ABSTRACT
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.
ABSTRACT
Background: The ongoing coronavirus disease 2019 (COVID-19) pandemic has posed an unprecedented challenge to public health in Southeast Asia, a tropical region with limited resources. This study aimed to investigate the evolutionary dynamics and spatiotemporal patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the region. Materials and Methods: A total of 1491 complete SARS-CoV-2 genome sequences from 10 Southeast Asian countries were downloaded from the Global Initiative on Sharing Avian Influenza Data (GISAID) database on November 17, 2020. The evolutionary relationships were assessed using maximum likelihood (ML) and time-scaled Bayesian phylogenetic analyses, and the phylogenetic clustering was tested using principal component analysis (PCA). The spatial patterns of SARS-CoV-2 spread within Southeast Asia were inferred using the Bayesian stochastic search variable selection (BSSVS) model. The effective population size (Ne) trajectory was inferred using the Bayesian Skygrid model. Results: Four major clades (including one potentially endemic) were identified based on the maximum clade credibility (MCC) tree. Similar clustering was yielded by PCA; the first three PCs explained 46.9% of the total genomic variations among the samples. The time to the most recent common ancestor (tMRCA) and the evolutionary rate of SARS-CoV-2 circulating in Southeast Asia were estimated to be November 28, 2019 (September 7, 2019 to January 4, 2020) and 1.446 × 10-3 (1.292 × 10-3 to 1.613 × 10-3) substitutions per site per year, respectively. Singapore and Thailand were the two most probable root positions, with posterior probabilities of 0.549 and 0.413, respectively. There were high-support transmission links (Bayes factors exceeding 1,000) in Singapore, Malaysia, and Indonesia; Malaysia involved the highest number (7) of inferred transmission links within the region. A twice-accelerated viral population expansion, followed by a temporary setback, was inferred during the early stages of the pandemic in Southeast Asia. Conclusions: With available genomic data, we illustrate the phylogeography and phylodynamics of SARS-CoV-2 circulating in Southeast Asia. Continuous genomic surveillance and enhanced strategic collaboration should be listed as priorities to curb the pandemic, especially for regional communities dominated by developing countries.
Subject(s)
COVID-19 , SARS-CoV-2 , Asia, Southeastern/epidemiology , Bayes Theorem , Genome, Viral/genetics , Humans , PhylogenyABSTRACT
Background: The global outbreak of coronavirus disease 2019 (COVID-19) has been ongoing in Southeast Asia since 13 January 2020. We conducted an observational study to investigate underlying disease patterns of COVID-19 in Southeast Asia, and consequently to guide intervention strategies against the pandemic.Methods: In this population-level observational study set in Southeast Asia, we compiled a list of patients with COVID-19 (n = 925) and daily country-level case counts (n = 1346) from 13 January 2020 through 16 March 2020. All epidemiological data were extracted from official websites of the WHO and health authorities of each Southeast Asian country. Relevant spatiotemporal distributions, demographic characteristics, and short-term trends were assessed.Results: A total of 1,346 confirmed cases of COVID-19, with 217 (16.1%) recoveries and 18 (1.3%) deaths, were reported in Southeast Asia as of 16 March 2020. Early transmission dynamics were examined with an exponential regression model: y=0.30e0.13x (p<0·01, adjusted R2 = 0.96). Using this model, we predicted that the cumulative number of reported COVID-19 cases in Southeast Asia would exceed 10,000 by early April 2020. A total of 74 cities across eight countries in Southeast Asia were affected by COVID-19. Most of the confirmed cases were located in five international metropolitan areas. Demographic analyses of the 925 confirmed cases indicated a median age of 44 years and a sex ratio of 1.25. The median age of the local patient population was significantly higher than that of the corresponding country’s general population (p<0·01), whereas the sex ratio did not significantly differ.Conclusions: The COVID-19 situation in Southeast Asia is unevenly geographically distributed and pessimistic in the short term. Age may play a significant role in both the susceptibility to and outcome of infection. Real-time active surveillance and targeted intervention strategies are urgently needed to contain the pandemic.