 Research
 Open Access
A conceptual model for optimizing vaccine coverage to reduce vectorborne infections in the presence of antibodydependent enhancement
 Biao Tang^{1, 2},
 Xi Huo^{2, 3},
 Yanni Xiao^{1},
 Shigui Ruan^{3} and
 Jianhong Wu^{2}Email author
https://doi.org/10.1186/s129760180085x
© The Author(s) 2018
 Received: 13 September 2017
 Accepted: 13 July 2018
 Published: 3 September 2018
Abstract
Background
Many vectorborne diseases cocirculate, as the viruses from the same family are also transmitted by the same vector species. For example, Zika and dengue viruses belong to the same Flavivirus family and are primarily transmitted by a common mosquito species Aedes aegypti. Zika outbreaks have also commonly occurred in dengueendemic areas, and cocirculation and coinfection of both viruses have been reported. As recent immunological crossreactivity studies have confirmed that convalescent plasma following dengue infection can enhance Zika infection, and as global efforts of developing dengue and Zika vaccines are intensified, it is important to examine whether and how vaccination against one disease in a large population may affect infection dynamics of another disease due to antibodydependent enhancement.
Methods
Through a conceptual coinfection dynamics model parametrized by reported dengue and Zika epidemic and immunological crossreactivity characteristics, we evaluate impact of a hypothetical dengue vaccination program on Zika infection dynamics in a single season when only one particular dengue serotype is involved.
Results
We show that an appropriately designed and optimized dengue vaccination program can not only help control the dengue spread but also, counterintuitively, reduce Zika infections. We identify optimal dengue vaccination coverages for controlling dengue and simultaneously reducing Zika infections, as well as the critical coverages exceeding which dengue vaccination will increase Zika infections.
Conclusion
This study based on a conceptual model shows the promise of an integrative vectorborne disease control strategy involving optimal vaccination programs, in regions where different viruses or different serotypes of the same virus cocirculate, and convalescent plasma following infection from one virus (serotype) can enhance infection against another virus (serotype). The conceptual model provides a first step towards welldesigned regional and global vectorborne disease immunization programs.
Keywords
 Zika
 Dengue
 Antibody dependent enhancement
 Optimized vaccination strategies
 Mathematical modelling
Background
Our conceptual modelling study is motivated by the observation that several vectorborne diseases (or several serotypes of the same disease) may share the same vector species, and convalescent plasma following infection of one disease (or one serotype) can enhance the infection to another disease (or another serotype). We wish to address the following hypothetical issue: if a vaccine product for one particular disease (or a particular serotype) becomes available and if the aforementioned antibodydependent enhancement does occur, is there an optimal vaccine coverage that can control the outbreak of the particular disease while simultaneously contributing to the control of other diseases (or serotypes) in the presence of antibody enhancement.
Our conceptual model formulation is guided by Zika outbreaks in dengue endemic areas. Dengue fever is caused by any of four closely related viruses or serotypes (DENV 1, DENV 2, DENV 3, DENV 4) and is transmitted between people by Aedes aegypti mosquitoes which are found throughout the world. Today about 2.5 billion people live in areas where there is a risk of dengue transmission with 50100 million infections yearly, including 500,000 dengue hemorrhagic fever (DHF) cases and 22,000 deaths [1–3]. The antigenic differences among four serotypes are so great that robust immunity to one conferred by recovery from infection does not confer immunity to the others. Instead, previous exposure to one serotype increases the risk of severe disease after infection by a second serotype, the phenomenon of antibodydependent enhancement (ADE) [4–6]. Studies based on modeling multiple DENV strains indicate that preexisting antibodies can significantly affect the dengue viral dynamics and disease transmission [7–9]. The crossreactivity and ADE have been imposing substantial challenges for the development of an ideal dengue vaccine since it needs to balance protective response against all four serotypes. This is illustrated by the experience of the first dengue vaccine, Dengvaxia produced by Sanofi Pasteur, that was approved for use in six countries [10], and WHO published the recommendations of the Strategic Advisory Group of Experts (SAGE) on Immunization on the use of Dengvaxia in May 2016. However, following the disclosure to WHO of additional data by Sanofi Pasteur, WHO initiated a process engaging independent external experts [11], and this process led to revised recommendations from SAGE on April 18 of 2018.
Zika virus (ZIKV), also a member of the Flavivirus family, was first isolated from a rhesus monkey in the Zika forest of Uganda in 1947 [12]. The first severe ZIKV outbreak occurred on Yap Island in the North Pacific in 2007 [13]. In 20132014, largescale ZIKV outbreaks were reported on other Pacific islands, including French Polynesia, New Caledonia, Easter Island, and Cook Island [14, 15]. After being transmitted to Brazil in 2015 [16], ZIKV was subsequently spread to other countries and territories in the Americas, and was estimated to become a potential threat to countries in Europe [17], Africa and the AsiaPacific region [18, 19]. By December 29 of 2016, 48 countries and territories in the Americas had confirmed autochthonous vectorborne transmission of ZIKV disease with more than 520,000 suspected cases [20]. Though nonvector borne transmission such as sexual transmission [21] and vertical transmission [22] has been reported, ZIKV is primarily transmitted by the bite of infected Aedes aegypti mosquitoes, the same mosquito species that transmits dengue viruses.
Since ZIKV outbreaks usually occurred in areas where dengue was endemic, cocirculation and coinfection of dengue and Zika has been reported [23, 24], and since there is evidence that immunological crossreactivity occurs between dengue and Zika and the ADE of dengue viruses can enhance Zika infections [25–27], it is natural to ask whether and how dengue vaccine (when available) utilization in a population impacts Zika infection dynamics [28, 29].
A previous study [30] reported that dengue vaccine may increase Zika infections. This study was based on the assumption of a very high effective vaccine coverage rate. Since the effective vaccine rate is the vaccine coverage rate times the vaccine efficacy while the vaccine efficacy of existing vaccine candidates is moderate, the effective vaccine rate is moderate in real settings. Hence, it is natural to ask if a largescale use of DENV vaccine with moderate effective vaccine rate feasible in real settings would increase the likelihood of ZIKV outbreak and lead to a larger number of ZIKV infections in the population. Our analysis provides a negative answer to this question, so we are led to ask if there is an optimal DENV vaccine coverage rate with which the dengue vaccination program not only controls the dengue transmission but also reduces ZIKV infections.
The main objective of this study is to address this question through a deterministic model for the coinfection of DENV and ZIKV among mosquitos and humans. We perform intensive simulations on a wide range of the basic reproduction numbers of dengue and Zika reported from different areas in the world, and show that under a wide range of circumstances, the use of a dengue vaccine in the population can be designed to not only help control the dengue outbreak but also, counterintuitively, reduce Zika infections. We remark that this conclusion is based on a hypothetical dengue vaccine being used in a population in a dengue epidemic area with a particular serotype.
Methods
Parameter definitions and values
Definitions  Value(range)  Reference  

c  Mosquito biting rate  0.8(0.3,1)  
β _{ d}  Mosquitotohuman transmission probability of dengue  Varied ^{∗} (0.045,0.32)  
β _{ z}  Mosquitotohuman transmission probability of Zika  Varied ^{∗} (0.125,0.281)  
η _{ d}  Humantomosquito transmission probability of dengue  0.5(0.3,0.75)  
η _{ z}  Humantomosquito transmission probability of Zika  0.5(0.3,0.75)  
γ _{ d}  Recovery rate of humans infected with dengue  0.2(0.017,0.33)  [35] 
γ _{ z}  Recovery rate of humans infected with Zika  0.2(0.14,0.33)  
μ  Mosquito mortality rate  0.1(0.028,0.25)  
κ  Antibody dependent enhancement/neutralization factor  Varied (0,3)  
of the susceptibility of ZIKV 
where Λ is the recruitment rate of mosquitoes, μ is the mosquito mortality rate, and c is the mosquito daily biting rate. {η_{i}}_{i=d,z} is the human to mosquito transmission probability of disease i per contact. Specifically, during a contact between a susceptible mosquito and a coinfected human, the probability of the mosquito getting contaminated by dengue, Zika, and both viruses are respectively η_{d}(1−η_{z}), (1−η_{d})η_{z}, and η_{d}η_{z}.
where {γ_{i}}_{i=d,z} is the human recovery rate from disease i, and {β_{i}}_{i=d,z} is the mosquito to human transmission probability of disease i only per contact. Thus during a contact between a susceptible human and a mosquito with both viruses, the probability of the human getting infected by dengue, Zika, and both viruses are respectively β_{d}(1−β_{z}), (1−β_{d})β_{z}, and β_{d}β_{z}.
In this study, we assume the human population N_{h} remains a constant.
Basic reproduction numbers
Parameter set up
As mentioned above, we use parameter κ to measure the ADE induced multiplication factor of the susceptibility to ZIKV. Thus in our model, the Zika infection force for people immunized to dengue is amplified by κ compared to the Zika infection force for people who have never had dengue infection or immunization, and κ>1 [7, 25–27]. We assume that dengue immunity from natural infection and vaccination are immunologically identical, that is, both denguerecovered and effectively denguevaccinated individuals are immune to dengue, and have the same degree of ADE for Zika infections. To what extent this assumption holds depends on the DENV serotype and the vaccine characteristics, and we will discuss this in the final section.
ADE and antibodydependent neutralization (ADN) also exist among the four serotypes of dengue viruses, of which the multiplication factors of susceptibility have been studied and estimated using epidemiological data [7–9, 32, 33]. Although the ADE effect of dengue on Zika infection has been observed in cellular level experiments [25, 26, 34], the actual ADE factor κ is difficult to be estimated from the experimental measurements [7]. So we adopt the parameter value κ estimated/assumed in the aforementioned dengue studies as κ∈[0,3], complemented by an intensive sensitivity analysis.
In our simulations, we fix the following parameter values: (1) the transmission probabilities from human to mosquito of both diseases are assumed as a constant value η_{d}=η_{z}=0.5; (2) The human recovery rates from both diseases are fixed as γ_{d}=γ_{z}=0.2day^{−1}; (3) the total human population is fixed as N_{h}=100,000, the recruitment rate of mosquito population is fixed as Λ=20,000. Table 1 gives the overview of the parameter ranges and literatures from which they are cited.
We vary the basic reproduction numbers R_{d} from 1.2 to 3.2 and R_{z} from 2 to 3 by adjusting values of β_{d} and β_{z} in credible ranges in agreements with those from [21, 35]. These basic reproduction number values are in broad agreement with previous dengue [21, 35] and Zika [21, 36–38] estimates.
When dengue vaccine is used, the initial values of the compartments S and R^{d} are changed accordingly while the others remain unchanged. Specifically, we use the following initial values for system (1)–(2) for the scenario with no vaccination:
For the scenario when a percentage of P_{v} human population is effectively covered by dengue vaccination at the onset of the outbreak, the initial conditions of compartments S and R^{d} are modified as \(\hat {S}(0)=(1P_{v})(N_{h}200)\) and \(\hat {R}^{d}(0)=P_{v} (N_{h}200)\) while the other components remain unchanged.
Results
Potential impact of dengue vaccination on the final size of Zika infections

Route 1  infection by ZIKV without prior dengue infection (i.e. S→I_{z});

Route 2  coinfection by dengue and Zika (i.e. S→I_{d}→I_{dz} and S→I_{dz});

Route 3  infection by ZIKV with prior recovered dengue infection \(\left (\text {i.e. } S\to I_{d}\to R^{d}\to J_{d}^{z}\right)\).

Added Route  infection by ZIKV with prior dengue vaccination \(\left (\text {i.e. } \hat {R}^{d}\to J_{d}^{z}\right)\).
In the scenario with dengue vaccination, we will regard Route 1A as the combination of Zika cases through both Route 1 and Added Route. Thus we can compare cases through Route 1A under dengue vaccination scenario with the cases through Route 1 under the scenario without vaccination.
Optimizing dengue vaccination to reduce Zika infections
The case of antibodydependent neutralization (ADN)
Discussion
There is increasing evidence of immunological crossreactivity between dengue and Zika viruses, which indicates that convalescent plasma following dengue infection can enhance the ZIKV infection. Through a conceptual mathematical model, we addressed the issue that vaccination against dengue may increase Zika infections in the presence of ADE. We found that under some conditions, dengue vaccination can reduce Zika infections. We computed explicitly the parameter ranges within which dengue vaccine increases or reduces Zika infections and examined how these results depend on the basic reproduction numbers of both diseases.
In Fig. 2a, where three different values of β_{d} were chosen while other parameters were fixed, we found two different types of impact of dengue vaccination: it either always increases Zika infections, or can reduce Zika infections when the vaccine coverage rate is in a safe interval. We illustrated in Fig. 2b that our models enable us to determine which scenario can occur when R_{d} varies between 1.2 and 3.2 while R_{z} varies from 2 to 3.
The optimal and critical effective dengue vaccine coverage rates can be calculated through numerical simulations for regions where both R_{d} and R_{z} can be estimated. Figure 7 gives the dengue vaccine isoclines, panel (a) for optimal and panel (b) for critical rates. This clearly shows that in the regions with similar dengue epidemics (same R_{d}), the higher the R_{z} the smaller the optimal dengue vaccine coverage rate; and in the regions with similar Zika epidemics (same R_{z}), the higher the R_{d} the higher the optimal dengue vaccine coverage rate. For example, in the area with the pair of basic reproduction numbers (R_{d},R_{z}) near (3.09, 2.1), (2.32, 2.33) and (2.93, 2.33), the optimal and critical effective dengue vaccine coverage rates are (73.6%, 99%), (51.4%, 88.2%), and (68.2%, 97.6%), respectively. If we use the efficacy of the dengue vaccine 71.6 and 76.9% for serotypes 3 and 4, 54.7 and 43.0% for serotypes 1 and 2 previously reported [44], then the optimal effective vaccine coverage for the regions with the aforementioned basic reproduction number pairs (for Dengue and for Zika) can be achieved. Our analysis shows that if the dengue vaccine efficacy is less than 90%, high dengue vaccination coverage in these regions contribute to the control of Zika. This result, in the aspect of potential dengue vaccination impacts on Zika outbreaks, reconciles the WHO’s former position on the use of the vaccine “for highly endemic areas” [45]. We are aware that WHO has revised its position for a given vaccine product given the updated data from the tetravalent dengue vaccine producer, but we are also aware there are other potential competitive vaccine products and our model analysis can be reproduced once the efficacy of these vaccine products becomes available.
The sensitivity analysis, illustrated in Fig. 9, shows that the optimal and critical dengue vaccine coverage rates are robust to uncertainty and estimation errors of dengue and Zika epidemic characteristics, and to the assumed ADE level (κ). This study thus shows the promise of an integrative dengueZika control strategy in dengue epidemic regions with access to dengue vaccine and immunization. In the presence of antibodydependent enhancement, caution has to be exercised to optimally design the dengue vaccine program with an appropriate coverage that can reduce the final size of Zika infections. Our study shows this optimal program is feasible.
We also investigated the impact of dengue vaccine on Zika infections if the convalescent plasma following dengue infection partially protects human from being infected by ZIKV (when κ<1). We concluded in Fig. 10 that the variation of ΔZ with respect to P_{v} becomes different than the case of κ>1. We found that in the case of κ=0.7, dengue immunization can either reduce total Zika cases regardless of vaccination coverage, or boost Zika cases with small coverages but reduce Zika cases with large coverages. Dengue vaccination becomes beneficial at any coverage level when R_{z} is larger than R_{d} to some extent.
Limitations
It has been shown that naturallyacquired dengue infection against a single serotype can be incomplete, resulting in individuals being infected multiple times by the same serotype [46, 47]. To incorporate this incomplete and/or waning natural protection, we will need to modify our model setup to allow recovered individuals from dengue infection to become partially susceptible to dengue infection, in addition to enhanced susceptibility to Zika infection. Should new evidence arise to indicate the difference of dengue immunity from natural infection and vaccination, our model parameters need to be modified by incorporating two different κ. The qualitative conclusion should remain since our sensitivity analysis indicates the robustness of our conclusion with respect to the change of κ, but accurate optimal and critical vaccine rates may be slightly changed.
Our model captures some important aspects of dengue and Zika transmission to address the impact of dengue vaccine usage on Zika infections in a homogeneous population within a single season and in a setting only one dengue serotype is involved. This conceptual model provides a basis for future studies to incorporate other important epidemiological characteristics such as different serotypes of dengue, asymptomatic infection, generation time of secondary Zika/Dengue infections, sexually transmission of ZIKV, and variation in transmission potential and severity (and hence risk, and costbenefit) for different age/gender groups [48]. Seasonal factors can and should also be incorporated to allow temporal variation of transmission parameters to address more logistic vaccination program design that must consider risk differentiation by gender, age and other demographic characteristics [10]. To examine the long term impact of dengue vaccine on Zika transmission, we should also consider the issue whether dengue vaccine offers only shortterm protection (and hence ADE), which can be modelled by allowing recovery to the denguesusceptible populations. Finally, in view of the recent study [49] on bidirectional ADE impact between dengue and Zika, and the substantial global efforts towards Zika vaccine development, our model should be modified by further stratification of the vector and human populations and additional costbenefit analyses to inform “longterm high prioritisation and adequate resources” [50].
Conclusions
In this paper, we evaluate the impact of dengue vaccination on Zika infection dynamics through a conceptual mathematical coinfection dynamics model. We show that an appropriately designed and optimized dengue vaccine usage plan can not only help control the dengue spread but also, counterintuitively, reduce Zika infections. We also identify optimal dengue vaccination coverages for controlling dengue and simultaneously reducing Zika infections, as well as the critical coverages exceeding which dengue vaccination will increase Zika infections. This study shows the promise of an integrative dengueZika control strategy in dengue epidemic regions with access to dengue vaccine, the mathematical model provides the first step towards welldesigned regional and global vectorborne disease immunization programs.
Notes
Declarations
Funding
This project was partially supported by the National Natural Science Foundation of China (NSFC, 11571273, 11631012 (YX)), the Fundamental Research Funds for the Central Universities (08143042 (YX)), National Science Foundation (DMS1412454 (SR)), the Canada Research Chair Program, the Natural Sciences and Engineering Research Council of Canada (JW), and the International Development Research Center (Ottawa, Canada, 104519010).
Authors’ contributions
BT, XH, YX, SR, and JW designed the study and carried out the analysis. BT performed numerical simulations. BT, XH, YX, SR, and JW contributed to writing the paper. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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Authors’ Affiliations
References
 Gubler DJ. Dengue and dengue hemorrhagic fever. Clin Microbiol Rev. 1998; 11(3):480–96.PubMedPubMed CentralGoogle Scholar
 World Health Organization (WHO). WHO Dengue and Severe Dengue, Fact Sheet No. 117, Updated May 2015. http://www.who.int/en/newsroom/factsheets/detail/dengueandseveredengue.
 Massad E, Burattini MN, Ximenes R, Amaku M, WilderSmith A. Dengue outlook for the World Cup in Brazil. Lancet Infect Dis. 2014; 14(7):552–3.View ArticlePubMedGoogle Scholar
 Halstead SB. Pathogenesis of dengue: challenges to molecular biology. Science. 1988; 239(4839):476–81.View ArticlePubMedGoogle Scholar
 Dejnirattisai W, Jumnainsong A, Onsirisakul N, Fitton P, Vasanawathana S, Limpitikul W, Puttikhunt C, Edwards C, Duangchinda T, Supasa S, et al. Crossreacting antibodies enhance dengue virus infection in humans. Science. 2010; 328(5979):745–8.View ArticlePubMedGoogle Scholar
 Ndifon W, Wingreen NS, Levin SA. Differential neutralization efficiency of hemagglutinin epitopes, antibody interference, and the design of influenza vaccines. Proc Natl Acad Sci USA. 2009; 106(21):8701–6.View ArticlePubMedGoogle Scholar
 Ferguson N, Anderson R, Gupta S. The effect of antibodydependent enhancement on the transmission dynamics and persistence of multiplestrain pathogens. Proc Natl Acad Sci USA. 1999; 96(2):790–4.View ArticlePubMedGoogle Scholar
 Cummings DA, Schwartz IB, Billings L, Shaw LB, Burke DS. Dynamic effects of antibodydependent enhancement on the fitness of viruses. Proc Natl Acad Sci USA. 2005; 102(42):15259–64.View ArticlePubMedGoogle Scholar
 Adams B, Holmes E, Zhang C, Mammen M, Nimmannitya S, Kalayanarooj S, Boots M. Crossprotective immunity can account for the alternating epidemic pattern of dengue virus serotypes circulating in Bangkok. Proc Natl Acad Sci USA. 2006; 103(38):14234–9.View ArticlePubMedGoogle Scholar
 Ferguson NM, RodríguezBarraquer I, Dorigatti I, MieryTeranRomero L, Laydon DJ, Cummings DA. Benefits and risks of the SanofiPasteur dengue vaccine: Modeling optimal deployment. Science. 2016; 353(6303):1033–6.View ArticlePubMedPubMed CentralGoogle Scholar
 World Health Organization (WHO). Updated Questions and Answers Related to the Dengue Vaccine Dengvaxia and Its Use. http://www.who.int/immunization/diseases/dengue/q_and_a_dengue_vaccine_dengvaxia_use/en/.
 Dick GWA, Kitchen SF, Haddow AJ. Zika Virus. Trans R Soc Trop Med Hyg. 1952; 46(5):509–20. https://doi.org/10.1016/00359203(52)900424.View ArticlePubMedGoogle Scholar
 Duffy MR, Chen TH, Hancock WT, Powers A, Kool JL, Lanciotti RS, Pretrick M, Marfel M, Holzbauer S, Dubray C, et al. Zika virus outbreak on Yap Island, federated states of Micronesia. N Engl J Med. 2009; 360:2536–43.View ArticlePubMedGoogle Scholar
 Musso D, Nilles EJ, CaoLormeau V. Rapid spread of emerging Zika virus in the Pacific area. Clin Microbiol Infect. 2014; 20(10):595–6. https://doi.org/10.1111/14690691.12707.View ArticleGoogle Scholar
 Petersen L, Jamieson D, Powers A, Honein M. Zika Virus. N Engl J Med. 2016; 374(16):1552–63. https://doi.org/10.1056/NEJMra1602113.View ArticlePubMedGoogle Scholar
 Campos GS, Bandeira AC, Sardi SI, et al. Zika virus outbreak, Bahia, Brazil. Emerg Infect Dis. 2015; 21(10):1885–6.View ArticlePubMedPubMed CentralGoogle Scholar
 Massad E, Tan SH, Khan K, WilderSmith A. Estimated Zika virus importations to Europe by travellers from Brazil. Glob Health Action. 2016; 9(1):31669. https://doi.org/10.3402/gha.v9.31669.View ArticlePubMedGoogle Scholar
 Bogoch II, Brady O, Kraemer M, German M, Creatore MI, Brent S, Watts AG, Hay SI, Kulkarni MA, Brownstein JS, et al. Potential for Zika virus introduction and transmission in resourcelimited countries in Africa and the AsiaPacific region: a modelling study. Lancet Infect Dis. 2016; 16(11):1237–45.View ArticlePubMedPubMed CentralGoogle Scholar
 Bogoch II, Brady O, Kraemer M, German M, Creatore MI, Kulkarni MA, Brownstein JS, Mekaru SR, Hay SI, Groot E, et al. Anticipating the international spread of Zika virus from Brazil. Lancet. 2016; 387:335–6.View ArticlePubMedPubMed CentralGoogle Scholar
 Pan American Health Organization (PAHO), World Health Organization (WHO). Zika  Epidemiological Update, 29 December 2016. https://www.paho.org/hq/dmdocuments/2016/2016dec29pheepiupdatezikavirus.pdf.Google Scholar
 Gao D, Lou Y, He D, Porco TC, Kuang Y, Chowell G, Ruan S. Prevention and control of Zika as a mosquitoborne and sexually transmitted disease: a mathematical modeling analysis. Sci Rep. 2016; 6:28070.View ArticlePubMedPubMed CentralGoogle Scholar
 Besnard M, Lastere S, Teissier A, CaoLormeau V, Musso D, et al. Evidence of perinatal transmission of Zika virus, French Polynesia, December 2013 and February 2014. Euro Surveill. 2014; 19(13):20751.View ArticlePubMedGoogle Scholar
 DupontRouzeyrol M, O’Connor O, Calvez E, Daures M, John M, Grangeon JP, Gourinat AC. Coinfection with Zika and dengue viruses in 2 patients, New Caledonia, 2014. Emerg Infect Dis. 2015; 21(2):381–2.View ArticlePubMedPubMed CentralGoogle Scholar
 Pessôa R., Patriota JV, de Souza MdL, Felix AC, Mamede N, Sanabani SS. Investigation into an outbreak of denguelike illness in pernambuco, brazil, revealed a cocirculation of zika, chikungunya, and dengue virus type 1. Medicine. 2016; 95:3201.View ArticleGoogle Scholar
 Dejnirattisai W, Supasa P, Wongwiwat W, Rouvinski A, BarbaSpaeth G, Duangchinda T, Sakuntabhai A, CaoLormeau VM, Malasit P, Rey FA, et al. Dengue virus serocrossreactivity drives antibodydependent enhancement of infection with zika virus. Nat Immunol. 2016; 17:1102–8.View ArticlePubMedPubMed CentralGoogle Scholar
 Priyamvada L, Quicke KM, Hudson WH, Onlamoon N, Sewatanon J, Edupuganti S, Pattanapanyasat K, Chokephaibulkit K, Mulligan MJ, Wilson PC, et al. Human antibody responses after dengue virus infection are highly crossreactive to Zika virus. Proc Natl Acad Sci USA. 2016; 113(28):7852–7.View ArticlePubMedGoogle Scholar
 Bardina SV, Bunduc P, Tripathi S, Duehr J, Frere JJ, Brown JA, Nachbagauer R, Foster GA, Krysztof D, Tortorella D, et al. Enhancement of Zika virus pathogenesis by preexisting antiflavivirus immunity. Science. 2017; 356(6334):175–80.View ArticlePubMedPubMed CentralGoogle Scholar
 Pierson TC, Graham BS. Zika virus: immunity and vaccine development. Cell. 2016; 167(3):625–31.View ArticlePubMedPubMed CentralGoogle Scholar
 Valentine G, Marquez L, Pammi M. Zika virus epidemic: an update. Expert Rev AntiInfect Ther. 2016; 14:1127–38. https://doi.org/10.1080/14787210.2016.1245614.View ArticlePubMedGoogle Scholar
 Tang B, Xiao Y, Wu J. Implication of vaccination against dengue for Zika outbreak. Sci Rep. 2016; 6:25623.View ArticleGoogle Scholar
 Van den Driessche P, Watmough J. Reproduction numbers and subthreshold endemic equilibria for compartmental models of disease transmission. Math Biosci. 2002; 180(12):29–48.View ArticlePubMedGoogle Scholar
 Wearing HJ, Rohani P. Ecological and immunological determinants of dengue epidemics. Proc Natl Acad Sci USA. 2006; 103(31):11802–7.View ArticlePubMedGoogle Scholar
 Recker M, Blyuss KB, Simmons CP, Hien TT, Wills B, Farrar J, Gupta S. Immunological serotype interactions and their effect on the epidemiological pattern of dengue. Proc R Soc Lond B Biol Sci. 2009; 276(1667):2541–8.View ArticleGoogle Scholar
 Charles AS, Christofferson RC. Utility of a denguederived monoclonal antibody to enhance Zika infection in vitro. Edition 1. PLoS Curr Outbreaks. 2016. https://doi.org/10.1371/currents.outbreaks.4ab8bc87c945eb41cd8a49e127082620https: //doi.org/10.1371/currents.outbreaks.4ab8bc87c945eb41cd8a49e127082620.Google Scholar
 Andraud M, Hens N, Marais C, Beutels P. Dynamic epidemiological models for dengue transmission: a systematic review of structural approaches. PLoS ONE. 2012; 7(11):49085.View ArticleGoogle Scholar
 Towers S, Brauer F, CastilloChavez C, Falconar AK, Mubayi A, RomeroVivas CM. Estimate of the reproduction number of the 2015 Zika virus outbreak in Barranquilla, Colombia, and estimation of the relative role of sexual transmission. Epidemics. 2016; 17:50–5.View ArticlePubMedGoogle Scholar
 Kucharski AJ, Funk S, Eggo RM, Mallet HP, Edmunds WJ, Nilles EJ. Transmission dynamics of Zika virus in island populations: a modelling analysis of the 2013–14 French Polynesia outbreak. PLoS Negl Trop Dis. 2016; 10(5):0004726.View ArticleGoogle Scholar
 Majumder MS, Cohn E, Fish D, Brownstein JS. Estimating a feasible serial interval range for Zika fever. Bull World Health Organ. 2016; 10:1–6.Google Scholar
 Zhang Q, Sun K, Chinazzi M, PastorePiontti A, Dean NE, Rojas DP, Merler S, Mistry D, Poletti P, Rossi L, Bray M, Halloran ME, Longini IM, Vespignani A. Projected spread of Zika virus in the Americas. bioRxiv. 2016. https://doi.org/10.1101/066456.Google Scholar
 Villela DAM, Bastos L, Carvalho LM, Cruz OG, Gomes MFC, Durovni B, Lemos MC, Saraceni V, Coelho FC, Codeco CT. Zika in Rio de Janeiro: Assessment of basic reproduction number and comparison with dengue outbreaks. Epidemiol Infect. 2017; 145:1649–57.View ArticlePubMedGoogle Scholar
 Rocklöv J, Quam MB, Sudre B, German M, Kraemer M, Brady O, Bogoch II, LiuHelmersson J, WilderSmith A, Semenza JC, et al. Assessing seasonal risks for the introduction and mosquitoborne spread of Zika virus in Europe. EBioMedicine. 2016; 9:250–6.View ArticlePubMedPubMed CentralGoogle Scholar
 Imai N, Dorigatti I, Cauchemez S, Ferguson NM. Estimating dengue transmission intensity from seroprevalence surveys in multiple countries. PLoS Negl Trop Dis. 2015; 9(4):0003719.View ArticleGoogle Scholar
 Chowell G, DiazDuenas P, Miller J, AlcazarVelazco A, Hyman J, Fenimore P, CastilloChavez C. Estimation of the reproduction number of dengue fever from spatial epidemic data. Math Biosci. 2007; 208(2):571–89.View ArticlePubMedGoogle Scholar
 World Health Organization (WHO). Questions and Answers on Dengue Vaccines. http://www.who.int/immunization/research/development/dengue_q_and_a/en/.
 World Health Organization (WHO). Updated Questions and Answers Related to Information Presented in the Sanofi Pasteur  Press Release on 30 November 2017 with Regards to the Dengue Vaccine Dengvaxia. http://www.who.int/immunization/QAdenguevaccine.pdf.Google Scholar
 Forshey BM, Reiner RC, Olkowski S, Morrison AC, Espinoza A, Long KC, Vilcarromero S, Casanova W, Wearing HJ, Halsey ES, et al. Incomplete protection against dengue virus type 2 reinfection in Peru. PLoS Negl Trop Dis. 2016; 10(2):0004398.View ArticleGoogle Scholar
 Waggoner JJ, Balmaseda A, Gresh L, Sahoo MK, Montoya M, Wang C, Abeynayake J, Kuan G, Pinsky BA, Harris E. Homotypic dengue virus reinfections in nicaraguan children. J Infect Dis. 2016; 214(7):986–93.View ArticlePubMedPubMed CentralGoogle Scholar
 Flasche S, Jit M, RodríguezBarraquer I, Coudeville L, Recker M, Koelle K, Milne G, Hladish TJ, Perkins TA, Cummings DA, et al. The longterm safety, public health impact, and costeffectiveness of routine vaccination with a recombinant, liveattenuated dengue vaccine (dengvaxia): a model comparison study. PLoS Med. 2016; 13(11):1002181.View ArticleGoogle Scholar
 Kawiecki AB, Christofferson RC. Zika Virus–Induced Antibody Response Enhances Dengue Virus Serotype 2 Replication In Vitro. J Infect Dis. 2016; 214(9):1357–60.View ArticlePubMedGoogle Scholar
 Pang T, Mak TK, Gubler DJ. Prevention and control of dengue—the light at the end of the tunnel. Lancet Infect Dis. 2017; 17(3):e79–e87.View ArticlePubMedGoogle Scholar