Understanding COVID-19 spread in the National Capital Region, Philippines using Genomic Sequences: A Phylodynamic Investigation

To understand the disease dynamics in a particular location, incidence reports are used to estimate key epidemiological parameters such as transmission rates and reproductive numbers. However, incidence data often suffer from underreporting due to logistical concerns in disease surveillance, insufficient testing, etc. One way to address this concern is to use information from viruses' genomes to infer the past ecological dynamics of the disease. Here, we use publicly available SARS-CoV-2 genomic sequences data from the National Capital Region of the Philippines. We use the BEAST2 software to model its dynamics using a Birth-Death Susceptible-Infected-Recovered (BDSIR) model and infer its transmission history. Nineteen whole-genome sequences from NCR, sampled from 3 April to 18 July 2020, were used. We also model the spread of COVID-19 using incidence data through a deterministic ODE-based SIR model. The estimated transmission rate using the genomic sequences is 4.2x10-6 which is greater than the estimated transmission rate using the incidence reports at 2.0x10-8. The estimated basic reproduction number of the BDSIR (2.21) is also higher than that of the SIR (1.21). These results point out the need to cautiously use the reported incidences as basis in making policies in managing infectious diseases outbreak.