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1. Genomic reconstruction of the SARS-CoV-2 epidemic in England

Author

Vöhringer, HS, Sanderson, T, Sinnott, M, De Maio, N, Nguyen, T, Goater, R, Schwach, F, Harrison, I, Hellewell, J, Ariani, CV, Gonçalves, S, Jackson, DK, Johnston, I, Jung, AW, Saint, C, Sillitoe, J, Suciu, M, Goldman, N, Panovska-Griffiths, J, Abnizova, I, Aigrain, L, Alderton, A, Ali, M, Allen, L, Amato, R, Anderson, R, Ariani, C, Austin-Guest, S, Bala, S, Barrett, J, Bassett, A, Battleday, K, Beal, J, Beale, M, Beaver, C, Bellany, S, Bellerby, T, Bellis, K, Berger, D, Berriman, M, Betteridge, E, Bevan, P, Binley, S, Bishop, J, Blackburn, K, Bonfield, J, Boughton, N, Bowker, S, Brendler-Spaeth, T, Bronner, I, Brooklyn, T, Buddenborg, SK, Bush, R, Caetano, C, Cagan, A, Carter, N, Cartwright, J, Monteiro, TC, Chapman, L, Chillingworth, T-J, Clapham, P, Clark, R, Clarke, A, Clarke, C, Cole, D, Cook, E, Coppola, M, Cornell, L, Cornwell, C, Corton, C, Crackett, A, Cranage, A, Craven, H, Craw, S, Crawford, M, Cutts, T, Dabrowska, M, Davies, M, Davies, R, Dawson, J, Day, C, Densem, A, Dibling, T, Dockree, C, Dodd, D, Dogga, S, Dorman, M, Dougan, G, Dougherty, M, Dove, A, Drummond, L, Drury, E, Dudek, M, Durham, J, Durrant, L, Easthope, E, Eckert, S, Ellis, P, Farr, B, Fenton, M, Ferrero, M, Flack, N, Fordham, H, Forsythe, G, Foulser, L, Francis, M, Fraser, A, Freeman, A, Galvin, A, Garcia-Casado, M, Gedny, A, Girgis, S, Glover, J, Goncalves, S, Goodwin, S, Gould, O, Gourtovaia, M, Gray, A, Gray, E, Griffiths, C, Gu, Y, Guerin, F, Hamilton, W, Hanks, H, Harrison, E, Harrott, A, Harry, E, Harvison, J, Heath, P, Hernandez-Koutoucheva, A, Hobbs, R, Holland, D, Holmes, S, Hornett, G, Hough, N, Huckle, L, Hughes-Hallet, L, Hunter, A, Inglis, S, Iqbal, S, Jackson, A, Jackson, D, James, K, Jamrozy, D, Verdejo, CJ, Jones, M, Kallepally, K, Kane, L, Kay, K, Kay, S, Keatley, J, Keith, A, King, A, Kitchin, L, Kleanthous, M, Klimekova, M, Korlevic, P, Krasheninnkova, K, Lane, G, Langford, C, Laverack, A, Law, K, Lawniczak, M, Lensing, S, Leonard, S, Letchford, L, Lewis, K, Lewis-Wade, A, Liddle, J, Lin, Q, Lindsay, S, Linsdell, S, Livett, R, Lo, S, Long, R, Lovell, J, Ludden, C, Mack, J, Maddison, M, Makunin, A, Mamun, I, Mansfield, J, Marriott, N, Martin, M, Mayho, M, McCarthy, S, McClintock, J, McGuigan, S, McHugh, S, McMinn, L, Meadows, C, Mobley, E, Moll, R, Morra, M, Morrow, L, Murie, K, Nash, S, Nathwani, C, Naydenova, P, Neaverson, A, Nelson, R, Nerou, E, Nicholson, J, Nimz, T, Noell, GG, O’Meara, S, Ohan, V, Oliver, K, Olney, C, Ormond, D, Oszlanczi, A, Palmer, S, Pang, YF, Pardubska, B, Park, N, Parmar, A, Patel, G, Patel, M, Payne, M, Peacock, S, Petersen, A, Plowman, D, Preston, T, Prestwood, L, Puethe, C, Quail, M, Rajan, D, Rajatileka, S, Rance, R, Rawlings, S, Redshaw, N, Reynolds, J, Reynolds, M, Rice, S, Richardson, M, Roberts, C, Robinson, K, Robinson, M, Robinson, D, Rogers, H, Rojo, EM, Roopra, D, Rose, M, Rudd, L, Sadri, R, Salmon, N, Saul, D, Scott, C, Seekings, P, Shirley, L, Simms, A, Sivadasan, S, Siwek, B, Sizer, D, Skeldon, K, Skelton, J, Slater-Tunstill, J, Sloper, L, Smerdon, N, Smith, C, Smith, J, Smith, K, Smith, M, Smith, S, Smith, T, Sneade, L, Soria, CD, Sousa, C, Souster, E, Sparkes, A, Spencer-Chapman, M, Squares, J, Stanley, R, Steed, C, Stickland, T, Still, I, Stratton, MR, Strickland, M, Swann, A, Swiatkowska, A, Sycamore, N, Swift, E, Symons, E, Szluha, S, Taluy, E, Tao, N, Taylor, K, Taylor, S, Thompson, S, Thompson, M, Thomson, M, Thomson, N, Thurston, S, Tonkin-Hill, G, Toombs, D, Topping, B, Tovar-Corona, J, Ungureanu, D, Uphill, J, Urbanova, J, Van Vuuren, PJ, Vancollie, V, Voak, P, Walker, D, Walker, M, Waller, M, Ward, G, Weatherhogg, C, Webb, N, Weldon, D, Wells, A, Wells, E, Westwood, L, Whipp, T, Whiteley, T, Whitton, G, Whitwham, A, Widaa, S, Williams, M, Wilson, M, Wright, S, Robson, SC, Connor, TR, Loman, NJ, Golubchik, T, Martinez Nunez, RT, Bonsall, D, Rambaut, A, Snell, LB, Corden, S, Nastouli, E, Nebbia, G, Lythgoe, K, Torok, ME, Goodfellow, IG, Prieto, JA, Saeed, K, Houlihan, C, Frampton, D, Hamilton, WL, Witney, AA, Bucca, G, Pope, CF, Moore, C, Thomson, EC, Harrison, EM, Smith, CP, Rogan, F, Beckwith, SM, Murray, A, Singleton, D, Eastick, K, Sheridan, LA, Randell, P, Jackson, LM, Fairley, DJ, Loose, MW, Watkins, J, Moses, S, Nicholls, S, Bull, M, Smith, DL, Aanensen, DM, Aggarwal, D, Shepherd, JG, Curran, MD, Parmar, S, Parker, MD, Williams, C, Glaysher, S, Underwood, AP, Bashton, M, Pacchiarini, N, Loveson, KF, Byott, M, Carabelli, AM, Templeton, KE, de Silva, TI, Wang, D, Langford, CF, Gunson, RN, Cottrell, S, O’Grady, J, Kwiatkowski, D, Lillie, PJ, Cortes, N, Moore, N, Thomas, C, Burns, PJ, Mahungu, TW, Liggett, S, Beckett, AH, Holden, MTG, Levett, LJ, Osman, H, Hassan-Ibrahim, MO, Simpson, DA, Chand, M, Gupta, RK, Darby, AC, Paterson, S, Pybus, OG, Volz, EM, de Angelis, D, Robertson, DL, Page, AJ, Bassett, AR, Wong, N, Taha, Y, Erkiert, MJ, Spencer Chapman, MH, Dewar, R, McHugh, MP, Mookerjee, S, Aplin, S, Harvey, M, Sass, T, Umpleby, H, Wheeler, H, McKenna, JP, Warne, B, Taylor, JF, Chaudhry, Y, Izuagbe, R, Jahun, AS, Young, GR, McMurray, C, McCann, CM, Nelson, A, Elliott, S, Lowe, H, Price, A, Crown, MR, Rey, S, Roy, S, Temperton, B, Shaaban, S, Hesketh, AR, Laing, KG, Monahan, IM, Heaney, J, Pelosi, E, Silviera, S, Wilson-Davies, E, Fryer, H, Adams, H, du Plessis, L, Johnson, R, Harvey, WT, Hughes, J, Orton, RJ, Spurgin, LG, Bourgeois, Y, Ruis, C, O’Toole, Á, Fraser, C, Edgeworth, J, Breuer, J, Michell, SL, Todd, JA, John, M, Buck, D, Gajee, K, Kay, GL, Peacock, SJ, Heyburn, D, Kitchman, K, McNally, A, Pritchard, DT, Dervisevic, S, Muir, P, Robinson, E, Vipond, BB, Ramadan, NA, Jeanes, C, Catalan, J, Jones, N, da Silva Filipe, A, Fuchs, M, Miskelly, J, Jeffries, AR, Park, NR, Ash, A, Koshy, C, Barrow, M, Buchan, SL, Mantzouratou, A, Clark, G, Holmes, CW, Campbell, S, Davis, T, Tan, NK, Brown, JR, Harris, KA, Kidd, SP, Grant, PR, Xu-McCrae, L, Cox, A, Madona, P, Pond, M, Randell, PA, Withell, KT, Graham, C, Denton-Smith, R, Swindells, E, Turnbull, R, Sloan, TJ, Bosworth, A, Hutchings, S, Pymont, HM, Casey, A, Ratcliffe, L, Jones, CR, Knight, BA, Haque, T, Hart, J, Irish-Tavares, D, Witele, E, Mower, C, Watson, LK, Collins, J, Eltringham, G, Crudgington, D, Macklin, B, Iturriza-Gomara, M, Lucaci, AO, McClure, PC, Carlile, M, Holmes, N, Storey, N, Rooke, S, Yebra, G, Craine, N, Perry, M, Alikhan, N-F, Bridgett, S, Cook, KF, Fearn, C, Goudarzi, S, Lyons, RA, Williams, T, Haldenby, ST, Davies, RM, Batra, R, Blane, B, Spyer, MJ, Smith, P, Yavus, M, Williams, RJ, Mahanama, AIK, Samaraweera, B, Girgis, ST, Hansford, SE, Green, A, Bellis, KL, Dorman, MJ, Quick, J, Poplawski, R, Reynolds, N, Mack, A, Morriss, A, Whalley, T, Patel, B, Georgana, I, Hosmillo, M, Pinckert, ML, Stockton, J, Henderson, JH, Hollis, A, Stanley, W, Yew, WC, Myers, R, Thornton, A, Adams, A, Annett, T, Asad, H, Birchley, A, Coombes, J, Evans, JM, Fina, L, Gatica-Wilcox, B, Gilbert, L, Graham, L, Hey, J, Hilvers, E, Jones, S, Jones, H, Kumziene-Summerhayes, S, McKerr, C, Powell, J, Pugh, G, Trotter, AJ, Williams, CA, Kermack, LM, Foulkes, BH, Gallis, M, Hornsby, HR, Louka, SF, Pohare, M, Wolverson, P, Zhang, P, MacIntyre-Cockett, G, Trebes, A, Moll, RJ, Ferguson, L, Goldstein, EJ, Maclean, A, Tomb, R, Starinskij, I, Thomson, L, Southgate, J, Kraemer, MUG, Raghwani, J, Zarebski, AE, Boyd, O, Geidelberg, L, Illingworth, CJ, Jackson, C, Pascall, D, Vattipally, S, Freeman, TM, Hsu, SN, Lindsey, BB, Tovar-Corona, JM, Cox, M, Abudahab, K, Menegazzo, M, Taylor, BEW, Yeats, CA, Mukaddas, A, Wright, DW, de Oliveira Martins, L, Colquhoun, R, Hill, V, Jackson, B, McCrone, JT, Medd, N, Scher, E, Keatley, J-P, Curran, T, Morgan, S, Maxwell, P, Eldirdiri, S, Kenyon, A, Holmes, AH, Price, JR, Wyatt, T, Mather, AE, Skvortsov, T, Hartley, JA, Guest, M, Kitchen, C, Merrick, I, Munn, R, Bertolusso, B, Lynch, J, Vernet, G, Kirk, S, Wastnedge, E, Idle, G, Bradley, DT, Poyner, J, Mori, M, Jones, O, Wright, V, Brooks, E, Churcher, CM, Fragakis, M, Galai, K, Jermy, A, Judges, S, McManus, GM, Smith, KS, Westwick, E, Attwood, SW, Bolt, F, Davies, A, De Lacy, E, Downing, F, Edwards, S, Meadows, L, Jeremiah, S, Smith, N, Charalampous, T, Patel, A, Berry, L, Boswell, T, Fleming, VM, Howson-Wells, HC, Joseph, A, Khakh, M, Lister, MM, Bird, PW, Fallon, K, Helmer, T, McMurray, CL, Odedra, M, Shaw, J, Tang, JW, Willford, NJ, Blakey, V, Raviprakash, V, Sheriff, N, Williams, L-A, Feltwell, T, Bedford, L, Cargill, JS, Hughes, W, Moore, J, Stonehouse, S, Atkinson, L, Lee, JCD, Shah, D, Alcolea, A, Ohemeng-Kumi, N, Ramble, J, Sehmi, J, Williams, R, Chatterton, W, Pusok, M, Everson, W, Castigador, A, Macnaughton, E, El Bouzidi, K, Lampejo, T, Sudhanva, M, Breen, C, Sluga, G, Ahmad, SSY, George, RP, Machin, NW, Binns, D, James, V, Blacow, R, Coupland, L, Smith, L, Barton, E, Padgett, D, Scott, G, Cross, A, Mirfenderesky, M, Greenaway, J, Cole, K, Clarke, P, Duckworth, N, Walsh, S, Bicknell, K, Impey, R, Wyllie, S, Hopes, R, Bishop, C, Chalker, V, Gifford, L, Molnar, Z, Auckland, C, Evans, C, Johnson, K, Partridge, DG, Raza, M, Baker, P, Bonner, S, Essex, S, Murray, LJ, Lawton, AI, Burton-Fanning, S, Payne, BAI, Waugh, S, Gomes, AN, Kimuli, M, Murray, DR, Ashfield, P, Dobie, D, Ashford, F, Best, A, Crawford, L, Cumley, N, Mayhew, M, Megram, O, Mirza, J, Moles-Garcia, E, Percival, B, Ensell, L, Lowe, HL, Maftei, L, Mondani, M, Chaloner, NJ, Cogger, BJ, Easton, LJ, Huckson, H, Lewis, J, Lowdon, S, Malone, CS, Munemo, F, Mutingwende, M, Nicodemi, R, Podplomyk, O, Somassa, T, Beggs, A, Richter, A, Cormie, C, Dias, J, Forrest, S, Higginson, EE, Maes, M, Young, J, Davidson, RK, Jackson, KA, Turtle, L, Keeley, AJ, Ball, J, Byaruhanga, T, Chappell, JG, Dey, J, Hill, JD, Park, EJ, Fanaie, A, Hilson, RA, Yaze, G, Afifi, S, Beer, R, Maksimovic, J, Masters, KM, Spellman, K, Bresner, C, Fuller, W, Marchbank, A, Workman, T, Shelest, E, Debebe, J, Sang, F, Zamudio, ME, Francois, S, Gutierrez, B, Vasylyeva, TI, Flaviani, F, Ragonnet-Cronin, M, Smollett, KL, Broos, A, Mair, D, Nichols, J, Nomikou, K, Tong, L, Tsatsani, I, O’Brien, S, Rushton, S, Sanderson, R, Perkins, J, Cotton, S, Gallagher, A, Allara, E, Pearson, C, Bibby, D, Dabrera, G, Ellaby, N, Gallagher, E, Hubb, J, Lackenby, A, Lee, D, Manesis, N, Mbisa, T, Platt, S, Twohig, KA, Morgan, M, Aydin, A, Baker, DJ, Foster-Nyarko, E, Prosolek, SJ, Rudder, S, Baxter, C, Carvalho, SF, Lavin, D, Mariappan, A, Radulescu, C, Singh, A, Tang, M, Morcrette, H, Bayzid, N, Cotic, M, Balcazar, CE, Gallagher, MD, Maloney, D, Stanton, TD, Williamson, KA, Manley, R, Michelsen, ML, Sambles, CM, Studholme, DJ, Warwick-Dugdale, J, Eccles, R, Gemmell, M, Gregory, R, Hughes, M, Nelson, C, Rainbow, L, Vamos, EE, Webster, HJ, Whitehead, M, Wierzbicki, C, Angyal, A, Green, LR, Whiteley, M, Bronner, IF, Farr, BW, Lensing, SV, McCarthy, SA, Quail, MA, Redshaw, NM, Thurston, SAJ, Rowe, W, Gaskin, A, Le-Viet, T, Birney, E, Volz, E, Funk, S, Martincorena, I, Barrett, JC, and Gerstung, M

Abstract

The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus leads to new variants that warrant timely epidemiological characterization. Here we use the dense genomic surveillance data generated by the COVID-19 Genomics UK Consortium to reconstruct the dynamics of 71 different lineages in each of 315 English local authorities between September 2020 and June 2021. This analysis reveals a series of subepidemics that peaked in early autumn 2020, followed by a jump in transmissibility of the B.1.1.7/Alpha lineage. The Alpha variant grew when other lineages declined during the second national lockdown and regionally tiered restrictions between November and December 2020. A third more stringent national lockdown suppressed the Alpha variant and eliminated nearly all other lineages in early 2021. Yet a series of variants (most of which contained the spike E484K mutation) defied these trends and persisted at moderately increasing proportions. However, by accounting for sustained introductions, we found that the transmissibility of these variants is unlikely to have exceeded the transmissibility of the Alpha variant. Finally, B.1.617.2/Delta was repeatedly introduced in England and grew rapidly in early summer 2021, constituting approximately 98% of sampled SARS-CoV-2 genomes on 26 June 2021.

Published
2021

2. A cross-sectional analysis of meteorological factors and SARS-CoV-2 transmission in 409 cities across 26 countries

Author

Sera, F., Armstrong, B., Abbott, S., Meakin, S., O’Reilly, K., von Borries, R., Schneider, R., Royé, D., Hashizume, M., Pascal, M., Tobias, A., Vicedo-Cabrera, A.M., Hu, W., Tong, S., Lavigne, E., Correa, P.M., Meng, X., Kan, H., Kynčl, J., Urban, A., Orru, H., Ryti, N.R.I., Jaakkola, J.J.K., Cauchemez, S., Dallavalle, M., Schneider, A., Zeka, A., Honda, Y., Ng, C.F.S., Alahmad, B., Rao, S., Di Ruscio, F., Carrasco Escobar, Gabriel, Seposo, X., Holobâcă, I.H., Kim, H., Lee, W., Íñiguez, C., Ragettli, M.S., Aleman, A., Colistro, V., Bell, M.L., Zanobetti, A., Schwartz, J., Dang, T.N., Scovronick, N., de Sousa Zanotti Stagliorio Coélho, M., Diaz, M.H., Zhang, Y., Russell, T.W., Koltai, M., Kucharski, A.J., Barnard, R.C., Quaife, M., Jarvis, C.I., Lei, J., Munday, J.D., Chan, Y.-W.D., Quilty, B.J., Eggo, R.M., Flasche, S., Foss, A.M., Clifford, S., Tully, D.C., Edmunds, W.J., Klepac, P., Brady, O., Krauer, F., Procter, S.R., Jombart, T., Rosello, A., Showering, A., Funk, S., Hellewell, J., Sun, F.Y., Endo, A., Williams, J., Gimma, A., Waterlow, N.R., Prem, K., Bosse, N.I., Gibbs, H.P., Atkins, K.E., Pearson, C.A.B., Jafari, Y., Villabona-Arenas, C.J., Jit, M., Nightingale, E.S., Davies, N.G., van Zandvoort, K., Liu, Y., Sandmann, F.G., Waites, W., Abbas, K., Medley, G., Knight, G.M., Gasparrini, A., Lowe, R., MCC Collaborative Research Network, CMMID COVID-19 Working Group, and European Commission

Subjects
Percentile, Meteorological Concepts, Cross-sectional study, Epidemiology, Psychological intervention, Basic Reproduction Number, General Physics and Astronomy, epidemic, law.invention, 0302 clinical medicine, law, environmental factor, risk factors, 030212 general & internal medicine, climate sciences, COVID-19, temperature, global, 0303 health sciences, education.field_of_study, Multidisciplinary, cross section, parasite transmission, Temperature, Regression analysis, 3. Good health, Geography, Transmission (mechanics), Regression Analysis, epidemiology, Seasons, SARS coronavirus, Science, Population, 610 Medicine & health, severe acute respiratory syndrome, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, Meta-Analysis as Topic, 360 Social problems & social services, Humans, Cities, education, Pandemics, Weather, 030304 developmental biology, Government, SARS-CoV-2, COVID-19, General Chemistry, Cross-Sectional Studies, Risk factors, 13. Climate action, Basic reproduction number, Climate sciences, Demography
Abstract

There is conflicting evidence on the influence of weather on COVID-19 transmission. Our aim is to estimate weather-dependent signatures in the early phase of the pandemic, while controlling for socio-economic factors and non-pharmaceutical interventions. We identify a modest non-linear association between mean temperature and the effective reproduction number (Re) in 409 cities in 26 countries, with a decrease of 0.087 (95% CI: 0.025; 0.148) for a 10 °C increase. Early interventions have a greater effect on Re with a decrease of 0.285 (95% CI 0.223; 0.347) for a 5th - 95th percentile increase in the government response index. The variation in the effective reproduction number explained by government interventions is 6 times greater than for mean temperature. We find little evidence of meteorological conditions having influenced the early stages of local epidemics and conclude that population behaviour and government interventions are more important drivers of transmission., This work was generated using Copernicus Climate Change Service (C3S) and Copernicus Atmosphere Monitoring Service (CAMS) information [2020]. The authors would like to thank the European Centre for Medium-Range Weather Forecasts (ECMWF) that implements the C3S and CAMS on behalf of the European Union. D.R. was supported by a postdoctoral research fellowship of the Xunta de Galicia (Spain). A.G. was funded by the Medical Research Council-UK (Grant ID: MR/R013349/1), the Natural Environment Research Council UK (Grant ID: NE/R009384/1) and the European Union’s Horizon 2020 Project Exhaustion (Grant ID: 820655). R.L. was supported by a Royal Society Dorothy Hodgkin Fellowship. S.A. and S.M. were funded by the Wellcome Trust (grant 210758/Z/18/Z210758/Z/18/Z). The following funding sources are acknowledged as providing funding for the MCC Collaborative Research Network authors: J.K. and A.U. were supported by the Czech Science Foundation, project 18-22125S. S.T. was supported by the Shanghai Municipal Science and Technology Commission (Grant 18411951600). N.S. is supported by the NIEHS-funded HERCULES Center (P30ES019776). H.K. was supported by the National Research Foundation of Korea (BK21 Center for Integrative Response to Health Disasters, Graduate School of Public Health, Seoul National University). A.S., F.D.R. and S.R. were funded by the European Union’s Horizon 2020 Project Exhaustion (Grant ID: 820655). Each member of the CMMID COVID-19 Working Group contributed to processing, cleaning and interpretation of data, interpreted findings, contributed to the manuscript and approved the work for publication. The following funding sources are acknowledged as providing funding for the CMMID COVID-19 working group authors. This research was partly funded by the Bill & Melinda Gates Foundation (INV-001754: M.Q; INV-003174: K.P., M.J., Y.L., J.L.; NTD Modelling Consortium OPP1184344: C.A.B.P., G.M.; OPP1180644: S.R.P.; OPP1183986: E.S.N.). BMGF (OPP1157270: K.E.A.). DFID/Wellcome Trust (Epidemic Preparedness Coronavirus research programme 221303/Z/20/Z: C.A.B.P.). EDCTP2 (RIA2020EF-2983-CSIGN: H.P.G.). ERC Starting Grant (#757699: M.Q.). This project has received funding from the European Union’s Horizon 2020 research and innovation programme—project EpiPose (101003688: K.P., M.J., P.K., R.C.B., W.J.E., Y.L.). This research was partly funded by the Global Challenges Research Fund (GCRF) project ‘RECAP’ managed through RCUK and ESRC (ES/P010873/1: A.G., C.I.J., T.J.). HDR UK (MR/S003975/1: R.M.E.). MRC (MR/N013638/1: N.R.W.; MR/V027956/1: W.W.). Nakajima Foundation (A.E.). This research was partly funded by the National Institute for Health Research (NIHR) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care (16/136/46: B.J.Q.; 16/137/109: B.J.Q., F.Y.S., M.J., Y.L.; Health Protection Research Unit for Immunisation NIHR200929: N.G.D.; Health Protection Research Unit for Modelling Methodology HPRU-2012-10096: T.J.; NIHR200908: R.M.E.; NIHR200929: F.G.S., M.J.; PR-OD-1017-20002: A.R., W.J.E.). Royal Society (Dorothy Hodgkin Fellowship: R.L.; RP\EA\180004: P.K.). UK DHSC/UK Aid/NIHR (PR-OD-1017-20001: H.P.G.). UK MRC (MC_PC_19065—Covid 19: Understanding the dynamics and drivers of the COVID-19 epidemic using real-time outbreak analytics: A.G., N.G.D., R.M.E., S.C., T.J., W.J.E., Y.L.; MR/P014658/1: G.M.K.). Authors of this research receive funding from the UK Public Health Rapid Support Team funded by the United Kingdom Department of Health and Social Care (T.J.). Wellcome Trust (206250/Z/17/Z: A.J.K., T.W.R.; 206471/Z/17/Z: O.B.; 208812/Z/17/Z: S.C.; 210758/Z/18/Z: J.D.M., J.H., N.I.B.; UNS110424: F.K.). No funding (A.M.F., A.S., C.J.V.-A., D.C.T., J.W., K.E.A., Y.-W.D.C.). LSHTM, DHSC/UKRI COVID-19 Rapid Response Initiative (MR/V028456/1: Y.L.). Innovation Fund of the Joint Federal Committee (01VSF18015: F.K.). Foreign, Commonwealth and Development Office/Wellcome Trust (221303/Z/20/Z: M.K.).

Published
2021

3. Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England

Author

Leclerc, QJ, Fuller, NM, Keogh, RH, Diaz-Ordaz, K, Sekula, R, Semple, MG, Baillie, JK, Openshaw, PJM, Carson, G, Alex, B, Bach, B, Barclay, WS, Bogaert, D, Chand, M, Cooke, GS, Docherty, AB, Dunning, J, da Silva Filipe, A, Fletcher, T, Green, CA, Harrison, EM, Hiscox, JA, Ho, AYW, Horby, PW, Ijaz, S, Khoo, S, Klenerman, P, Law, A, Lim, WS, Mentzer, AJ, Merson, L, Meynert, AM, Noursadeghi, M, Moore, SC, Palmarini, M, Paxton, WA, Pollakis, G, Price, N, Rambaut, A, Robertson, DL, Russell, CD, Sancho-Shimizu, V, Scott, JT, de Silva, T, Sigfrid, L, Solomon, T, Sriskandan, S, Stuart, D, Summers, C, Tedder, RS, Thomson, EC, Thompson, AAR, Thwaites, RS, Turtle, LCW, Zambon, M, Hardwick, H, Donohue, C, Lyons, R, Griffiths, F, Oosthuyzen, W, Norman, L, Pius, R, Drake, TM, Fairfield, CJ, Knight, S, Mclean, KA, Murphy, D, Shaw, CA, Dalton, J, Lee, J, Plotkin, D, Girvan, M, Saviciute, E, Roberts, S, Harrison, J, Marsh, L, Connor, M, Halpin, S, Jackson, C, Gamble, C, Petersen, C, Mullaney, S, Leeming, G, Wham, M, Clohisey, S, Hendry, R, Scott-Brown, J, Greenhalf, W, Shaw, V, McDonald, S, Keating, S, Ahmed, KA, Armstrong, JA, Ashworth, M, Asiimwe, IG, Bakshi, S, Barlow, SL, Booth, L, Brennan, B, Bullock, K, Catterall, BWA, Clark, JJ, Clarke, EA, Cole, S, Cooper, L, Cox, H, Davis, C, Dincarslan, O, Dunn, C, Dyer, P, Elliott, A, Evans, A, Finch, L, Fisher, LWS, Foster, T, Garcia-Dorival, I, Gunning, P, Hartley, C, Ho, A, Jensen, RL, Jones, CB, Jones, TR, Khandaker, S, King, K, Kiy, RT, Koukorava, C, Lake, A, Lant, S, Latawiec, D, Lavelle-Langham, L, Lefteri, D, Lett, L, Livoti, LA, Mancini, M, McEvoy, L, McLauchlan, J, Metelmann, S, Miah, NS, Middleton, J, Mitchell, J, Murphy, EG, Penrice-Randal, R, Pilgrim, J, Prince, T, Reynolds, W, Ridley, PM, Sales, D, Shaw, VE, Shears, RK, Small, B, Subramaniam, KS, Szemiel, A, Taggart, A, Tanianis-Hughes, J, Thomas, J, Trochu, E, van Tonder, L, Wilcock, E, Zhang, JE, Adeniji, K, Agranoff, D, Agwuh, K, Ail, D, Alegria, A, Angus, B, Ashish, A, Atkinson, D, Bari, S, Barlow, G, Barnass, S, Barrett, N, Bassford, C, Baxter, D, Beadsworth, M, Bernatoniene, J, Berridge, J, Best, N, Bothma, P, Brealey, D, Brittain-Long, R, Bulteel, N, Burden, T, Burtenshaw, A, Caruth, V, Chadwick, D, Chambler, D, Chee, N, Child, J, Chukkambotla, S, Clark, T, Collini, P, Cosgrove, C, Cupitt, J, Cutino-Moguel, M-T, Dark, P, Dawson, C, Dervisevic, S, Donnison, P, Douthwaite, S, DuRand, I, Dushianthan, A, Dyer, T, Evans, C, Eziefula, C, Fegan, C, Finn, A, Fullerton, D, Garg, S, Garg, A, Gkrania-Klotsas, E, Godden, J, Goldsmith, A, Graham, C, Hardy, E, Hartshorn, S, Harvey, D, Havalda, P, Hawcutt, DB, Hobrok, M, Hodgson, L, Hormis, A, Jacobs, M, Jain, S, Jennings, P, Kaliappan, A, Kasipandian, V, Kegg, S, Kelsey, M, Kendall, J, Kerrison, C, Kerslake, I, Koch, O, Koduri, G, Koshy, G, Laha, S, Laird, S, Larkin, S, Leiner, T, Lillie, P, Limb, J, Linnett, V, Little, J, MacMahon, M, MacNaughton, E, Mankregod, R, Masson, H, Matovu, E, McCullough, K, McEwen, R, Meda, M, Mills, G, Minton, J, Mirfenderesky, M, Mohandas, K, Mok, Q, Moon, J, Moore, E, Morgan, P, Morris, C, Mortimore, K, Moses, S, Mpenge, M, Mulla, R, Murphy, M, Nagel, M, Nagarajan, T, Nelson, M, Otahal, I, Pais, M, Panchatsharam, S, Paraiso, H, Patel, B, Pattison, N, Pepperell, J, Peters, M, Phull, M, Pintus, S, Pooni, JS, Post, F, Price, D, Prout, R, Rae, N, Reschreiter, H, Reynolds, T, Richardson, N, Roberts, M, Roberts, D, Rose, A, Rousseau, G, Ryan, B, Saluja, T, Shah, A, Shanmuga, P, Sharma, A, Shawcross, A, Sizer, J, Shankar-Hari, M, Smith, R, Snelson, C, Spittle, N, Staines, N, Stambach, T, Stewart, R, Subudhi, P, Szakmany, T, Tatham, K, Thompson, C, Thompson, R, Tridente, A, Tupper-Carey, D, Twagira, M, Ustianowski, A, Vallotton, N, Vincent-Smith, L, Visuvanathan, S, Vuylsteke, A, Waddy, S, Wake, R, Walden, A, Welters, I, Whitehouse, T, Whittaker, P, Whittington, A, Wijesinghe, M, Williams, M, Wilson, L, Wilson, S, Winchester, S, Wiselka, M, Wolverson, A, Wooton, DG, Workman, A, Yates, B, Young, P, Quaife, M, Jarvis, CI, Meakin, SR, Quilty, BJ, Prem, K, Villabona-Arenas, CJ, Sun, FY, Abbas, K, Auzenbergs, M, Gimma, A, Tully, DC, Sherratt, K, Rosello, A, Davies, NG, Liu, Y, Lowe, R, Gibbs, HP, Waterlow, NR, Edmunds, WJ, Simons, D, Medley, G, Munday, JD, Flasche, S, Sandmann, FG, Showering, A, Eggo, RM, Chan, Y-WD, Pearson, CAB, Kucharski, AJ, Foss, AM, Russell, TW, Bosse, NI, Jit, M, Abbott, S, Williams, J, Endo, A, Clifford, S, Gore-Langton, GR, Klepac, P, Brady, O, Hellewell, J, Funk, S, van Zandvoort, K, Barnard, RC, Nightingale, ES, Jombart, T, Atkins, KE, Procter, SR, and Knight, GM

Abstract

Background\ud \ud Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient’s “bed pathway” - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy.\ud \ud \ud \ud Methods\ud \ud We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020.\ud \ud \ud \ud Results\ud \ud In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: “Ward, CC, Ward”, “Ward, CC”, “CC” and “CC, Ward”. Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days.\ud \ud \ud \ud For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities.\ud \ud \ud \ud Conclusions\ud \ud We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19.\ud \ud \ud \ud Trial registration\ud \ud The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.

Published
2021

4. Assessing the impact of low-technology emanators alongside long-lasting insecticidal nets to control malaria: Spatial repellents and malaria control

Author

Hellewell, J, Sherrard-Smith, E, Ogoma, S, and Churcher, TS

Subjects
fungi, parasitic diseases
Abstract

Malaria control in sub-Saharan Africa relies on the widespread use of long-lasting insecticidal nets (LLINs) or the indoor residual spraying of insecticide. Disease transmission may be maintained even when these indoor interventions are universally used as some mosquitoes will bite in the early morning and evening when people are outside. As countries seek to eliminate malaria, they can target outdoor biting using new vector control tools such as spatial repellent emanators, which emit airborne insecticide to form a protective area around the user. Field data are used to incorporate a low-technology emanator into a mathematical model of malaria transmission to predict its public health impact across a range of scenarios. Targeting outdoor biting by repeatedly distributing emanators alongside LLINs increases the chance of elimination, but the additional benefit depends on the level of anthropophagy in the local mosquito population, emanator effectiveness and the pre-intervention proportion of mosquitoes biting outdoors. High proportions of pyrethroid-resistant mosquitoes diminish LLIN impact because of reduced mosquito mortality. When mosquitoes are highly anthropophagic, this reduced mortality leads to more outdoor biting and a reduced additional benefit of emanators, even if emanators are assumed to retain their effectiveness in the presence of pyrethroid resistance. Different target product profiles are examined, which show the extra epidemiological benefits of spatial repellents that induce mosquito mortality. This article is part of the theme issue 'Novel control strategies for mosquito-borne diseases'.

Published
2021

5. Global and national estimates of the number of healthcare workers at high risk of SARS-CoV-2 infection

Author

McCarthy, C.V., Sandmann, F.G., Jit, M., Eggo, R.M., Knight, G.M., Flasche, S., Foss, A.M., Klepac, P., Jafari, Y., Waterlow, N.R., Meakin, S.R., Lei, J., Villabona-Arenas, C.J., Procter, S.R., Abbott, S., Funk, S., Bosse, N.I., O'Reilly, K., Waites, W., Abbas, K., Gimma, A., Showering, A., Jarvis, C.I., Kucharski, A.J., Endo, A., Jombart, T., Medley, G., Brady, O., Barnard, R.C., Williams, J., Davies, N.G., Edmunds, W.J., Munday, J.D., Pearson, C.A.B., Liu, Y., Atkins, K.E., Gibbs, H.P., Russell, T.W., Tully, D.C., Lowe, R., Clifford, S., Nightingale, E.S., Hellewell, J., Rosello, A., Quaife, M., Krauer, F., Desmond Chan, Y.-W., Sun, F.Y., van Zandvoort, K., Quilty, B.J., Koltai, M., and Prem, K.

Subjects
Microbiology (medical), 2019-20 coronavirus outbreak, COVID-19 Vaccines, Vaccination Coverage, Coronavirus disease 2019 (COVID-19), Health Personnel, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), MEDLINE, Global Health, World Health Organization, Regional Health Planning, Health personnel, Environmental health, Health care, Medicine, Humans, Letter to the Editor, Population Density, business.industry, Health Priorities, Immunization Programs, SARS-CoV-2, Research, Vaccination, COVID-19, General Medicine, Infectious Diseases, business
Abstract

Objective To provide global, regional, and national estimates of target population sizes for coronavirus disease 2019 (covid-19) vaccination to inform country specific immunisation strategies on a global scale. Design Descriptive study. Setting 194 member states of the World Health Organization. Population Target populations for covid-19 vaccination based on country specific characteristics and vaccine objectives (maintaining essential core societal services; reducing severe covid-19; reducing symptomatic infections and stopping virus transmission). Main outcome measure Size of target populations for covid-19 vaccination. Estimates use country specific data on population sizes stratified by occupation, age, risk factors for covid-19 severity, vaccine acceptance, and global vaccine production. These data were derived from a multipronged search of official websites, media sources, and academic journal articles. Results Target population sizes for covid-19 vaccination vary markedly by vaccination goal and geographical region. Differences in demographic structure, presence of underlying conditions, and number of essential workers lead to highly variable estimates of target populations at regional and country levels. In particular, Europe has the highest share of essential workers (63.0 million, 8.9%) and people with underlying conditions (265.9 million, 37.4%); these two categories are essential in maintaining societal functions and reducing severe covid-19, respectively. In contrast, South East Asia has the highest share of healthy adults (777.5 million, 58.9%), a key target for reducing community transmission. Vaccine hesitancy will probably impact future covid-19 vaccination programmes; based on a literature review, 68.4% (95% confidence interval 64.2% to 72.6%) of the global population is willing to receive covid-19 vaccination. Therefore, the adult population willing to be vaccinated is estimated at 3.7 billion (95% confidence interval 3.2 to 4.1 billion). Conclusions The distribution of target groups at country and regional levels highlights the importance of designing an equitable and efficient plan for vaccine prioritisation and allocation. Each country should evaluate different strategies and allocation schemes based on local epidemiology, underlying population health, projections of available vaccine doses, and preference for vaccination strategies that favour direct or indirect benefits.

Published
2021
Full Text
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6. Short-term forecasts to inform the response to the Covid-19 epidemic in the UK

Author

Funk, S, Abbott, S, Atkins, BD, Baguelin, M, Baillie, JK, Birrell, P, Blake, J, Bosse, NI, Burton, J, Carruthers, J, Davies, NG, De Angelis, D, Dyson, L, Edmunds, WJ, Eggo, RM, Ferguson, NM, Gaythorpe, K, Gorsich, E, Guyver-Fletcher, G, Hellewell, J, Hill, EM, Holmes, A, House, TA, Jewell, C, Jit, M, Jombart, T, Joshi, I, Keeling, MJ, Kendall, E, Knock, ES, Kucharski, AJ, Lythgoe, KA, Meakin, SR, Munday, JD, Openshaw, PJM, Overton, CE, Pagani, F, Pearson, J, Perez-Guzman, PN, Pellis, L, Scarabel, F, Semple, MG, Sherratt, K, Tang, M, Tildesley, MJ, Van Leeuwen, E, and Whittles, LK

Abstract

BackgroundShort-term forecasts of infectious disease can aid situational awareness and planning for outbreak response. Here, we report on multi-model forecasts of Covid-19 in the UK that were generated at regular intervals starting at the end of March 2020, in order to monitor expected healthcare utilisation and population impacts in real time.MethodsWe evaluated the performance of individual model forecasts generated between 24 March and 14 July 2020, using a variety of metrics including the weighted interval score as well as metrics that assess the calibration, sharpness, bias and absolute error of forecasts separately. We further combined the predictions from individual models into ensemble forecasts using a simple mean as well as a quantile regression average that aimed to maximise performance. We compared model performance to a null model of no change.ResultsIn most cases, individual models performed better than the null model, and ensembles models were well calibrated and performed comparatively to the best individual models. The quantile regression average did not noticeably outperform the mean ensemble.ConclusionsEnsembles of multi-model forecasts can inform the policy response to the Covid-19 pandemic by assessing future resource needs and expected population impact of morbidity and mortality.

Published
2020

8. Ivermectin as a novel complementary malaria control tool to reduce incidence and prevalence: a modelling study

Author

Slater, H.C., Foy, Brian D., Kobylinski, K., Chaccour, Carlos, Watson, Oliver J., Hellewell, J., Bousema, T., Ghani, Azra C., Smit, Menno R., Slater, H.C., Foy, Brian D., Kobylinski, K., Chaccour, Carlos, Watson, Oliver J., Hellewell, J., Bousema, T., Ghani, Azra C., and Smit, Menno R.

Abstract

Contains fulltext : 217762.pdf (Publisher’s version ) (Closed access)

Published
2020

9. Reconstructing the early global dynamics of under-ascertained COVID-19 cases and infections

Author

Russell, TW, Golding, N, Hellewell, J, Abbott, S, Wright, L, Pearson, CAB, van Zandvoort, K, Jarvis, CI, Gibbs, H, Liu, Y, Eggo, RM, Edmunds, WJ, Kucharski, AJ, Russell, TW, Golding, N, Hellewell, J, Abbott, S, Wright, L, Pearson, CAB, van Zandvoort, K, Jarvis, CI, Gibbs, H, Liu, Y, Eggo, RM, Edmunds, WJ, and Kucharski, AJ

Abstract

BACKGROUND: Asymptomatic or subclinical SARS-CoV-2 infections are often unreported, which means that confirmed case counts may not accurately reflect underlying epidemic dynamics. Understanding the level of ascertainment (the ratio of confirmed symptomatic cases to the true number of symptomatic individuals) and undetected epidemic progression is crucial to informing COVID-19 response planning, including the introduction and relaxation of control measures. Estimating case ascertainment over time allows for accurate estimates of specific outcomes such as seroprevalence, which is essential for planning control measures. METHODS: Using reported data on COVID-19 cases and fatalities globally, we estimated the proportion of symptomatic cases (i.e. any person with any of fever ≥ 37.5 °C, cough, shortness of breath, sudden onset of anosmia, ageusia or dysgeusia illness) that were reported in 210 countries and territories, given those countries had experienced more than ten deaths. We used published estimates of the baseline case fatality ratio (CFR), which was adjusted for delays and under-ascertainment, then calculated the ratio of this baseline CFR to an estimated local delay-adjusted CFR to estimate the level of under-ascertainment in a particular location. We then fit a Bayesian Gaussian process model to estimate the temporal pattern of under-ascertainment. RESULTS: Based on reported cases and deaths, we estimated that, during March 2020, the median percentage of symptomatic cases detected across the 84 countries which experienced more than ten deaths ranged from 2.4% (Bangladesh) to 100% (Chile). Across the ten countries with the highest number of total confirmed cases as of 6 July 2020, we estimated that the peak number of symptomatic cases ranged from 1.4 times (Chile) to 18 times (France) larger than reported. Comparing our model with national and regional seroprevalence data where available, we find that our estimates are consistent with observed values. Finally

Published
2020

10. Reconstructing the early global dynamics of under-ascertained COVID-19 cases and infections

Author

Russell, T, Golding, N, Hellewell, J, Abbott, S, Wright, L, Pearson, C, Zandvoort, KV, Jarvis, C, Gibbs, H, Liu, Y, Eggo, R, Edmunds, J, Kucharski, A, Russell, T, Golding, N, Hellewell, J, Abbott, S, Wright, L, Pearson, C, Zandvoort, KV, Jarvis, C, Gibbs, H, Liu, Y, Eggo, R, Edmunds, J, and Kucharski, A

Abstract

Background

Asymptomatic or subclinical SARS-CoV-2 infections are often unreported, which means that confirmed case counts may not accurately reflect underlying epidemic dynamics. Understanding the level of ascertainment (the ratio of confirmed symptomatic cases to the true number of symptomatic individuals) and undetected epidemic progression is crucial to informing COVID-19 response planning, including the introduction and relaxation of control measures. Estimating case ascertainment over time allows for accurate estimates of specific outcomes such as seroprevalence, which is essential for planning control measures.

Methods

Using reported data on COVID-19 cases and fatalities globally, we estimated the proportion of symptomatic cases (i.e. any person with any of fever >= 37.5°C, cough, shortness of breath, sudden onset of anosmia, ageusia or dysgeusia illness) that were reported in 210 countries and territories, given those countries had experienced more than ten deaths. We used published estimates of the baseline case fatality ratio (CFR), which was adjusted for delays and under-ascertainment, then calculated the ratio of this baseline CFR to an estimated local delay-adjusted CFR to estimate the level of under-ascertainment in a particular location. We then fit a Bayesian Gaussian process model to estimate the temporal pattern of under-ascertainment.

Results

Based on reported cases and deaths, we estimated that, during March 2020, the median percentage of symptomatic cases detected across the 84 countries which experienced more than ten deaths ranged from 2.4% (Bangladesh) to 100% (Chile). Across the ten countries with the highest number of total confirmed cases as of 6th July 2020, we estimated that the peak number of symptomatic cases ranged from 1.4 times (Chile) to 18 times (France) larger than reported. Comparing our model with national and regional seroprevalence data where available, we find that our estimates are consistent with

Published
2020

11. Reconstructing the early global dynamics of under-ascertained COVID-19 cases and infections

Author

Russell, T.W., Golding, Nick, Hellewell, J., Abbott, S., Wright, L., Pearson, C.A.B., van Zandvoort, K., Jarvis, C.I., Gibbs, H., Liu, Y., Eggo, R.M., Edmunds, W.J., Kucharski, A.J., Deol, A.K., Villabona-Arenas, C.J., Jombart, T., O’Reilly, K., Munday, J.D., Meakin, S.R., Lowe, R., Gimma, A., Endo, A., Nightingale, E.S., Medley, G., Foss, A.M., Knight, G.M., Prem, K., Hué, S., Diamond, C., Rudge, J.W., Atkins, K.E., Auzenbergs, M., Flasche, S., Houben, R.M.G.J., Quilty, B.J., Klepac, P., Quaife, M., Funk, S., Leclerc, Q.J., Emery, J.C., Jit, M., Simons, D., Bosse, N.I., Procter, S.R., Sun, F.Y., Clifford, S., Sherratt, K., Rosello, A., Davies, N.G., Brady, O., Tully, D.C., Gore-Langton, G.R., Russell, T.W., Golding, Nick, Hellewell, J., Abbott, S., Wright, L., Pearson, C.A.B., van Zandvoort, K., Jarvis, C.I., Gibbs, H., Liu, Y., Eggo, R.M., Edmunds, W.J., Kucharski, A.J., Deol, A.K., Villabona-Arenas, C.J., Jombart, T., O’Reilly, K., Munday, J.D., Meakin, S.R., Lowe, R., Gimma, A., Endo, A., Nightingale, E.S., Medley, G., Foss, A.M., Knight, G.M., Prem, K., Hué, S., Diamond, C., Rudge, J.W., Atkins, K.E., Auzenbergs, M., Flasche, S., Houben, R.M.G.J., Quilty, B.J., Klepac, P., Quaife, M., Funk, S., Leclerc, Q.J., Emery, J.C., Jit, M., Simons, D., Bosse, N.I., Procter, S.R., Sun, F.Y., Clifford, S., Sherratt, K., Rosello, A., Davies, N.G., Brady, O., Tully, D.C., and Gore-Langton, G.R.

Abstract

Background: Asymptomatic or subclinical SARS-CoV-2 infections are often unreported, which means that confirmed case counts may not accurately reflect underlying epidemic dynamics. Understanding the level of ascertainment (the ratio of confirmed symptomatic cases to the true number of symptomatic individuals) and undetected epidemic progression is crucial to informing COVID-19 response planning, including the introduction and relaxation of control measures. Estimating case ascertainment over time allows for accurate estimates of specific outcomes such as seroprevalence, which is essential for planning control measures. Methods: Using reported data on COVID-19 cases and fatalities globally, we estimated the proportion of symptomatic cases (i.e. any person with any of fever ≥ 37.5 °C, cough, shortness of breath, sudden onset of anosmia, ageusia or dysgeusia illness) that were reported in 210 countries and territories, given those countries had experienced more than ten deaths. We used published estimates of the baseline case fatality ratio (CFR), which was adjusted for delays and under-ascertainment, then calculated the ratio of this baseline CFR to an estimated local delay-adjusted CFR to estimate the level of under-ascertainment in a particular location. We then fit a Bayesian Gaussian process model to estimate the temporal pattern of under-ascertainment. Results: Based on reported cases and deaths, we estimated that, during March 2020, the median percentage of symptomatic cases detected across the 84 countries which experienced more than ten deaths ranged from 2.4% (Bangladesh) to 100% (Chile). Across the ten countries with the highest number of total confirmed cases as of 6 July 2020, we estimated that the peak number of symptomatic cases ranged from 1.4 times (Chile) to 18 times (France) larger than reported. Comparing our model with national and regional seroprevalence data where available, we find that our estimates are consistent with observed values. Finally

Published
2020

15. Variation in natural exposure to anopheles mosquitoes and its effects on malaria transmission

Author

Guelbeogo, W.M., Goncalves, B.P., Grignard, L., Bradley, J., Serme, Samuel S., Hellewell, J., Lanke, K.H., Bousema, T., and Drakeley, C.

Subjects
All institutes and research themes of the Radboud University Medical Center, lnfectious Diseases and Global Health Radboud Institute for Health Sciences [Radboudumc 4], GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries)
Abstract

Contains fulltext : 187807.pdf (Publisher’s version ) (Open Access)

Published
2018

19. Examining the human infectious reservoir for Plasmodium falciparum malaria in areas of differing transmission intensity

Author

Goncalves, B.P., Kapulu, M.C., Sawa, P., Guelbeogo, W.M., Tiono, A.B., Grignard, L., Stone, W.J.R., Hellewell, J., Lanke, K.H.W., Bastiaens, G.J.H., Bradley, J., Nebie, I., Ngoi, J.M., Oriango, R., Mkabili, D., Nyaurah, M., Midega, J., Wirth, D.F., Marsh, K., Churcher, T.S., Bejon, P., Sirima, S.B., Drakeley, C., Bousema, J.T., Goncalves, B.P., Kapulu, M.C., Sawa, P., Guelbeogo, W.M., Tiono, A.B., Grignard, L., Stone, W.J.R., Hellewell, J., Lanke, K.H.W., Bastiaens, G.J.H., Bradley, J., Nebie, I., Ngoi, J.M., Oriango, R., Mkabili, D., Nyaurah, M., Midega, J., Wirth, D.F., Marsh, K., Churcher, T.S., Bejon, P., Sirima, S.B., Drakeley, C., and Bousema, J.T.

Abstract

Contains fulltext : 182487.pdf (publisher's version ) (Open Access), A detailed understanding of the human infectious reservoir is essential for improving malaria transmission-reducing interventions. Here we report a multi-regional assessment of population-wide malaria transmission potential based on 1209 mosquito feeding assays in endemic areas of Burkina Faso and Kenya. Across both sites, we identified 39 infectious individuals. In high endemicity settings, infectious individuals were identifiable by research-grade microscopy (92.6%; 25/27), whilst one of three infectious individuals in the lowest endemicity setting was detected by molecular techniques alone. The percentages of infected mosquitoes in the different surveys ranged from 0.05 (4/7716) to 1.6% (121/7749), and correlate positively with transmission intensity. We also estimated exposure to malaria vectors through genetic matching of blood from 1094 wild-caught bloodfed mosquitoes with that of humans resident in the same houses. Although adults transmitted fewer parasites to mosquitoes than children, they received more mosquito bites, thus balancing their contribution to the infectious reservoir.

Published
2017

20. Comparison of buspirone with diazepam and fluvoxamine on aversive classical conditioning in humans.

Author

Hellewell, J. S. E., Guimaraes, F. S., Wang, Mike, Deakin, J F W, Hellewell, J S, Wang, M, and Deakin, J F

Subjects
*BUSPIRONE, *DIAZEPAM, *CLASSICAL conditioning, *AMINES, *AROUSAL (Physiology), *CLINICAL trials, *COMPARATIVE studies, *CONDITIONED response, *LEARNING, *RESEARCH methodology, *MEDICAL cooperation, *ORAL drug administration, *RESEARCH, *SEX distribution, *SKIN physiology, *TRANQUILIZING drugs, *EVALUATION research, *RANDOMIZED controlled trials, *ACOUSTIC stimulation, *PHARMACODYNAMICS
Abstract

The effects of buspirone, fluvoxamine and diazepam were investigated, using healthy volunteers, in an aversive conditioning paradigm, a putative model for conditioned anxiety. The main prediction was that buspirone, an anxiolytic agent which reduces activity in serotonin (5-hydroxytryptophan, 5-HT) neurones, would attenuate aversively conditioned skin conductance responses. Skin conductance responses were recorded to 10 neutral tones (habituation phase). Tone 11 was immediately followed by a 1-s 90-dB aversive white noise (unconditioned stimulus). The conditioning trial reinstated responding to a second presentation of the tones (extinction phase). Skin conductance response amplitude, inter-response level and spontaneous fluctuations were recorded. There were five treatment groups comprising five men and five women. One control group took placebo, another control group received nothing; there was no effect of placebo on any measure. Diazepam (2 mg, p.o.), a positive comparator, markedly reduced the amplitude of skin conductance responses at all phases of the experiment, but only in women. Buspirone (5 mg, p.o.) had the predicted effect of accelerating extinction but also of unexpectedly accelerated habituation of skin conductance responses. There was a trend to reduce spontaneous fluctuations and no effect on skin conductance level. The effects of buspirone were thus specific to responses to stimuli. Fluvoxamine (25 mg, p.o.) had similar effects to buspirone and diazepam in women. An action common to buspirone, fluvoxamine and diazepam, which may account for their shared effect on conditioned autonomic responses, is the suppression of neural activity in the dorsal raphe nucleus. It is argued that enhanced habituation must involve a different mechanism, such as enhanced 5-HT1A function in the terminal fields of the median raphe nucleus. [ABSTRACT FROM AUTHOR]

Published
1999
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28. 5HT2 receptors and anxiety

Author

Connell, J, primary, Graeff, F, additional, Guimareas, F, additional, Hellewell, J SE, additional, Hettem, L A, additional, and Deakin, J FW, additional

Published
1995
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30. Thought disorder in schizophrenia is associated with both executive dysfunction and circumscribed impairments in semantic function.

Author

Stirling J, Hellewell J, Blakey A, and Deakin W

Abstract

BACKGROUND: Formal thought disorder (FTD) has long been regarded as a key sign of schizophrenia but little is known about its origins or aetiology. One suggestion is that it is directly related to disordered language functioning; a second is that it is a reflection of poor neurocognitive functioning. A current model posits that it is related to a combination of executive dysfunction and impaired semantic processing. METHOD: To examine these alternative ideas, a heterogeneous group of 30 patients, all carrying a diagnosis of schizophrenia, and 18 non-patient controls completed a series of neurocognitive and psycholinguistic tests, and a clinical review that, inter alia, permitted assessment of thought disorder (TD) using the Thought, Language and Communication Scale (TLC). RESULTS: Patients generally performed at a lower level on most components of the test battery, but there was little evidence of a relationship between TD and syntactic psycholinguistic function. However, schizophrenic patients manifesting higher levels of TD performed at a lower level on tests sensitive to executive dysfunction and semantic impairments. CONCLUSIONS: The origins of TD seem more closely linked to deficits in executive functioning and semantic processing than to impairments in other language functions or general cognition. [ABSTRACT FROM AUTHOR]

Published
2006
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31. Functional anatomy of verbal fluency in people with schizophrenia and those at genetic risk. Focal dysfunction and distributed disconnectivity reappraised.

Author

Spence, Sean A., Liddle, Peter F., Stefan, Martin D., Hellewell, Jonathan S. E., Sharma, Tonmoy, Friston, Karl J., Hirsch, Steven R., Frith, Christopher D., Murray, Robin M., Deakin, J. F. William, Grasby, Paul M., Spence, S A, Liddle, P F, Stefan, M D, Hellewell, J S, Sharma, T, Friston, K J, Hirsch, S R, Frith, C D, and Murray, R M

Subjects
SCHIZOPHRENIA, VERBAL ability, FLUENCY (Language learning), CEREBRAL cortex, PREFRONTAL cortex, CEREBRAL hemispheres
Abstract

Background: PET studies of verbal fluency in schizophrenia report a failure of 'deactivation' of left superior temporal gyrus (STG) in the presence of activation of left dorsolateral prefrontal cortex (DLPFC), which deficit has been attributed to underlying 'functional disconnectivity'.Aim: To test whether these findings provide trait-markers for schizophrenia.Method: We used H2(15)O PET to examine verbal fluency in 10 obligate carriers of the predisposition to schizophrenia, 10 stable patients and 10 normal controls.Results: We found no evidence of a failure of left STG deactivation in carriers or patients. Instead, patients failed to deactivate the precuneus relative to other groups. We found no differences in functional connectivity between left DLPFC and left STG but patients exhibited significant disconnectivity between left DLPFC and anterior cingulate cortex.Conclusions: Failure of left STG 'deactivation' and left fronto-temporal disconnectivity are not consistent findings in schizophrenia; neither are they trait-markers for genetic risk. Prefrontal functional disconnectivity here may characterise the schizophrenic phenotype. [ABSTRACT FROM AUTHOR]

Published
2000
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41. Antipsychotic induced hyperprolactinaemia: a series of illustrative case reports.

Author

Haddad, Peter, Hellewell, Jonathan S E, Wieck, Angelika, Haddad, P M, Hellewell, J S, and Wieck, A

Subjects
AMENORRHEA, ANTIPSYCHOTIC agents, LACTATION disorders, HYPERPROLACTINEMIA
Abstract

Hyperprolactinaemia is a common side-effect of many antipsychotic drugs but, in comparison to extrapyramidal side-effects, it has received little attention. Four case reports are presented which illustrate important clinical and pharmacological aspects of the syndrome. Two of the cases were caused by conventional antipsychotic drugs and two by risperidone, an atypical antipsychotic. Symptoms included gynaecomastia, galactorrhoea, amenorrhoea and sexual dysfunction. Three patients were switched to a prolactin sparing antipsychotic leading to normalization of serum prolactin and resolution of the symptoms. Patients prescribed prolactin elevating antipsychotics should, where possible, have this issue explained to them prior to commencing treatment and be screened for symptoms suggestive of hyperprolactinaemia before starting treatment and regularly thereafter. [ABSTRACT FROM AUTHOR]

Published
2001
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45. Genomic reconstruction of the SARS-CoV-2 epidemic in England

Author

Vöhringer, HS, Sanderson, T, Sinnott, M, De Maio, N, Nguyen, T, Goater, R, Schwach, F, Harrison, I, Hellewell, J, Ariani, CV, Gonçalves, S, Jackson, DK, Johnston, I, Jung, AW, Saint, C, Sillitoe, J, Suciu, M, Goldman, N, Panovska-Griffiths, J, Abnizova, I, Aigrain, L, Alderton, A, Ali, M, Allen, L, Amato, R, Anderson, R, Ariani, C, Austin-Guest, S, Bala, S, Barrett, J, Bassett, A, Battleday, K, Beal, J, Beale, M, Beaver, C, Bellany, S, Bellerby, T, Bellis, K, Berger, D, Berriman, M, Betteridge, E, Bevan, P, Binley, S, Bishop, J, Blackburn, K, Bonfield, J, Boughton, N, Bowker, S, Brendler-Spaeth, T, Bronner, I, Brooklyn, T, Buddenborg, SK, Bush, R, Caetano, C, Cagan, A, Carter, N, Cartwright, J, Monteiro, TC, Chapman, L, Chillingworth, TJ, Clapham, P, Clark, R, Clarke, A, Clarke, C, Cole, D, Cook, E, Coppola, M, Cornell, L, Cornwell, C, Corton, C, Crackett, A, Cranage, A, Craven, H, Craw, S, Crawford, M, Cutts, T, Dabrowska, M, Davies, M, Davies, R, Dawson, J, Day, C, Densem, A, Dibling, T, Dockree, C, Dodd, D, Dogga, S, Dorman, M, Dougan, G, Dougherty, M, Dove, A, Drummond, L, Drury, E, Dudek, M, Durham, J, Durrant, L, Easthope, E, Eckert, S, Ellis, P, Farr, B, Fenton, M, Ferrero, M, Flack, N, Fordham, H, Forsythe, G, Foulser, L, Francis, M, Fraser, A, Freeman, A, Galvin, A, Garcia-Casado, M, Gedny, A, Girgis, S, Glover, J, Goncalves, S, Goodwin, S, Gould, O, Gourtovaia, M, Gray, A, Gray, E, Griffiths, C, Gu, Y, Guerin, F, Hamilton, W, Hanks, H, Harrison, E, Harrott, A, Harry, E, Harvison, J, Heath, P, Hernandez-Koutoucheva, A, Hobbs, R, Holland, D, Holmes, S, Hornett, G, Hough, N, Huckle, L, Hughes-Hallet, L, Hunter, A, Inglis, S, Iqbal, S, Jackson, A, Jackson, D, James, K, Jamrozy, D, Verdejo, CJ, Jones, M, Kallepally, K, Kane, L, Kay, K, Kay, S, Keatley, J, Keith, A, King, A, Kitchin, L, Kleanthous, M, Klimekova, M, Korlevic, P, Krasheninnkova, K, Lane, G, Langford, C, Laverack, A, Law, K, Lawniczak, M, Lensing, S, Leonard, S, Letchford, L, Lewis, K, Lewis-Wade, A, Liddle, J, Lin, Q, Lindsay, S, Linsdell, S, Livett, R, Lo, S, Long, R, Lovell, J, Ludden, C, Mack, J, Maddison, M, Makunin, A, Mamun, I, Mansfield, J, Marriott, N, Martin, M, Mayho, M, McCarthy, S, McClintock, J, McGuigan, S, McHugh, S, McMinn, L, Meadows, C, Mobley, E, Moll, R, Morra, M, Morrow, L, Murie, K, Nash, S, Nathwani, C, Naydenova, P, Neaverson, A, Nelson, R, Nerou, E, Nicholson, J, Nimz, T, Noell, GG, O’Meara, S, Ohan, V, Oliver, K, Olney, C, Ormond, D, Oszlanczi, A, Palmer, S, Pang, YF, Pardubska, B, Park, N, Parmar, A, Patel, G, Patel, M, Payne, M, Peacock, S, Petersen, A, Plowman, D, Preston, T, Prestwood, L, Puethe, C, Quail, M, Rajan, D, Rajatileka, S, Rance, R, Rawlings, S, Redshaw, N, Reynolds, J, Reynolds, M, Rice, S, Richardson, M, Roberts, C, Robinson, K, Robinson, M, Robinson, D, Rogers, H, Rojo, EM, Roopra, D, Rose, M, Rudd, L, Sadri, R, Salmon, N, Saul, D, Scott, C, Seekings, P, Shirley, L, Simms, A, Sivadasan, S, Siwek, B, Sizer, D, Skeldon, K, Skelton, J, Slater-Tunstill, J, Sloper, L, Smerdon, N, Smith, C, Smith, J, Smith, K, Smith, M, Smith, S, Smith, T, Sneade, L, Soria, CD, Sousa, C, Souster, E, Sparkes, A, Spencer-Chapman, M, Squares, J, Stanley, R, Steed, C, Stickland, T, Still, I, Stratton, MR, Strickland, M, Swann, A, Swiatkowska, A, Sycamore, N, Swift, E, Symons, E, Szluha, S, Taluy, E, Tao, N, Taylor, K, Taylor, S, Thompson, S, Thompson, M, Thomson, M, Thomson, N, Thurston, S, Tonkin-Hill, G, Toombs, D, Topping, B, Tovar-Corona, J, Ungureanu, D, Uphill, J, Urbanova, J, Van Vuuren, PJ, Vancollie, V, Voak, P, Walker, D, Walker, M, Waller, M, Ward, G, Weatherhogg, C, Webb, N, Weldon, D, Wells, A, Wells, E, Westwood, L, Whipp, T, Whiteley, T, Whitton, G, Whitwham, A, Widaa, S, Williams, M, Wilson, M, Wright, S, Robson, SC, Connor, TR, Loman, NJ, Golubchik, T, Martinez Nunez, RT, Bonsall, D, Rambaut, A, Snell, LB, Corden, S, Nastouli, E, Nebbia, G, Lythgoe, K, Torok, ME, Goodfellow, IG, Prieto, JA, Saeed, K, Houlihan, C, Frampton, D, Hamilton, WL, Witney, AA, Bucca, G, Pope, CF, Moore, C, Thomson, EC, Harrison, EM, Smith, CP, Rogan, F, Beckwith, SM, Murray, A, Singleton, D, Eastick, K, Sheridan, LA, Randell, P, Jackson, LM, Fairley, DJ, Loose, MW, Watkins, J, Moses, S, Nicholls, S, Bull, M, Smith, DL, Aanensen, DM, Aggarwal, D, Shepherd, JG, Curran, MD, Parmar, S, Parker, MD, Williams, C, Glaysher, S, Underwood, AP, Bashton, M, Pacchiarini, N, Loveson, KF, Byott, M, Carabelli, AM, Templeton, KE, de Silva, TI, Wang, D, Langford, CF, Gunson, RN, Cottrell, S, O’Grady, J, Kwiatkowski, D, Lillie, PJ, Cortes, N, Moore, N, Thomas, C, Burns, PJ, Mahungu, TW, Liggett, S, Beckett, AH, Holden, MTG, Levett, LJ, Osman, H, Hassan-Ibrahim, MO, Simpson, DA, Chand, M, Gupta, RK, Darby, AC, Paterson, S, Pybus, OG, Volz, EM, de Angelis, D, Robertson, DL, Page, AJ, Bassett, AR, Wong, N, Taha, Y, Erkiert, MJ, Spencer Chapman, MH, Dewar, R, McHugh, MP, Mookerjee, S, Aplin, S, Harvey, M, Sass, T, Umpleby, H, Wheeler, H, McKenna, JP, Warne, B, Taylor, JF, Chaudhry, Y, Izuagbe, R, Jahun, AS, Young, GR, McMurray, C, McCann, CM, Nelson, A, Elliott, S, Lowe, H, Price, A, Crown, MR, Rey, S, Roy, S, Temperton, B, Shaaban, S, Hesketh, AR, Laing, KG, Monahan, IM, Heaney, J, Pelosi, E, Silviera, S, Wilson-Davies, E, Fryer, H, Adams, H, du Plessis, L, Johnson, R, Harvey, WT, Hughes, J, Orton, RJ, Spurgin, LG, Bourgeois, Y, Ruis, C, O’Toole, Á, Fraser, C, Edgeworth, J, Breuer, J, Michell, SL, Todd, JA, John, M, Buck, D, Gajee, K, Kay, GL, Peacock, SJ, Heyburn, D, Kitchman, K, McNally, A, Pritchard, DT, Dervisevic, S, Muir, P, Robinson, E, Vipond, BB, Ramadan, NA, Jeanes, C, Catalan, J, Jones, N, da Silva Filipe, A, Fuchs, M, Miskelly, J, Jeffries, AR, Park, NR, Ash, A, Koshy, C, Barrow, M, Buchan, SL, Mantzouratou, A, Clark, G, Holmes, CW, Campbell, S, Davis, T, Tan, NK, Brown, JR, Harris, KA, Kidd, SP, Grant, PR, Xu-McCrae, L, Cox, A, Madona, P, Pond, M, Randell, PA, Withell, KT, Graham, C, Denton-Smith, R, Swindells, E, Turnbull, R, Sloan, TJ, Bosworth, A, Hutchings, S, Pymont, HM, Casey, A, Ratcliffe, L, Jones, CR, Knight, BA, Haque, T, Hart, J, Irish-Tavares, D, Witele, E, Mower, C, Watson, LK, Collins, J, Eltringham, G, Crudgington, D, Macklin, B, Iturriza-Gomara, M, Lucaci, AO, McClure, PC, Carlile, M, Holmes, N, Storey, N, Rooke, S, Yebra, G, Craine, N, Perry, M, Alikhan, NF, Bridgett, S, Cook, KF, Fearn, C, Goudarzi, S, Lyons, RA, Williams, T, Haldenby, ST, Davies, RM, Batra, R, Blane, B, Spyer, MJ, Smith, P, Yavus, M, Williams, RJ, Mahanama, AIK, Samaraweera, B, Girgis, ST, Hansford, SE, Green, A, Bellis, KL, Dorman, MJ, Quick, J, Poplawski, R, Reynolds, N, Mack, A, Morriss, A, Whalley, T, Patel, B, Georgana, I, Hosmillo, M, Pinckert, ML, Stockton, J, Henderson, JH, Hollis, A, Stanley, W, Yew, WC, Myers, R, Thornton, A, Adams, A, Annett, T, Asad, H, Birchley, A, Coombes, J, Evans, JM, Fina, L, Gatica-Wilcox, B, Gilbert, L, Graham, L, Hey, J, Hilvers, E, Jones, S, Jones, H, Kumziene-Summerhayes, S, McKerr, C, Powell, J, Pugh, G, Trotter, AJ, Williams, CA, Kermack, LM, Foulkes, BH, Gallis, M, Hornsby, HR, Louka, SF, Pohare, M, Wolverson, P, Zhang, P, MacIntyre-Cockett, G, Trebes, A, Moll, RJ, Ferguson, L, Goldstein, EJ, Maclean, A, Tomb, R, Starinskij, I, Thomson, L, Southgate, J, Kraemer, MUG, Raghwani, J, Zarebski, AE, Boyd, O, Geidelberg, L, Illingworth, CJ, Jackson, C, Pascall, D, Vattipally, S, Freeman, TM, Hsu, SN, Lindsey, BB, Tovar-Corona, JM, Cox, MG, Abudahab, K, Menegazzo, M, Taylor, BEW, Yeats, CA, Mukaddas, A, Wright, DW, de Oliveira Martins, L, Colquhoun, R, Hill, V, Jackson, B, McCrone, JT, Medd, N, Scher, E, Keatley, JP, Curran, T, Morgan, S, Maxwell, P, Eldirdiri, S, Kenyon, A, Holmes, AH, Price, JR, Wyatt, T, Mather, AE, Skvortsov, T, Hartley, JA, Guest, M, Kitchen, C, Merrick, I, Munn, R, Bertolusso, B, Lynch, J, Vernet, G, Kirk, S, Wastnedge, E, Idle, G, Bradley, DT, Poyner, J, Mori, M, Jones, O, Wright, V, Brooks, E, Churcher, CM, Fragakis, M, Galai, K, Jermy, A, Judges, S, McManus, GM, Smith, KS, Westwick, E, Attwood, SW, Bolt, F, Davies, A, De Lacy, E, Downing, F, Edwards, S, Meadows, L, Jeremiah, S, Smith, N, Charalampous, T, Patel, A, Berry, L, Boswell, T, Fleming, VM, Howson-Wells, HC, Joseph, A, Khakh, M, Lister, MM, Bird, PW, Fallon, K, Helmer, T, McMurray, CL, Odedra, M, Shaw, J, Tang, JW, Willford, NJ, Blakey, V, Raviprakash, V, Sheriff, N, Williams, LA, Feltwell, T, Bedford, L, Cargill, JS, Hughes, W, Moore, J, Stonehouse, S, Atkinson, L, Lee, JCD, Shah, D, Alcolea, A, Ohemeng-Kumi, N, Ramble, J, Sehmi, J, Williams, R, Chatterton, W, Pusok, M, Everson, W, Castigador, A, Macnaughton, E, El Bouzidi, K, Lampejo, T, Sudhanva, M, Breen, C, Sluga, G, Ahmad, SSY, George, RP, Machin, NW, Binns, D, James, V, Blacow, R, Coupland, L, Smith, L, Barton, E, Padgett, D, Scott, G, Cross, A, Mirfenderesky, M, Greenaway, J, Cole, K, Clarke, P, Duckworth, N, Walsh, S, Bicknell, K, Impey, R, Wyllie, S, Hopes, R, Bishop, C, Chalker, V, Gifford, L, Molnar, Z, Auckland, C, Evans, C, Johnson, K, Partridge, DG, Raza, M, Baker, P, Bonner, S, Essex, S, Murray, LJ, Lawton, AI, Burton-Fanning, S, Payne, BAI, Waugh, S, Gomes, AN, Kimuli, M, Murray, DR, Ashfield, P, Dobie, D, Ashford, F, Best, A, Crawford, L, Cumley, N, Mayhew, M, Megram, O, Mirza, J, Moles-Garcia, E, Percival, B, Ensell, L, Lowe, HL, Maftei, L, Mondani, M, Chaloner, NJ, Cogger, BJ, Easton, LJ, Huckson, H, Lewis, J, Lowdon, S, Malone, CS, Munemo, F, Mutingwende, M, Nicodemi, R, Podplomyk, O, Somassa, T, Beggs, A, Richter, A, Cormie, C, Dias, J, Forrest, S, Higginson, EE, Maes, M, Young, J, Davidson, RK, Jackson, KA, Turtle, L, Keeley, AJ, Ball, J, Byaruhanga, T, Chappell, JG, Dey, J, Hill, JD, Park, EJ, Fanaie, A, Hilson, RA, Yaze, G, Afifi, S, Beer, R, Maksimovic, J, Masters, KMC, Spellman, K, Bresner, C, Fuller, W, Marchbank, A, Workman, T, Shelest, E, Debebe, J, Sang, F, Zamudio, ME, Francois, S, Gutierrez, B, Vasylyeva, TI, Flaviani, F, Ragonnet-Cronin, M, Smollett, KL, Broos, A, Mair, D, Nichols, J, Nomikou, K, Tong, L, Tsatsani, I, O’Brien, S, Rushton, S, Sanderson, R, Perkins, J, Cotton, S, Gallagher, A, Allara, E, Pearson, C, Bibby, D, Dabrera, G, Ellaby, N, Gallagher, E, Hubb, J, Lackenby, A, Lee, D, Manesis, N, Mbisa, T, Platt, S, Twohig, KA, Morgan, M, Aydin, A, Baker, DJ, Foster-Nyarko, E, Prosolek, SJ, Rudder, S, Baxter, C, Carvalho, SF, Lavin, D, Mariappan, A, Radulescu, C, Singh, A, Tang, M, Morcrette, H, Bayzid, N, Cotic, M, Balcazar, CE, Gallagher, MD, Maloney, D, Stanton, TD, Williamson, KA, Manley, R, Michelsen, ML, Sambles, CM, Studholme, DJ, Warwick-Dugdale, J, Eccles, R, Gemmell, M, Gregory, R, Hughes, M, Nelson, C, Rainbow, L, Vamos, EE, Webster, HJ, Whitehead, M, Wierzbicki, C, Angyal, A, Green, LR, Whiteley, M, Bronner, IF, Farr, BW, Lensing, SV, McCarthy, SA, Quail, MA, Redshaw, NM, Thurston, SAJ, Rowe, W, Gaskin, A, Le-Viet, T, Birney, E, Volz, E, Funk, S, Martincorena, I, Barrett, JC, Gerstung, M, Vöhringer, HS, Sanderson, T, Sinnott, M, De Maio, N, Nguyen, T, Goater, R, Schwach, F, Harrison, I, Hellewell, J, Ariani, CV, Gonçalves, S, Jackson, DK, Johnston, I, Jung, AW, Saint, C, Sillitoe, J, Suciu, M, Goldman, N, Panovska-Griffiths, J, Abnizova, I, Aigrain, L, Alderton, A, Ali, M, Allen, L, Amato, R, Anderson, R, Ariani, C, Austin-Guest, S, Bala, S, Barrett, J, Bassett, A, Battleday, K, Beal, J, Beale, M, Beaver, C, Bellany, S, Bellerby, T, Bellis, K, Berger, D, Berriman, M, Betteridge, E, Bevan, P, Binley, S, Bishop, J, Blackburn, K, Bonfield, J, Boughton, N, Bowker, S, Brendler-Spaeth, T, Bronner, I, Brooklyn, T, Buddenborg, SK, Bush, R, Caetano, C, Cagan, A, Carter, N, Cartwright, J, Monteiro, TC, Chapman, L, Chillingworth, TJ, Clapham, P, Clark, R, Clarke, A, Clarke, C, Cole, D, Cook, E, Coppola, M, Cornell, L, Cornwell, C, Corton, C, Crackett, A, Cranage, A, Craven, H, Craw, S, Crawford, M, Cutts, T, Dabrowska, M, Davies, M, Davies, R, Dawson, J, Day, C, Densem, A, 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Gerstung, M

Abstract

The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus leads to new variants that warrant timely epidemiological characterization. Here we use the dense genomic surveillance data generated by the COVID-19 Genomics UK Consortium to reconstruct the dynamics of 71 different lineages in each of 315 English local authorities between September 2020 and June 2021. This analysis reveals a series of subepidemics that peaked in early autumn 2020, followed by a jump in transmissibility of the B.1.1.7/Alpha lineage. The Alpha variant grew when other lineages declined during the second national lockdown and regionally tiered restrictions between November and December 2020. A third more stringent national lockdown suppressed the Alpha variant and eliminated nearly all other lineages in early 2021. Yet a series of variants (most of which contained the spike E484K mutation) defied these trends and persisted at moderately increasing proportions. However, by accounting for sustained introductions, we found that the transmissibility of these variants is unlikely to have exceeded the transmissibility of the Alpha variant. Finally, B.1.617.2/Delta was repeatedly introduced in England and grew rapidly in early summer 2021, constituting approximately 98% of sampled SARS-CoV-2 genomes on 26 June 2021.

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