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Katharine Sherratt; Hugo Gruson; Rok Grah; Helen Johnson; Rene Niehus; Bastian Prasse; Frank Sandman; Jannik Deuschel; Daniel Wolffram; Sam Abbott; Alexander Ullrich; Graham Gibson; Evan L Ray; Nicholas G Reich; Daniel Sheldon; Yijin Wang; Nutcha Wattanachit; Lijing Wang; Jan Trnka; Guillaume Obozinski; Tao Sun; Dorina Thanou; Loic Pottier; Ekaterina Krymova; Maria Vittoria Barbarossa; Neele Leithauser; Jan Mohring; Johanna Schneider; Jaroslaw Wlazlo; Jan Fuhrmann; Berit Lange; Isti Rodiah; Prasith Baccam; Heidi Gurung; Steven Stage; Bradley Suchoski; Jozef Budzinski; Robert Walraven; Inmaculada Villanueva; Vit Tucek; Martin Smid; Milan Zajicek; Cesar Perez Alvarez; Borja Reina; Nikos I Bosse; Sophie Meakin; Pierfrancesco Alaimo Di Loro; Antonello Maruotti; Veronika Eclerova; Andrea Kraus; David Kraus; Lenka Pribylova; Bertsimas Dimitris; Michael Lingzhi Li; Soni Saksham; Jonas Dehning; Sebastian Mohr; Viola Priesemann; Grzegorz Redlarski; Benjamin Bejar; Giovanni Ardenghi; Nicola Parolini; Giovanni Ziarelli; Wolfgang Bock; Stefan Heyder; Thomas Hotz; David E. Singh; Miguel Guzman-Merino; Jose L Aznarte; David Morina; Sergio Alonso; Enric Alvarez; Daniel Lopez; Clara Prats; Jan Pablo Burgard; Arne Rodloff; Tom Zimmermann; Alexander Kuhlmann; Janez Zibert; Fulvia Pennoni; Fabio Divino; Marti Catala; Gianfranco Lovison; Paolo Giudici; Barbara Tarantino; Francesco Bartolucci; Giovanna Jona Lasinio; Marco Mingione; Alessio Farcomeni; Ajitesh Srivastava; Pablo Montero-Manso; Aniruddha Adiga; Benjamin Hurt; Bryan Lewis; Madhav Marathe; Przemyslaw Porebski; Srinivasan Venkatramanan; Rafal Bartczuk; Filip Dreger; Anna Gambin; Krzysztof Gogolewski; Magdalena Gruziel-Slomka; Bartosz Krupa; Antoni Moszynski; Karol Niedzielewski; Jedrzej Nowosielski; Maciej Radwan; Franciszek Rakowski; Marcin Semeniuk; Ewa Szczurek; Jakub Zielinski; Jan Kisielewski; Barbara Pabjan; Kirsten Holger; Yuri Kheifetz; Markus Scholz; Marcin Bodych; Maciej Filinski; Radoslaw Idzikowski; Tyll Krueger; Tomasz Ozanski; Johannes Bracher; Sebastian Funk.
Preprint in English | medRxiv | ID: ppmedrxiv-22276024

ABSTRACT

BackgroundShort-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. MethodsWe used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported from a standardised source over the next one to four weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models past predictive performance. ResultsOver 52 weeks we collected and combined up to 28 forecast models for 32 countries. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 84% of participating models forecasts of incident cases (with a total N=862), and 92% of participating models forecasts of deaths (N=746). Across a one to four week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over four weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. ConclusionsOur results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than two weeks. Code and data availabilityAll data and code are publicly available on Github: covid19-forecast-hub-europe/euro-hub-ensemble.

2.
Preprint in English | medRxiv | ID: ppmedrxiv-22276321

ABSTRACT

IntroductionSince the start of the pandemic SARS-CoV-2 infection has most commonly been confirmed using reverse transcriptase polymerase chain reaction (RT-PCR), with results translated into a binary positive/negative outcomes. Previous studies have found that there is additional useful information in the level of the Cycle threshold (Ct value) of positive cases. Here we characterise variation in Ct values as a proxy for viral loads in more than 3 million test-positive COVID-19 cases in England with the aim of better quantifying the utility of such data. MethodsWe used individual N gene Ct values from symptomatic PCR positive (with Ct value less than 30) Pillar 2 cases in England who self-reported the date of symptom onset, and for whom age, reinfection status, variant status, and the number of vaccines received was available. Those with a positive test result more than 6 days after their reported symptom onset were excluded to mitigate the potential impact of recall bias. We used a generalised additive model, to estimate Ct values empirical mean Ct values for each strata of interest independently as well as to predict Ct values using a model that adjusted for a range of demographic and epidemiological covariates jointly. We present empirical Ct values and compare them to predicted mean Ct values. ResultsWe found that mean Ct values varied by vaccine status, and reinfection status with the number of vaccine doses having little apparent effect. Modelling Ct values as a smooth function of time since onset and other variables struggled to reproduce the individual variation in the data but did match the population-level variation over time relatively well with this being apparently dominated by large differences between variants. Other variation over time was also captured to some degree though their remained several periods where the model could not capture the empirical means with a potential explanation being epidemic phase bias. ConclusionsAnalysing a large dataset of routine Ct values from symptomatic COVID-19 cases in England we found variation based on time since symptom onset, vaccine status, age, and variant. Ct values were highest 1-3 days after symptom onset and differed most due to variant status. We found no clear correlation between previously estimated differences in intrinsic transmissibility and Ct values indicating that this is potentially mediated at least partly by factors other than viral load as estimated using Ct values. We found evidence that a model adjusting for a range of covariates could explain some of the population-level variation over time but systematically underestimated Ct values when incidence was increasing, and overestimated them when incidence was decreasing. This indicates the utility of Ct values from this data source as a tool for surveillance, potentially avoiding some of the biases of aggregated positive counts.

3.
Preprint in English | medRxiv | ID: ppmedrxiv-22273101

ABSTRACT

BackgroundRepeated measurements of cross-sectional prevalence of Polymerase Chain Reaction (PCR) positivity or seropositivity provide rich insight into the dynamics of an infection. The UK Office for National Statistics (ONS) Community Infection Survey publishes such measurements for SARS-CoV-2 on a weekly basis based on testing enrolled households, contributing to situational awareness in the country. Here we present estimates of time-varying and static epidemiological quantities that were derived from the estimates published by ONS. MethodsWe used a gaussian process to model incidence of infections and then estimated observed PCR prevalence by convolving our modelled incidence estimates with a previously published PCR detection curve describing the probability of a positive test as a function of the time since infection. We refined our incidence estimates using time-varying estimates of antibody prevalence combined with a model of antibody positivity and waning that moved individuals between compartments with or without antibodies based on estimates of new infections, vaccination, probability of seroconversion and waning. ResultsWe produced incidence curves of infection describing the UK epidemic from late April 2020 until early 2022. We used these estimates of incidence to estimate the time-varying growth rate of infections, and combined them with estimates of the generation interval to estimate time-varying reproduction numbers. Biological parameters describing seroconversion and waning, while based on a simple model, were broadly in line with plausible ranges from individual-level studies. ConclusionsBeyond informing situational awareness and allowing for estimates using individual-level data, repeated cross-sectional studies make it possible to estimate epidemiological parameters from population-level models. Studies or public health surveillance methods based on similar designs offer opportunities for further improving our understanding of the dynamics of SARS-CoV-2 or other pathogens and their interaction with population-level immunity.

4.
Preprint in English | medRxiv | ID: ppmedrxiv-22268920

ABSTRACT

BackgroundEarly estimates from South Africa indicated that the Omicron COVID-19 variant may be both more transmissible and have greater immune escape than the previously dominant Delta variant. The rapid turnover of the latest epidemic wave in South Africa as well as initial evidence from contact tracing and household infection studies has prompted speculation that the generation time of the Omicron variant may be shorter in comparable settings than the generation time of the Delta variant. MethodsWe estimated daily growth rates for the Omicron and Delta variants in each UKHSA region from the 23rd of November to the 23rd of December 2021 using surveillance case counts by date of specimen and S-gene target failure status with an autoregressive model that allowed for time-varying differences in the transmission advantage of the Delta variant where the evidence supported this. By assuming a gamma distributed generation distribution we then estimated the generation time distribution and transmission advantage of the Omicron variant that would be required to explain this time varying advantage. We repeated this estimation process using two different prior estimates for the generation time of the Delta variant first based on household transmission and then based on its intrinsic generation time. ResultsVisualising our growth rate estimates provided initial evidence for a difference in generation time distributions. Assuming a generation time distribution for Delta with a mean of 2.5-4 days (90% credible interval) and a standard deviation of 1.9-3 days we estimated a shorter generation time distribution for Omicron with a mean of 1.5-3.2 days and a standard deviation of 1.3-4.6 days. This implied a transmission advantage for Omicron in this setting of 160%-210% compared to Delta. We found similar relative results using an estimate of the intrinsic generation time for Delta though all estimates increased in magnitude due to the longer assumed generation time. ConclusionsWe found that a reduction in the generation time of Omicron compared to Delta was able to explain the observed variation over time in the transmission advantage of the Omicron variant. However, this analysis cannot rule out the role of other factors such as differences in the populations the variants were mixing in, differences in immune escape between variants or bias due to using the test to test distribution as a proxy for the generation time distribution.

5.
Preprint in English | medRxiv | ID: ppmedrxiv-21266598

ABSTRACT

1Forecasts based on epidemiological modelling have played an important role in shaping public policy throughout the COVID-19 pandemic. This modelling combines knowledge about infectious disease dynamics with the subjective opinion of the researcher who develops and refines the model and often also adjusts model outputs. Developing a forecast model is difficult, resource- and time-consuming. It is therefore worth asking what modelling is able to add beyond the subjective opinion of the researcher alone. To investigate this, we analysed different real-time forecasts of cases of and deaths from COVID-19 in Germany and Poland over a 1-4 week horizon submitted to the German and Polish Forecast Hub. We compared crowd forecasts elicited from researchers and volunteers, against a) forecasts from two semi-mechanistic models based on common epidemiological assumptions and b) the ensemble of all other models submitted to the Forecast Hub. We found crowd forecasts, despite being overconfident, to outperform all other methods across all forecast horizons when forecasting cases (weighted interval score relative to the Hub ensemble 2 weeks ahead: 0.89). Forecasts based on computational models performed comparably better when predicting deaths (rel. WIS 1.26), suggesting that epidemiological modelling and human judgement can complement each other in important ways.

6.
Preprint in English | medRxiv | ID: ppmedrxiv-21267056

ABSTRACT

BackgroundLocal estimates of the time-varying effective reproduction number (Rt) of COVID-19 in England became increasingly heterogeneous during April and May 2021. This may have been attributable to the spread of the Delta SARS-CoV-2 variant. This paper documents real-time analysis that aimed to investigate the association between changes in the proportion of positive cases that were S-gene positive, an indicator of the Delta variant against a background of the previously predominant Alpha variant, and the estimated time-varying Rt at the level of upper-tier local authorities (UTLA). MethodWe explored the relationship between the proportion of samples that were S-gene positive and the Rt of test-positive cases over time from the 23 February 2021 to the 25 May 2021. Effective reproduction numbers were estimated using the EpiNow2 R package independently for each local authority using two different estimates of the generation time. We then fit a range of regression models to estimate a multiplicative relationship between S-gene positivity and weekly mean Rt estimate. ResultsWe found evidence of an association between increased mean Rt estimates and the proportion of S-gene positives across all models evaluated with the magnitude of the effect increasing as model flexibility was decreased. Models that adjusted for either national level or NHS region level time-varying residuals were found to fit the data better, suggesting potential unexplained confounding. ConclusionsOur results indicated that even after adjusting for time-varying residuals between NHS regions, S-gene positivity was associated with an increase in the effective reproduction number of COVID-19. These findings were robust across a range of models and generation time assumptions, though the specific effect size was variable depending on the assumptions used. The lower bound of the estimated effect indicated that the reproduction number of Delta was above 1 in almost all local authorities throughout the period of investigation.

7.
Preprint in English | medRxiv | ID: ppmedrxiv-21265810

ABSTRACT

We report on the second and final part of a pre-registered forecasting study on COVID-19 cases and deaths in Germany and Poland. Fifteen independent research teams provided forecasts at lead times of one through four weeks from January through mid-April 2021. Compared to the first part (October-December 2020), the number of participating teams increased, and a number of teams started providing subnational-level forecasts. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions more straightforward than in the first part of our study. In both countries, case counts declined initially, before rebounding due to the rise of the B.1.1.7 variant. Deaths declined through most of the study period in Germany while in Poland they increased after a prolonged plateau. Many, though not all, models outperformed a simple baseline model up to four weeks ahead, with ensemble methods showing very good relative performance. Major trend changes in reported cases, however, remained challenging to predict.

8.
Preprint in English | medRxiv | ID: ppmedrxiv-21265046

ABSTRACT

BackgroundForecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources. MethodsWe made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all, and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the Weighted Interval Score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known. ResultsAll models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons. ConclusionsAssuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings.

9.
Preprint in English | medRxiv | ID: ppmedrxiv-21262480

ABSTRACT

BackgroundSARS-CoV-2 spreads in hospitals, but the contribution of these settings to the overall COVID-19 burden at a national level is unknown. MethodsWe used comprehensive national English datasets and simulation modelling to determine the total burden (identified and unidentified) of symptomatic hospital-acquired infections. Those unidentified would either be 1) discharged before symptom onset ("missed"), or 2) have symptom onset 7 days or fewer from admission ("misclassified"). We estimated the contribution of "misclassified" cases and transmission from "missed" symptomatic infections to the English epidemic before 31st July 2020. FindingsIn our dataset of hospitalised COVID-19 patients in acute English Trusts with a recorded symptom onset date (n = 65,028), 7% were classified as hospital-acquired (with symptom onset 8 or more days after admission and before discharge). We estimated that only 30% (range across weeks and 200 simulations: 20-41%) of symptomatic hospital-acquired infections would be identified. Misclassified cases and onward transmission from missed infections could account for 15% (mean, 95% range over 200 simulations: 14{middle dot}1%-15{middle dot}8%) of cases currently classified as community-acquired COVID-19. From this, we estimated that 26,600 (25,900 to 27,700) individuals acquired a symptomatic SARS-CoV-2 infection in an acute Trust in England before 31st July 2020, resulting in 15,900 (15,200-16,400) or 20.1% (19.2%-20.7%) of all identified hospitalised COVID-19 cases. ConclusionsTransmission of SARS-CoV-2 to hospitalised patients likely caused approximately a fifth of identified cases of hospitalised COVID-19 in the "first wave", but fewer than 1% of all SARS-CoV-2 infections in England. Using symptom onset as a detection method for hospital-acquired SARS-CoV-2 likely misses a substantial proportion (>60%) of hospital-acquired infections. FundingNational Institute for Health Research, UK Medical Research Council, Society for Laboratory Automation and Screening, UKRI, Wellcome Trust, Singapore National Medical Research Council. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed with the terms "((national OR country) AND (contribution OR burden OR estimates) AND ("hospital-acquired" OR "hospital-associated" OR "nosocomial")) AND Covid-19" for articles published in English up to July 1st 2021. This identified 42 studies, with no studies that had aimed to produce comprehensive national estimates of the contribution of hospital settings to the COVID-19 pandemic. Most studies focused on estimating seroprevalence or levels of infection in healthcare workers only, which were not our focus. Removing the initial national/country terms identified 120 studies, with no country level estimates. Several single hospital setting estimates exist for England and other countries, but the percentage of hospital-associated infections reported relies on identified cases in the absence of universal testing. Added value of this studyThis study provides the first national-level estimates of all symptomatic hospital-acquired infections with SARS-CoV-2 in England up to the 31st July 2020. Using comprehensive data, we calculate how many infections would be unidentified and hence can generate a total burden, impossible from just notification data. Moreover, our burden estimates for onward transmission suggest the contribution of hospitals to the overall infection burden. Implications of all the available evidenceLarge numbers of patients may become infected with SARS-CoV-2 in hospitals though only a small proportion of such infections are identified. Further work is needed to better understand how interventions can reduce such transmission and to better understand the contributions of hospital transmission to mortality.

10.
Preprint in English | medRxiv | ID: ppmedrxiv-21257973

ABSTRACT

BackgroundDuring the COVID-19 pandemic, the UK government imposed public health policies in England to reduce social contacts in hopes of curbing virus transmission. We measured contact patterns weekly from March 2020 to March 2021 to estimate the impact of these policies, covering three national lockdowns interspersed by periods of lower restrictions. MethodsData were collected using online surveys of representative samples of the UK population by age and gender. We calculated the mean daily contacts reported using a (clustered) bootstrap and fitted a censored negative binomial model to estimate age-stratified contact matrices and estimate proportional changes to the basic reproduction number under controlled conditions using the change in contacts as a scaling factor. ResultsThe survey recorded 101,350 observations from 19,914 participants who reported 466,710 contacts over 53 weeks. Contact patterns changed over time and by participants age, personal risk factors, and perception of risk. The mean of reported contacts among adults have reduced compared to previous surveys with adults aged 18 to 59 reporting a mean of 2.39 (95% CI 2.20 - 2.60) contacts to 4.93 (95% CI 4.65 - 5.19) contacts, and the mean contacts for school-age children was 3.07 (95% CI 2.89 - 3.27) to 15.11 (95% CI 13.87 - 16.41). The use of face coverings outside the home has remained high since the government mandated use in some settings in July 2020. ConclusionsThe CoMix survey provides a unique longitudinal data set for a full year since the first lockdown for use in statistical analyses and mathematical modelling of COVID-19 and other diseases. Recorded contacts reduced dramatically compared to pre-pandemic levels, with changes correlated to government interventions throughout the pandemic. Despite easing of restrictions in the summer of 2020, mean reported contacts only returned to about half of that observed pre-pandemic.

11.
Preprint in English | medRxiv | ID: ppmedrxiv-21257633

ABSTRACT

The evolution of the SARS-CoV-2 pandemic continuously produces new variants, which warrant timely epidemiological characterisation. Here we use the dense genomic surveillance 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 sub-epidemics that peaked in the early autumn of 2020, followed by a jump in transmissibility of the B.1.1.7/Alpha lineage. Alpha 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 Alpha and eliminated nearly all other lineages in early 2021. However, a series of variants (mostly containing the spike E484K mutation) defied these trends and persisted at moderately increasing proportions. Accounting for sustained introductions, however, indicates that their transmissibility is unlikely to have exceeded that of Alpha. Finally, B.1.617.2/Delta was repeatedly introduced to England and grew rapidly in the early summer of 2021, constituting approximately 98% of sampled SARS-CoV-2 genomes on June 26.

12.
Preprint in English | medRxiv | ID: ppmedrxiv-21256545

ABSTRACT

Inferring the relative strength (i.e., the ratio of reproduction numbers, [R]var/[R]wt) and relative speed (i.e., the difference between growth rates, rvar -rwt) of new SARS-CoV-2 variants compared to their wild types is critical to predicting and controlling the course of the current pandemic. Multiple studies have estimated the relative strength of new variants from the observed relative speed, but they typically neglect the possibility that the new variants have different generation intervals (i.e., time between infection and transmission), which determines the relationship between relative strength and speed. Notably, the increasingly predominant B.1.1.7 variant may have a longer infectious period (and therefore, a longer generation interval) than prior dominant lineages. Here, we explore how differences in generation intervals between a new variant and the wild type affect the relationship between relative strength and speed. We use simulations to show how neglecting these differences can lead to biases in estimates of relative strength in practice and to illustrate how such biases can be assessed. Finally, we discuss implications for control: if new variants have longer generation intervals then speed-like interventions such as contact tracing become more effective, whereas strength-like interventions such as social distancing become less effective.

13.
Preprint in English | medRxiv | ID: ppmedrxiv-21252964

ABSTRACT

BackgroundSchools have been closed in England since the 4th of January 2021 as part of the national restrictions to curb transmission of SARS-CoV-2. The UK Government plans to reopen schools on the 8th of March. Although there is evidence of lower individual-level transmission risk amongst children compared to adults, the combined effects of this with increased contact rates in school settings are not clear. MethodsWe measured social contacts when schools were both open or closed, amongst other restrictions. We combined these data with estimates of the susceptibility and infectiousness of children compared with adults to estimate the impact of reopening schools on the reproduction number. ResultsOur results suggest that reopening all schools could increase R from an assumed baseline of 0.8 to between 1.0 and 1.5, or to between 0.9 and 1.2 reopening primary or secondary schools alone. ConclusionOur results suggest that reopening schools is likely to halt the fall in cases observed in recent months and risks returning to rising infections, but these estimates rely heavily on the current estimates or reproduction number and the current validity of the susceptibility and infectiousness profiles we use.

14.
Estee Y Cramer; Evan L Ray; Velma K Lopez; Johannes Bracher; Andrea Brennen; Alvaro J Castro Rivadeneira; Aaron Gerding; Tilmann Gneiting; Katie H House; Yuxin Huang; Dasuni Jayawardena; Abdul H Kanji; Ayush Khandelwal; Khoa Le; Anja Muehlemann; Jarad Niemi; Apurv Shah; Ariane Stark; Yijin Wang; Nutcha Wattanachit; Martha W Zorn; Youyang Gu; Sansiddh Jain; Nayana Bannur; Ayush Deva; Mihir Kulkarni; Srujana Merugu; Alpan Raval; Siddhant Shingi; Avtansh Tiwari; Jerome White; Neil F Abernethy; Spencer Woody; Maytal Dahan; Spencer Fox; Kelly Gaither; Michael Lachmann; Lauren Ancel Meyers; James G Scott; Mauricio Tec; Ajitesh Srivastava; Glover E George; Jeffrey C Cegan; Ian D Dettwiller; William P England; Matthew W Farthing; Robert H Hunter; Brandon Lafferty; Igor Linkov; Michael L Mayo; Matthew D Parno; Michael A Rowland; Benjamin D Trump; Yanli Zhang-James; Samuel Chen; Stephen V Faraone; Jonathan Hess; Christopher P Morley; Asif Salekin; Dongliang Wang; Sabrina M Corsetti; Thomas M Baer; Marisa C Eisenberg; Karl Falb; Yitao Huang; Emily T Martin; Ella McCauley; Robert L Myers; Tom Schwarz; Daniel Sheldon; Graham Casey Gibson; Rose Yu; Liyao Gao; Yian Ma; Dongxia Wu; Xifeng Yan; Xiaoyong Jin; Yu-Xiang Wang; YangQuan Chen; Lihong Guo; Yanting Zhao; Quanquan Gu; Jinghui Chen; Lingxiao Wang; Pan Xu; Weitong Zhang; Difan Zou; Hannah Biegel; Joceline Lega; Steve McConnell; VP Nagraj; Stephanie L Guertin; Christopher Hulme-Lowe; Stephen D Turner; Yunfeng Shi; Xuegang Ban; Robert Walraven; Qi-Jun Hong; Stanley Kong; Axel van de Walle; James A Turtle; Michal Ben-Nun; Steven Riley; Pete Riley; Ugur Koyluoglu; David DesRoches; Pedro Forli; Bruce Hamory; Christina Kyriakides; Helen Leis; John Milliken; Michael Moloney; James Morgan; Ninad Nirgudkar; Gokce Ozcan; Noah Piwonka; Matt Ravi; Chris Schrader; Elizabeth Shakhnovich; Daniel Siegel; Ryan Spatz; Chris Stiefeling; Barrie Wilkinson; Alexander Wong; Sean Cavany; Guido Espana; Sean Moore; Rachel Oidtman; Alex Perkins; David Kraus; Andrea Kraus; Zhifeng Gao; Jiang Bian; Wei Cao; Juan Lavista Ferres; Chaozhuo Li; Tie-Yan Liu; Xing Xie; Shun Zhang; Shun Zheng; Alessandro Vespignani; Matteo Chinazzi; Jessica T Davis; Kunpeng Mu; Ana Pastore y Piontti; Xinyue Xiong; Andrew Zheng; Jackie Baek; Vivek Farias; Andreea Georgescu; Retsef Levi; Deeksha Sinha; Joshua Wilde; Georgia Perakis; Mohammed Amine Bennouna; David Nze-Ndong; Divya Singhvi; Ioannis Spantidakis; Leann Thayaparan; Asterios Tsiourvas; Arnab Sarker; Ali Jadbabaie; Devavrat Shah; Nicolas Della Penna; Leo A Celi; Saketh Sundar; Russ Wolfinger; Dave Osthus; Lauren Castro; Geoffrey Fairchild; Isaac Michaud; Dean Karlen; Matt Kinsey; Luke C. Mullany; Kaitlin Rainwater-Lovett; Lauren Shin; Katharine Tallaksen; Shelby Wilson; Elizabeth C Lee; Juan Dent; Kyra H Grantz; Alison L Hill; Joshua Kaminsky; Kathryn Kaminsky; Lindsay T Keegan; Stephen A Lauer; Joseph C Lemaitre; Justin Lessler; Hannah R Meredith; Javier Perez-Saez; Sam Shah; Claire P Smith; Shaun A Truelove; Josh Wills; Maximilian Marshall; Lauren Gardner; Kristen Nixon; John C. Burant; Lily Wang; Lei Gao; Zhiling Gu; Myungjin Kim; Xinyi Li; Guannan Wang; Yueying Wang; Shan Yu; Robert C Reiner; Ryan Barber; Emmanuela Gaikedu; Simon Hay; Steve Lim; Chris Murray; David Pigott; Heidi L Gurung; Prasith Baccam; Steven A Stage; Bradley T Suchoski; B. Aditya Prakash; Bijaya Adhikari; Jiaming Cui; Alexander Rodriguez; Anika Tabassum; Jiajia Xie; Pinar Keskinocak; John Asplund; Arden Baxter; Buse Eylul Oruc; Nicoleta Serban; Sercan O Arik; Mike Dusenberry; Arkady Epshteyn; Elli Kanal; Long T Le; Chun-Liang Li; Tomas Pfister; Dario Sava; Rajarishi Sinha; Thomas Tsai; Nate Yoder; Jinsung Yoon; Leyou Zhang; Sam Abbott; Nikos I Bosse; Sebastian Funk; Joel Hellewell; Sophie R Meakin; Katharine Sherratt; Mingyuan Zhou; Rahi Kalantari; Teresa K Yamana; Sen Pei; Jeffrey Shaman; Michael L Li; Dimitris Bertsimas; Omar Skali Lami; Saksham Soni; Hamza Tazi Bouardi; Turgay Ayer; Madeline Adee; Jagpreet Chhatwal; Ozden O Dalgic; Mary A Ladd; Benjamin P Linas; Peter Mueller; Jade Xiao; Yuanjia Wang; Qinxia Wang; Shanghong Xie; Donglin Zeng; Alden Green; Jacob Bien; Logan Brooks; Addison J Hu; Maria Jahja; Daniel McDonald; Balasubramanian Narasimhan; Collin Politsch; Samyak Rajanala; Aaron Rumack; Noah Simon; Ryan J Tibshirani; Rob Tibshirani; Valerie Ventura; Larry Wasserman; Eamon B O'Dea; John M Drake; Robert Pagano; Quoc T Tran; Lam Si Tung Ho; Huong Huynh; Jo W Walker; Rachel B Slayton; Michael A Johansson; Matthew Biggerstaff; Nicholas G Reich.
Preprint in English | medRxiv | ID: ppmedrxiv-21250974

ABSTRACT

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naive baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance StatementThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

15.
Preprint in English | medRxiv | ID: ppmedrxiv-20248826

ABSTRACT

We report insights from ten weeks of collaborative COVID-19 forecasting for Germany and Poland (12 October - 19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.

16.
Preprint in English | medRxiv | ID: ppmedrxiv-20248822

ABSTRACT

A novel SARS-CoV-2 variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in November 2020 and is rapidly spreading towards fixation. Using a variety of statistical and dynamic modelling approaches, we estimate that this variant has a 43-90% (range of 95% credible intervals 38-130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine roll-out, COVID-19 hospitalisations and deaths across England in 2021 will exceed those in 2020. Concerningly, VOC 202012/01 has spread globally and exhibits a similar transmission increase (59-74%) in Denmark, Switzerland, and the United States.

17.
Preprint in English | medRxiv | ID: ppmedrxiv-20248559

ABSTRACT

BackgroundMortality rates of UK patients hospitalised with COVID-19 appeared to fall during the first wave. We quantify potential drivers of this change and identify groups of patients who remain at high risk of dying in hospital. MethodsThe International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) WHO Clinical Characterisation Protocol UK recruited a prospective cohort admitted to 247 acute UK hospitals with COVID-19 in the first wave (March to August 2020). Outcome was hospital mortality within 28 days of admission. We performed a three-way decomposition mediation analysis using natural effects models to explore associations between week of admission and hospital mortality adjusting for confounders (demographics, comorbidity, illness severity) and quantifying potential mediators (respiratory support and steroids). FindingsUnadjusted hospital mortality fell from 32.3% (95%CI 31.8, 32.7) in March/April to 16.4% (95%CI 15.0, 17.8) in June/July 2020. Reductions were seen in all ages, ethnicities, both sexes, and in comorbid and non-comorbid patients. After adjustment, there was a 19% reduction in the odds of mortality per 4 week period (OR 0.81, 95%CI 0.79, 0.83). 15.2% of this reduction was explained by greater disease severity and comorbidity earlier in the epidemic. The use of respiratory support changed with greater use of non-invasive ventilation (NIV). 22.2% (OR 0.94, 95%CI 0.94, 0.96) of the reduction in mortality was mediated by changes in respiratory support. InterpretationThe fall in hospital mortality in COVID-19 patients during the first wave in the UK was partly accounted for by changes in case mix and illness severity. A significant reduction was associated with differences in respiratory support and critical care use, which may partly reflect improved clinical decision making. The remaining improvement in mortality is not explained by these factors, and may relate to community behaviour on inoculum dose and hospital capacity strain. FundingNIHR & MRC Key points / Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSRisk factors for mortality in patients hospitalised with COVID-19 have been established. However there is little literature regarding how mortality is changing over time, and potential explanations for why this might be. Understanding changes in mortality rates over time will help policy makers identify evolving risk, strategies to manage this and broader decisions about public health interventions. Added value of this studyMortality in hospitalised patients at the beginning of the first wave was extremely high. Patients who were admitted to hospital in March and early April were significantly more unwell at presentation than patients who were admitted in later months. Mortality fell in all ages, ethnic groups, both sexes and in patients with and without comorbidity, over and above contributions from falling illness severity. After adjustment for these variables, a fifth of the fall in mortality was explained by changes in the use of respiratory support and steroid treatment, along with associated changes in clinical decision-making relating to supportive interventions. However, mortality was persistently high in patients who required invasive mechanical ventilation, and in those patients who received non-invasive ventilation outside of critical care. Implications of all the available evidenceThe observed reduction in hospital mortality was greater than expected based on the changes seen in both case mix and illness severity. Some of this fall can be explained by changes in respiratory care, including clinical learning. In addition, introduction of community policies including wearing of masks, social distancing, shielding of vulnerable patients and the UK lockdown potentially resulted in people being exposed to less virus. The decrease in mortality varied depending on the level of respiratory support received. Patients receiving invasive mechanical ventilation have persistently high mortality rates, albeit with a changing case-mix, and further research should target this group. Severe COVID-19 disease has primarily affected older people in the UK. Many of these people, but not all have significant frailty. It is essential to ensure that patients and their families remain at the centre of decision-making, and we continue with an individualised approach to their treatment and care.

18.
Preprint in English | medRxiv | ID: ppmedrxiv-20240648

ABSTRACT

Slovakia conducted multiple rounds of population-wide rapid antigen testing for SARS-CoV-2 in late 2020, combined with a period of additional contact restrictions. Observed prevalence decreased by 58% (95% CI: 57-58%) within one week in the 45 counties that were subject to two rounds of mass testing, an estimate that remained robust when adjusting for multiple potential confounders. Adjusting for epidemic growth of 4.4% (1.1-6.9%) per day preceding the mass testing campaign, the estimated decrease in prevalence compared to a scenario of unmitigated growth was 70% (67-73%). Modelling suggests that this decrease cannot be explained solely by infection control measures, but requires the additional impact of isolation as well as quarantine of household members of those testing positive.

19.
Preprint in English | medRxiv | ID: ppmedrxiv-20220962

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.

20.
Preprint in English | medRxiv | ID: ppmedrxiv-20226969

ABSTRACT

During infectious disease epidemics, an important question is whether cases travelling to new locations will trigger local outbreaks. The risk of this occurring depends on the transmissibility of the pathogen, the susceptibility of the host population and, crucially, the effectiveness of surveillance in detecting cases and preventing onward spread. For many pathogens, transmission from presymptomatic and/or asymptomatic (together referred to as nonsymptomatic) infectious hosts can occur, making effective surveillance challenging. Here, using SARS-CoV-2 as a case-study, we show how the risk of local outbreaks can be assessed when nonsymptomatic transmission can occur. We construct a branching process model that includes nonsymptomatic transmission, and explore the effects of interventions targeting nonsymptomatic or symptomatic hosts when surveillance resources are limited. We consider whether the greatest reductions in local outbreak risks are achieved by increasing surveillance and control targeting nonsymptomatic or symptomatic cases, or a combination of both. We find that seeking to increase surveillance of symptomatic hosts alone is typically not the optimal strategy for reducing outbreak risks. Adopting a strategy that combines an enhancement of surveillance of symptomatic cases with efforts to find and isolate nonsymptomatic infected hosts leads to the largest reduction in the probability that imported cases will initiate a local outbreak.

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