Subpopulationb	Median age (yr) (IQR)c	Total no. of individuals tested for ZIKV by ELISA	ZIKV IgM		ZIKV IgG		ZIKV PRNT		CHIKV IgG		DENV IgGd
			n	%	n	%	n/total no.	%	n/total no.	%	n/total no.	%
HIV patients
2013	36.7 (16.4)	96	0	0	7	7.3			7/96	7.3	52/84	61.9
2014	38.8 (17.8)	89	0	0	2	2.3			6/89	6.7	57/82	69.5
2015	36.6 (17.4)	92	2	2.2	16	17.4			1/92	1.1	46/68	67.6
Total retrospective		277
HIV patients 2016	44.7 (15.4)	263	2	0.8	139	52.9	31/61	50.8	22/263	8.4	88/110	80.4
MC pregnancies 2015–2016	28.5 (10.8)	19	1	5.3	18	94.7	14/15	93.3	3/19	15.8	0/1	0
Non-MC pregnancies 2015–2016	28.9 (10.9)	257	1	0.4	178	69.3	114/171	66.6	15/257	5.8	52/69	75.4
Tuberculosis patients 2016	45.1 (22.2)	55	2	3.6	47	85.5	14/20	70	4/55	7.3	8/8	100
University employees 2016	33.8 (12.3)	39	2	5.1	19	48.7	14/32	43.8	3/39	7.7	8/18	44.4
Total 2015–2016		633	8	1.3	401	63.3	187/299	62.5	47/633	7.4	156/206	75.7
Total study		910

Subpopulation	No. of specimens within subpopulations	No. of specimens tested by PRNT (%)	ELISA result	No. of specimens with the following PRNT result:		% specimens with divergent ELISA/PRNT results	Sensitivity (95% CI)	Specificity (95% CI)	PPV (95% CI)	NPV (95% CI)
				+	−
MC pregnancies	19	15 (78.4)	+	14	0	0	1 (0.76−1)	1 (0.25−1)	1 (0.76−1)	1 (0.25−1)
			−	0	1	0
Non-MC pregnancies	257	171 (66.5)	+	105	21	12.3	0.92* (0.85−0.96)	0.63* (0.49−0.75)	0.83* (0.76−0.89)	0.80* (0.65−0.90)
			−	9	36	5.3
HIV patients	263	61 (23.2)	+	26	6	9.8	0.83* (0.66−0.95)	0.80* (0.63−0.92)	0.81* (0.65−0.93)	0.83* (0.64−0.94)
			−	5	24	8.2
Tuberculosis patients	55	20 (36.4)	+	14	0	0	1* (0.76−1)	1* (0.54−1)	1* (0.76−1)	1* (0.54−1)
			−	0	6	0
University employees	39	32 (82.1)	+	12	1	3.1	0.86* (0.57−0.98)	0.94* (0.73−0.99)	0.92* (0.64−0.99)	0.89* (0.67−0.99)
			−	2	17	6.3
Total specimens	633	299 (47.2)	+	171	28	9.4	0.91* (0.86−0.95)	0.75* (0.66−0.83)	0.86* (0.80−0.91)	0.84* (0.75−0.90)
			−	16	84	5.4

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^aThe positive (+) and negative (−) ELISA and plaque reduction neutralization test (PRNT) results are shown. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), and adjusted Wald confidence intervals (95% CI) are shown; values with an P value of <0.0001 are indicated by an asterisk. MC, microcephaly.

TABLE S2 

Gender distribution in prospectively sampled subpopulations. The proportions of male and female individuals and results of serological tests for Zika virus within subpopulations are shown. Download TABLE S2, DOCX file, 0.02 MB.

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TABLE S3 

Age distribution of study participants compared to the population of Salvador, Brazil. The proportions of participants in different age groups comprised in this study compared to the proportion of the same age groups within the general population of Salvador, as well as the results of serological tests for Zika virus, dengue virus, and Chikungunya virus per age group are shown. Download TABLE S3, DOCX file, 0.02 MB.

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Differential spread of ZIKV and CHIKV.

The emergence of CHIKV in the Americas parallels that of ZIKV spatiotemporally with the introduction and transcontinental spread in 2013 to 2014, and both viruses use Aedes mosquitos as vectors (23,–25). However, CHIKV seroprevalence remained consistently low in HIV patients during 2013 to 2016 at 1.1 to 8.4% (χ² = 5.9; P = 0.12; Fig. 3B, light gray), reaching an overall seroprevalence of 7.4% across all study groups in 2016 (CI, 5.6 to 9.8%; Fig. 3D and Table 1). Our CHIKV seroprevalence estimate was consistent with that of an independent study from Salvador, Brazil (26). Our data thus suggested an accelerated dissemination of ZIKV compared to CHIKV in Salvador, Brazil.

Low rate of acute ZIKV infections in 2016.

Cases of acute exanthematic disease, the lead symptom of ZIKV infection in adults, reported in the Bahia state within the Brazilian surveillance system SINAN were retrieved and compared to our observations. Of the 67,454 cases reported in Bahia during 2015, 35,261 originated from Salvador (52.3%) (Fig. 3F, blue line). The first year of the ZIKV outbreak in Bahia was thus concentrated in Salvador. In contrast, in 2016, 59,054 ZIKV cases were reported all over Bahia (Fig. 3F, red line) of which only 928 cases originated from Salvador (1.6%). To see whether the decline of notified cases from Salvador in 2016 could be confirmed by laboratory tests, we tested all specimens for ZIKV RNA and ZIKV IgM. None of the specimens tested positive for ZIKV RNA. In agreement with the high ZIKV seroprevalence from 2015 onwards, IgM-based incidence was detected only in 2015 (2.2%; CI, 0.1 to 8.0%) and 2016 (1.3%; CI, 0.6 to 2.5%) (Fig. 3F, arrows). Until mid-2017, the number of reported cases remained consistently low from both Salvador and the state of Bahia. This suggests that the outbreak ceased due to the lack of acute cases.

Modelling the trajectory of the epidemic in Salvador, Brazil.

To test whether population immunity would limit future cases in Salvador, Brazil, we fitted a mathematical model of ZIKV transmission jointly to the independent case notification data from Salvador and the seroprevalence results from our study. Our results showed that the observed data are consistent with a single-year continuous epidemic that began early in 2015 and declined toward the end of 2015 (Fig. 4A and ​andBB).

FIG 4 

Transmission model and projected trajectory of the Zika epidemic in Salvador, Brazil. (A) Model fit to ZIKV incidence in Salvador. The red circles show the reported ZIKV cases. The black line shows the median model estimate. The shaded regions depict the interquartile range and 95% CI. (B) ZIKV seroprevalence over time in the study population (n = 633). The black line shows the median model estimate. The shaded regions depict the interquartile range (IQR) and 95% CI. The red circle shows the observed proportion of seropositive individuals. (C) Estimated seasonal variation in ZIKV transmission. (D) Estimated change in effective reproduction number over time.

The estimated basic reproduction number (R₀) for ZIKV was 2.1 (CI, 1.8 to 2.5) at the onset of the outbreak with, on average, 2.0% (CI, 1.8 to 2.2%) of ZIKV infections reported in the national surveillance system. Projecting the model forward into 2016 suggested a continued decline in transmission despite the return of peak arbovirus season. Due to the lack of susceptible individuals, the effective reproductive number (R_eff) was not predicted to exceed one in subsequent years, a condition required for another ZIKV epidemic wave (Fig. 4C and ​andDD).

Impact of SES.

To further investigate the impact of low SES on ZIKV infection rates, the home addresses of study participants were georeferenced onto 147 spatial units classified into human development units (HDUs) according to sociodemographic characteristics (Fig. 5A and Fig. S1). HDUs showing ZIKV-positive cases represented significantly lower SES in 65 (32.2%) of 201 indicators (Table S4). The latter included all 56 available population indicators, as well as less regular garbage recollection, a higher proportion of child and youth labor, inferior schooling, and lower income in ZIKV-positive HDUs (Fig. 5B shows the most significant indicators per category). No significant differences were observed regarding the occurrence of microcephaly in HDUs. Logistic regression analyses were conducted to identify which SES-associated indicators were most associated with ZIKV-positive HDUs. However, the high degree of multicollinearity between sociodemographic indicators prevented model convergence.

 

FIG 5 

Association of socioeconomic status and ZIKV exposure. (A) Maps showing Brazil, the state of Bahia, metropolitan Salvador, and sample distribution onto human development units (HDUs). (B) Sociodemographic indicators differing significantly between ZIKV-positive and ZIKV-negative HDUs. Boxplots show medians, interquartile range (box length), outliers (circles), and extreme values (squares). Values that are significantly different are indicated by bars and asterisks as follows: *, P ≤ 0.05; **, P < 0.01. (C) Distribution of samples used for nested case-control study. ZIKV-positive and -negative cases and microcephaly pregnancies (stars; n = 11) are shown. One additional case was outside the area shown in the map. Seven other cases were insufficiently georeferenced. Due to geographic proximity of home adresses of some controls, not all 72 controls are visible.

TABLE S4 

Association between ZIKV infections and sociodemographic indicators. Comparison of sociodemographic indicators between those human development units (HDU) comprising households of individuals testing positive and those HDUs comprising households of individuals testing negative for Zika virus infection in a serological assay (NS1 antigen ELISA). Download TABLE S4, DOCX file, 0.05 MB.

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Finally, a nested case-control approach was conducted to investigate whether low-SES-associated indicators influenced the occurrence of microcephaly independently of ZIKV infection. To that end, six pregnant women matched for age (within 2 years) for each of 12 microcephaly cases living in HDUs within metropolitan Salvador, Brazil, were chosen, and the sociodemographic indicators of the respective HDUs were attributed to cases and controls (Fig. 5C). Only the ZIKV serostatus differed significantly between cases and controls (χ² = 4.1; P = 0.043), in contrast to the sociodemographic indicators.

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DISCUSSION

Here we present the results of what is, to the best of our knowledge, the first arboviral seroprevalence survey in Latin America since the beginning of the Zika epidemic. We demonstrate a high ZIKV infection rate of about 63% in Salvador, the third-largest Brazilian city with about 2.7 million inhabitants in northeastern Brazil. This rate was comparable to the 66 to 73% seroprevalence found on Yap, Micronesia, and French Polynesia, although these ZIKV outbreaks occurred in 10- to 300-fold smaller island populations (10, 27). The similar seroprevalence rates suggest effective ZIKV spread irrespective of different geographic settings.

The reasons for the differential spread of ZIKV and CHIKV in Salvador, Brazil, remain unclear. Hypothetically, the faster spread of ZIKV might be associated with viral properties affecting transmission. However, a putative replicative advantage of ZIKV over CHIKV in Brazilian Aedes mosquitos is not warranted by vector competence studies (28, 29). Similarly, increased availability of ZIKV to mosquito vectors during feeding on viremic humans is unlikely, since viral loads can be considerably higher in CHIKV infections than in ZIKV infections (5). An alternative explanation may include amplification of CHIKV in sylvatic cycles prior to its putative introduction into urban cycles in Salvador, Brazil. However, whether CHIKV may enter a sylvatic cycle in the Americas remains to be determined (30). Finally, whereas sexual transmission of ZIKV may have contributed to its initial spread, the predominant route of transmission likely remains vector-borne, opposing a relatively faster spread of ZIKV due to sexual transmission (31). So far, the most plausible explanation may include differences in the geospatial introduction of CHIKV and ZIKV within northeastern Brazil. Indeed, the main foci of CHIKV infections in the Brazilian state of Bahia were initially centered in the hinterland, whereas ZIKV may have been directly introduced to the densely populated Atlantic coast, including Salvador, facilitating efficient spread in relatively larger, more connected human populations (32, 33).

Our modeling estimates of the basic reproduction number R₀ were lower than in estimates for Pacific island populations (34) but consistent with recent estimates from several independent studies (8, 31, 35). Moreover, our data and modeling projections suggest that ZIKV was able to reach the critical population immunity threshold within a single year and that community protective immunity could restrict ZIKV spread in this area until susceptible individuals are replaced by birth or migration. This finding is consistent with the near-complete lack of reported cases from Salvador, Brazil, since 2016 and with previous model-based projections that predicted the cessation of the current Latin American outbreak within the next few years (8). The limitation of ZIKV spread due to community protective immunity is probably analogous to CHIKV, because both viruses show limited antigenic variability. Consistent with our data, CHIKV infection rates exceeding 60% have been associated with the cessation of outbreak activity (36). In Africa and probably in Asia as well, CHIKV can emerge cyclically from nonhuman primate reservoirs upon replenishment of sufficient numbers of susceptible individuals (36). Whether ZIKV can establish a sylvatic transmission cycle in Latin America thus requires urgent investigation (37).

The high rate of ZIKV-positive mothers of microcephaly cases in our study substantiates the recent case-control study from Recife, Brazil (18) in identifying ZIKV as the cause of the surge in microcephaly cases in northeastern Brazil. Additionally, our data enable more precise risk estimates of congenital ZIKV disease. In the absence of serological data, the risk of fetal microcephaly upon maternal ZIKV infection in the first trimester has previously been modeled across a seroprevalence interval spanning 10 to 80% (10). According to that study (10), the 63% seroprevalence rate found in this study implies a risk of fetal microcephaly in Bahia of about 1% during the first trimester. This risk is analogous to the 0.95% risk modeled for French Polynesia assuming a similar ZIKV infection rate of 66% (27) and similar to the 1.7% prevalence of microcephaly found in ZIKV-infected mothers in a cohort study in French Guiana (38).

Finally, our results suggest an impact of low SES on the probability of ZIKV infection. Whether the increased ZIKV infection rate correlates with increased risk of microcephaly remains to be determined, but it is in line with anecdotal evidence from the Brazilian Ministry of Health (39). Our data correspond to a previous study demonstrating higher DENV infection rates in lower social strata from northeastern Brazil (22). However, other etiologic factors associated with low SES remain to be determined in large prospective epidemiological studies, including detailed assessments of individual-level determinants of SES, exhaustive assessments of infectious and noninfectious causes of congenital malformations, clinical symptoms other than microcephaly, and differences in access to abortion practice between different social strata in Latin America, which may cause a relatively higher incidence of neonates with malformations in lower social strata because higher social strata may have a relatively easier access to antenatal care, including imaging techniques allowing premature identification of malformations leading to abortion practices (40,–42). Of note, our data may imply that individuals and areas with a relatively higher SES may represent a potential reservoir for focal reemergence of ZIKV in Salvador, Brazil. However, whether high-SES strata may represent a sufficient community size to allow ZIKV resurgence in Salvador remains to be determined.

The strengths of our study include the large sample from different subpopulations that can identify key variations in transmission rates, the longitudinal analysis of patients before, during, and after the Zika outbreak, the multidisciplinary approach allowing insights into geospatial and sociodemographic factors affecting ZIKV exposure, and the comparison of seroprevalence of multiple arboviruses using a range of laboratory tests. A principal limitation of this study is the availability-based sample of individuals which may not be representative of the general population. However, the age distribution of individuals across the pooled samples was comparable to that of the general population, and infection rates in pregnant women were comparable to the overall seroprevalence from the combined subpopulations. Finally, seroprevalence results were comparable to (i) the independent case data from Salvador, Brazil, (ii) previous ZIKV seroprevalence surveys in other areas, and (iii) the seroprevalence results for DENV and CHIKV in other settings, suggesting that our study is robust despite our nonsystematic sampling design. Importantly, our seroprevalence data enabled an estimate of R₀ that was highly consistent with estimates from other studies not containing serological information from the current American outbreak (8, 31, 35). The similarities between those modeling approaches and our data were thus supportive of the appropriateness of our data set. However, a principal challenge to our study arises from the high levels of cross-reactivity of antibodies elicited by different flaviviruses in serological tests, limiting the ability to obtain unequivocal serological results (43). Previous studies assessing the specificity of the NS1-based ELISA we used in our study yielded conflicting results (15, 44). However, the majority of studies aiming at test validation investigated patients with acute febrile illness and included only a few or no sera from individuals living in areas where DENV is endemic, limiting the ability to extrapolate results from those studies to our study population. Recent studies investigating asymptomatic blood donors from Martinique and Cameroon suggested applicability of the NS1-based ELISA, despite a high DENV burden in these areas (45, 46). Furthermore, our NS1-based ELISA results were largely congruent with PRNT-based analyses conducted within subpopulations. Of note, recent data suggest that PRNT specificity in late convalescent-phase sera may be high enough to retain its utility as a tool for population-level ZIKV serosurveillance (47). In sum, our seroprevalence data for samples collected during four consecutive years, before and during the dissemination of ZIKV in Salvador, Brazil, using two different ZIKV antigens for ELISA, and confirmation of ELISA results by PRNT strongly suggest that our data are valid despite the limitations of any serological investigation of ZIKV-specific antibody responses in areas in which other flaviviruses are hyperendemic. Of note, applicability of the NS1-based ELISA in our population-based study does not translate into a recommendation of its usage for patient diagnostics, which may require further validation and innovative tools that are not yet broadly available, such as a recently published monoclonal antibody-based competitive ELISA (48).

In summary, our data demonstrate high ZIKV infection rates in a Brazilian setting and suggest that the ZIKV outbreak ceased due to community protective immunity. Prevention of congenital ZIKV disease may need to incorporate responses to low SES-associated cofactors in addition to pathogen-oriented measures. Further studies of outbreak settings are urgently needed outside northeastern Brazil to determine whether such explosive and underrecognized ZIKV epidemics have also occurred. Ideally, these studies should include sera from neonates with congenital disease and their mothers sampled early during pregnancy, as well as specimens from adults suffering from severe ZIKV disease to identify whether determinants of severe ZIKV disease are shared among congenital and adult infections.

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MATERIALS AND METHODS

Ethical clearance, sampling sites, and sample storage.

Sampling and testing were approved by the Federal University of Bahia (UFBA) research ethics board Climério de Oliveira under protocol 1.408.499. HIV patients were sampled at the UFBA teaching hospital. Tuberculosis patients were sampled at the José Silveira Foundation-Brazilian Institute for Investigation of Tuberculosis. Pregnant women were sampled at the time of delivery at the UFBA maternity hospital Climério de Oliveira. All patients attended during the study period accepted participation in the protocol. Microcephaly was diagnosed when the measurement of the cephalic circumference was 2 standard deviations below that of the corresponding gestational age, based on intergrowth charts from the World Health Organization in addition to clinical and imaging data as recommended (49).

Laboratory analyses.

All samples were analyzed for viral RNA using real-time reverse transcription-PCR (RT-PCR) assays for ZIKV (5). Serological testing was performed by using enzyme-linked immunosorbent assays (ELISAs) for ZIKV IgM/IgG (Euroimmun, Lübeck, Germany) (NS1 antigen), DENV IgG (Euroimmun) (full virus lysates), and CHIKV IgG (Euroimmun) (recombinant structural protein) according to the manufacturer’s instructions. Briefly, sera diluted 1:101 in sample buffer were added to the wells and allowed to react for 60 min at 37°C. Before IgM detection, sera were preincubated with sample buffer containing IgG/rheumatoid factor absorbent (Euroimmun) to remove class IgG antibodies. Bound antibodies were detected by applying goat anti-human IgM peroxidase conjugate or rabbit anti-human IgG peroxidase conjugate for 30 min at room temperature. The competitive ELISA using a mutant E protein of ZIKV was conducted according to reference 50 for DENV and is described in detail elsewhere (56). Briefly, the quadruple mutant E protein from ZIKV (strain H/PF/2013, E-protein amino acid residues 1 to 406, GenBank accession no. KJ776791) bearing the point mutations T76A, Q77G, W101R, and L107R was expressed in Drosophila S2 cells. Serum samples (diluted 1:100 in 100 µl blocking solution) were preincubated with 2 µg/sample of mutant DENV E proteins (mixture of the four DENV serotypes [50] for 1 h to remove DENV antibodies and/or cross-reacting antibodies). Following the preincubations, samples were transferred to 96-well plates coated overnight with ZIKV mutant E protein (150 ng/well), and the assay was completed following standard ELISA procedures.

Due to 100% cross-reactivity of ZIKV-specific IgG antibodies with the DENV ELISA antigen (43), only clearly ZIKV-negative specimens were used for assessments of DENV seroprevalence (Fig. S2). Plaque reduction neutralization tests (PRNTs) for ZIKV (51) were used for confirmation in 199 ELISA-positive specimens and 100 ELISA-negative specimens from 2016 for which sufficient serum volumes were available (Table 2). All sera were heat inactivated (56°C, 30 min) prior to neutralization testing. Two microliters of serum was diluted in 1% Dulbecco modified Eagle medium (DMEM) at 1:25, 1:250, 1:2,500, and 1:25,000 and incubated at 37°C for 60 minutes with 50 plaque-forming units (PFU) of ZIKV outbreak strain H/PF/2013 resurrected from subgenomic cDNA fragments transfected into BHK cells as described previously (52). A second incubation was done at 37°C for 60 min in 12-well plates, followed by an agarose-DMEM (containing 2% fetal calf serum and 0.6% final agarose concentration) overlay. Cells were incubated for 4 days before formaldehyde fixation, staining with crystal violet, and plaque counting. Serum titers reducing ZIKV PFU by ≥50% compared to controls in any dilution were considered positive. NS1 ELISA ratios of sera tested by PRNT did not differ significantly from those not tested by PRNT (P = 0.20 by t test).

FIG S2 

ELISA ratios. The numbers of sera tested for ZIKV antibodies (NS1 antigen) and CHIKV antibodies were as follows: 96 in 2013, 89 in 2014, 92 in 2015, and 263 in 2016 for HIV-infected patients (HIV); 257 for non-microcephaly pregnancies (PRG); 19 for microcephaly pregnancies (MC); 55 for tuberculosis patients (TBC); and 39 for university employees (UNI). The numbers of sera tested for DENV were 84 in 2013, 82 in 2014, 69 in 2015, and 110 in 2016 for HIV; 69 for PRG, 1 for MC, 8 for TBC, and 18 for UNI. Dashed lines indicate signal-to-cutoff ratios of ≥1.1, which are considered positive, and ratios between 0.8 and 1.1, which are considered borderline by the manufacturer (conservatively considered negative in this study together with all ratios of <0.8). Horizontal lines in plots indicate mean ratios. Only clearly ZIKV-negative specimens yielding signal-to-cutoff ratios of <0.8 as stated in the manufacturer’s instructions were used for assessments of DENV seroprevalence. Download FIG S2, PDF file, 1.4 MB.

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Georeference and demographic data.

The home addresses of study participants were georeferenced onto spatial units (human development units [HDUs]) according to census data from the Instituto Brasileiro de Geografia e Estatística, dividing the metropolitan region of Salvador, Brazil, into socioeconomically homogenous areas, taking into account a minimum of 400 permanent households at a first classification step, and socioeconomic homogeneity at a second step, provided within the Brazilian Human development atlas (http://www.atlasbrasil.org.br/2013/pt/consulta/) from the United Nations Development Programme (UNDP). HDUs are described by seven different categories: population, demography, housing, labor, education, income, and vulnerability. Maps were generated using ArcGIS 10.3 (ESRI, Redlands, CA, USA).

Statistical analyses.

Statistical analyses included χ² and Fisher’s exact tests for comparisons of seroprevalence rates (EpiInfo V7.2; http://www.cdc.gov/epiinfo), two-tailed Mann-Whitney U tests for comparisons of sociodemographic indicators and logistic regression with stepwise backward elimination of variables for multivariate analyses (SPSS V23; IBM, Ehningen, Germany), done on one variable per HDU category, selected according to highest P values in bivariate comparisons. Diagnostic test parameters were calculated using OpenEpi (http://www.openepi.com).

Transmission dynamic modeling.

ZIKV outbreak dynamics were analyzed using a susceptible-exposed-infectious-recovered (SEIR) model. The vector population was not explicitly taken into account, but variation in mosquito numbers was modeled through annual seasonal forces acting on the transmission rate. The model was implemented in a Bayesian framework using the LibBi library via the RBi and RBi.helpers packages (53,–55). The model was jointly fitted to reported ZIKV incidence data from Salvador, Brazil, from the beginning of January 2015 to the end of October 2015 and the proportion of individuals (401/633) who were seropositive to ZIKV in 2016. Incidence was fitted using a Poisson likelihood with overdispersion and approximated with a truncated Gaussian distribution, and seroprevalence was fitted using a binomial likelihood. Informative prior probability distributions were used for the delay between infection and infectiousness (the sum of the mosquito-to-human generation time and the intrinsic incubation period) centered around 17.8 days, and the infectious period in humans centered around 4.7 days, respectively (8), and for the peak of seasonality in mid-May based on dengue transmission dynamics in Salvador, Brazil. Uniform prior probability distributions were used for the amplitude of seasonality, proportion of cases reported, and basic reproduction number, R₀. Regularizing prior probability distributions were used for the initial numbers of infected and overdispersion of reporting. All model parameters were estimated using Markov chain Monte Carlo. Full prior and posterior probability distributions are shown in Fig. S3.

FIG S3 

Parameter estimates for the fitted mathematical model. Posterior and prior parameter probability density plots for the model. D_inc, duration between infection caused by mosquitoes in a human and the subsequent human case to become infectious; D_inf, duration of human infectiousness; R₀, basic reproduction number; r, proportion of cases reported; ϕ, overdispersion of reporting; Peak week, week of peak Zika transmission; Peak amplitude, peak amplitude (as a proportion of the average transmission rate); I₀, initial number of infectives. Download FIG S3, PDF file, 0.04 MB.

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Data availability.

A document containing the code necessary to reproduce the modeling results is provided in Data Set S1.

Data Set S1 

Script to reproduce mathematical modeling in R. Download Data Set S1, PDF file, 0.6 MB.

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ACKNOWLEDGMENTS

We thank Monika Eschbach-Bludau, Janett Wieseler, Carlo Fischer, Victor M. Corman, Tobias Bleicker, Sebastian Brünink, Valdélio de Oliveira Marques, María Belen Arriaga Gutierrez, Ludy Alexandra Vargas Torres, André Pessoa Bonfim Guimarães, Katja Steinhagen, Jens Miguel Warnecke, and Erik Lattwein for their support.

This work was supported by the German Centre for Infection Research (DZIF) through the ZIKApath project to J.F.D. and T.J., and the European Union’s Horizon 2020 research and innovation program through the ZIKAlliance project (grant agreement 734548) to J.F.D., T.J., B.M.K., O.J.B., and X.D.L.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

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FOOTNOTES

Citation Netto EM, Moreira-Soto A, Pedroso C, Höser C, Funk S, Kucharski AJ, Rockstroh A, Kümmerer BM, Sampaio GS, Luz E, Vaz SN, Dias JP, Bastos FA, Cabral R, Kistemann T, Ulbert S, de Lamballerie X, Jaenisch T, Brady OJ, Drosten C, Sarno M, Brites C, Drexler JF. 2017. High Zika virus seroprevalence in Salvador, northeastern Brazil limits the potential for further outbreaks. mBio 8:e01390-17. https://doi.org/10.1128/mBio.01390-17.

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CONTRIBUTOR INFORMATION

Ann M. Powers, Centers for Disease Control and Prevention.

James M. Hughes, Emory University School of Medicine.

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REFERENCES

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