MicrobeTrace: Retooling molecular epidemiology for rapid public health response

Abstract

Outbreak investigations use data from interviews, healthcare providers, laboratories and surveillance systems. However, integrated use of data from multiple sources requires a patchwork of software that present challenges in usability, interoperability, confidentiality, and cost. Rapid integration, visualization and analysis of data from multiple sources can guide effective public health interventions. We developed MicrobeTrace to facilitate rapid public health responses by overcoming barriers to data integration and exploration in molecular epidemiology. MicrobeTrace is a web-based, client-side, JavaScript application (https://microbetrace.cdc.gov) that runs in Chromium-based browsers and remains fully operational without an internet connection. Using publicly available data, we demonstrate the analysis of viral genetic distance networks and introduce a novel approach to minimum spanning trees that simplifies results. We also illustrate the potential utility of MicrobeTrace in support of contact tracing by analyzing and displaying data from an outbreak of SARS-CoV-2 in South Korea in early 2020. MicrobeTrace is developed and actively maintained by the Centers for Disease Control and Prevention. Users can email microbetrace@cdc.gov for support. The source code is available at https://github.com/cdcgov/microbetrace.

Tracing the trajectories of SARS-CoV-2 variants of concern between December 2020 and September 2021 in the Canary Islands (Spain)

“…When Delta emerged, it was associated with a surge of cases in the archipelago throughout the summer, mimicking what occurred in the UK (Riley et al ., 2021) and in the rest of Europe (Hodcroft, 2021). We attribute this not only to the intrinsically higher transmissibility of the Delta variant compared to the previously circulating VOCs (Campbell et al ., 2021) but also to the concurrent lifting of COVID-19 restrictions in the archipelago and nationwide. Since August 2021, we observed the emergence of diverse Delta sub-lineages (AY.4 and AY.12), similar to what occurred worldwide throughout the summer (Angeletti et al ., 2021; Eales et al ., 2021).…”

Genotyping Cyclospora cayetanensis From Multiple Outbreak Clusters With An Emphasis on a Cluster Linked to Bagged Salad Mix—United States, 2020

“…B, The matrix used to generate the network shown was calculated using the Barratt-Plucinski ensemble of algorithms, as described elsewhere [ 2 , 3 ]. Links were filtered in MicrobeTrace [ 5 ] to link specimens separated by a distance of <0.15 for visualization purposes. Color coding reflects specimens belonging to each temporal-genetic cluster (TGC), determined from the same distance matrix using Ward’s clustering method, as described elsewhere [ 2 ].…”

Exploring the Evolutionary History and Phylodynamics of Human Immunodeficiency Virus Type 1 Outbreak From Unnao, India Using Phylogenetic Approach

“…For the purpose of constructing the transmission network using Microbe Trace (Campbell et al, 2021 ), the initial ML tree was employed. The information regarding the Unnao sequences was provided as the node list.…”

“…Viral sequence data have been known to provide meaningful insights into infection transmission networks, origin, history, and dynamics of an epidemic (Pybus and Rambaut, 2009 ; Frost and Volz, 2010 ; Stadler et al, 2012 , 2013 ; Ragonnet-Cronin et al, 2013 ; Campbell et al, 2021 ). Phylogenetic and phylodynamic analysis have been applied earlier in the case of localized HIV-1 outbreaks to unearth the transmission patterns, history, and demographic reconstruction of an outbreak (Rouet et al, 2018 ; Abidi et al, 2021 ).…”