Project 1 focuses on how viral genomic variation; adoption of public health mitigation measures and mobility patterns contribute to spatially and temporally explicit pathways of SARS-CoV-2 transmission at local and regional scales.
Dr. Stafford received her BA in psychology from Notre Dame of Maryland University, her MPH in Health Policy and Management from the Johns Hopkins Bloomberg School of Public Health, and her PhD in Epidemiology from the Department of Epidemiology and Public Health at the University of Maryland School of Medicine. Dr. Stafford is an infectious disease epidemiologist with 25 years of experience in the design, implementation, and evaluation of domestic and international HIV care and treatment programs.
University of Minnesota & University of Port Harcourt
To start assessing the potential effectiveness of COVID-19 public health measures in sub-Saharan Africa, we conducted an ecological study using publicly-available data from Nigeria and South Africa from April 23rd, 2020 to May 1st, 2022 to describe patterns between population-level adoption of public health measures with the number of reported new COVID-19 cases across the different COVID-19 waves in these two countries. Our preliminary results demonstrated how Nigeria’s consistent self-reported mask use never reached levels of 70% and its use declined with each wave. While in South Africa, mask use levels reached the upper nineties. The least adopted measures in both countries were avoiding grocery markets/pharmacies, avoiding spending time with individuals outside their households, and working at home. Consistent mask use and vaccination at high levels and preferably with the addition of at least one booster were shown to have an indirect relationship with new COVID-19 cases in these settings. As we face new COVID-19 variants and limited vaccine supply in Africa, it is essential to identify which public health measures are implementable with high adoption and fidelity for this unique context.
Dr. Thomas Kono, a bioinformatics analyst at the University of Minnesota Supercomputing Institute, has led the effort to assess SARS-CoV-2 phylogenetic changes and transmission potential over time within Nigeria. This Project 1 project involves the genetic analysis of SARS-CoV-2 genomes collected from patients in Nigeria and submitted to GISAID. Working with other researchers on the INFORM project, we were able to identify which viral sequences were collected during various “waves” of viral spread through Nigeria and which geographic region they were collected from. Comparisons among the samples revealed a pattern of increasing viral diversity over time, especially in the spike protein region, consistent with global patterns of SARS-CoV-2 evolution. Future efforts will involve identification of potential recombinant viral lineages and characterization of variation in mutation rate, both across Nigeria and across the viral genome.
Additionally, Project 1 continues to collaborate with Akros, our industry partner to strengthen the Reveal geospatial platform. Reveal is an open-source platform that uses spatial intelligence to drive the delivery of life-saving interventions. Akros has developed several additions to the tool including, the creation of the visualization module that supports dataset import and geospatial data tagging, data attribute selection and visualization, basic data aggregation, heat mapping, import and visualization of GPS-point type data, and download of multiple overlaid datasets and geospatial features as one dataset. We are currently developing functionality for multiple attribute overlays that will inform the creation of risk layers and additional geospatial analysis and modeling. Under project 1, we will be conducting deeper geospatial analysis on the overlaid project datasets; the INFORM consortium will utilize Reveal moving forward to visualize and geospatially analyze datasets across all projects. The eventual goal is to take these data and create targeted public health plans through the Reveal platform in order to guide and track in-field delivery of health interventions at a granular level with each area of interest.