The risk of sustained sexual transmission of Zika is underestimated

Abstract

Pathogens often follow more than one transmission route during outbreaks-from needle sharing plus sexual transmission of HIV to small droplet aerosol plus fomite transmission of influenza. Thus, controlling an infectious disease outbreak often requires characterizing the risk associated with multiple mechanisms of transmission. For example, during the Ebola virus outbreak in West Africa, weighing the relative importance of funeral versus health care worker transmission was essential to stopping disease spread. As a result, strategic policy decisions regarding interventions must rely on accurately characterizing risks associated with multiple transmission routes. The ongoing Zika virus (ZIKV) outbreak challenges our conventional methodologies for translating case-counts into route-specific transmission risk. Critically, most approaches will fail to accurately estimate the risk of sustained sexual transmission of a pathogen that is primarily vectored by a mosquito-such as the risk of sustained sexual transmission of ZIKV. By computationally investigating a novel mathematical approach for multi-route pathogens, our results suggest that previous epidemic threshold estimates could under-estimate the risk of sustained sexual transmission by at least an order of magnitude. This result, coupled with emerging clinical, epidemiological, and experimental evidence for an increased risk of sexual transmission, would strongly support recent calls to classify ZIKV as a sexually transmitted infection.

Fig 1. Women and men are shown in orange and blue, respectively, with squares indicating vector-infected individuals and circles corresponding to infections acquired sexually.

Since most vector-infected individuals typically transmit ZIKV to 0 or 1 sexual partners, a biased estimate of the relevant reproductive number for sexual transmission risk ($R_0^{\text{total}}$), will be obtained when considering all infections, thus inaccurately suggesting that ZIKV cannot be a self-sustaining STI (one would correctly determine that $R_0^{\text{total}}⪡1$; however, this quantity underestimates the risk of sexual transmission). Considering only transmissions caused by sexually-infected individuals corrects for the bias, but still may not provide a complete assessment of the threat posed by ZIKV as an STI ($R_0^{\text{sexual}}<1$). In fact, because of the highly asymmetric sex-dependent probability of transmission of ZIKV, an epidemic is likely to occur in the men-who-have-sex-with-men (MSM) community with some spillover transmission to the population not active in the MSM community, a situation that can only be modeled through a well-suited community-specific reproductive number (e.g., $R_0^{\text{MSM}}>1$).

Fig 2

left panel: Considering a scenario in which women are 10 times less likely to transmit ZIKV than men (ε = 10), the dotted, dashed and solid lines show three estimates of the basic reproductive number R₀ associated with sexual transmissions: (i) $R_0^{\text{total}}$ is the average number of secondary infections due to all infected individuals, including by the vector, (ii) $R_0^{\text{sexual}}$ is the average number of secondary infections resulting from individuals infected sexually, and (iii) $R_0^{\text{MSM}}$ is the average number of secondary infections resulting from individuals in the MSM community who were infected sexually. These estimates are computed by counting the number of sexual infections caused by the individuals infected by the vector and/or their direct neighbors (see Methods). These various R₀ estimates indicate that the transmission probability is at the epidemic threshold when they are equal to one (large dots). The community-specific $R_0^{\text{MSM}}$ is the only observable that adequately measures when ZIKV could invade the MSM community; the other metrics falsely imply that sustained sexual transmission is unlikely; critically, $R_0^{\text{total}}$ leads to an overestimation of the epidemic threshold, T_c, by an order of magnitude (i.e., 0.35 versus 0.03).right panel: Prevalence of ZIKV in the whole population and in two sub-populations (MSM and all heterosexuals) as a function of T using ε = 2 and only considering individuals with more than one sexual partner (as a rough approximation of the sexually active population). The vertical, dashed-gray lines show the threshold values for a self-sustained epidemic within the MSM community (with subcritical spillovers in the rest of the population, shaded area), and within the entire population (i.e., supercritical outbreaks exist both within and outside of the MSM community), see Eqs (1) and (2). Note that the prevalence outside of the MSM community start rising at the threshold for endemic transmission in the MSM population because of sub-critical spillovers. Note also that we used ε = 2 instead of ε = 10 for pedagogical reasons since it facilitated the presentation of the results and is clearly conservative with respect to our conclusions.

Fig 3

left panel: Validation that the ratio of incidence in heterosexual men and women equals T〈k_e〉_WH/ε, as predicted in the main text using the same results of numerical simulations as for the right panel of Fig 2. The blue and yellow solid lines show the prevalence of ZIKV within the heterosexual men and women sub-populations, and the red solid line shows their ratio. The vertical gray lines and shaded area are the same as on the right panel of Fig 2. right panel: The black dashed lines show the different predictions for the prevalence ratios. The green solid line shows the results of the numerical simulations for the ratio using ε = 5 while keeping the same values for 〈k_e〉_WH and 〈k_e〉_MH as in the left panel (respectively 2.03 and 2.20). The red solid line shows the results for ε = 3, an increased value of 〈k_e〉_MH (3.51) and an unchanged value of 〈k_e〉_WH. Notice how the slope increases by a factor 5/3. The blue solid line shows a similar scenario as the red one, but one where 〈k_e〉_WH has also been increased (2.75), thus yielding a steeper slope. Finally, the orange dotted line (almost completely under the blue one) shows the same scenario as the blue line, but one in which the average excess degree of bisexual men has been increased (from 5.40 to 7.72 without any effect, as predicted). The vertical dashed gray lines show the different thresholds corresponding to their respective R₀ equals 1 (see Eqs (1) and (2)). Altogether, these results confirm that 〈k_e〉_WH and T/ϵ are the only quantities affecting the risk factor of heterosexual women compared to men.