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Preprint in English | medRxiv | ID: ppmedrxiv-21267615


BackgroundA rapid increase in cases due to the SARS-CoV-2 Omicron (B.1.1.529) variant in highly vaccinated populations has raised concerns about the effectiveness of current vaccines. MethodsWe used a test-negative case-control design to estimate vaccine effectiveness (VE) against symptomatic disease caused by the Omicron and Delta variants in England. VE was calculated after primary immunisation with two BNT162b2 or ChAdOx1 doses, and at 2+ weeks following a BNT162b2 booster. ResultsBetween 27 November and 06 December 2021, 581 and 56,439 eligible Omicron and Delta cases respectively were identified. There were 130,867 eligible test-negative controls. There was no effect against Omicron from 15 weeks after two ChAdOx1 doses, while VE after two BNT162b2 doses was 88.0% (95%CI: 65.9 to 95.8%) 2-9 weeks after dose 2, dropping to between 34 and 37% from 15 weeks post dose 2.From two weeks after a BNT162b2 booster, VE increased to 71.4% (95%CI: 41.8 to 86.0%) for ChAdOx1 primary course recipients and 75.5% (95%CI: 56.1 to 86.3%) for BNT162b2 primary course recipients. For cases with Delta, VE was 41.8% (95%CI: 39.4-44.1%) at 25+ weeks after two ChAdOx1 doses, increasing to 93.8% (95%CI: 93.2-94.3%) after a BNT162b2 booster. With a BNT162b2 primary course, VE was 63.5% (95%CI: 61.4 to 65.5%) 25+ weeks after dose 2, increasing to 92.6% (95%CI: 92.0-93.1%) two weeks after the booster. ConclusionsPrimary immunisation with two BNT162b2 or ChAdOx1 doses provided no or limited protection against symptomatic disease with the Omicron variant. Boosting with BNT162b2 following either primary course significantly increased protection.

Preprint in English | medRxiv | ID: ppmedrxiv-21260746


BackgroundAs of July 2021, more than 180,000,000 cases of COVID-19 have been reported across the world, with more than 4 million deaths. Mathematical modelling and forecasting efforts have been widely used to inform policy-making and to create situational awareness. Methods and FindingsFrom 8th March to 29th November 2020, we produced weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for countries with evidence of sustained transmission. The estimates and forecasts were based on an ensemble model comprising of three models that were calibrated using only the reported number of COVID-19 cases and deaths in each country. We also developed a novel heuristic to combine weekly estimates of transmissibility and potential changes in population immunity due to infection to produce forecasts over a 4-week horizon. We evaluated the robustness of the forecasts using relative error, coverage probability, and comparisons with null models. ConclusionsDuring the 39-week period covered by this study, we produced short- and medium-term forecasts for 81 countries. Both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. We could accurately characterise the overall phase of the epidemic up to 4-weeks ahead in 84.9% of country-days. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax stringent public health measures that were implemented to contain the pandemic.

Preprint in English | medRxiv | ID: ppmedrxiv-20194258


Background: Unprecedented public health interventions including travel restrictions and national lockdowns have been implemented to stem the COVID-19 epidemic, but the effectiveness of non-pharmaceutical interventions is still debated. International comparisons are hampered by highly variable conditions under which epidemics spread and differences in the timing and scale of interventions. Cumulative COVID-19 morbidity and mortality are functions of both the rate of epidemic growth and the duration of uninhibited growth before interventions were implemented. Incomplete and sporadic testing during the early COVID-19 epidemic makes it difficult to identify how long SARS-CoV-2 was circulating in different places. SARS-CoV-2 genetic sequences can be analyzed to provide an estimate of both the time of epidemic origin and the rate of early epidemic growth in different settings. Methods: We carried out a phylogenetic analysis of more than 29,000 publicly available whole genome SARS-CoV-2 sequences from 57 locations to estimate the time that the epidemic originated in different places. These estimates were cross-referenced with dates of the most stringent interventions in each location as well as the number of cumulative COVID-19 deaths following maximum intervention. Phylodynamic methods were used to estimate the rate of early epidemic growth and proxy estimates of epidemic size. Findings: The time elapsed between epidemic origin and maximum intervention is strongly associated with different measures of epidemic severity and explains 46% of variance in numbers infected at time of maximum intervention. The reproduction number is independently associated with epidemic severity. In multivariable regression models, epidemic severity was not associated with census population size. The time elapsed between detection of initial COVID-19 cases to interventions was not associated with epidemic severity, indicating that many locations experienced long periods of cryptic transmission. Interpretation: Locations where strong non-pharmaceutical interventions were implemented earlier experienced much less severe COVID-19 morbidity and mortality during the period of study.

Darlan da Silva Candido; Ingra Morales Claro; Jaqueline Goes de Jesus; William Marciel de Souza; Filipe Romero Rebello Moreira; Simon Dellicour; Thomas A. Mellan; Louis du Plessis; Rafael Henrique Moraes Pereira; Flavia Cristina da Silva Sales; Erika Regina Manuli; Julien Theze; Luis Almeida; Mariane Talon de Menezes; Carolina Moreira Voloch; Marcilio Jorge Fumagalli; Thais de Moura Coletti; Camila Alves Maia Silva; Mariana Severo Ramundo; Mariene Ribeiro Amorim; Henrique Hoeltgebaum; Swapnil Mishra; Mandev Gill; Luiz Max Carvalho; Lewis Fletcher Buss; Carlos Augusto Prete Jr.; Jordan Ashworth; Helder Nakaya; Pedro da Silva Peixoto; Oliver J Brady; Samuel M. Nicholls; Amilcar Tanuri; Atila Duque Rossi; Carlos Kaue Vieira Braga; Alexandra Lehmkuhl Gerber; Ana Paula Guimaraes; Nelson Gaburo Jr.; Cecilia Salete Alencar; Alessandro Clayton de Souza Ferreira; Cristiano Xavier Lima; Jose Eduardo Levi; Celso Granato; Giula Magalhaes Ferreira; Ronaldo da Silva Francisco Jr.; Fabiana Granja; Marcia Teixeira Garcia; Maria Luiza Moretti; Mauricio Wesley Perroud Jr.; Terezinha Marta Pereira Pinto Castineiras; Carolina Dos Santos Lazari; Sarah C Hill; Andreza Aruska de Souza Santos; Camila Lopes Simeoni; Julia Forato; Andrei Carvalho Sposito; Angelica Zaninelli Schreiber; Magnun Nueldo Nunes Santos; Camila Zolini Sa; Renan Pedra Souza; Luciana Cunha Resende Moreira; Mauro Martins Teixeira; Josy Hubner; Patricia Asfora Falabella Leme; Rennan Garcias Moreira; Mauricio Lacerda Nogueira; - CADDE-Genomic-Network; Neil Ferguson; Silvia Figueiredo Costa; Jose Luiz Proenca-Modena; Ana Tereza Vasconcelos; Samir Bhatt; Philippe Lemey; Chieh-Hsi Wu; Andrew Rambaut; Nick J Loman; Renato Santana Aguiar; Oliver G Pybus; Ester Cerdeira Sabino; Nuno Rodrigues Faria.
Preprint in English | medRxiv | ID: ppmedrxiv-20128249


Brazil currently has one of the fastest growing SARS-CoV-2 epidemics in the world. Due to limited available data, assessments of the impact of non-pharmaceutical interventions (NPIs) on virus transmission and epidemic spread remain challenging. We investigate the impact of NPIs in Brazil using epidemiological, mobility and genomic data. Mobility-driven transmission models for Sao Paulo and Rio de Janeiro cities show that the reproduction number (Rt) reached below 1 following NPIs but slowly increased to values between 1 to 1.3 (1.0-1.6). Genome sequencing of 427 new genomes and analysis of a geographically representative genomic dataset from 21 of the 27 Brazilian states identified >100 international introductions of SARS-CoV-2 in Brazil. We estimate that three clades introduced from Europe emerged between 22 and 27 February 2020, and were already well-established before the implementation of NPIs and travel bans. During this first phase of the epidemic establishment of SARS-CoV-2 in Brazil, we find that the virus spread mostly locally and within-state borders. Despite sharp decreases in national air travel during this period, we detected a 25% increase in the average distance travelled by air passengers during this time period. This coincided with the spread of SARS-CoV-2 from large urban centers to the rest of the country. In conclusion, our results shed light on the role of large and highly connected populated centres in the rapid ignition and establishment of SARS-CoV-2, and provide evidence that current interventions remain insufficient to keep virus transmission under control in Brazil. One Sentence SummaryJoint analysis of genomic, mobility and epidemiological novel data provide unique insight into the spread and transmission of the rapidly evolving epidemic of SARS-CoV-2 in Brazil.

Preprint in English | medRxiv | ID: ppmedrxiv-20033357


BackgroundA range of case fatality ratio (CFR) estimates for COVID-19 have been produced that differ substantially in magnitude. MethodsWe used individual-case data from mainland China and cases detected outside mainland China to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the CFR by relating the aggregate distribution of cases by dates of onset to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for the demography of the population, and age- and location-based under-ascertainment. We additionally estimated the CFR from individual line-list data on 1,334 cases identified outside mainland China. We used data on the PCR prevalence in international residents repatriated from China at the end of January 2020 to obtain age-stratified estimates of the infection fatality ratio (IFR). Using data on age-stratified severity in a subset of 3,665 cases from China, we estimated the proportion of infections that will likely require hospitalisation. FindingsWe estimate the mean duration from onset-of-symptoms to death to be 17.8 days (95% credible interval, crI 16.9-19.2 days) and from onset-of-symptoms to hospital discharge to be 22.6 days (95% crI 21.1-24.4 days). We estimate a crude CFR of 3.67% (95% crI 3.56%-3.80%) in cases from mainland China. Adjusting for demography and under-ascertainment of milder cases in Wuhan relative to the rest of China, we obtain a best estimate of the CFR in China of 1.38% (95% crI 1.23%-1.53%) with substantially higher values in older ages. Our estimate of the CFR from international cases stratified by age (under 60 / 60 and above) are consistent with these estimates from China. We obtain an overall IFR estimate for China of 0.66% (0.39%-1.33%), again with an increasing profile with age. InterpretationThese early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and demonstrate a strong age-gradient in risk.