Pandemic dynamics of SARS-CoV-2 using epidemic stage, instantaneous reproductive number and pathogen genome identity (GENI) score: a genomic epidemiological study
DJ Bandoy, Bart Weimer
Received date: 18th May 2020
Background The spread of SARS-CoV-2 created a pandemic crisis with >150,000 cumulative cases in >65 counties within a few months. The reproductive number (R) is a metric to estimate the transmission of a pathogen during an outbreak. Preliminary published estimates were based on the initial outbreak in China. Whole genome sequences (WGS) analysis found mutational variations in the viral genome; however, previous comparisons failed to show a direct relationship between viral genome diversity and the epidemic severity. Methods COVID-19 incidences from different countries were modeled over the epidemic curve. Estimates of the instantaneous R (Wallinga and Teunis method) with a short and standard serial interval were done. WGS were used to determine the populations genomic variation and underpinned creation of the pathogen genome identity (GENI) score, which was merged with the outbreak curve in four distinct phases. Inference of transmission time was based on a mutation rate of 2 mutations/month. Results R estimates revealed differences in the variable infection dynamics between and within outbreak progression for each country examined. Outside China, our R estimates observed propagating dynamics indicating that other countries were poised to move to the takeoff and exponential stages. Population density and local temperatures had no clear relationship to the outbreak progression. Integration of incidence data with the GENI score directly predicted increases in cases as the genome variation increased. Conclusion Integrating the outbreak curve, dynamic R, and WGS variation found a direct association between increasing cases and genome variation. By defining epidemic curve into four stages and integrating the instantaneous country-specific R with the GENI score, we directly connected changes in individual outbreaks based on changes in the virus genome. This resulted in the ability to forecast potential increases in cases as each outbreak progressed. By using instantaneous R estimations and WGS, outbreak dynamics were defined to be linked to viral mutations, indicating that WGS, as a surveillance tool that predicted shifts in each outbreak examined. Integrating epidemiology with genome sequences allows for evidence-based disease outbreak tracking with predictive evidence in near real time.
This is an abstract of a preprint hosted on a preprint server, which is currently undergoing peer review at Scientific Reports. The findings have yet to be thoroughly evaluated, nor has a decision on ultimate publication been made. Therefore, the results reported should not be considered conclusive, and these findings should not be used to inform clinical practice, or public health policy, or be promoted as verified information.