Infectious disease phylodynamics with occurrence data

1. Phylodynamic models use pathogen genome sequence data to infer epidemiological dynamics. With the increasing genomic surveillance of pathogens, especially during the SARS-CoV-2 pandemic, new practical questions about their use are emerging. 2. One such question focuses on the inclusion of un-sequenced case occurrence data alongside sequenced data to improve phylodynamic analyses. This approach can be particularly valuable if sequencing efforts vary over time. 3. Using simulations, we demonstrate that birth-death phylodynamic models can employ occurrence data to eliminate bias in estimates of the basic reproductive number due to misspecification of the sampling process. In contrast, the coalescent exponential model is robust to such sampling biases, but in the absence of a sampling model it cannot exploit occurrence data. Subsequent analysis of the SARS-CoV-2 epidemic in the northwest USA supports these results. 4. We conclude that occurrence data are a valuable source of information in combination with birth-death models. These data should be used to bolster phylodynamic analyses of infectious diseases and other rapidly spreading species in the future.

COMPARISON OF SARS-COV2 GENOME SEQUENCES FROM PAKISTAN WITH GENOME SEQUENCES FROM OTHER COUNTRIES

OBJECTIVE: To analyze SARS-CoV-2 whole genome reported of Pakistan and compare them with other viral strains collected from other world region to better understand the origin and genetic characterization of the virus. METHODS: All the available genomic information of SARS-CoV-2 including Pakistani strains were collected from various online sources. Phylogenetic analysis of 131 sequences from 11 countries (Brazil, China, India, Italy, Nepal, Pakistan, Spain, Sweden, Taiwan, USA and Viet-Nam) were performed and compared with other related coronaviruses to find the evolution of virus and its origin. Individual SARS-CoV-2 gene, spike (S) glycoprotein and the receptor binding domain (RBD) with closely related coronaviruses were compared to further explore genetic variations and the likely RBD properties of the virus. RESULTS: The analysis shows that genome of all analyzed 131 SARS-CoV-2 strains collected from different geographical area were extremely similar, exhibiting >99% sequence identity. Notably, genome of the SARS-CoV-2 has high similarity (89.1% sequence identity) with the two bat-derived severe acute respiratory syndrome (SARS) like betacoronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21 but was sufficiently divergent from SARS-CoV (82.3% similarity) and MERS-CoV (50% similarity). Phylogenetic analysis shows that the SARS-CoV-2 has relatively similar spike glycoprotein with bat-SL-CoVZC45, however, the RBD was more like that of SARS-CoVGZ02. CONCLUSION: Using different bioinformatics tools, we determined that SARS-CoV-2 has high similarities to bat-derived SARS like betacoronaviruses than SARS-CoV at the whole genome level, however, the RBD was more like that of SARS-CoVGZ02, which shows that they use similar ACE2 as a cell receptor.

[Sars-CoV-2 genomic and phylodynamic analyses]. / Analyses génomiques et phylodynamiques du Sars-CoV-2.

In line with the recent Ebola and Zika virus epidemics, the Covid-19 pandemic has led to an avalanche of genomic data. These data made it possible to better understand the origin of this virus, to date its emergence in China, but also in France, and to analyse the spread of the epidemic using techniques from the emerging field of phylodynamics.