Imperial College London

DrAnneCori

Faculty of MedicineSchool of Public Health

Lecturer
 
 
 
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+44 (0)20 7594 3229a.cori

 
 
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Location

 

G27Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Publication Type
Year
to

86 results found

Pickles M, Cori A, Probert WJM, Sauter R, Hinch R, Fidler S, Ayles H, Bock P, Donnell D, Wilson E, Piwowar-Manning E, Floyd S, Hayes RJ, Fraser C, HPTN 071 PopART Study Teamet al., 2021, PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial., PLoS Comput Biol, Vol: 17

Mathematical models are powerful tools in HIV epidemiology, producing quantitative projections of key indicators such as HIV incidence and prevalence. In order to improve the accuracy of predictions, such models need to incorporate a number of behavioural and biological heterogeneities, especially those related to the sexual network within which HIV transmission occurs. An individual-based model, which explicitly models sexual partnerships, is thus often the most natural type of model to choose. In this paper we present PopART-IBM, a computationally efficient individual-based model capable of simulating 50 years of an HIV epidemic in a large, high-prevalence community in under a minute. We show how the model calibrates within a Bayesian inference framework to detailed age- and sex-stratified data from multiple sources on HIV prevalence, awareness of HIV status, ART status, and viral suppression for an HPTN 071 (PopART) study community in Zambia, and present future projections of HIV prevalence and incidence for this community in the absence of trial intervention.

Journal article

Abbas M, Nunes TR, Cori A, Cordey S, Laubscher F, Baggio S, Jombart T, Iten A, Vieux L, Teixeira D, Perez M, Pittet D, Frangos E, Graf CE, Zingg W, Harbarth Set al., 2021, Explosive nosocomial outbreak of SARS-CoV-2 in a rehabilitation clinic: the limits of genomics for outbreak reconstruction., J Hosp Infect

BACKGROUND: Nosocomial outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are frequent despite implementation of conventional infection control measures. We performed an outbreak investigation using advanced genomic and statistical techniques to reconstruct likely transmission chains and assess the role of healthcare workers (HCWs) in SARS-CoV-2 transmission. METHODS: We investigated a nosocomial SARS-CoV-2 outbreak in a university-affiliated rehabilitation clinic, involving patients and HCWs, with high coverage of pathogen whole genome sequences (WGS). We estimated the time-varying reproduction number from epidemiological data (Rt) and produced a maximum likelihood phylogeny to assess genetic diversity of the pathogen. We combined genomic and epidemiological data into a Bayesian framework to model directionality of transmission. We performed a Case-control study to investigate risk factors for nosocomial SARS-CoV-2 acquisition in patients. FINDINGS: The outbreak spanned from March 14 to April 12, 2020 and involved 37 patients (31 with WGS) and 39 employees (31 with WGS) of whom 37 are HCWs. We estimated a peak Rt between 2.2 - 3.6. The phylogenetic tree showed very limited genetic diversity, with 60/62 (96.7%) isolates forming one large cluster of identical genomes. Despite the resulting uncertainty in reconstructed transmission events, our analyses suggest that HCWs (one of whom was the index Case) played an essential role in cross-transmission, with a significantly larger fraction of infections (p < 2.2e-16) attributable to HCWs (70.7%) than expected given the number of HCWs cases (46.7%). The excess of transmission from HCWs was larger when considering infection of patients (79.0%; 95%CI 78.5% - 79.5%), and especially frail patients (Clinical Frailty Scale >5: 82.3%; 95%CI 81.8% - 83.4%). Furthermore, frail patients were found to be at greater risk for nosocomial COVID-19 than other patients (adjusted OR 6.94; 95%CI 2.13 - 22.57). IN

Journal article

Mishra S, Scott JA, Laydon DJ, Flaxman S, Gandy A, Mellan TA, Unwin HJT, Vollmer M, Coupland H, Ratmann O, Monod M, Zhu HH, Cori A, Gaythorpe KAM, Whittles LK, Whittaker C, Donnelly CA, Ferguson NM, Bhatt Set al., 2021, Comparing the responses of the UK, Sweden and Denmark to COVID-19 using counterfactual modelling, SCIENTIFIC REPORTS, Vol: 11, Pages: 1-9, ISSN: 2045-2322

The UK and Sweden have among the worst per-capita COVID-19 mortality in Europe. Sweden stands out for its greater reliance on voluntary, rather than mandatory, control measures. We explore how the timing and effectiveness of control measures in the UK, Sweden and Denmark shaped COVID-19 mortality in each country, using a counterfactual assessment: what would the impact have been, had each country adopted the others’ policies? Using a Bayesian semi-mechanistic model without prior assumptions on the mechanism or effectiveness of interventions, we estimate the time-varying reproduction number for the UK, Sweden and Denmark from daily mortality data. We use two approaches to evaluate counterfactuals which transpose the transmission profile from one country onto another, in each country’s first wave from 13th March (when stringent interventions began) until 1st July 2020. UK mortality would have approximately doubled had Swedish policy been adopted, while Swedish mortality would have more than halved had Sweden adopted UK or Danish strategies. Danish policies were most effective, although differences between the UK and Denmark were significant for one counterfactual approach only. Our analysis shows that small changes in the timing or effectiveness of interventions have disproportionately large effects on total mortality within a rapidly growing epidemic.

Journal article

Knock ES, Whittles LK, Lees JA, Perez-Guzman PN, Verity R, FitzJohn RG, Gaythorpe KAM, Imai N, Hinsley W, Okell LC, Rosello A, Kantas N, Walters CE, Bhatia S, Watson OJ, Whittaker C, Cattarino L, Boonyasiri A, Djaafara BA, Fraser K, Fu H, Wang H, Xi X, Donnelly CA, Jauneikaite E, Laydon DJ, White PJ, Ghani AC, Ferguson NM, Cori A, Baguelin Met al., 2021, Key epidemiological drivers and impact of interventions in the 2020 SARS-CoV-2 epidemic in England, Science Translational Medicine, Vol: 13, Pages: 1-12, ISSN: 1946-6234

We fitted a model of SARS-CoV-2 transmission in care homes and the community to regional surveillance data for England. Compared with other approaches, our model provides a synthesis of multiple surveillance data streams into a single coherent modelling framework allowing transmission and severity to be disentangled from features of the surveillance system. Of the control measures implemented, only national lockdown brought the reproduction number (Rteff ) below 1 consistently; if introduced one week earlier it could have reduced deaths in the first wave from an estimated 48,600 to 25,600 (95% credible interval [95%CrI]: 15,900-38,400). The infection fatality ratio decreased from 1.00% (95%CrI: 0.85%-1.21%) to 0.79% (95%CrI: 0.63%-0.99%), suggesting improved clinical care. The infection fatality ratio was higher in the elderly residing in care homes (23.3%, 95%CrI: 14.7%-35.2%) than those residing in the community (7.9%, 95%CrI: 5.9%-10.3%). On 2nd December 2020 England was still far from herd immunity, with regional cumulative infection incidence between 7.6% (95%CrI: 5.4%-10.2%) and 22.3% (95%CrI: 19.4%-25.4%) of the population. Therefore, any vaccination campaign will need to achieve high coverage and a high degree of protection in vaccinated individuals to allow non-pharmaceutical interventions to be lifted without a resurgence of transmission.

Journal article

O'Driscoll M, Harry C, Donnelly CA, Cori A, Dorigatti Iet al., 2021, A comparative analysis of statistical methods to estimate the reproduction number in emerging epidemics with implications for the current COVID-19 pandemic, Clinical Infectious Diseases, Vol: 73, Pages: e215-e223, ISSN: 1058-4838

As the SARS-CoV-2 pandemic continues its rapid global spread, quantification of local transmission patterns has been, and will continue to be, critical for guiding pandemic response. Understanding the accuracy and limitations of statistical methods to estimate the reproduction number, R0, in the context of emerging epidemics is therefore vital to ensure appropriate interpretation of results and the subsequent implications for control efforts. Using simulated epidemic data we assess the performance of 6 commonly-used statistical methods to estimate R0 as they would be applied in a real-time outbreak analysis scenario - fitting to an increasing number of data points over time and with varying levels of random noise in the data. Method comparison was also conducted on empirical outbreak data, using Zika surveillance data from the 2015-2016 epidemic in Latin America and the Caribbean. We find that all methods considered here frequently over-estimate R0 in the early stages of epidemic growth on simulated data, the magnitude of which decreases when fitted to an increasing number of time points. This trend of decreasing bias over time can easily lead to incorrect conclusions about the course of the epidemic or the need for control efforts. We show that true changes in pathogen transmissibility can be difficult to disentangle from changes in methodological accuracy and precision, particularly for data with significant over-dispersion. As localised epidemics of SARS-CoV-2 take hold around the globe, awareness of this trend will be important for appropriately cautious interpretation of results and subsequent guidance for control efforts.

Journal article

Risher K, Cori A, Reniers G, Marston M, Calvert C, Crampin A, Dadirai T, Dube A, Gregson S, Herbst K, Lutalo T, Moorhouse L, Mtenga B, Nabukalu D, Newton R, Price AJ, Tlhajoane M, Todd J, Tomlin K, Urassa M, Vandormael A, Fraser C, Slaymaker E, Eaton Jet al., 2021, Age patterns of HIV incidence in eastern and southern Africa: a collaborative analysis of observational general population cohort studies, The Lancet HIV, Vol: 8, Pages: e429-e439, ISSN: 2405-4704

Background: As the HIV epidemic in sub-Saharan Africa matures, evidence about the age distribution of new HIV infections and how this has changed over the epidemic is needed to guide HIV prevention. We assessed trends in age-specific HIV incidence in six population-based cohort studies in eastern and southern Africa, reporting changes in average age at infection, age distribution of new infections, and birth cohort cumulative incidence. Methods: We used a Bayesian model to reconstruct age-specific HIV incidence from repeated observations of individuals’ HIV serostatus and survival collected among population HIV cohorts in rural Malawi, South Africa, Tanzania, Uganda, and Zimbabwe. The HIV incidence rate by age, time and sex was modelled using smooth splines functions. Incidence trends were estimated separately by sex and study. Estimated incidence and prevalence results for 2000-2017, standardised to study population distribution, were used to estimate average age at infection and proportion of new infections by age. Findings: Age-specific incidence declined at all ages, though the timing and pattern of decline varied by study. The average age at infection was higher in men (cohort means: 27·8-34·6 years) than women (cohort means: 24·8-29·6 years). Between 2000 and 2017, the average age at infection increased slightly: cohort means 0·5-2·8 years among men and -0·2-2·5 years among women. Across studies, between 38-63%(cohort means)of women’s infections were among 15-24-year-olds and between 30-63% of men’s infections were in 20-29-year-olds. Lifetime risk of HIV declined for successive birth cohorts. Interpretation: HIV incidence declined in all age groups and shifted slightly, but not dramatically, to older ages. Disproportionate new HIV infections occur among 15-24-year-old 4women and20-29-year-oldmen, supporting focused prevention in these groups. But 40-60% of infections were outside these

Journal article

Thomas R, Probert W, Sauter R, Mwenge L, Singh S, Kanema S, Vanqa N, Harper A, Burger R, Cori A, Pickles M, Bell-Mandla N, Yang B, Bwalya J, Phiri M, Shanaube K, Floyd S, Donnell D, Bock P, Ayles H, Fidler S, Hayes R, Fraser C, Hauck Ket al., 2021, Cost and cost-effectiveness of a universal HIV testing and treatment intervention in Zambia and South Africa: evidence and projections from the HPTN 071 (PopART) trial, The Lancet Global Health, Vol: 9, Pages: e668-e680, ISSN: 2214-109X

BackgroundThe HPTN 071 (PopART) trial showed that a combination HIV prevention package including universal HIV testing and treatment (UTT) reduced population-level incidence of HIV compared with standard care. However, evidence is scarce on the costs and cost-effectiveness of such an intervention.MethodsUsing an individual-based model, we simulated the PopART intervention and standard care with antiretroviral therapy (ART) provided according to national guidelines for the 21 trial communities in Zambia and South Africa (for all individuals aged >14 years), with model parameters and primary cost data collected during the PopART trial and from published sources. Two intervention scenarios were modelled: annual rounds of PopART from 2014 to 2030 (PopART 2014–30; as the UNAIDS Fast-Track target year) and three rounds of PopART throughout the trial intervention period (PopART 2014–17). For each country, we calculated incremental cost-effectiveness ratios (ICERs) as the cost per disability-adjusted life-year (DALY) and cost per HIV infection averted. Cost-effectiveness acceptability curves were used to indicate the probability of PopART being cost-effective compared with standard care at different thresholds of cost per DALY averted. We also assessed budget impact by projecting undiscounted costs of the intervention compared with standard care up to 2030.FindingsDuring 2014–17, the mean cost per person per year of delivering home-based HIV counselling and testing, linkage to care, promotion of ART adherence, and voluntary medical male circumcision via community HIV care providers for the simulated population was US$6·53 (SD 0·29) in Zambia and US$7·93 (0·16) in South Africa. In the PopART 2014–30 scenario, median ICERs for PopART delivered annually until 2030 were $2111 (95% credible interval [CrI] 1827–2462) per HIV infection averted in Zambia and $3248 (2472–3963) per HIV infection averted in South Afric

Journal article

Bhatia S, Lassmann B, Cohn E, Desai AN, Carrion M, Kraemer MUG, Herringer M, Brownstein J, Madoff L, Cori A, Nouvellet Pet al., 2021, Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread, npj Digital Medicine, Vol: 4, ISSN: 2398-6352

Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013–2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1–4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak.

Journal article

Ragonnet-Cronin M, Boyd O, Geidelberg L, Jorgensen D, Nascimento F, Siveroni I, Johnson R, Baguelin M, Cucunuba Z, Jauneikaite E, Mishra S, Watson O, Ferguson N, Cori A, Donnelly C, Volz Eet al., 2021, Genetic evidence for the association between COVID-19 epidemic severity and timing of non-pharmaceutical interventions, Nature Communications, Vol: 12, Pages: 1-7, ISSN: 2041-1723

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. 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 examined in relation to the dates of the most stringent interventions in each location as well as to the number of cumulative COVID-19 deaths and phylodynamic estimates of epidemic size. Here we report that the time elapsed between epidemic origin and maximum intervention is associated with different measures of epidemic severity and explains 11% of the variance in reported deaths one month after the most stringent intervention. Locations where strong non-pharmaceutical interventions were implemented earlier experienced 30 much less severe COVID-19 morbidity and mortality during the period of study.

Journal article

Nouvellet P, Bhatia S, Cori A, Ainslie K, Baguelin M, Bhatt S, Boonyasiri A, Brazeau N, Cattarino L, Cooper L, Coupland H, Cucunuba Perez Z, Cuomo-Dannenburg G, Dighe A, Djaafara A, Dorigatti I, Eales O, van Elsland S, NASCIMENTO F, Fitzjohn R, Gaythorpe K, Geidelberg L, green W, Hamlet A, Hauck K, Hinsley W, Imai N, Jeffrey, Jeffrey B, Knock E, Laydon D, Lees J, Mangal T, Mellan T, Nedjati Gilani G, Parag K, Pons Salort M, Ragonnet-Cronin M, Riley S, Unwin H, Verity R, Vollmer M, Volz E, Walker P, Walters C, Wang H, Watson O, Whittaker C, Whittles L, Xi X, Ferguson N, Donnelly Cet al., 2021, Reduction in mobility and COVID-19 transmission, Nature Communications, Vol: 12, ISSN: 2041-1723

In response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts.Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world.Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27-77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49-91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12-48%]) post-relaxation.In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.

Journal article

Knock E, Whittles L, Lees J, Perez Guzman P, Verity R, Fitzjohn R, Gaythorpe K, Imai N, Hinsley W, Okell L, Rosello A, Kantas N, Walters C, Bhatia S, Watson O, Whittaker C, Cattarino L, Boonyasiri A, Djaafara A, Fraser K, Fu H, Wang H, Xi X, Donnelly C, Jauneikaite E, Laydon D, White P, Ghani A, Ferguson N, Cori A, Baguelin Met al., 2020, Report 41: The 2020 SARS-CoV-2 epidemic in England: key epidemiological drivers and impact of interventions

England has been severely affected by COVID-19. We fitted a model of SARS-CoV-2 transmission in care homes and the community to regional 2020 surveillance data. Only national lockdown brought the reproduction number below 1 consistently; introduced one week earlier in the first wave it could have reduced mortality by 23,300 deaths on average. The mean infection fatality ratio was initially ~1.3% across all regions except London and halved following clinical care improvements. The infection fatality ratio was two-fold lower throughout in London, even when adjusting for demographics. The infection fatality ratio in care homes was 2.5-times that in the elderly in the community. Population-level infection-induced immunity in England is still far from herd immunity, with regional mean cumulative attack rates ranging between 4.4% and 15.8%.

Report

Djaafara B, Imai N, Hamblion E, Impouma B, Donnelly C, Cori Aet al., 2020, A quantitative framework for defining the end of an infectious disease outbreak: application to Ebola virus disease, American Journal of Epidemiology, Vol: 190, Pages: 642-651, ISSN: 0002-9262

The end-of-outbreak declaration is an important step in controlling infectious disease outbreaks. Objective estimation of the confidence level that an outbreak is over is important to reduce the risk of postdeclaration flare-ups. We developed a simulation-based model with which to quantify that confidence and tested it on simulated Ebola virus disease data. We found that these confidence estimates were most sensitive to the instantaneous reproduction number, the reporting rate, and the time between the symptom onset and death or recovery of the last detected case. For Ebola virus disease, our results suggested that the current World Health Organization criterion of 42 days since the recovery or death of the last detected case is too short and too sensitive to underreporting. Therefore, we suggest a shift to a preliminary end-of-outbreak declaration after 63 days from the symptom onset day of the last detected case. This preliminary declaration should still be followed by 90 days of enhanced surveillance to capture potential flare-ups of cases, after which the official end of the outbreak can be declared. This sequence corresponds to more than 95% confidence that an outbreak is over in most of the scenarios examined. Our framework is generic and therefore could be adapted to estimate end-of-outbreak confidence for other infectious diseases.

Journal article

Pickles M, Cori A, Probert W, Sauter R, Fidler S, Ayles H, Bock P, Donnell D, Wilson E, Piwowar-Manning E, Floyd S, Hayes R, Frase Cet al., 2020, PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial

<jats:title>Abstract</jats:title><jats:p>Mathematical models are powerful tools in HIV epidemiology, producing quantitative projections of key indicators such as HIV incidence and prevalence. In order to improve the accuracy of predictions, such models need to incorporate a number of behavioural and biological heterogeneities, especially those related to the sexual network within which HIV transmission occurs. An individual-based model, which explicitly models sexual partnerships, is thus often the most natural type of model to choose. In this paper we present PopART-IBM, a computationally efficient individual-based model capable of simulating 50 years of an HIV epidemic in a large, high-prevalence community in under a minute. We show how the model calibrates within a Bayesian inference framework to detailed age- and sex-stratified data from multiple sources on HIV prevalence, awareness of HIV status, ART status, and viral suppression for an HPTN 071 (PopART) study community in Zambia, and present future projections of HIV prevalence and incidence for this community in the absence of trial intervention.</jats:p>

Journal article

Bhatia S, Imai N, Cuomo-Dannenburg G, Baguelin M, Boonyasiri A, Cori A, Cucunuba Perez Z, Dorigatti I, Fitzjohn R, Fu H, Gaythorpe K, Ghani A, Hamlet A, Hinsley W, Laydon D, Nedjati Gilani G, Okell L, Riley S, Thompson H, van Elsland S, Volz E, Wang H, Wang Y, Whittaker C, Xi X, Donnelly CA, Ferguson NMet al., 2020, Estimating the number of undetected COVID-19 cases among travellers from mainland China, Publisher: F1000 Research Ltd

Background: Since the start of the COVID-19 epidemic in late 2019, there have been more than 152 affected regions and countries with over 110,000 confirmed cases outside mainland China.Methods: We analysed COVID-19 cases among travellers from mainland China to different regions and countries, comparing the region- and country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different regions and countries.Results: Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that more than two thirds (70%, 95% CI: 54% - 80%, compared to Singapore; 75%, 95% CI: 66% - 82%, compared to multiple countries) of cases exported from mainland China have remained undetected.Conclusions: These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China.

Working paper

Nouvellet P, Bhatia S, Cori A, Ainslie K, Baguelin M, Bhatt S, Boonyasiri A, Brazeau N, Cattarino L, Cooper L, Coupland H, Cucunuba Perez Z, Cuomo-Dannenburg G, Dighe A, Djaafara A, Dorigatti I, Eales O, van Elsland S, Nscimento F, Fitzjohn R, Gaythorpe K, Geidelberg L, Grassly N, Green W, Hamlet A, Hauck K, Hinsley W, Imai N, Jeffrey B, Knock E, Laydon D, Lees J, Mangal T, Mellan T, Nedjati Gilani G, Parag K, Pons Salort M, Ragonnet-Cronin M, Riley S, Unwin H, Verity R, Vollmer M, Volz E, Walker P, Walters C, Wang H, Watson O, Whittaker C, Whittles L, Xi X, Ferguson N, Donnelly Cet al., 2020, Report 26: Reduction in mobility and COVID-19 transmission

In response to the COVID-19 pandemic, countries have sought to control transmission of SARS-CoV-2by restricting population movement through social distancing interventions, reducing the number ofcontacts.Mobility data represent an important proxy measure of social distancing. Here, we develop aframework to infer the relationship between mobility and the key measure of population-level diseasetransmission, the reproduction number (R). The framework is applied to 53 countries with sustainedSARS-CoV-2 transmission based on two distinct country-specific automated measures of humanmobility, Apple and Google mobility data.For both datasets, the relationship between mobility and transmission was consistent within andacross countries and explained more than 85% of the variance in the observed variation intransmissibility. We quantified country-specific mobility thresholds defined as the reduction inmobility necessary to expect a decline in new infections (R<1).While social contacts were sufficiently reduced in France, Spain and the United Kingdom to controlCOVID-19 as of the 10th of May, we find that enhanced control measures are still warranted for themajority of countries. We found encouraging early evidence of some decoupling of transmission andmobility in 10 countries, a key indicator of successful easing of social-distancing restrictions.Easing social-distancing restrictions should be considered very carefully, as small increases in contactrates are likely to risk resurgence even where COVID-19 is apparently under control. Overall, strongpopulation-wide social-distancing measures are effective to control COVID-19; however gradualeasing of restrictions must be accompanied by alternative interventions, such as efficient contacttracing, to ensure control.

Report

Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, Cuomo-Dannenburg G, Thompson H, Walker PGT, Fu H, Dighe A, Griffin JT, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunubá Z, FitzJohn R, Gaythorpe K, Green W, Hamlet A, Hinsley W, Laydon D, Nedjati-Gilani G, Riley S, van Elsland S, Volz E, Wang H, Wang Y, Xi X, Donnelly CA, Ghani AC, Ferguson NMet al., 2020, Estimates of the severity of coronavirus disease 2019: a model-based analysis., Lancet Infectious Diseases, Vol: 20, Pages: 669-677, ISSN: 1473-3099

BACKGROUND: In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases. METHODS: We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation. FINDINGS: Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9-19·2) and to hospital discharge to be 24·7 days (22·9-28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for cen

Journal article

Cori A, Kamvar ZN, Stockwin J, Jombart T, Dahlqwist E, Fitzjohn R, Thompson Ret al., 2020, mrc-ide/EpiEstim: 2.2-3

Fixed bugs in draw_one_set_of_ancestries resulting from incorrect lengths and an undefined variable (issue #92) (#93, @jstockwin)Fixed incorrect quantiles (issue #88) (#89, @jstockwin)

Software

ODriscoll M, Harry C, Donnelly CA, Cori A, Dorigatti Iet al., 2020, A comparative analysis of statistical methods to estimate the reproduction number in emerging epidemics with implications for the current COVID-19 pandemic, Publisher: Cold Spring Harbor Laboratory

<jats:title>Abstract</jats:title><jats:p>As the SARS-CoV-2 pandemic continues its rapid global spread, quantification of local transmission patterns has been, and will continue to be, critical for guiding pandemic response. Understanding the accuracy and limitations of statistical methods to estimate the reproduction number, R<jats:sub>0</jats:sub>, in the context of emerging epidemics is therefore vital to ensure appropriate interpretation of results and the subsequent implications for control efforts. Using simulated epidemic data we assess the performance of 6 commonly-used statistical methods to estimate R<jats:sub>0</jats:sub> as they would be applied in a real-time outbreak analysis scenario – fitting to an increasing number of data points over time and with varying levels of random noise in the data. Method comparison was also conducted on empirical outbreak data, using Zika surveillance data from the 2015–2016 epidemic in Latin America and the Caribbean. We find that all methods considered here frequently over-estimate R0 in the early stages of epidemic growth on simulated data, the magnitude of which decreases when fitted to an increasing number of time points. This trend of decreasing bias over time can easily lead to incorrect conclusions about the course of the epidemic or the need for control efforts. We show that true changes in pathogen transmissibility can be difficult to disentangle from changes in methodological accuracy and precision, particularly for data with significant over-dispersion. As localised epidemics of SARS-CoV-2 take hold around the globe, awareness of this trend will be important for appropriately cautious interpretation of results and subsequent guidance for control efforts.</jats:p><jats:sec><jats:title>Significance Statement</jats:title><jats:p>In line with a real-time outbreak analysis we use simulated epidemic data to assess the performance of 6

Working paper

Gaythorpe K, Imai N, Cuomo-Dannenburg G, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunuba Perez Z, Dighe A, Dorigatti I, Fitzjohn R, Fu H, Green W, Hamlet A, Hinsley W, Laydon D, Nedjati Gilani G, Okell L, Riley S, Thompson H, van Elsland S, Volz E, Wang H, Wang Y, Whittaker C, Xi X, Donnelly C, Ghani A, Ferguson Net al., 2020, Report 8: Symptom progression of COVID-19

The COVID-19 epidemic was declared a Public Health Emergency of International Concern (PHEIC) by WHO on 30th January 2020 [1]. As of 8 March 2020, over 107,000 cases had been reported. Here, we use published and preprint studies of clinical characteristics of cases in mainland China as well as case studies of individuals from Hong Kong, Japan, Singapore and South Korea to examine the proportional occurrence of symptoms and the progression of symptoms through time.We find that in mainland China, where specific symptoms or disease presentation are reported, pneumonia is the most frequently mentioned, see figure 1. We found a more varied spectrum of severity in cases outside mainland China. In Hong Kong, Japan, Singapore and South Korea, fever was the most frequently reported symptom. In this latter group, presentation with pneumonia is not reported as frequently although it is more common in individuals over 60 years old. The average time from reported onset of first symptoms to the occurrence of specific symptoms or disease presentation, such as pneumonia or the use of mechanical ventilation, varied substantially. The average time to presentation with pneumonia is 5.88 days, and may be linked to testing at hospitalisation; fever is often reported at onset (where the mean time to develop fever is 0.77 days).

Report

Thompson H, Imai N, Dighe A, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunuba Perez Z, Cuomo-Dannenburg G, Dorigatti I, Fitzjohn R, Fu H, Gaythorpe K, Ghani A, Green W, Hamlet A, Hinsley W, Laydon D, Nedjati Gilani G, Okell L, Riley S, van Elsland S, Volz E, Wang H, Yuanrong W, Whittaker C, Xi X, Donnelly C, Ferguson Net al., 2020, Report 7: Estimating infection prevalence in Wuhan City from repatriation flights

Since the end of January 2020, in response to the growing COVID-19 epidemic, 55 countries have repatriated over 8000 citizens from Wuhan City, China. In addition to quarantine measures for returning citizens, many countries implemented PCR screening to test for infection regardless of symptoms. These flights therefore give estimates of infection prevalence in Wuhan over time. Between 30th January and 1st February (close to the peak of the epidemic in Wuhan), infection prevalence was 0.87% (95% CI: 0.32% - 1.89%). As countries now start to repatriate citizens from Iran and northern Italy, information from repatriated citizens could help inform the level of response necessary to help control the outbreaks unfolding in newly affected areas.

Report

Bhatia S, Imai N, Cuomo-Dannenburg G, Baguelin M, Boonyasiri A, Cori A, Cucunuba Perez Z, Dorigatti I, Fitzjohn R, Fu H, Gaythorpe K, Ghani A, Hamlet A, Hinsley W, Laydon D, Nedjati Gilani G, Thompson H, Okell L, Riley S, van Elsland S, Volz E, Wang H, Wang Y, Whittaker C, Xi X, Donnelly C, Ferguson Net al., 2020, Report 6: Relative sensitivity of international surveillance, Report 6: Relative sensitivity of international surveillance

Since the start of the COVID-19 epidemic in late 2019, there are now 29 affected countries with over 1000 confirmed cases outside of mainland China. In previous reports, we estimated the likely epidemic size in Wuhan City based on air traffic volumes and the number of detected cases internationally. Here we analysed COVID-19 cases exported from mainland China to different regions and countries, comparing the country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different countries. Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that about two thirds of COVID-19 cases exported from mainland China have remained undetected worldwide, potentially resulting in multiple chains of as yet undetected human-to-human transmission outside mainland China.

Report

Djaafara BA, Imai N, Hamblion E, Impouma B, Donnelly CA, Cori Aet al., 2020, A quantitative framework to define the end of an outbreak: application to Ebola Virus Disease, Publisher: Cold Spring Harbor Laboratory

<jats:p>Declaring the end of an outbreak is an important step in controlling infectious disease outbreaks. An objective estimation of the probability of cases arising in the future is important to reduce the risk of post-declaration flare-ups. We developed a simulation-based model to quantify that probability. We tested it on simulated Ebola Virus Disease (EVD) data and found this probability was most sensitive to the instantaneous reproduction number, the reporting rate, and the delay between symptom onset and recovery or death of the last detected case. For EVD, our results suggest that the current WHO criterion of 42 days since the outcome of the last detected case is too short and very sensitive to underreporting. The 90 days of enhanced surveillance period after the end-of-outbreak declaration is therefore crucial to capture potential flare-ups of cases. Hence, we suggest a shift to a preliminary end-of-outbreak declaration after 63 days from the symptom onset day of the last detected case. This should be followed by a 90-day enhanced surveillance, after which the official end-of-outbreak can be declared. This corresponds to less than 5% probability of flare ups in most of the scenarios examined. Our quantitative framework could be adapted to define end-of-outbreak criteria for other infectious diseases.</jats:p>

Working paper

Volz E, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunuba Perez Z, Cuomo-Dannenburg G, Donnelly C, Dorigatti I, Fitzjohn R, Fu H, Gaythorpe K, Ghani A, Hamlet A, Hinsley W, Imai N, Laydon D, Nedjati Gilani G, Okell L, Riley S, van Elsland S, Wang H, Wang Y, Xi X, Ferguson Net al., 2020, Report 5: Phylogenetic analysis of SARS-CoV-2

Genetic diversity of SARS-CoV-2 (formerly 2019-nCoV), the virus which causes COVID-19, provides information about epidemic origins and the rate of epidemic growth. By analysing 53 SARS-CoV-2 whole genome sequences collected up to February 3, 2020, we find a strong association between the time of sample collection and accumulation of genetic diversity. Bayesian and maximum likelihood phylogenetic methods indicate that the virus was introduced into the human population in early December and has an epidemic doubling time of approximately seven days. Phylodynamic modelling provides an estimate of epidemic size through time. Precise estimates of epidemic size are not possible with current genetic data, but our analyses indicate evidence of substantial heterogeneity in the number of secondary infections caused by each case, as indicated by a high level of over-dispersion in the reproduction number. Larger numbers of more systematically sampled sequences – particularly from across China – will allow phylogenetic estimates of epidemic size and growth rate to be substantially refined.

Report

Dorigatti I, Okell L, Cori A, Imai N, Baguelin M, Bhatia S, Boonyasiri A, Cucunuba Perez Z, Cuomo-Dannenburg G, Fitzjohn R, Fu H, Gaythorpe K, Hamlet A, Hinsley W, Hong N, Kwun M, Laydon D, Nedjati Gilani G, Riley S, van Elsland S, Volz E, Wang H, Walters C, Xi X, Donnelly C, Ghani A, Ferguson Net al., 2020, Report 4: Severity of 2019-novel coronavirus (nCoV)

We present case fatality ratio (CFR) estimates for three strata of 2019-nCoV infections. For cases detected in Hubei, we estimate the CFR to be 18% (95% credible interval: 11%-81%). For cases detected in travellers outside mainland China, we obtain central estimates of the CFR in the range 1.2-5.6% depending on the statistical methods, with substantial uncertainty around these central values. Using estimates of underlying infection prevalence in Wuhan at the end of January derived from testing of passengers on repatriation flights to Japan and Germany, we adjusted the estimates of CFR from either the early epidemic in Hubei Province, or from cases reported outside mainland China, to obtain estimates of the overall CFR in all infections (asymptomatic or symptomatic) of approximately 1% (95% confidence interval 0.5%-4%). It is important to note that the differences in these estimates does not reflect underlying differences in disease severity between countries. CFRs seen in individual countries will vary depending on the sensitivity of different surveillance systems to detect cases of differing levels of severity and the clinical care offered to severely ill cases. All CFR estimates should be viewed cautiously at the current time as the sensitivity of surveillance of both deaths and cases in mainland China is unclear. Furthermore, all estimates rely on limited data on the typical time intervals from symptom onset to death or recovery which influences the CFR estimates.

Report

Imai N, Cori A, Dorigatti I, Baguelin M, Donnelly C, Riley S, Ferguson Net al., 2020, Report 3: Transmissibility of 2019-nCoV

Self-sustaining human-to-human transmission of the novel coronavirus (2019-nCov) is the only plausible explanation of the scale of the outbreak in Wuhan. We estimate that, on average, each case infected 2.6 (uncertainty range: 1.5-3.5) other people up to 18th January 2020, based on an analysis combining our past estimates of the size of the outbreak in Wuhan with computational modelling of potential epidemic trajectories. This implies that control measures need to block well over 60% of transmission to be effective in controlling the outbreak. It is likely, based on the experience of SARS and MERS-CoV, that the number of secondary cases caused by a case of 2019-nCoV is highly variable – with many cases causing no secondary infections, and a few causing many. Whether transmission is continuing at the same rate currently depends on the effectiveness of current control measures implemented in China and the extent to which the populations of affected areas have adopted risk-reducing behaviours. In the absence of antiviral drugs or vaccines, control relies upon the prompt detection and isolation of symptomatic cases. It is unclear at the current time whether this outbreak can be contained within China; uncertainties include the severity spectrum of the disease caused by this virus and whether cases with relatively mild symptoms are able to transmit the virus efficiently. Identification and testing of potential cases need to be as extensive as is permitted by healthcare and diagnostic testing capacity – including the identification, testing and isolation of suspected cases with only mild to moderate disease (e.g. influenza-like illness), when logistically feasible.

Report

Imai N, Dorigatti I, Cori A, Donnelly C, Riley S, Ferguson Net al., 2020, Report 2: Estimating the potential total number of novel Coronavirus cases in Wuhan City, China

We estimate that a total of 4,000 cases of 2019-nCoV in Wuhan City (uncertainty range: 1,000 – 9,700) had onset of symptoms by 18th January 2020 (the last reported onset date of any case) [15].Our estimates should not be interpreted as implying the outbreak has doubled in size in the period 12th January to 18th January – delays in confirming and reporting exported cases and incomplete information about dates of symptom onset together with the still very small numbers of exported cases mean we are unable to estimate the epidemic growth rate at the current time.This estimate is based on the following assumptions:• Wuhan International Airport has a catchment population of 19 million individuals [1].• There is a mean 10-day delay between infection and detection, comprising a 5-6 day incubation period [16,17] and a 4-5 day delay from symptom onset to detection/hospitalisation of a case (the cases detected in Thailand and Japan were hospitalised 3 and 7 days after onset, respectively) [4,18].• Total volume of international travel from Wuhan over the last two months has been 3,301 passengers per day. This estimate is derived from the 3,418 foreign passengers per day in the top 20 country destinations based on 2018 IATA data [19], and uses 2016 IATA data held by Imperial College London to correct for the travel surge at Chinese New Year present in the latter data (which has not happened yet this year) and for travel to countries outside the top 20 destination list.• Exit screening (which reportedly came into force on the 15th January [13]) had no impact on exported cases reported up to 16th January. Exit screening may have reduced exports in recent days, in which case our baseline prediction may be an underestimate of the true number of cases in Wuhan.• We assume all cases in travellers flying to destinations outside mainland China are being detected at those destinations. This may well not be the case. If cases are being missed in other

Report

Imai N, Dorigatti I, Cori A, Riley S, Ferguson Net al., 2020, Report 1: Estimating the potential total number of novel Coronavirus cases in Wuhan City, China

We estimate that a total of 1,723 cases of 2019-nCoV in Wuhan City (95% CI: 427 – 4,471) had onset of symptoms by 12th January 2020 (the last reported onset date of any case).This estimate is based on the following assumptions:• Wuhan International Airport has a catchment population of 19 million individuals [1].• There is a mean 10-day delay between infection and detection, comprising a 5-6 day incubation period [8,9] and a 4-5 day delay from symptom onset to detection/hospitalisation of a case (the cases detected in Thailand and Japan were hospitalised 3 and 7 days after onset, respectively) [4,10].• Total volume of international travel from Wuhan over the last two months has been 3,301 passengers per day. This estimate is derived from the 3,418 foreign passengers per day in the top 20 country destinations based on 2018 IATA data [11], and uses 2016 IATA data held by Imperial College to correct for the travel surge at Chinese New Year present in the latter data (which has not happened yet this year) and for travel to countries outside the top 20 destination list.

Report

Thompson R, Stockwin J, van Gaalen R, Polonsky J, Kamvar Z, Demarsh A, Dahlqwist E, Miguel E, Jombart T, Lessler J, Cauchemez S, Cori Aet al., 2019, Improved inference of time-varying reproduction numbers during infectious disease outbreaks, Epidemics, Vol: 29, Pages: 1-11, ISSN: 1755-4365

Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.

Journal article

Desai AN, Kraemer MUG, Bhatia S, Cori A, Nouvellet P, Herringer M, Cohn EL, Carrion M, Brownstein JS, Madoff LC, Lassmann Bet al., 2019, Real-time epidemic forecasting: challenges and opportunities, Health Security, Vol: 17, Pages: 268-275, ISSN: 2326-5094

Infectious disease outbreaks play an important role in global morbidity and mortality. Real-time epidemic forecasting provides an opportunity to predict geographic disease spread as well as case counts to better inform public health interventions when outbreaks occur. Challenges and recent advances in predictive modeling are discussed here. We identified data needs in the areas of epidemic surveillance, mobility, host and environmental susceptibility, pathogen transmissibility, population density, and healthcare capacity. Constraints in standardized case definitions and timely data sharing can limit the precision of predictive models. Resource-limited settings present particular challenges for accurate epidemic forecasting due to the lack of granular data available. Incorporating novel data streams into modeling efforts is an important consideration for the future as technology penetration continues to improve on a global level. Recent advances in machine-learning, increased collaboration between modelers, the use of stochastic semi-mechanistic models, real-time digital disease surveillance data, and open data sharing provide opportunities for refining forecasts for future epidemics. Epidemic forecasting using predictive modeling is an important tool for outbreak preparedness and response efforts. Despite the presence of some data gaps at present, opportunities and advancements in innovative data streams provide additional support for modeling future epidemics.

Journal article

Cori A, Kamvar ZN, Stockwin J, Jombart T, Thompson R, Dahlqwist Eet al., 2019, annecori/EpiEstim: EpiEstim Cran 2.2-1

new CRAN version of EpiEstim including all new features described in Thompson et al. (currently in review in Epidemics journal).

Software

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