Molecular Phylogenesis and Spatiotemporal Spread of SARS-CoV-2 in Southeast Asia

Dispersion of SARS-CoV-2 lineage BA.5.1.25 and its descendants in Peru during two COVID-19 waves in 2022

During the third year of the pandemic in Peru, the persistent transmission of SARS-CoV-2 led to the appearance of more transmissible and immune-evasive Omicron sublineages; in that context, the National Genomic Surveillance of SARS-CoV-2 performed by the Peruvian National Institute of Health detected spike mutations in the circulating Omicron BA.5.1.25 sublineage which was later designated as DJ.1 and increased during the fourth COVID-19 wave, this eventually branched into new sublineages. The introduction, emergence, and timing of the most recent common ancestor (tMRCA) of BA.5.1.25 and its descendants (DJ.1, DJ.1.1, DJ.1.2, and DJ.1.3) were investigated in this paper as well as the time lags between their emergence and identification by the Peruvian National Institute of Health. Our findings show that ongoing genomic surveillance of SARS-CoV-2 is critical for understanding its phylogenetic evolution and the emergence of novel variations.

Sequential viral introductions and spread of BA.1 across Pakistan provinces during the Omicron wave

BACKGROUND

COVID-19 waves caused by specific SARS-CoV-2 variants have occurred globally at different times. We focused on Omicron variants to understand the genomic diversity and phylogenetic relatedness of SARS-CoV-2 strains in various regions of Pakistan.

METHODS

We studied 276,525 COVID-19 cases and 1,031 genomes sequenced from December 2021 to August 2022. Sequences were analyzed and visualized using phylogenetic trees.

RESULTS

The highest case numbers and deaths were recorded in Sindh and Punjab, the most populous provinces in Pakistan. Omicron variants comprised 93% of all genomes, with BA.2 (32.6%) and BA.5 (38.4%) predominating. The first Omicron wave was associated with the sequential identification of BA.1 in Sindh, then Islamabad Capital Territory, Punjab, Khyber Pakhtunkhwa (KP), Azad Jammu Kashmir (AJK), Gilgit-Baltistan (GB) and Balochistan. Phylogenetic analysis revealed Sindh to be the source of BA.1 and BA.2 introductions into Punjab and Balochistan during early 2022. BA.4 was first introduced in AJK and BA.5 in Punjab. Most recent common ancestor (MRCA) analysis revealed relatedness between the earliest BA.1 genome from Sindh with Balochistan, AJK, Punjab and ICT, and that of first BA.1 from Punjab with strains from KPK and GB.

CONCLUSIONS

Phylogenetic analysis provides insights into the introduction and transmission dynamics of the Omicron variant in Pakistan, identifying Sindh as a hotspot for viral dissemination. Such data linked with public health efforts can help limit surges of new infections.

Tracking the molecular evolution and transmission patterns of SARS-CoV-2 lineage B.1.466.2 in Indonesia based on genomic surveillance data

Background

As a new epi-center of COVID-19 in Asia and a densely populated developing country, Indonesia is facing unprecedented challenges in public health. SARS-CoV-2 lineage B.1.466.2 was reported to be an indigenous dominant strain in Indonesia (once second only to the Delta variant). However, it remains unclear how this variant evolved and spread within such an archipelagic nation.

Methods

For statistical description, the spatiotemporal distributions of the B.1.466.2 variant were plotted using the publicly accessible metadata in GISAID. A total of 1302 complete genome sequences of Indonesian B.1.466.2 strains with high coverage were downloaded from the GISAID’s EpiCoV database on 28 August 2021. To determine the molecular evolutionary characteristics, we performed a time-scaled phylogenetic analysis using the maximum likelihood algorithm and called the single nucleotide variants taking the Wuhan-Hu-1 sequence as reference. To investigate the spatiotemporal transmission patterns, we estimated two dynamic parameters (effective population size and effective reproduction number) and reconstructed the phylogeography among different islands.

Results

As of the end of August 2021, nearly 85% of the global SARS-CoV-2 lineage B.1.466.2 sequences (including the first one) were obtained from Indonesia. This variant was estimated to account for over 50% of Indonesia’s daily infections during the period of March–May 2021. The time-scaled phylogeny suggested that SARS-CoV-2 lineage B.1.466.2 circulating in Indonesia might have originated from Java Island in mid-June 2020 and had evolved into two disproportional and distinct sub-lineages. High-frequency non-synonymous mutations were mostly found in the spike and NSP3; the S-D614G/N439K/P681R co-mutations were identified in its larger sub-lineage. The demographic history was inferred to have experienced four phases, with an exponential growth from October 2020 to February 2021. The effective reproduction number was estimated to have reached its peak (11.18) in late December 2020 and dropped to be less than one after early May 2021. The relevant phylogeography showed that Java and Sumatra might successively act as epi-centers and form a stable transmission loop. Additionally, several long-distance transmission links across seas were revealed.

Conclusions

SARS-CoV-2 variants circulating in the tropical archipelago may follow unique patterns of evolution and transmission. Continuous, extensive and targeted genomic surveillance is essential.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12985-022-01830-1.

A mixture model for determining SARS-Cov-2 variant composition in pooled samples

MOTIVATION

Despite of the fast development of highly effective vaccines to control the current COVID-19 pandemics, the unequal distribution and availability of these vaccines worldwide and the number of people infected in the world lead to the continuous emergence of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) variants of concern. Therefore, it is likely that real-time genomic surveillance will be continuously needed as an unceasing monitoring tool, necessary to follow the spread of the disease and the evolution of the virus. In this context, new genomic variants of SARS-CoV-2, including variants refractory to current vaccines, makes genomic surveillance programs tools of utmost importance. Nevertheless, the lack of appropriate analytical tools to quickly and effectively access the viral composition in meta-transcriptomic sequencing data, including environmental surveillance, represent possible challenges that may impact the fast adoption of this approach to mitigate the spread and transmission of viruses.

RESULTS

We propose a statistical model for the estimation of the relative frequencies of SARS-CoV-2 variants in pooled samples. This model is built by considering a previously defined selection of genomic polymorphisms that characterize SARS-CoV-2 variants. The methods described here support both raw sequencing reads for polymorphisms-based markers calling and predefined markers in the variant call format. Results obtained using simulated data show that our method is quite effective in recovering the correct variant proportions. Further, results obtained by considering longitudinal data from wastewater samples of two locations in Switzerland agree well with those describing the epidemiological evolution of COVID-19 variants in clinical samples of these locations. Our results show that the described method can be a valuable tool for tracking the proportions of SARS-CoV-2 variants in complex mixtures such as waste water and environmental samples.

AVAILABILITY AND IMPLEMENTATION

http://github.com/rvalieris/LCS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Circulation and Evolution of SARS-CoV-2 in India: Let the Data Speak

The COVID-19 pandemic is a global challenge that impacted 200+ countries. India ranks in the second and third positions in terms of number of reported cases and deaths. Being a populous country with densely packed cities, SARS-CoV-2 spread exponentially. India sequenced ≈0.14% isolates from confirmed cases for pandemic surveillance and contributed ≈1.58% of complete genomes sequenced globally. This study was designed to map the circulating lineage diversity and to understand the evolution of SARS-CoV-2 in India using comparative genomics and population genetics approaches. Despite varied sequencing coverage across Indian States and Union Territories, isolates belonging to variants of concern (VoC) and variants of interest (VoI) circulated, persisted, and diversified during the first seventeen months of the pandemic. Delta and Kappa lineages emerged in India and spread globally. The phylogenetic tree shows lineage-wise monophyletic clusters of VoCs/VoIs and diversified tree topologies for non-VoC/VoI lineages designated as 'Others' in this study. Evolutionary dynamics analyses substantiate a lack of spatio-temporal clustering, which is indicative of multiple global and local introductions. Sites under positive selection and significant variations in spike protein corroborate with the constellation of mutations to be monitored for VoC/VoI as well as substitutions that are characteristic of functions with implications in virus-host interactions, differential glycosylation, immune evasion, and escape from neutralization.

Early Spatiotemporal Patterns and Population Characteristics of the COVID-19 Pandemic in Southeast Asia

This observational study aims to investigate the early disease patterns of coronavirus disease 2019 (COVID-19) in Southeast Asia, consequently providing historical experience for further interventions. Data were extracted from official websites of the WHO and health authorities of relevant countries. A total of 1346 confirmed cases of COVID-19, with 217 recoveries and 18 deaths, were reported in Southeast Asia as of 16 March 2020. The basic reproductive number (R0) of COVID-19 in the region was estimated as 2.51 (95% CI:2.31 to 2.73), and there were significant geographical variations at the subregional level. Early transmission dynamics were examined with an exponential regression model: y = 0.30e0.13x (p < 0.01, R2 = 0.96), which could help predict short-term incidence. Country-level disease burden was positively correlated with Human Development Index (r = 0.86, p < 0.01). A potential early shift in spatial diffusion patterns and a spatiotemporal cluster occurring in Malaysia and Singapore were detected. Demographic analyses of 925 confirmed cases indicated a median age of 44 years and a sex ratio (male/female) of 1.25. Age may play a significant role in both susceptibilities and outcomes. The COVID-19 situation in Southeast Asia is challenging and unevenly geographically distributed. Hence, enhanced real-time surveillance and more efficient resource allocation are urgently needed.