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Revealing regional disparities in the transmission potential of SARS-CoV-2 from interventions in Southeast Asia.

ABSTRACT: SARS-CoV-2 is a new pathogen responsible for the coronavirus disease 2019 (COVID-19) outbreak. Southeast Asia was the first region to be affected outside China, and although COVID-19 cases have been reported in all countries of Southeast Asia, both the policies and epidemic trajectories differ substantially, potentially due to marked differences in social distancing measures that have been implemented by governments in the region. This paper studies the across-country relationships between social distancing and each population's response to policy, the subsequent effects of these responses to the transmissibility and epidemic trajectories of SARS-CoV-2. The analysis couples COVID-19 case counts with real-time mobility data across Southeast Asia to estimate the effects of host population response to social distancing policy and the subsequent effects on the transmissibility and epidemic trajectories of SARS-CoV-2. A novel inference strategy for the time-varying reproduction number is developed to allow explicit inference of the effects of social distancing on the transmissibility of SARS-CoV-2 through a regression structure. This framework replicates the observed epidemic trajectories across most Southeast Asian countries, provides estimates of the effects of social distancing on the transmissibility of disease and can simulate epidemic histories conditional on changes in the degree of intervention scenarios and compliance within Southeast Asia.

SUBMITTER: Lim JT

PROVIDER: S-EPMC7482285 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Revealing regional disparities in the transmission potential of SARS-CoV-2 from interventions in Southeast Asia.

Lim Jue Tao JT Dickens Borame Sue Lee BSL Choo Esther Li Wen ELW Chew Lawrence Zheng Xiong LZX Koo Joel Rui Han JRH Tam Clarence C Park Minah M Cook Alex R AR

Proceedings. Biological sciences 20200826 1933

SARS-CoV-2 is a new pathogen responsible for the coronavirus disease 2019 (COVID-19) outbreak. Southeast Asia was the first region to be affected outside China, and although COVID-19 cases have been reported in all countries of Southeast Asia, both the policies and epidemic trajectories differ substantially, potentially due to marked differences in social distancing measures that have been implemented by governments in the region. This paper studies the across-country relationships between socia ...[more]

PMID: 32842911

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Project description: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.

Project description:BackgroundSARS-CoV-2, the virus that caused the global COVID-19 pandemic, has severely impacted Central Asia; in spring 2020, high numbers of cases and deaths were reported in this region. The second wave of the COVID-19 pandemic is currently breaching the borders of Central Asia. Public health surveillance is necessary to inform policy and guide leaders; however, existing surveillance explains past transmissions while obscuring shifts in the pandemic, increases in infection rates, and the persistence of the transmission of COVID-19.ObjectiveThe goal of this study is to provide enhanced surveillance metrics for SARS-CoV-2 transmission that account for weekly shifts in the pandemic, including speed, acceleration, jerk, and persistence, to better understand the risk of explosive growth in each country and which countries are managing the pandemic successfully.MethodsUsing a longitudinal trend analysis study design, we extracted 60 days of COVID-19-related data from public health registries. We used an empirical difference equation to measure the daily number of cases in the Central Asia region as a function of the prior number of cases, level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.ResultsCOVID-19 transmission rates were tracked for the weeks of September 30 to October 6 and October 7-13, 2020, in Central Asia. The region averaged 11,730 new cases per day for the first week and 14,514 for the second week. Infection rates increased across the region from 4.74 per 100,000 persons to 5.66. Russia and Turkey had the highest 7-day moving averages in the region, with 9836 and 1469, respectively, for the week of October 6 and 12,501 and 1603, respectively, for the week of October 13. Russia has the fourth highest speed in the region and continues to have positive acceleration, driving the negative trend for the entire region as the largest country by population. Armenia is experiencing explosive growth of COVID-19; its infection rate of 13.73 for the week of October 6 quickly jumped to 25.19, the highest in the region, the following week. The region overall is experiencing increases in its 7-day moving average of new cases, infection, rate, and speed, with continued positive acceleration and no sign of a reversal in sight.ConclusionsThe rapidly evolving COVID-19 pandemic requires novel dynamic surveillance metrics in addition to static metrics to effectively analyze the pandemic trajectory and control spread. Policy makers need to know the magnitude of transmission rates, how quickly they are accelerating, and how previous cases are impacting current caseload due to a lag effect. These metrics applied to Central Asia suggest that the region is trending negatively, primarily due to minimal restrictions in Russia.

Project description:The unprecedented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) global sequencing effort has suffered from an analytical bottleneck. Many existing methods for phylogenetic analysis are designed for sparse, static datasets and are too computationally expensive to apply to densely sampled, rapidly expanding datasets when results are needed immediately to inform public health action. For example, public health is often concerned with identifying clusters of closely related samples, but the sheer scale of the data prevents manual inspection and the current computational models are often too expensive in time and resources. Even when results are available, intuitive data exploration tools are of critical importance to effective public health interpretation and action. To help address this need, we present a phylogenetic heuristic that quickly and efficiently identifies newly introduced strains in a region, resulting in clusters of infected individuals, and their putative geographic origins. We show that this approach performs well on simulated data and yields results largely congruent with more sophisticated Bayesian phylogeographic modeling approaches. We also introduce Cluster-Tracker (https://clustertracker.gi.ucsc.edu/), a novel interactive web-based tool to facilitate effective and intuitive SARS-CoV-2 geographic data exploration and visualization across the USA. Cluster-Tracker is updated daily and automatically identifies and highlights groups of closely related SARS-CoV-2 infections resulting from the transmission of the virus between two geographic areas by travelers, streamlining public health tracking of local viral diversity and emerging infection clusters. The site is open-source and designed to be easily configured to analyze any chosen region, making it a useful resource globally. The combination of these open-source tools will empower detailed investigations of the geographic origins and spread of SARS-CoV-2 and other densely sampled pathogens.

Project description:BackgroundThe role played by regional organisations in climate change adaptation and health is growing in Southeast Asia, with the Asian Development Bank and the Asia-Pacific Regional Forum on Health and Environment both supporting health and adaptation initiatives. There is, however, a lack of empirical research on the value that regional organisations add to national health-related adaptation. This qualitative research compares regional project and governance-based models of adaptation and health support in Southeast Asia, providing an analysis of strengths and weaknesses of each, as well as possibilities for improvement.MethodsAn existing adaptation assessment framework was modified for this research, and used as a guide to gather and analyse data from academic and grey literature, policy documents and interviews in order to qualitatively assess two organisations and their different models of adaptation and health support.ResultsThis research found differing strengths in the approaches to climate change and health used by the Asian Development Bank and by the Asia-Pacific Regional Forum on Health and Environment. The regional forum has vision, high levels of perceived legitimacy, and access to 'in-house' expertise in public health and climate change. Conversely, the Asian Development Bank has strengths in project management and access to significant financial resources to support work in climate change and health.ConclusionWhen regional organisations, such as the Asian Development Bank and the Asia-Pacific Regional Forum on Health and Environment, have membership and mandate overlaps, their work will likely benefit from well designed, institutionalised and incentivised coordination mechanisms. Coordination can reduce redundancies as well as the administrative workload on partner government agencies. In the case-study examined, the Asian Development Bank's project management expertise complements the vision and high levels of perceived legitimacy of the Asia-Pacific Regional Forum on Health and Environment, thus a coordinated approach could deliver improved adaptation and health outcomes.

Dataset Information

Revealing regional disparities in the transmission potential of SARS-CoV-2 from interventions in Southeast Asia.

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Revealing regional disparities in the transmission potential of SARS-CoV-2 from interventions in Southeast Asia.

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