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Polotan FGM, Salazar CRP, Morito HLE, Abulencia MFB, Pantoni RAR, Mercado ES, Hué S, Ditangco RA. Reconstructing the phylodynamic history and geographic spread of the CRF01_AE-predominant HIV-1 epidemic in the Philippines from PR/RT sequences sampled from 2008 to 2018. Virus Evol 2023; 9:vead073. [PMID: 38131006 PMCID: PMC10735293 DOI: 10.1093/ve/vead073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 11/22/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023] Open
Abstract
The Philippines has had a rapidly growing human immunodeficiency virus (HIV) epidemic with a shift in the prevalent subtype from B to CRF01_AE. However, the phylodynamic history of CRF01_AE in the Philippines has yet to be reconstructed. We conducted a descriptive retrospective study reconstructing the history of HIV-1 CRF01_AE transmissions in the Philippines through molecular epidemiology. Partial polymerase sequences (n = 1144) collected between 2008 and 2018 from three island groups were collated from the Research Institute for Tropical Medicine drug resistance genotyping database. Estimation of the time to the most recent common ancestor (tMRCA), effective reproductive number (Re), effective viral population size (Ne), relative migration rates, and geographic spread of CRF01_AE was performed with BEAST. Re and Ne were compared between CRF01_AE and B. Most CRF01_AE sequences formed a single clade with a tMRCA of June 1996 [95 per cent highest posterior density (HPD): December 1991, October 1999]. An increasing CRF01_AE Ne was observed from the tMRCA to 2013. The CRF01_AE Re reached peaks of 2.46 [95 per cent HPD: 1.76, 3.27] in 2007 and 2.52 [95 per cent HPD: 1.83, 3.34] in 2015. A decrease of CRF01_AE Re occurred in the intervening years of 2007 to 2011, reaching as low as 1.43 [95 per cent HPD: 1.06, 1.90] in 2011, followed by a rebound. The CRF01_AE epidemic most likely started in Luzon and then spread to the other island groups of the country. Both CRF01_AE and Subtype B exhibited similar patterns of Re fluctuation over time. These results characterize the subtype-specific phylodynamic history of the largest CRF01_AE cluster in the Philippines, which contextualizes and may inform past, present, and future public health measures toward controlling the HIV epidemic in the Philippines.
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Affiliation(s)
- Francisco Gerardo M Polotan
- Molecular Biology Laboratory, Research Institute for Tropical Medicine, 9002, Research Drive, Filinvest Corporate City, Alabang, Muntinlupa City, Metro Manila 1781, The Philippines
| | - Carl Raymund P Salazar
- Laboratory of Microbiology, Wageningen University and Research, Stippeneng 4, Wageningen 6700 EH, The Netherlands
| | - Hannah Leah E Morito
- Molecular Biology Laboratory, Research Institute for Tropical Medicine, 9002, Research Drive, Filinvest Corporate City, Alabang, Muntinlupa City, Metro Manila 1781, The Philippines
| | - Miguel Francisco B Abulencia
- Molecular Biology Laboratory, Research Institute for Tropical Medicine, 9002, Research Drive, Filinvest Corporate City, Alabang, Muntinlupa City, Metro Manila 1781, The Philippines
| | - Roslind Anne R Pantoni
- Molecular Biology Laboratory, Research Institute for Tropical Medicine, 9002, Research Drive, Filinvest Corporate City, Alabang, Muntinlupa City, Metro Manila 1781, The Philippines
| | - Edelwisa S Mercado
- Molecular Biology Laboratory, Research Institute for Tropical Medicine, 9002, Research Drive, Filinvest Corporate City, Alabang, Muntinlupa City, Metro Manila 1781, The Philippines
| | - Stéphane Hué
- Centre for the Mathematical Modelling of Infectious Diseases (CMMID), London School of Hygiene & Tropical Medicine, Keppel Street, London, Camden WC1E 7HT , UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London, Camden WC1E 7HT , UK
| | - Rossana A Ditangco
- AIDS Research Group, Research Institute for Tropical Medicine, 9002, Research Drive, Filinvest Corporate City, Alabang, Muntinlupa City, Metro Manila 1781, The Philippines
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Inward RPD, Parag KV, Faria NR. Using multiple sampling strategies to estimate SARS-CoV-2 epidemiological parameters from genomic sequencing data. Nat Commun 2022; 13:5587. [PMID: 36151084 PMCID: PMC9508174 DOI: 10.1038/s41467-022-32812-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 08/16/2022] [Indexed: 11/09/2022] Open
Abstract
The choice of viral sequences used in genetic and epidemiological analysis is important as it can induce biases that detract from the value of these rich datasets. This raises questions about how a set of sequences should be chosen for analysis. We provide insights on these largely understudied problems using SARS-CoV-2 genomic sequences from Hong Kong, China, and the Amazonas State, Brazil. We consider multiple sampling schemes which were used to estimate Rt and rt as well as related R0 and date of origin parameters. We find that both Rt and rt are sensitive to changes in sampling whilst R0 and the date of origin are relatively robust. Moreover, we find that analysis using unsampled datasets result in the most biased Rt and rt estimates for both our Hong Kong and Amazonas case studies. We highlight that sampling strategy choices may be an influential yet neglected component of sequencing analysis pipelines.
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Affiliation(s)
| | - Kris V Parag
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK.
- NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, UK.
| | - Nuno R Faria
- Department of Zoology, University of Oxford, Oxford, UK.
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK.
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil.
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