Molecular epidemiology of SARS-CoV-2 genome sentinel surveillance in commercial COVID-19 testing sites targeting asymptomatic individuals during Japan's seventh epidemic wave.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

COVID-19*/epidemiology ; COVID-19*/virology ; COVID-19*/transmission ; COVID-19*/diagnosis ; SARS-CoV-2*/genetics ; SARS-CoV-2*/isolation & purification ; Genome, Viral* ; Phylogeny* ; Sentinel Surveillance*; Humans ; Japan/epidemiology ; Molecular Epidemiology ; Asymptomatic Infections/epidemiology

Eight peaks of coronavirus disease 2019 (COVID-19) outbreak occurred in Japan, each associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern. The National Epidemiological Surveillance of Infectious Diseases (NESID) analyzed viral genome sequences from symptomatic patients and submitted the results to GISAID. Meanwhile, commercial testing services occasionally sequence samples from asymptomatic individuals. We compared a total of 1248 SARS-CoV-2 full-genome sequences obtained from the SB Coronavirus Inspection Center Corp. (SBCVIC) during Japan's seventh wave, which was dominated by Omicron variants, with 1764 sequences obtained in Japan from GISAID during the same period using chronological phylogenies and molecular transmission networks. The number of SBCVIC sequences was consistent with the number of cases reported by NESID. The SBCVIC detected a shift in the PANGO lineage from BA.2 to BA.5 earlier than that of GISAID. BA.2 lineages from the SBCVIC were distributed at different locations in the transmission network dominated by GISAID entries, whereas BA.5 lineages from SBCVIC and GISAID often formed distinct subclusters. Test-based sentinel surveillance of asymptomatic individuals may be a more manageable approach compared to notifiable disease surveillance; however, it may not necessarily capture all infection populations throughout Japan.
(© 2024. The Author(s).)

WHO. Japan: WHO Coronavirus Disease (COVID-19) Dashboard with Vaccination Data. https://covid19.who.int/region/wpro/country/jp (2023).
Ministry of Health, Labour and Welfare, Japan. Novel Coronavirus (COVID-19). https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000164708_00079.html .
Kitahara, K., Nishikawa, Y., Yokoyama, H., Kikuchi, Y. & Sakoi, M. An overview of the reclassification of COVID-19 of the infectious diseases control law in Japan. Glob. Health Med. 5, 70–74 (2023).
National Institute of Infectious Diseases, Japan. Summary on SARS-CoV-2 variants of concern for increased infectivity/transmissibility and antigenic changes (No. 26). https://www.niid.go.jp/niid/en/2019-ncov-e/2551-cepr/11909-summary-on-sars-cov-2-variants-of-concern-for-increased-infectivity-transmissibility-and-antigenic-changes-no-26-en-2.html (2023).
Khare, S. et al. GISAID’s role in pandemic response. China CDC Wkly. 3, 1049–1051 (2021). (PMID: 10.46234/ccdcw2021.255349345148668406)
Elbe, S. & Buckland-Merrett, G. Data, disease and diplomacy: GISAID’s innovative contribution to global health. Glob. Chall. 1, 33–46 (2017). (PMID: 10.1002/gch2.1018315652586607375)
Shu, Y. & McCauley, J. GISAID: Global initiative on sharing all influenza data – from vision to reality. Eurosurveillance. 22, 30494 (2017). (PMID: 10.2807/1560-7917.ES.2017.22.13.30494283829175388101)
Ferdinand, A. S. et al. An implementation science approach to evaluating pathogen whole genome sequencing in public health. Genome Med. 13, 121 (2021). (PMID: 10.1186/s13073-021-00934-7343210768317677)
German, R. R. et al. Updated guidelines for evaluating public health surveillance systems: Recommendations from the Guidelines Working Group. MMWR Recomm. Rep. 50, 1–35 (2001). (PMID: 18634202)
Buitrago-Garcia, D. et al. Occurrence and transmission potential of asymptomatic and presymptomatic SARSCoV-2 infections: A living systematic review and meta-analysis. PLoS Med. 17, e1003346 (2020). (PMID: 10.1371/journal.pmed.1003346329608817508369)
Casey-Bryars, M. et al. Presymptomatic transmission of SARS-CoV-2 infection: A secondary analysis using published data. BMJ Open. 11, e041240 (2021). (PMID: 10.1136/bmjopen-2020-04124034183334)
Furuse, Y. Properties of the omicron variant of SARS-CoV-2 affect public health measure effectiveness in the COVID-19 epidemic. Int. J. Environ. Res. Public Health. 19, 4930 (2022). (PMID: 10.3390/ijerph19094930355643259099739)
Lemey, P. et al. Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2. PLoS Pathog. 10, e1003932 (2014). (PMID: 10.1371/journal.ppat.1003932245861533930559)
Magee, D. & Scotch, M. The effects of random taxa sampling schemes in Bayesian virus phylogeography. Infect. Genet. Evol. 64, 225–230 (2018). (PMID: 10.1016/j.meegid.2018.07.003299914556123251)
Edmond, M., Wong, C. & Chuang, S. K. Evaluation of sentinel surveillance system for monitoring hand, foot and mouth disease in Hong Kong. Public Health. 125, 777–783 (2011). (PMID: 10.1016/j.puhe.2011.09.00222036193)
Nuvey, F. S. et al. Evaluation of the sentinel surveillance system for influenza-like illnesses in the Greater Accra region, Ghana, 2018. PLoS One. 14, e0213627 (2019). (PMID: 10.1371/journal.pone.0213627308704896417674)
Babakazo, P. et al. Evaluation of the influenza sentinel surveillance system in the Democratic Republic of Congo, 2012–2015. BMC Public Health. 19, 1652 (2019). (PMID: 10.1186/s12889-019-8008-2318237636902419)
Oltean, H. N. et al. Sentinel Surveillance system implementation and evaluation for SARS-CoV-2 genomic data, Washington, USA, 2020–2021. Emerg. Infect. Dis. 29, 242–251 (2023). (PMID: 10.3201/eid2902.221482365965659881772)
Ginige, S. et al. Protocol for a winter sentinel surveillance program of notifiable respiratory viruses in Queensland. PLoS One. 17, e0277895 (2022). (PMID: 10.1371/journal.pone.0277895364416999704554)
Terada-Hirashima, J. et al. Investigation of the use of PCR testing prior to ship boarding to prevent the spread of SARS-CoV-2 from urban areas to less populated remote islands. Glob. Health Med. 4, 174–179 (2022). (PMID: 10.35772/ghm.2022.01008358550679243411)
Hodcroft, E.B. CoVariants: SARS-CoV-2 mutations and variants of interest. https://covariants.org/ (2021).
Hadfield, J. et al. NextStrain: Real-time tracking of pathogen evolution. Bioinformatics 34, 4121–4123 (2018). (PMID: 10.1093/bioinformatics/bty407297909396247931)
O’Toole, Á. et al. Assignment of epidemiological lineages in an emerging pandemic using the pangolin tool. Virus Evol. 7, veab064 (2021).
Furukawa, N. W., Furukawa, N. W., Brooks, J. T. & Sobel, J. Evidence supporting transmission of severe acute respiratory syndrome coronavirus 2 while presymptomatic or asymptomatic. Emerg. Infect. Dis. 26, e201595 (2020). (PMID: 10.3201/eid2607.201595)
Bae, S., Lim, J. S., Kim, J. Y., Jung, J. & Kim, S. H. Transmission characteristics of Sars-Cov-2 that hinder effective control. Immune Netw. 21, e9 (2021). (PMID: 10.4110/in.2021.21.e9)
Murray, J. & Cohen, A.L. Infectious Disease Surveillance. In International Encyclopedia of Public Health, pp. 222–229 (Elsevier Inc., 2016).
Davis, J. T. et al. Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave. Nature. 600, 127–132 (2021). (PMID: 10.1038/s41586-021-04130-w346958378636257)
Nabeshima, T. et al. COVID-19 cryptic transmission and genetic information blackouts: Need for effective surveillance policy to better understand disease burden. Lancet Reg, Health. West Pac. 7, 100104 (2021).
Padilha, D. A. et al. Genomic surveillance of SARS-CoV-2 in healthcare workers: A critical sentinel group for monitoring the SARS-CoV-2 variant shift. Viruses. 15, 984 (2023). (PMID: 10.3390/v150409843711296410146896)
Sekizuka, T. et al. COVID-19 genome surveillance at international airport quarantine stations in Japan. J. Travel. Med. 28, taaa217 (2021).
Borges, V. et al. Nosocomial outbreak of Sars-Cov-2 in a “non-COVID-19” hospital ward: Virus genome sequencing as a key tool to understand cryptic transmission. Viruses. 13, 604 (2021). (PMID: 10.3390/v13040604339162058065743)
Barabási, A. L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999). (PMID: 10.1126/science.286.5439.50910521342)
Pastor-Satorras, R. & Vespignani, A. Epidemic dynamics and endemic states in complex networks. Phys. Rev. E. 63, 066117 (2001). (PMID: 10.1103/PhysRevE.63.066117)
Shiino, T. Phylodynamic analysis of a viral infection network. Front. Microbiol. 3, 278 (2012). (PMID: 10.3389/fmicb.2012.00278)
Lewis, F., Hughes, G.J., Rambaut, A., Pozniak, A. & Leigh Brown, A. J. Episodic sexual transmission of HIV revealed by molecular phylodynamics. PLoS Med. 5, e50 (2008).
Hughes, G.J., Fearnhill, E., Dunn, D., Lycett, S.J., Rambaut, A. & Leigh Brown, A.J. Molecular phylodynamics of the heterosexual HIV epidemic in the United Kingdom. PLoS Pathog. 5, e1000590 (2009).
Romano, C. M. et al. Social networks shape the transmission dynamics of hepatitis C virus. PLoS One. 5, e11170 (2010). (PMID: 10.1371/journal.pone.0011170)
Hoch, M. et al. Weekly SARS-CoV-2 sentinel surveillance in primary schools, kindergartens, and nurseries, Germany, June-November 2020. Emerg. Infect. Dis. 27, 2192–2196 (2021). (PMID: 10.3201/eid2708.204859340870888314813)
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013). (PMID: 10.1093/molbev/mst010233296903603318)
Sanderson, T. & Barrett, J. C. Variation at Spike position 142 in SARS-CoV-2 Delta genomes is a technical artifact caused by dropout of a sequencing amplicon. Wellcome Open Res. 6, 305 (2021). (PMID: 10.12688/wellcomeopenres.17295.1)
Tamura, K., Stecher, G. & Kumar, S. MEGA11: Molecular evolutionary genetics analysis version 11. Mol. Biol. Evol. 38, 3022–3027 (2021). (PMID: 10.1093/molbev/msab120338924918233496)
Ihaka, R. & Gentleman, R. R: A language for data analysis and graphics. J. Comput. Graph. Stat. 5, 299–314 (1996). (PMID: 10.1080/10618600.1996.10474713)
Smith, D. M. et al. A public health model for the molecular surveillance of HIV transmission in San Diego California. AIDS. 23, 225–232 (2009). (PMID: 10.1097/QAD.0b013e32831d2a8119098493)
Little, S. J. et al. Using HIV networks to inform real time prevention interventions. PLoS One. 9, e98443 (2014). (PMID: 10.1371/journal.pone.0098443)
Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007). (PMID: 10.1186/1471-2148-7-214179960362247476)
Drummond, A. J., Rambaut, A., Shapiro, B. & Pybus, O. G. Bayesian coalescent inference of past population dynamics from molecular sequences. Mol. Biol. Evol. 22, 1185–1192 (2005). (PMID: 10.1093/molbev/msi10315703244)

Keywords: Genome surveillance; SARS-CoV-2; Transmission network

SARS-CoV-2 variants

Date Created: 20240909 Date Completed: 20240909 Latest Revision: 20240913

20240914

PMC11385524

10.1038/s41598-024-71953-8

39251760