animal HealthProgramme, R7985, ‘Investigating the impact of brucel-losis on human health and livestock health and reproduc-tion in Tanzania’.

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