Pandemic dynamics of COVID-19 using epidemic stage, instantaneous reproductive number and pathogen genome identity (GENI) score: modeling molecular epidemiology

BackgroundGlobal spread of COVID-19 created an unprecedented infectious disease crisis that progressed to a pandemic with >180,000 cases in >100 countries. Reproductive number (R) is an outbreak metric estimating the transmission of a pathogen. Initial R values were published based on the early outbreak in China with limited number of cases with whole genome sequencing. Initial comparisons failed to show a direct relationship viral genomic diversity and epidemic severity was not established for SARS-Cov-2.MethodsEach country’s COVID-19 outbreak status was classified according to epicurve stage (index, takeoff, exponential, decline). Instantaneous R estimates (Wallinga and Teunis method) with a short and standard serial interval examined asymptomatic spread. Whole genome sequences were used to quantify the pathogen genome identity score that were used to estimate transmission time and epicurve stage. Transmission time was estimated based on evolutionary rate of 2 mutations/month.FindingsThe country-specific R revealed variable infection dynamics between and within outbreak stages. Outside China, R estimates revealed propagating epidemics poised to move into the takeoff and exponential stages. Population density and local temperatures had variable relationship to the outbreaks. GENI scores differentiated countries in index stage with cryptic transmission. Integration of incidence data with genome variation directly increases in cases with increased genome variation.InterpretationR was dynamic for each country and during the outbreak stage. Integrating the outbreak dynamic, dynamic R, and genome variation found a direct association between cases and genome variation. Synergistically, GENI provides an evidence-based transmission metric that can be determined by sequencing the virus from each case. We calculated an instantaneous country-specific R at different stages of outbreaks and formulated a novel metric for infection dynamics using viral genome sequences to capture gaps in untraceable transmission. Integrating epidemiology with genome sequencing allows evidence-based dynamic disease outbreak tracking with predictive evidence.FundingPhilippine California Advanced Research Institute (Quezon City, Philippines) and the Weimer laboratory.Research in contextReproductive number is (R) an epidemiological parameter that defines outbreak transmission dynamics. While early estimates of R exist for COVID-19, the sample size is relatively small (<2000 individuals) taken during the early stages of the disease in China. The outbreak is now a pandemic and a more comprehensive assessment is needed to guide public health efforts in making informed decisions to control regional outbreaks. Commonly, R is computed using a sliding window approach, hence assessment of impact of intervention is more difficult to estimate and often underestimates the dynamic nature of R as the outbreak progresses and expands to different regions of the world. Parallel to epidemiological metrics, pathogen whole genome sequencing is being used to infer transmission dynamics. Viral genome analysis requires expert knowledge in understanding viral genomics that can be integrated with the rapid responses needed for public health to advance outbreak mitigation. This study establishes integrative approaches of genome sequencing with established epidemiological outbreak metrics to provide an easily understandable estimate of transmission dynamics aimed at public health response using evidence-based estimates.Added value of this studyEstimates of R are dynamic within the progression of the epidemic curve. Using the framework defined in this study with dynamic estimates of R specific to each epicurve stage combined with whole genome sequencing led to creation of a novel metric called GENI (pathogen genome identity) that provides genomic evolution and variation in the context of the outbreak dynamics. The GENI scores were directly linked and proportional to outbreak changes when using disease incidence from epicurve stages (index, takeoff, exponential, and decline). By simulating short and standard (2 day and 7 day, respectively) serial intervals, we calculated instantaneous R followed by a global comparison that was associated with changes in GENI. This approach quantified R values that are impacted by public health intervention to change the outbreak trajectory and were linked to case incidence (i.e. exponential expansion or decelerating) by country. Integrating viral whole genome sequences to estimate GENI we were able to infer circulation time, local transmission, and index case introduction. Systematic integration of viral whole genome sequences with epidemiological parameters resulted in a simplified approach in assessing the status of outbreak that facilitates decisions using evidence from genomics and epidemiology in combination.Implications of all the available evidenceThis study created a framework of evidence-based intervention by integrating whole genome sequencing and epidemiology during the COVID-19 pandemic. Calculating instantaneous R at different stages of the epicurve for different countries provided an evidence-based assessment of control measures as well as the underlying genomic variation globally that changed the outbreak trajectory for all countries examined. Use of the GENI score translates sequencing data into a public health metric that can be directly integrated in epidemiology for outbreak intervention and global preparedness systems.