Downloaded by guest on September 28, 2021 www.pnas.org/cgi/doi/10.1073/pnas.1801095115 a Ezenwa O. Vanessa Gorsich E. Erin and scales individual population at coinfection of outcomes Opposite fia buffalo African that the at pathogens infections level. secondary individual coinfecting facilitates pathogen between one when occurs competition of data-driven a example provide results these Therefore, brucellosis. populations without basic modeled and (R prevalence In number endemic level. reproduction the negative present, population strong was the a brucellosis at where have bTB to on brucellosis model impact of our presence level, bTB individual the Whereas the predicted scales: at population infection impacts brucellosis and opposite facilitated individual predicted at and reproduced coinfection data model of the The in fecundity. was were patterns in infection coinfection infections neither reductions Both and with bTB. survival associated in acquiring between reductions of with association factor risk associated no risk the found a and we as brucellosis but bTB brucellosis, identified indi- acquiring results the for empirical a At esti- our contexts. across to level, environmental brucellosis, vidual and buffalo and demographic African of (bTB) range in tuberculosis bacterial two bovine study with coinfection pathogens, cohort of effects 4-y individual-level the mate a model, this conducted parameterize we produce To these to patterns. how up infection investigate scale population-level to coinfection model of includes consequences the that individual-level used model fecundity, We mortality, a transmission. coinfection: coin- and constructed of effects we of clear individual-level Here, consequences three a lacking. However, population-level is progression. the fection disease of and individual- understanding for risk consequences infection their to 2018) challenge level 20, due January leading health review public a for global (received remain 2018 for pathogens 8, June and approved parasites and Norway, Coinfecting Oslo, Oslo, of University Stenseth, Chr. Nils by Edited and 30602; 30602 GA GA Athens, Athens, Georgia, Georgia, of of University University Medicine, Veterinary of Netherlands; College Ecology, The Groningen, CC 9700 Groningen, 97331; OR hnoeptoe,ifcinwt n ahgnmyb n of one be may more pathogen with one ecosystem with infection an pathogen, in one example, than For envi- variation. and demographic ronmental natural multiple, across synthesizing processes requires individual-level scales these population parasite Bridging (10). and population individual of a within relevant occur patterns mechanisms control while disease for host, dis- the the or within susceptibility that progression—occur to ease changes is resulting their coinfection interaction—and of quences multiple of target success that 9). the (8, programs to infections control fundamental and be treatment therefore, the coinfection integrated may, at of levels effects infections both the of Understanding at 7). spread These (6, the level 5). influence population (4, also influence infection may to of interactions host outcomes clinical the individual-level within the interact that can suggests pathogens evidence Mounting coinfecting (1–3). schistosomiasis HIV, taxa hepatitis-C, and as pathogen TB, such 270 infections, chronic over important includes many and ubiquity Their (1)]. pathogens O eateto imdclSine,Oeo tt nvriy ovli,O 97331; OR Corvallis, University, State Oregon Sciences, Biomedical of Department n hleg opeitn h pdmooia conse- epidemiological the predicting to challenge One eafce ycifcin[ocretifcinb multiple by infection [concurrent coinfection to by estimated is affected population be human global the of one-sixth ver c eateto ilg,Clrd tt nvriy otClis O80523; CO Collins, Fort University, State Colorado Biology, of Department | brucellosis a,b,c,1 aplS Etienne S. Rampal , f,g n naE Jolles E. Anna and , 0 | fbBwr oe hni populations in than lower were bTB of ) tuberculosis | coinfection d e a Medlock Jan , eateto ihre n idie rgnSaeUiest,Crals R97331; OR Corvallis, University, State Oregon Wildlife, and Fisheries of Department a | competition a ran .Beechler R. Brianna , 1073/pnas.1801095115/-/DCSupplemental hsatcecnan uprigifrainoln at online information supporting contains article This 1 the under Published Submission. Direct PNAS a is article This paper; interest. of the conflict wrote data. no A.E.J. ana- the declare collected authors and A.E.J. A.E.J. The V.O.E., and and V.O.E., R.S.S., V.O.E., R.S.S., J.M.S., J.M.S., B.R.B., B.R.B., B.R.B., E.E.G., J.M., E.E.G., J.M., research; and R.S.E., R.S.E., designed E.E.G., E.E.G., A.E.J. research; data; and lyzed performed V.O.E., J.M. J.M., and R.S.E., R.S.E., E.E.G., contributions: Author t optoe neatos hlegn oe eeomn and development model complex- and challenging increased interactions, (14). brings pathogen (1) host to patterns ity the coinfections infection in alter of presence because protracted dramatically majority Their to context the Chronic potential this the for with 22). in have responsible coinfection ref. interest are see of particular they of (but effects are limited the In coinfections are on infections. infections theory immunizing long-lasting and acute, child- data for of contrast, dynamics database understanding data-driven coinfection detailed a a of providing on thereby infections, builds hood work theoretical 18–21). (15, This mortality disease-induced and mechanisms convalescence dramat- ecological as can such through (6, coinfection dynamics pathogens infection pathogens, modify coinfecting unrelated ically for by Even generated 17). nonlinear dynamics 16, of potentially range these the of effects 15). net (14, processes the individual-level are coinfection by 13). of the influenced (4, consequences infection population-level moderate of the costs also However, decreased fecundity or and may survival increased a pathogens individual-level in with Coinfecting infection resulting for 12), transmission. risk (11, individual-level pathogen of second predictors best the owo orsodnesol eadesd mi:[email protected] Email: addressed. be should correspondence whom To o te hoi,imnsprsieptoessc sHIV as consequences such TB. pathogens or important immunosuppressive chronic, have other could for of which generality mechanism, the assessing this recommend We convalescence. or host, occurs the within and competition resource mechanisms cross-immunity, without described first previously unique the with is competition of compared of prevalence mechanism This the reduced. is pathogen, pathogen second level. the pro- and population of transmission gression the the infections both at facilitates pathogen The compete one When study coinfection. this of level in transmission characterized population consequences individual-level the mortality the at and both dynam- prediction quantifying coinfection accurate chronic requires that of shows model ics the data-driven and infec- Our long-lasting common one tion. least is at involve species coinfections of parasite majority multiple with Infection Significance ttepplto ee,tertclsuishv highlighted have studies theoretical level, population the At b eateto nertv ilg,Oeo tt nvriy Corvallis, University, State Oregon Biology, Integrative of Department d rnne nttt o vltoayLf cecs nvriyof University Sciences, Life Evolutionary for Institute Groningen g eateto netosDsae,Cleeo eeiayMedicine, Veterinary of College Diseases, Infectious of Department a oaneM Spaan M. 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ECOLOGY evaluation. Detailed longitudinal sampling or experimental stud- dynamics at the population level. In addition, we assess the rel- ies are required to unravel their precise mechanisms and poten- ative importance of each individual-level process on coinfection tially asymmetric outcomes of interaction (22). Few datasets dynamics. simultaneously estimate the individual-level transmission, sur- vival, and fecundity consequences of coinfection. To address Results this gap, we provide a data-driven investigation of coinfection Individual-Level Consequences of Coinfection: Model Parameteriza- dynamics for chronic pathogens. tion. bTB and brucellosis were associated with multiplicative We focus our research on two chronic bacterial infections, increases in mortality (Fig. 2A and SI Appendix, section 1, Table bovine tuberculosis (bTB) and brucellosis, in a wild popula- S1). Approximate annual mortality rates in the data were 0.056 tion of African buffalo (Syncerus caffer) to ask: How do the (10 mortalities/175.75 animal years) in uninfected buffalo, 0.108 individual-level consequences of coinfection scale up to pro- (6 mortalities/55.5 animal years) in buffalo with bTB alone, duce population-level infection patterns? This system allows us 0.144 in buffalo with brucellosis alone (13 mortalities/90.5 ani- to simultaneously monitor both individual and population lev- mal years), and 0.21 in coinfected buffalo (9 mortalities/43.8 els of the infection process (4, 7) in a natural reservoir host (23, animal years). After accounting for environmental and demo- 24). Furthermore, bTB and brucellosis have well-characterized graphic covariates with a Cox proportional hazards regression and asymmetric effects on the within-host environment. bTB is model, bTB was associated with a 2.82 (95% CI 1.43–5.58)- a directly transmitted, life-long respiratory infection that causes fold increase in mortality, and infection with brucellosis was dramatic and systemic changes to host immunity (25). African associated with a 3.02 (95% CI 1.52–6.01)-fold increase in mor- buffalo infected with bTB have reduced innate immune function tality compared with uninfected buffalo. Coinfected buffalo were and increased inflammatory responses (4). Conversely, brucel- associated with an 8.58 (95% CI 3.20–22.71)-fold increase in losis is a persistent infection of the reproductive system. It mortality compared with uninfected buffalo (Fig. 2A). Mortal- persists within phagocytic cells (26), and although infection also ity rates were also influenced by buffalo age and capture site, but invokes an inflammatory response, it is less severe and more the effect of coinfection remained consistent across all ages and localized compared to the immune response to bTB (27). These in both sites. Neither infection was associated with reductions in differences and our ability to observe the natural history of both fecundity (described in detail in SI Appendix, section 1, Fig. S1). infections make bTB and brucellosis an ideal system to explore Uninfected buffalo were observed with a calf 68% (11/16) of the disease dynamics across scales. time compared with 37% (6/16), 29% (7/24), and 57% (4/7) in Our approach combines a mathematical model of the coinfec- bTB-positive, brucellosis-positive, and coinfected adult buffalo. tion dynamics of bTB and brucellosis and a 4-y cohort study of The consequences of coinfection on infection risk were 151 buffalo (Fig. 1). For this model, all parameters describing the asymmetric, with bTB facilitating brucellosis infection but not consequence of coinfection were estimated from field data; they vice versa (Fig. 2B and SI Appendix, section 1, Table S2). include the individual-level, per capita consequences of coinfec- Approximate brucellosis incidence rates were 0.05 (18 infec- tion on mortality, fecundity, and infection risk. We quantified tions/340 animal years) in uninfected buffalo compared with these parameters by tracking the individual infection profiles of 0.08 (8 infections/104 animal years) in buffalo with bTB (SI each buffalo, which were monitored at approximately 6-mo inter- Appendix, section 1, Fig. S2). Approximate bTB incidence rates vals and resulted in over 4,386 animal months of observation time were 0.08 (27 infection/340 animal years) in uninfected buffalo from two capture sites. We show that the model accurately repro- and 0.07 (9 infections/138 animal years) in buffalo with bru- duces observed coinfection patterns and use the model to predict cellosis. After accounting for demographic covariates in a Cox the reciprocal effects of brucellosis and bTB on each other’s proportional hazards regression model, brucellosis infection risk

Fig. 1. Conceptual diagram of the data, model, and evaluation. (Center) A schematic representation of the disease model defined in SI Appendix, section 2. Hosts are represented as susceptible (S), infected with bTB only (IT ), infected with brucellosis only (IB), coinfected with both infections (IC ), persistently infected with brucellosis only but no longer infectious (RB), and persistently infected with brucellosis but no longer infectious and coinfected with bTB (RC ). (Left) A detailed cohort study informs model parameterization by quantifying the mortality, transmission, and fecundity consequences of coinfection (Right) as well as the transmission parameters for both infections. (Right) The prevalence plot illustrates that the model accurately reproduces coinfection patterns in the data. The bars represent the proportion of single (S) and coinfected (C) individuals in the model results and the solid circles represent the data.

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R in without number and reproduction with basic and lence osc tal. et Gorsich in reductions large in results single a by population. generated susceptible cases a secondary in of infection number average the as data the in observed brucellosis 1, and (Fig. bTB between S4 Table association 0.0013; transmission 2, brucellosis, bTB section with buffalo Appendix, 1.20; in SI and bTB, buffalo with brucellosis uninfected in 0.576; buffalo rate buffalo, in (brucel- uninfected rate parameters in transmission rate S4). rate transmission Fig. transmission prevalence resulting brucellosis 2, losis and The section 27% after 34%. was model Appendix , data was the the (SI in from equilibrium prevalence sample bTB reached age-matched had an errors in in it squared prevalence and of infection data brucellosis sum and our the bTB overall minimizing the by between brucellosis and bTB model into translated were analyses data parameters. coinfec- our of in consequences quantified the tion how defines compared S4 and and parameterization bTB buffalo. S3 uninfected with Tables for 2, buffalo section rate for transmission represent rate the transmission to spec- with we higher example, bTB, a with For buffalo ify in sites. risk infection across brucellosis increased bru- risk on early bTB infection in of effect brucellosis cellosis average acquiring the and and of buffalo risk reproductive-aged 1 increased (Fig. represents there- parameterization, fore, coinfection model disease Our of 2). pre- section into Appendix, effects above population-level quantified effects dicted individual-level the translate Reproduction Prevalence. Basic and Coinfection: Number of Consequences Population-Level age. risk. an infection buffalo identified increased an and also had risk buffalo model reproductive-aged infection Early regression brucellosis The between S2). association Table 1, (inter- section bTB site for other a the term to at site risk action one higher at 1.51–12.37)-fold CI change (95% no and 4.32 from bTB ranging between risk association infection infection the brucellosis with brucellosis site, and capture bTB by associa- varied with The risk infection brucellosis. prior with between bTB buffalo tion with and buffalo buffalo similar uninfected in was risk in higher infection bTB times buffalo. susceptible 0.89–4.91) with compared CI (95% 2.09 was with line dashed risk. infected The in buffalo. are change uninfected buffalo no in indicates risk for when the risk estimates to or relative infection predicted pathogen bTB another in The or change (B) brucellosis proportional buffalo. with proportional the uninfected infected the to are for relative SE buffalo and coinfected when median mortality predicted in The change (A) study. cohort the of 2. 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ECOLOGY risk (e.g., transmission rate) and mortality consequences of the second pathogen, the latter via mortality. When infection coinfection to explore other environmental contexts where the with one pathogen modifies only the transmission or only the individual-level effects of coinfection may be reduced or exacer- mortality rate of the second pathogen, the prevalence of the bated (Fig. 4). For two pathogens, A and B, the results suggest second pathogen predictively increases or decreases (Fig. 4 that pathogen A will have a negative effect on the prevalence of and SI Appendix, section 2, Figs. S7 and S8). Previous work pathogen B if coinfected individuals have elevated mortality and has quantified the disease-dynamic consequences of changes in infection with pathogen A results in reduced or similar suscep- transmission through a range of mechanisms: cross-immunity, tibility to pathogen B. In contrast, pathogen A is predicted to antibody-mediated enhancement, immunosuppression, and con- have a positive effect on the prevalence of pathogen B if infec- valescence (16, 20, 28, 29). Here, we show that transmission tion results in an increased transmission rate for pathogen B and and mortality should be considered concurrently, following the- minimal changes in mortality with coinfection. When coinfection oretical predictions (14, 22, 30). When pathogens modify both is associated with changes in both the transmission and mortality processes, nonlinear responses mediated through the coinfect- rates, the population-level consequences of coinfection depend ing pathogen can have a large impact on population-level disease on the type of pathogen considered. Specifically, bTB preva- dynamics. lence is lower in modeled populations with brucellosis for most By exploring the coinfection dynamics of bTB and brucellosis, parameter values while the effect of bTB on brucellosis is more we also provide a data-driven example of competition between variable. pathogens in a natural population. Here, the mechanism driv- At the parameter values quantified in our empirical dataset, ing competition is different from previously described examples these results illustrate that the lower bTB prevalence in popula- that focus on cross-immunity (29), resource competition within tions where brucellosis co-occurs is driven by two mechanisms: the host (31, 32), or ecological competition by convalescence (i) bTB is associated with increases in the transmission rate of (20, 21). The mechanism of parasite interaction in these exam- brucellosis but not vice versa and (ii) coinfection is associated ples occurs when one infection reduces the transmission of the with increased mortality. As a result, at the individual level, second pathogen. By contrast, in our study system, we did not buffalo infected with bTB are more likely to become infected see a reduced transmission rate for bTB or brucellosis during with brucellosis and die than their uninfected counterparts. The coinfection. Individuals infected with bTB were associated with a resulting reductions in infection duration mean that the pres- higher rate of acquiring brucellosis in at least one of our sites but ence of brucellosis is predicted to reduce bTB prevalence at the appeared to have no effect in the other site. Brucellosis appeared population level. These results are robust to several important to have no effect on the transmission of bTB (Fig. 2). Because changes in the model structure, including alternative forms of coinfection was associated with elevated mortality, coinfected density dependence, a range of values for the model param- individuals were also removed from the population at a faster eters (SI Appendix, section 2, Figs. S6–S8), and density- vs. rate. Competition, therefore, occurs at the population level: bTB T frequency-dependent transmission terms. Model dynamics in all is predicted to have a lower prevalence and lower R0 in pop- formulations are qualitatively similar, although there is some ulations where brucellosis occurs compared with populations variation in overall magnitude of change with coinfection. without brucellosis. The model structure in this study is informed by our empirical Discussion data. As a result, it incorporates realistic age-specific transmis- Our study provides a mechanistic understanding of how chronic sion and mortality rates as well as data-driven estimates of the coinfections mediate each other’s dynamics. Model dynamics consequences of coinfection. However, additional detail could show that a pathogen can increase or decrease the preva- be added to our model. Specifically, we do not know the con- lence of a second pathogen, depending on the net effect of sequences of either infection on the other’s infection duration infection on the transmission rate and infection duration of or infectiousness, two processes likely to influence persistent infections (18, 22). We also do not consider genetic variation within our buffalo population that may mediate susceptibility to either pathogen. However, our model’s ability to accurately rep- resent coinfection patterns with the mechanisms characterized suggests that we have captured the most important processes. Furthermore, our empirical results account for natural variation in demographic and environmental conditions. Thus, our results highlight the importance of coinfection in generating population- level association patterns relative to environmental or genetic drivers of infection. Given the ubiquity and documented individual-level impacts of chronic coinfections on the host, these results highlight two core challenges in the design and application of integrated con- trol strategies. First, it remains unclear how commonly competi- Fig. 4. The difference between predicted (Left) bTB or (Right) brucellosis tion between coinfecting pathogens is occurring. Understanding prevalence values in populations where one or both pathogens are present. which pathogens may be competing in coinfected host popula- The axes represent a range of transmission rate and mortality consequences tions is crucial to estimating the costs and benefits of disease of coinfection. Proportional increases in mortality represent the mortality control interventions. For example, in the presence of pathogen rate in coinfected individuals divided by the rate in susceptible individuals. competition, removing one pathogen may unintentionally lead Proportional increases in the transmission rate represent the transmission to a resurgence of or increases in prevalence of a competing rate of the focal pathogen in individuals infected with the second pathogen pathogen. Our results suggest that competition at the population divided by the transmission rate of the focal pathogen for susceptible indi- level can occur between unrelated pathogens and in the absence viduals. Red indicates that the prevalence of the focal pathogen is higher of competition for shared resources within the host. Competition in populations where the second pathogen is present; blue indicates that the prevalence of the focal pathogen is lower in populations where the sec- appears to be strongest when pathogens have asymmetric effects ond pathogen is present; yellow indicates no change. Contour lines indicate on transmission. Similar asymmetries in transmission occur in changes in prevalence by 20%. Circles and error bars indicate median and HIV–malaria (6) and HIV–HCV coinfections (33), suggesting a SE parameter values estimated in the data. role for this mechanism in other systems.

4 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1801095115 Gorsich et al. 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(to for 016.140.616 Organization Grant Vici Netherlands - Improvement and (NWO) Dissertation E.E.G.), Research Doctoral by (to NSF DEB-121094 Graduate supported and NSF Grant V.O.E.), was Program and Fellowship study EF- A.E.J. Research Grants (to This Diseases EF-0723928 Infectious support. and of Buss, 0723918/DEB-1102493 Ecology technical P. (NSF) comments Foundation and for thank Science group National manuscript Depart- We laboratory Webb Services Kruger. the the Wildlife and in on Veterinary Schrama M. study SANParks thank entire We this ment. the conduct and Hofmeyr, to M. permission their for ACKNOWLEDGMENTS. used calculated was (47). pathogens We method both 1). of (Fig. prevalence overall fitting the for only pat- as coinfection function. data, recreate the to this in ability minimize age-matched its terns algo- to comparing in by R Nelder–Mead model in our and the function evaluated used We data optim We the the with model. implemented in the rithm from brucellosis prevalence and of bTB preva- estimates the for between estimates differences squared lence of sum the minimizing numerically R ecluae rvlnei h oe fe thdrahdequilibrium reached had as it prevalence after I bTB model representing the (46). package in by deSolve prevalence the calculated using numerically We calculated were prevalence of .HtzP,e l 20)Icroaigarpdipc akg o elce tropical neglected for package rapid-impact a Incorporating (2006) al. et tuberculosis PJ, with Hotez therapy antiretroviral 9. of Integration (2011) al. et SS, Karim microbial Abdool on treatment anthelmintic 8. of effects Opposite (2015) AE Jolles VO, Ezenwa 7. B C + easse h fet fcifcino einmraiyrtsand rates mortality median on coinfection of effects the assessed We + children. Parasitology in malaria. and tuberculosis, HIV/AIDS, for programs with diseases IMJAp Math Appl J SIAM population. mouse wild a in crashes population. wildlife a in treatment. scales. population versus individual at infection R I B B n rcloi rvlneas prevalence brucellosis and ) + R rcRScLn ilSci Biol B Lond Soc R Proc B .Tetasiso ae fbt ahgn eeetmtdby estimated were pathogens both of rates transmission The ). nlJMed J Engl N dRlisnD(cdmc xod,Vl8,p 321–369. pp 82, Vol Oxford), (Academic, D Rollinson ed , 66:843–872. etakSuhArcnNtoa ak (SANParks) Parks National African South thank We Science 365:1492–1501. -oitrasutlJn–coe 02 Dur- 2012. June–October until intervals ∼6-mo 330:243–246. aaee ausfrtetasiso rate transmission the for values Parameter 280:20122813. R nmEcol Anim J 0 ueial sn h next-generation the using numerically π π T B = = (I (I Science T 77:370–377. B + + NSLts Articles Latest PNAS I R C B + 347:175–177. + eto 3. section Appendix, SI R I C C )/(S + .Mdlestimates Model ). LSMed PLoS R C + )/(S I T + + I 3:e102. C I Advances T | + + f6 of 5 R I C C + +

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