Sequence analysis of new variants of porcine epidemic diarrhea virus in Luzon, Philippines, in 2017.

Porcine epidemic diarrhea virus (PEDV) is a coronavirus that causes emaciation and watery diarrhea in pigs. First identified in Europe in 1977, it eventually spread to Asia and North America, causing deadly outbreaks in neonatal piglets. In the Philippines, PEDV has caused several recorded outbreaks since 2005. However, DNA sequencing studies of local PEDV strains remain few and are limited to gene and gene fragment sequencing. Therefore, to provide updated sequence information about recent PEDV strains in the country, we performed reverse transcription PCR and sequencing of PEDV from swab samples collected from swine farms in the Philippines in 2017. Here, we report the first published whole genome sequence of PEDV from the Philippines as well as CO-26K equivalent (COE) domain sequences of strains from three provinces in Luzon where PEDV was detected in 2017. Sequence analysis suggested that PEDV from both the classical (genotype 1) and pandemic (genotype 2) groups are present in the Philippines, with possible East Asian and North American origins.

Analysis of SARS-CoV-2 genomic epidemiology reveals disease transmission coupled to variant emergence and allelic variation.

The spread of SARS-CoV-2 created a pandemic crisis with > 150,000 cumulative cases in > 65 countries 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, transmission, and the epidemic severity. 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 that 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. R estimates revealed differences in the transmission and 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 that led to new variants. Integrating the outbreak curve, dynamic R, and SNP variation found a direct association between increasing cases and transmission genome evolution. By defining the 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 via SNPs. This resulted in the ability to forecast potential increases in cases as well as mutations that may defeat PCR screening and the infection process. By using instantaneous R estimations and WGS, outbreak dynamics were defined to be linked to viral mutations, indicating that WGS, as a surveillance tool, is required to predict shifts in each outbreak that will provide actionable decision making information. Integrating epidemiology with genome sequencing and modeling allows for evidence-based disease outbreak tracking with predictive therapeutically valuable insights in near real time.