Vertical transmission of zika virus and its outcomes: a Bayesian synthesis of prospective studies

A E Ades, Antoni Soriano-Arandes, Ana Alarcon, Francesco Bonfante, Claire Thorne, Catherine Peckham, Carlo Giaquinto

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Prospective studies of Zika virus (ZIKV) in pregnancy have reported rates of Congenital Zika Syndrome (CZS) and other adverse outcomes by trimester. However, ZIKV may infect and damage the fetus in utero, but clear before delivery. The true vertical transmission rate is therefore unknown.

We applied Bayesian latent class analysis to data from 7 prospective studies, in order to simultaneously estimate: vertical transmission (VT) rates, rates of ZIKV-related and non-ZIKV related adverse outcomes, and the diagnostic sensitivity of markers of congenital infection. We allowed for variation between studies in these parameters, and utilised information from women in comparison groups with no PCR-confirmed infection, where available.

Following maternal infection in the 1 st , 2 nd , and 3 rd trimesters, the estimated average risks of VT were 46%, 28% and 25%; 8.5%, 2.9%, and 0.8% deliveries had symptoms consistent with Congenital Zika Syndrome (CZS). We estimated that in 1st, 2nd , and 3rd trimesters respectively 13.4%, 3.3% and 0.3% of pregnancies had adverse outcomes attributable to ZIKV in pregnancy. Diagnostic sensitivity of markers of congenital infection is lowest in the 1 st trimester (42%) but about 80%
subsequently. There was substantial between-study variation in the risks of VT and CZS.

This preliminary analysis recovers the causal effects of ZIKV from disparate study designs. Higher transmission in the first trimester is unusual with congenital infections but accords with laboratory evidence of decreasing susceptibility of placental cells to infection during pregnancy.
Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalThe Lancet Infectious Diseases
Early online date14 Oct 2020
Publication statusE-pub ahead of print - 14 Oct 2020


  • ZIKV
  • Pregnancy
  • Vertical transmission
  • evidence synthesis
  • latent mixture model

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