Inside-Out: Modeling the link between Zika virus viral dynamics within hosts and transmission to vectors across host species and virus strains

Inside-Out: Modeling the link between Zika virus viral dynamics within hosts and transmission to vectors across host species and virus strains | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Inside-Out: Modeling the link between Zika virus viral dynamics within hosts and transmission to vectors across host species and virus strains View ORCID Profile Hélène Cecilia , Benjamin M. Althouse , View ORCID Profile Sasha R. Azar , View ORCID Profile Shannan L. Rossi , View ORCID Profile Nikos Vasilakis , View ORCID Profile Kathryn A. Hanley doi: https://doi.org/10.1101/2025.04.23.650178 Hélène Cecilia 1 Department of Biology, New Mexico State University , Las Cruces, NM 88003, USA 2 Oniris, INRAE, BIOEPAR , 4433 Nantes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hélène Cecilia For correspondence: helene.cecilia{at}inrae.fr Benjamin M. Althouse 1 Department of Biology, New Mexico State University , Las Cruces, NM 88003, USA 3 Information School, University of Washington , Seattle, WA 98105, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sasha R. Azar 4 Department of Pathology, University of Texas Medical Branch , Galveston, TX 77555-0609, USA 5 Center for Tissue Engineering, Department of Surgery, Houston Methodist Research Institute, Houston Methodist Hospital , Houston, TX 77030, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sasha R. Azar Shannan L. Rossi 4 Department of Pathology, University of Texas Medical Branch , Galveston, TX 77555-0609, USA 6 Department of Microbiology and Immunology, Unviersity of Texas Medical Branch , Galveston, TX 77555, USA 7 Center for Vector-Borne and Zoonotic Diseases, University of Texas Medical Branch , Galveston, TX 77555-0609, USA 8 Institute for Human Infection and Immunity, University of Texas Medical Branch , Galveston, TX 77555-0610, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shannan L. Rossi Nikos Vasilakis 4 Department of Pathology, University of Texas Medical Branch , Galveston, TX 77555-0609, USA 7 Center for Vector-Borne and Zoonotic Diseases, University of Texas Medical Branch , Galveston, TX 77555-0609, USA 8 Institute for Human Infection and Immunity, University of Texas Medical Branch , Galveston, TX 77555-0610, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nikos Vasilakis Kathryn A. Hanley 1 Department of Biology, New Mexico State University , Las Cruces, NM 88003, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kathryn A. Hanley Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Epidemiological models of mosquito-borne virus transmission often lack accurate estimates of host-to-vector transmission probability. Here, we estimated this probability for two strains of Zika virus (ZIKV)—one sylvatic and one human-endemic—from two monkey species to Aedes albopictus mosquitoes using experimental infection data. Viral dynamics did not differ between monkey species, although one (cynomolgus macaque) is a native ZIKV host and the other (squirrel monkey) a novel host, but differed between strains, with viremia for the human-endemic strain peaking later and lower than the sylvatic strain. Only the sylvatic strain was transmitted to mosquitoes. In mosquitoes, anatomical barriers influence viral progression to salivary glands, complicating host infectiousness estimation. We quantified the probability of viral dissemination to the legs in Ae. albopictus , which increased with host viral load and was higher after feeding on squirrel monkeys than on cynomolgus macaques. We also found a positive relationship between virus titer in mosquito legs and virus detection in saliva after a 14-day extrinsic incubation period. Combining these factors, we found that squirrel monkeys were on average 1.5 times more infectious to Ae. albopictus than cynomolgus macaques. These estimates will help assess ZIKV’s potential to establish an enzootic, sylvatic cycle in the Americas. Introduction Understanding the factors that influence pathogen transmission is essential for predicting and controlling the spread of infectious diseases. In particular, estimating infectiousness—the probability that an infected host transmits a pathogen—is crucial for developing accurate epidemiological models [ 1 ]. Modeling at the within-host scale has been employed to derive infectiousness estimates of SARS-CoV-2 and influenza in humans [ 2 ], foot-and-mouth disease virus in cattle [ 3 ], and influenza virus in pigs [ 3 ], among others. For vector-borne pathogens, the infectiousness of one host to the next also depends on the vector that bridges the two hosts, as different vector taxa can vary in their competence. Infectiousness estimates exist for dengue virus (DENV) from humans to Aedes aegypti mosquitoes [ 4 ], Rift Valley fever virus from cattle, sheep, and goats to Aedes spp. and Culex spp. mosquitoes [ 5 ], Usutu virus from birds to Culex quinquefasciatus mosquitoes [ 6 ], and Zika virus (ZIKV) from humans to Aedes albopictus mosquitoes [ 7 ]. For mosquito-borne viruses, it is commonly assumed that within-host viral load is positively related to the probability of host-to-vector transmission [ 8 ], although some counter examples have been observed (e.g. DENV in [ 9 ]). In the vector, a virus must overcome sequential anatomic barriers (i.e, the midgut infection barrier, the midgut escape barrier, and the salivary gland infection and escape barrier) before becoming infectious [ 10 , 11 ]. Experimental constraints can make it difficult to gather enough information to characterize host infectiousness with regard to capacity to generate saliva-positive vectors over the complete course of host infection. Hence, existing estimates of host infectiousness are often limited to vector infection or disseminated infection [ 4 , 5 , 7 ]. Besides, transmission experiments sometimes deliver virus to hosts via needle rather than the bite of an infected vector [ 5 , 6 ], or use artificial blood meals to infect vectors [ 7 ], which makes their infectiousness estimates less reflective of the natural transmission arc. The current study focuses on Zika virus, which originated in a sylvatic cycle involving non-human primate (NHP) hosts and arboreal Aedes mosquitoes in Africa [ 12 , 13 ]. It eventually spilled over into humans, establishing human-endemic transmission in the paleotropics mediated by Ae. aegypti and Ae. albopictus [ 14 ]. In addition to its role in epidemics, Ae. albopictus may also contribute to both spillover and spillback, as a bridge vector [ 15 , 16 ]. ZIKV reached the Americas early in the 2010s, resulting in widespread human infections [ 17 ]. It is not yet known whether the virus will establish new sylvatic cycles within American NHP populations [ 13 , 18 , 19 ]. Should it do so, control of ZIKV in the Americas will become more complicated and eradication will likely become impossible. In this context, it is crucial to explore the potential for novel monkey hosts to sustain ZIKV transmission. To this end, we aimed to compare the infectiousness of native and novel monkey host species, resulting from the coupling between their within-host viral dynamics and the associated transmission to Ae. albopictus , following experimental infection with ZIKV strains derived from sylvatic [ 9 ] and human-endemic cycles (this paper). Here, we used these data for phenomenological modeling at the within-host scale to characterize viral load dynamics of ZIKV, focusing on the impact of monkey species, viral strain, initial viral dose received, and inter-individual heterogeneity. We then described host-to-vector transmission occurring during those experimental infections, using dose-response relationships, for disseminated infection and saliva infection of Ae. albopictus . Finally, we estimated the infectiousness profile of native and novel monkey hosts over the course of their infection, which can readily be used by models of ZIKV transmission dynamics at the population scale. Material and Methods Ethics statement Our study complies with all relevant ethical guidelines and all community standards for containment of infected arthropod vectors; all procedures conducted on non-human primates were approved via UTMB Institutional Animal Care and Use Committee (IACUC) protocol 1912100, approved on December 1, 2019. Overview of the experiment Here we leverage data from previous experimental infections of 3 (1M, 2F) adult cynomolgus macaques ( Macaca fascicularis ) and 10 (5M, 5F) adult squirrel monkeys ( Saimiri boliviensis ) with sylvatic Zika virus strain DakAr 41525 (GenBank accession number EF105379.1 ) delivered by the bites of batches of 15 Ae. albopictus mosquitoes, described in Hanley et al. 2024 [ 9 ] (Figure S1). Infecting mosquitoes had been inoculated intrathoracically with the virus [ 9 ]. We also leverage data from another arm of the study (Figure S1), not previously reported, in which 4 adult cynomolgus macaques (2 males, weighing 4.7 and 5.2 kg at the start of the experiment, and 2 females, 2.8 and 2.9 kg at the start of the experiment) were infected using identical methods with human-endemic ZIKV strain PRVABC-59 (passaged six times in Vero cells, GenBank accession number KU501215 ). The number of mosquitoes salivating virus and the total dose of virus delivered was estimated by forced salivation conducted 2 days after infected mosquitoes fed on monkeys; the values for ZIKV DakAr 41525 and PRVABC-59 can be found in Supplemental Table S1. Quantification of viremia from serum as well as feeding of uninfected Ae. albopictus mosquitoes on each animal was done regularly between infection and 28 days post-infection [ 9 ] (Figure S1). Viral load was measured by serial dilution of sera which were used to infect Vero cells (CCL-81) followed by immunostaining to yield measurements in plaque forming units (PFU, [ 9 ]). Engorged mosquitoes from all feeding days were tested for presence of virus in their bodies and legs, 14 days after feeding (unique extrinsic incubation period (EIP) tested), as described in [ 9 ]. Mosquito saliva was collected and tested only for those mosquitoes that fed on days 3 and 4 (sylvatic strain) or 3, 4, and 5 (human-endemic strain). The viremia of monkeys over time can be found in Supplemental Table S2 for the ZIKV sylvatic strain and Supplemental Table S3 for the ZIKV human-endemic strain. Of the four cynomolgus macaques infected with human-endemic ZIKV (FR1565), one is excluded from the analyses presented in this paper as it never became detectably viremic nor transmitted to mosquitoes. Data on transmission to mosquitoes can be found in Supplemental Table S4, for ZIKV sylvatic strain only. Indeed, we only detected one disseminated infection in mosquitoes for the human-endemic strain of ZIKV, and no positive saliva, so we did not analyze this dyad further for host infectiousness. Within-host viral dynamics To describe within-host viral dynamics, we used a phenomenological model adapted to capture the pattern of acute, short-lived infections, which relied on the simple equation [ 3 , 20 ] : where viral load V at time t is expressed as a function of peak viral load V p on a log 10 scale, time of peak T p , and rates of the exponential growth ( l g ) and decay ( l d ) phases. We fitted this equation to viral load data from all monkeys simultaneously, using a non-linear mixed effect modeling approach. We allowed all four parameters to vary with monkey species, viral strain, and dose delivered to the monkeys (fixed effects on three covariates) as well as between individuals (random effects). We used the stochastic approximation expectation maximization (SAEM) algorithm, implemented in the R package saemix, version 3.3 [ 21 ]. Parameters were assumed to follow a log-normal distribution, meaning that parameters θ i of individual i can be expressed as θ . e f i , with θ representing the population-level estimate, and f i = c i . β + η i , with c i representing the subject-specific covariates, β the fixed effects, and η i the random effects, which follow a multinormal distribution η ~ N (0, ω ). In addition, we used a constant residual error model. Data below the limit of detection of the assay was set arbitrarily as half the limit of detection if transmission to mosquitoes was detected, 0 otherwise. Further details about the fitting procedure and the computation of uncertainty can be found in Supplemental text S1. Vector infection As mosquito saliva was only tested on a subset of days, we could not estimate host infectiousness over their whole infection period in a single step. Instead, we first analyzed the drivers influencing ZIKV disseminated infection in mosquitoes on all days post infection (dpi), and we then explored the relationship between disseminated infection and ZIKV presence in mosquito saliva, to ultimately infer the whole dynamics of host infectiousness. Disseminated infection We investigated the combined effects of host viral load, host species, and dpi, on the probability of disseminated infection in vectors ( P leg ). This probability was defined as the ratio of virus-positive legs over the total number of mosquitoes tested. Sometimes, the ratio of virus-positive legs over the number of positive mosquito bodies is used to measure dissemination [ 22 ], but in our case an important proportion of mosquito legs were positive when the corresponding bodies were negative [ 9 ], which made this ratio uninformative. We performed a model selection procedure similar to what was conducted by Lambrechts et al. [ 23 ], to allow non-linear effects (Supplemental text S2.1). A similar approach was used to model the value of the virus titer measured in mosquito legs ( V leg ). Probability of ZIKV presence in saliva We then explored the relationship between V leg and the probability of ZIKV being detected in that mosquito’s saliva ( P saliva ), both measured after the same EIP. To do so, we selected among three functional forms with a sigmoidal shape, and between the use of a binomial or a betabinomial likelihood, the latter accounting for overdispersion in the data ([ 9 ], Supplemental text S2.2). The selection was done based on the corrected Akaike Information Criterion (AICc, the lowest AICc was selected). Host infectiousness over time We expressed host infectiousness over time as variations of the transmission rate which, in vector competence studies, is defined as the proportion of saliva-positive mosquitoes among all mosquitoes tested. To do so, we combined the different steps presented above, with the associated uncertainties, as presented in Figure S2. Results Within-host viral dynamics Dose delivered to the monkeys had no significant effect on the parameters of Eq. 1 (Table S1). Below, we present the results of a model including only monkey species and viral strain as covariates. Viral strain had a significant effect on V p and T p ( Table 1 ). Indeed, in macaques, the human-endemic strain of ZIKV induced significantly lower and later peak titers than the sylvatic strain ( Figure 1 C,E ). Monkey species had no significant effect, as macaques and squirrel monkeys infected with the sylvatic strain showed similar viral dynamics ( Figure 1 A,C ). View this table: View inline View popup Table 1: Results of the non-linear mixed effect model of within-host viral dynamics, testing the effect of monkey species and viral strain. Parameter estimates (relative standard error, %) p value (for covariates species and strain only). Squirrel monkeys infected with the sylvatic strain of ZIKV are taken as the reference. SD: standard deviation, dpi: days post infection. Download figure Open in new tab Figure 1: Data and model fit for within-host viral dynamics. A-B: squirrel monkeys infected with sylvatic ZIKV. C-D: cynomolgus macaques infected with sylvatic ZIKV. E-F: cynomolgus macaques infected with human-endemic ZIKV. Left column (A,C,E) shows the group-level model fit (line) and associated parameters. The shaded band is the confidence interval of the fixed effects. Right column (B,D,F) shows the individual fits, uncertainty, and associated parameters. Points are raw data. As no transmission to mosquitoes was detected with the hu7man-endemic strain in cynomolgus macaques, we do not analyze this dyad further for host infectiousness. LOD = limit of detection. There was very little variability among the three macaques infected with the sylvatic strain ( Figure 1 D ) and more inter-individual variability among the two other groups ( Figure 1 B,F ). l d and T p were the most and least variable parameters, respectively (Table S2). Vector infection Disseminated infection The model selected to describe P leg included a non-linear, non-decreasing effect of viremia, a non-linear, non-monotonic effect of day post infection, and an effect of monkey species ( Figure 2 , Supplemental text S2.1.1). Viral load had a significant effect (p = 9.5e-12, Figure 2 A,B ). Monkey species had a significant effect (p = 2.5e-5), with transmission from squirrel monkeys being more efficient than from cynomolgus macaques (OR = 4.20, 95% CI [2.15;8.18], Figure 2 A,B ). Download figure Open in new tab Figure 2: Probability of ZIKV disseminated infection in Ae. albopictus as a function of viral load (A,B), day post infection (C,D), and monkey species (A,C; B,D). Each circle represents a single batch of mosquitoes fed on a given host on a given day, and the size of the circle is proportionate to the number of mosquitoes analyzed. Lines represent the regression fits to the data. Lines and data are color coded according to day post infection (A,B) or host viremia (C,D). In D, note that several points are overlapping for days 4 and 5 and dissemination probability of 1, corresponding to viremia ranging between [ 4 ; 6 ] log 10 PFU/ml. Although day post infection did not have a significant effect (p = 0.065), an interesting pattern emerged, with P leg possibly maximized at 4 dpi even when controlling for viremia ( Figure 2 C,D ). We note that the significance of dpi varied from one model to the next in our selection process, and even among models with a non-linear, bell-shaped effect of dpi, 2 out of 4 models tested estimated the effect of dpi to be significant. A simple linear model was selected to describe V leg as a function of host viremia, day post infection, and monkey species (Supplemental text S2.1.2). Viremia was the only variable with a significant effect (p = 7.6e-10). We noticed a strong heterogeneity of leg titers, particularly when host viral load was between 4 and 5 log 10 PFU/ml, for both monkey species (Figure S3A). This heterogeneity would not be sufficiently captured in the selected model, as viremia only explained 25% of the variance. To remain simple, for the estimation of host infectiousness, we decided to sample V leg in the whole distribution of leg titers obtained in our experiment. This distribution was bimodal, with frequent sampling around 2.5 and 5.7 log 10 PFU/ml (Figure S3B). Probability of ZIKV presence in saliva The relationship between V leg and P saliva was best described using a logistic equation with binomial likelihood (Eq. S1), with log ( β 0 ) = 3.47 [2.75; 4.04] and β 1 = 0.85 [0.56; 1.18] ( Figure 3 ). This relationship corresponds to mosquito infections arising from both monkey species. Since the distribution of leg titers was similar regardless of the transmitting species (Figure S3C) we fitted the relationship for both species combined. Download figure Open in new tab Figure 3: Relationship between ZIKV titer measured in mosquito legs, and probability of mosquito saliva testing positive for ZIKV. Points show raw data, colored by monkey species, each point representing a single mosquito. The line shows the best fit, obtained with Eq. S1 and a binomial likelihood. The shaded band shows the associated 95% confidence interval. Host infectiousness over time The combination of the different steps resulted in the estimation of transmission rates in Ae. albopictus over time when feeding on each monkey species infected with sylvatic ZIKV. Squirrel monkeys were overall more infectious than cynomolgus macaques, mainly because their transmission rate increased earlier in infection ( Figure 4 ). This was due to viral loads reaching levels above LOD one day earlier in squirrel monkeys than cynomolgus macaques (Figure S4A), and further exagerated by more efficient dissemination in Ae. albopictus after feeding on squirrel monkeys than cynomolgus macaques (Figure S4B). Download figure Open in new tab Figure 4: Infectiousness of squirrel monkeys and cynomolgus macaques towards Ae. albopictus over time. Lines show the mean transmission rate (probability of mosquito saliva testing positive for ZIKV over the total number of mosquitoes fed) and shaded bands are the associated 95% confidence interval. Comparing the area under the curve (AUC) of their infectiousness profiles, we estimated that squirrel monkeys were on average 1.53 times more infectious (95% confidence interval [1.42; 1.63]) than cynomolgus macaques. This is assuming both species are subject to similar contact rates with vectors. The maximum transmission rate was similar for both species (mean 0.56 [0.45; 0.66] for cynomolgus macaques, 0.59 [0.49; 0.68] for squirrel monkeys, Figure 4 ), due to a common sampling distribution for V leg and P saliva . This maximum was reached at 4 dpi ( Figure 4 ). As a conservative estimate, we also derived the infectiousness profiles assuming viral loads below LOD would not give rise to any infection (Figure S5). The ratio of AUC between species remained similar (1.49 [1.39; 1.59]). Discussion In this study, we leveraged data from an experiment coupling ZIKV infection of monkeys with transmission to Ae. albopictus vectors to derive estimates of monkey infectiousness over time. The experiment was replicated across two species of monkey, one (cynomolgus macaque) a native host of the virus and one (squirrel monkey) a novel host with potential to serve as a New World reservoir in the future, as well as two strains of the virus, one each from the sylvatic and human-endemic ecotype [ 24 ]. We found that monkey species had no significant effect on within-host viral dynamics, while virus strain did, with the human-endemic strain inducing later and lower viremia peaks than the sylvatic strain. Moreover, the human-endemic strain failed to transmit to Ae. albopictus mosquitoes, but the sylvatic strain robustly transmitted. For the sylvatic strain, host viral load and host species both influenced disseminated infection in mosquitoes ( P leg ), with more efficient transmission at higher viremia, and from squirrel monkeys than cynomolgus macaques. Turning to infectious mosquitoes, we found a positive relationship between V leg and P saliva after an EIP of 14 days. Taken together, these results allowed us to conclude that squirrel monkeys were overall 1.5 times more infectious than cynomolgus macaques towards Ae. albopictus , when infected with a sylvatic strain of ZIKV, while the trend in human-endemic ZIKV could not be investigated due to a dearth of transmission. The transmission rates estimated here can readily be used to improve population-level models, such as Althouse et al. [ 18 ]. This underscores the utility of the framework we have developed, which exploits all aspects of the experimental design to provide an accurate picture of monkey infectiousness over time. Indeed, had we only focused on within-host viral dynamics, we would have concluded that squirrel monkeys and cynomolgus macaques were likely to infect mosquitoes similarly, which would have been a mistake. Of note, quantifying viral titer in mosquito legs was instrumental for us to link the different components of infectiousness, but this type of measure is not done systematically in vector competence studies. In the future, our framework can easily be applied to similar experiments involving other arbovirus, hosts, or vectors. The modeling techniques we used were adapted to the quantity of data available, but do not provide mechanistic insight to explain observed differences between viral strains and monkey species. The equation used to describe within-host viral dynamics has the advantage of only having four parameters, which can all be estimated at the individual and population levels. In comparison, even simple, target-cell limited, mechanistic within-host models, with no compartments for the immune response, which have been used in previous studies of within-host ZIKV dynamics [ 7 , 25 – 27 ], have 4 to 5 parameters and 2 initial conditions. When these are fitted to datasets of viral loads over time, some of these parameters and initial conditions must be fixed, sometimes arbitrarily or with the help of the literature, which can limit the mechanistic insight gained. When the objective is to capture the overall dynamics to accurately estimate host infectiousness over time, as was the case here, phenomenological modeling is sufficient and easier to implement. If one wishes to dive deeper into the processes of host-pathogen interactions, to explore different immune mechanisms or the effect of treatment, the mechanistic approach may be better suited. This endeavor will however require fitting to data other than viral loads alone [ 20 , 28 ]. Regarding mosquito infection, the development of a dynamic mechanistic model was not possible as we only had data for a unique extrinsic incubation period. Acquiring data at different extrinsic incubation periods usually requires sacrificing mosquitoes, which needs to be accounted for in models and also limits the number of individuals tested per time step. To bypass this issue, possible alternatives consist in monitoring viral shedding in mosquito excreta [ 29 ], or having live mosquitoes salivate on sugar pads [ 30 ]. The human-endemic ZIKV strain used here (PRVABC59) has been used in previous experimental infections of monkeys, albeit comparisons with our study are limited by differences in monkey species used (rhesus macaques Macaca mulatta in [ 25 , 27 , 31 – 35 ], cynomolgus macaques in [ 36 , 37 ]), inoculation route (subcutaneous in all except Dudley et al. [ 31 ] who used both mosquito bite and needle delivery), or type of viremia measured (genome copies in all except Triplett et al. [ 32 ]). Dudley et al. [ 31 ] used mosquito ( Ae. aegypti ) bites to deliver PRVABC59 to rhesus macaques, and measured viremia via qRT-PCR. Similar to our results, they observed that viremia peaked at day 5 or 6 pi. In Dudley et al. however, viremia was detected earlier (from day 1 pi for 3/4 individuals vs never before 3 dpi in our case) and lasted longer (at least until day 8 vs never after day 7 in our case). This likely reflects differences in detectability and persistence between viral genome copies and infectious virus. Indeed, in Triplett et al. [ 32 ], infectious viremia peaked at 2.5-3 log 10 PFU/ml in rhesus macaques, similar to our study, and was resolved by day 5. Dudley et al. noted that peak viremia was reached 1-2 days later with mosquito-bite inoculation compared to subcutaneous inoculation. We observe the same delay when comparing our results to studies subcutaneously inoculating doses between 3 and 4 log 10 PFU ZIKV [ 25 , 27 , 35 , 37 ], which is similar to the dose we estimated was delivered by mosquitoes in the current experiment (min-max [2.80-4.47] log 10 PFU ZIKV). Studies using a sylvatic strain closely related to the one we used (Dakar 41524), all infected pregnant rhesus macaques subcutaneously, and measured viremia through genome copies [ 35 , 38 , 39 ]. Interestingly in a subset of these, Crooks et al. [ 35 ] were able to compare viral dynamics between strain PRVABC59 and Dakar 51424 and observed later and lower peak viremia for PRVABC59, similar to what we found, although these differences were not significant in their case. Regarding the drivers of host-to-vector transmission, we observed a pattern similar to what was recently reported by Lambrechts et al. [ 23 ], who found maximum transmission of DENV from humans to Ae. aegypti at day 2 post symptom onset, after controlling for levels of viremia. In our case, maximum transmission was reached at 4 dpi, but this association was not significant in the final selected model. The fact that this pattern emerged in our study, where we measured infectious viremia, compared to the data in Lambrechts et al., who measured genome copies, suggests that infectivity of viral particles per se is not responsible for this difference in transmission between early and late infection. The mechanistic basis of this phenomenon remains to be elucidated. The likelihood of ZIKV establishing a sylvatic transmission cycle in the Neotropics cannot be inferred from our data alone. The human-endemic strain tested here belongs to the clade circulating in the New World, and did not transmit well to NHPs in the present study. However, at least one study has reported circulation of African lineage ZIKV in Brazil [ 40 ], and sylvatic DENV has been transported multiple times out of both Africa and Asia to other continents [ 41 – 43 ], demonstrating the potential for sylvatic ZIKV to be introduced to the Americas. Furthermore, the sylvatic strain we tested transmitted efficiently between squirrel monkeys and Ae. albopictus , but this is only one host-vector combination out of many possible in the field. The current experimental design already provided evidence that trophic preferences are likely to play a role in transmission dynamics, as engorgement rates were on average higher on cynomolgus macaques than on squirrel monkeys [ 44 ]. Finally, the co-occurrence of vectors and hosts must be mapped at high granularity, and mosquito blood meals must be analyzed to identify which vectors competent for ZIKV feed on both humans and NHPs and the frequency at which they do so. Such studies would need to be stratified vertically, as the community composition at ground level can greatly differ from that at canopy level [ 45 , 46 ]. Acknowledgements This research was funded by grant 1R01AI145918-02 (to K.A.H., N.V., B.M.A., and S.L.R.) and in part by the Centers for Research in Emerging Infectious Diseases “The Coordinating Research on Emerging Arboviral Threats Encompassing the Neotropics (CREATE-NEO)’ ‘grant 1U01AI151807 from the US National Institutes of Health (to N.V. and K.A.H.). Squirrel monkeys acquired from the UT MD Anderson Cancer Center, Michael E. Keeling Center for Comparative Medicine and Research, were supported by the NIH grant 5P40OD010938-36 prior to sale to UTMB. Funding US National Institute of Health, , 1R01AI145918-02 , 1U01AI151807 , 5P40OD010938-36 References 1. ↵ Martin LB et al. 2019 Extreme Competence: Keystone Hosts of Infections . Trends in Ecology & Evolution 34 , 303 – 314 . 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