Transmission of B.1.617.2 Delta variant between vaccinated healthcare workers

Abstract

Breakthrough infections with SARS-CoV-2 Delta variant have been reported in doubly-vaccinated recipients and as re-infections. Studies of viral spread within hospital settings have highlighted the potential for transmission between doubly-vaccinated patients and health care workers and have highlighted the benefits of high-grade respiratory protection for health care workers. However the extent to which vaccination is preventative of viral spread in health care settings is less well studied. Here, we analysed data from 118 vaccinated health care workers (HCW) across two hospitals in India, constructing two probable transmission networks involving six HCWs in Hospital A and eight HCWs in Hospital B from epidemiological and virus genome sequence data, using a suite of computational approaches. A maximum likelihood reconstruction of transmission involving known cases of infection suggests a high probability that doubly vaccinated HCWs transmitted SARS-CoV-2 between each other and highlights potential cases of virus transmission between individuals who had received two doses of vaccine. Our findings show firstly that vaccination may reduce rates of transmission, supporting the need for ongoing infection control measures even in highly vaccinated populations, and secondly we have described a novel approach to identifying transmissions that is scalable and rapid, without the need for an infection control infrastructure.

Figure 1

Maximum-likelihood phylogenies of double-vaccinated individuals in Hospitals A and B, with 100 randomly selected community-origin Delta variant SARS-CoV-2 genomes from the state of Delhi, India. Pangolin lineages of all sequences are indicated by the adjacent heatmap. Vaccination status of HCW is indicated by the far-right heatmap. Community sequences have unknown vaccination status.

Figure 2

Potential transmission networks between HCWs. Individual labels are coloured according to vaccine status, including the timing prior to infection at which the second vaccine was given, where relevant. The thickness of lines between individuals show the probabilities of distinct pairwise transmission events between individuals; these probabilities are conditional on transmission having occurred between the individuals observed in each network. Labels show the relative dates on which individuals became symptomatic, and respective gains of nucleotides in sequences collected from each individual with respect to the mutual consensus.

Figure 3

Assessments of network consistency. Data from simulations conducted on the transmission networks inferred for (A, B) hospital A and (C, D) hospital B. Simulations used the evolutionary model for the Delta variant of SARS-CoV-2 used to infer transmission networks. Simulated data were assessed to measure the total number of independent SNPs arising across the course of the transmission network, and the number of individuals in the network with SNPs that were unique to themselves. Histograms show distributions of each statistic across simulations, while the vertical dashed line shows the statistic calculated for the inferred network. A high value within the inferred network suggests the likely presence of missing data. Thus, data from the networks inferred for hospital A were consistent with the statistics of the simulations, though the data from hospital B showed  a significant outlier in terms of the number of individuals in the network with unique SNPs. These results provide evidence that the networks derived for hospital B is affected by missing data, though no such conclusion could be made for the networks derived for hospital B.