A conceptual model for optimizing vaccine coverage to reduce vector-borne infections in the presence of antibody-dependent enhancement

Abstract

Background: Many vector-borne diseases co-circulate, 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 dengue-endemic areas, and co-circulation and co-infection of both viruses have been reported. As recent immunological cross-reactivity 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 antibody-dependent enhancement.

Methods: Through a conceptual co-infection dynamics model parametrized by reported dengue and Zika epidemic and immunological cross-reactivity 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, counter-intuitively, 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 vector-borne disease control strategy involving optimal vaccination programs, in regions where different viruses or different serotypes of the same virus co-circulate, and convalescent plasma following infection from one virus (serotype) can enhance infection against another virus (serotype). The conceptual model provides a first step towards well-designed regional and global vector-borne disease immunization programs.

Keywords: Antibody dependent enhancement; Dengue; Mathematical modelling; Optimized vaccination strategies; Zika.

Fig. 1

Model compartmental diagram

Fig. 2

Regions of R_d and R_z where dengue vaccination increases/reduces Zika infections. a The red curve represents the variation of ΔZ with respect to P_v when R_z and R_d falls into the red region in (b). The blue curves refer to the case when R_z and R_d fall into the blue region, $P_v^{\ast }$ and $P_v^c$ are the optimal and critical dengue vaccine rates to reduce Zika infections. Here κ=2. b Dengue vaccine can always increase Zika infections if the basic reproduction numbers R_d and R_z are in the red region. The vaccine can reduce Zika infections if the vaccine coverage rate is within a certain interval when R_d and R_z are in the blue region

Fig. 3

Δ_Z curves under various parameter sets. a β_z=0.06; b β_z=0.09; c β_z=0.12; d β_z=0.15

Fig. 4

Zika transmission diagram showing how dengue vaccination affects the total number of Zika infections by the end of an outbreak. Through Route 1 individuals are directly infected by ZIKV without a previous dengue infection; through Added Route individuals are infected by ZIKV with a prior dengue vaccination; through Route 2 individuals are coinfected with Zika and dengue; and through Route 3 individuals are subsequently infected by ZIKV with a prior recovery from dengue infection. The width of the pink bars, based on model simulations, represent the contributions toward Zika infections from each route and the total Zika infections of the two scenarios. While the accumulated number of human infected with ZIKV could increase significantly through Route 1 and Added Route with enhanced transmission of ZIKV after dengue vaccine and due to ADE, because of the cocirculation of dengue and Zika viruses, the accumulated number of ZIKV infections can decrease significantly through Route 2 or/and Route 3 since the susceptible humans who can gain Zika through Route 2 and/or Route 3 are proportionally decreased

Fig. 5

Accumulated Zika infections via each transmission route. a through Route 1 (individuals are infected by ZIKV with no prior dengue infection) for the case without vaccination; through Route 1A (individuals are infected by ZIKV either with no prior dengue infection or with prior dengue vaccination) for the case with vaccination; b through Route 2 (individuals are coinfected with Zika and dengue); c through Route 3 (individuals, who were infected with dengue previously and recovered, are subsequently infected with ZIKV); d through all routes. The dash curves denote the number with dengue vaccine while the solid curves are the number without dengue vaccine. Here we fix β_d=0.07, β_z=0.07, κ=2 and P_v=0.3

Fig. 6

Sensitivity analysis on Zika infections via all transmission routes. PRCCs of the cumulative Zika infections through Route 1, Route 2, Route 3, and the total number of cases, with parameters uniformly distributed in the ranges κ∈[1,3], β_z∈[0.125,0.281] and β_d∈[0.045,0.32]. P_v=50% is fixed in this analysis

Fig. 7

Contour plots of the critical and optimal dengue vaccine coverage rates with respect to R_d and R_z. a Contour plot of the optimal dengue vaccine coverage rate. b Contour plot of the critical dengue vaccine coverage rate. Here κ=2

Fig. 8

Critical and optimal dengue vaccine coverage rates under four special cases. The solid and dash curves represent the changing relationship of the critical and optimal values of dengue vaccine coverage rate with respect to the basic reproduction number for dengue R_d, respectively. Here the antibody-dependent enhancement rate is κ=2

Fig. 9

Sensitivity analysis on optimal and critical vaccine coverages. a optimal vaccine coverage rate; b critical vaccine coverage rate. For each country shown in the figure, we sample the ADE factor κ in a large range of [1.2,2.5], and the corresponding local Zika and dengue basic reproduction numbers estimated from literatures. Each box plot has its first, second, and third quartiles marked. We sample (R_d,R_z) from ranges cited from literatures: [2.89,3.29]×[2,2.3] ; [2.07,2.6]×[2.13,2.53]; and [2.73,3.13]×[2.13,2.53]

Fig. 10

Scenarios for antibody-dependent neutralization with κ=0.7. Here the Zika basic reproduction number is fixed as 2.7. a Variation of ΔZ with respect to dengue vaccine coverage rate P_v. b Regions of R_d and R_z where dengue vaccination affects Zika infections in two ways as illustrated in (a)