Travel Surveillance and Genomics Uncover a Hidden Zika Outbreak during the Waning Epidemic

Abstract

publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Article Travel Surveillance and Genomics Uncover a Hidden Zika Outbreak during the Waning EpidemicGraphical Abstract Highlights d Travel surveillance and genomics uncovered hidden Zika transmission d An unreported and 1-year delayed Zika outbreak was detected in Cuba d Mosquito control may delay, not prevent, Zika virus establishment d A surveillance framework to detect hidden outbreaks was created

Genomic and Epidemiological Surveillance of Zika Virus in the Amazon Region

“…To better understand the establishment and transmission of ZIKV in Manaus, we added our newly generated consensus genome sequences to a global dataset of 423 ZIKV genomes, including recently released ZIKV genomes from Angola and Cuba (Hill et al, 2019;Grubaugh et al, 2019), and we estimated an initial maximum likelihood (ML) phylogenetic tree ( Figure 5). We find that 93% (55 of 59) of the novel Manaus isolates fall within a single large well-supported monophyletic clade (bootstrap score Figure 2.…”

“…Two complete or near-complete ZIKV genome datasets were generated. Dataset 1 (n = 482) comprised the data reported in this study (n = 59) plus a larger and updated dataset including recently released data from the ZIKV epidemic in Angola and Cuba (Hill et al, 2019;Grubaugh et al, 2019). Subsequently, to investigate the dynamic of the ZIKV infection within Manaus, genetic analyses were conducted on a smaller dataset including only sequences pertaining to the largest clade of virus strains circulating in Manaus (n = 56).…”

A Department of Defense Laboratory Consortium Approach to Next Generation Sequencing and Bioinformatics Training for Infectious Disease Surveillance in Kenya

“…Utilizing a global network of partner DoD medical research and public health laboratories, GEIS funds surveillance activities in over 70 countries to inform force health protection through timely and actionable infectious disease surveillance information (Chakhunashvili et al, 2017;Chang et al, 2018;Coleman et al, 2018;Koka et al, 2018;Anyamba et al, 2019;Guerra et al, 2019;Juma et al, 2019;Rivers et al, 2019;Rocha et al, 2019;Sugiharto et al, 2019). Unsurprisingly, development of NGS and bioinformatics methods for infectious disease surveillance and control has enabled a rapid expansion of GEIS partner studies that utilize pathogen genomic information (Frey et al, 2016;Maljkovic Berry et al, 2016, 2019aLee et al, 2017;Mullins et al, 2017;Salje et al, 2017;Cowell et al, 2018;LaBreck et al, 2018;Srijan et al, 2018;Grubaugh et al, 2019;Kim et al, 2019;Mbala-Kingebeni et al, 2019;Millar et al, 2019;Pollett et al, 2019;Wiley et al, 2019). However, NGS and bioinformatics can generally be technically challenging, as it requires specific knowledge of complex wet lab and bioinformatics processes (Maljkovic Berry et al, 2019b).…”

nosoi: a stochastic agent-based transmission chain simulation framework in R

“…These reconstructions rely on evolution occurring on the same time scale as the epidemic or transmission process, which is the case for most fast-evolving pathogens such as RNA viruses [8,9]. The inferred evolutionary history has been used in recent years to estimate the timing, the origin, or the effectiveness of mitigation measures of several epidemics [10][11][12][13].The accuracy, validity, or limitations of both currently available and future methods, however, generally lack formal assessment on datasets for which we have been able to observe the actual geographical spread and the complex factors that shaped its pattern. In that context, a simulated dataset is extremely useful as the exact transmission history is known and can be compared to the histories inferred from different software packages.…”

“…These reconstructions rely on evolution occurring on the same time scale as the epidemic or transmission process, which is the case for most fast-evolving pathogens such as RNA viruses [8,9]. The inferred evolutionary history has been used in recent years to estimate the timing, the origin, or the effectiveness of mitigation measures of several epidemics [10][11][12][13].…”