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

Zhu, Mingjian; Shen, Jian; Zeng, Qianli; Tan, Joanna Weihui; Jirapat, Kleepbua; Chew, Ian; Law, Jia Xian; Chew, Sien Ping; Tangathajinda, Anita; Latthitham, Natthjija; Li, Lanjuan

doi:10.3389/fpubh.2021.685315

Cited by 12 publications

(11 citation statements)

References 70 publications

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“…According to the spatiotemporal scanning, Malaysia as the center, along with Singapore, had formed the most likely spatiotemporal cluster of COVID-19 incidence in Southeast Asia since early March 2020, and the corresponding aggregation risk was 72.07 times that of the rest. This could be supported by the results of our previous phylogeographical study using genomic data [ 41 ]. Notably, the spread of the virus between neighboring countries is more in line with the spatial pattern of expansion diffusion.…”

Section: Discussionsupporting

confidence: 79%

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

et al. 2021

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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.

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Section: Discussionsupporting

confidence: 79%

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

et al. 2021

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“…Regression analysis (root-to-tip distance) based on complete genomes of pan India isolates (Dataset_3; Supplementary File S4) depicted temporal signals. Similar observations have been reported earlier [51,52]. The NSR estimated using complete genomes of Indian isolates (6.73 × 10 −2 subs/site/year; 95% HPD: 5 × 10 −3 to 7 × 10 −2 ) is higher as compared to that reported for Southeast Asia data (1.44 × 10 −3 subs/site/year; 95% HPD 1.292 × 10 −3 to 1.613 × 10 −3 ) and global (representative) VoC isolates (6.5 × 10 −4 subs/site/year; 95% HPD 0.58 − 0.77 × 10 −3 ) [51][52][53].…”

Section: Discussionsupporting

confidence: 93%

“…Similar observations have been reported earlier [51,52]. The NSR estimated using complete genomes of Indian isolates (6.73 × 10 −2 subs/site/year; 95% HPD: 5 × 10 −3 to 7 × 10 −2 ) is higher as compared to that reported for Southeast Asia data (1.44 × 10 −3 subs/site/year; 95% HPD 1.292 × 10 −3 to 1.613 × 10 −3 ) and global (representative) VoC isolates (6.5 × 10 −4 subs/site/year; 95% HPD 0.58 − 0.77 × 10 −3 ) [51][52][53]. The estimated NSR in Indian data is higher than the NSR of known RNA viruses and similar to that reported for viroids [54].…”

Section: Discussionsupporting

confidence: 93%

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

Limaye

Kasibhatla

Ramtirthkar

et al. 2021

Viruses

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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.

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“…The evolutionary rate of Southeast Asian SARS-CoV-2 strains (isolated before September 2020) was estimated to be 1.446 × 10 −3 substitutions per site per year [49], while the corresponding estimate for the Indonesian B.1.466.2 variant was almost halved. The decline in the rate of evolution probably occurred under its RNA proofreading mechanism and partly reflected that this variant was more adaptable to the local environment than the original strain [50,51].…”

Section: Discussionmentioning

confidence: 97%

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

Zhu

Zeng²,

Saputro

et al. 2022

Virol J

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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.

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

Cited by 12 publications

References 70 publications

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

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

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

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

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