Imperial College London

DrAnneCori

Faculty of MedicineSchool of Public Health

Senior Lecturer in Infectious Disease Modelling
 
 
 
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Contact

 

+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
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127 results found

Martoma RA, Washam M, Martoma JC, Cori A, Majumder MSet al., 2024, Corrigendum to "Modeling vaccination coverage during the 2022 central Ohio measles outbreak: a cross-sectional study" [The Lancet Regional Health-Americas 2023; 23: 100533]., Lancet Reg Health Am, Vol: 30

[This corrects the article DOI: 10.1016/j.lana.2023.100533.].

Journal article

Christen P, van Elsland S, Saulo D, Cori A, Fitzner Jet al., 2024, Advanced analytics to inform decision making during public health emergencies, Advanced Analytics to Inform Decision Making During Public Health Emergencies, Publisher: WHO, Imperial College London

The COVID-19 pandemic has led to significant understanding of evidence-informed decision making during public health emergencies. Imperial College London and the World Health Organization (WHO) Hub for Pandemic and Epidemic Intelligence (WHO Hub) jointly organized a workshop to generate an understanding of the context and ways in which advanced analytics were used for decision making during the COVID-19 pandemic and to identify opportunities to strengthen the data-to-decisions pathways. Held on 9 – 10 May 2023 at the WHO Hub in Berlin, Germany, the workshop brought together mathematical modellers specialized in infectious disease modelling and scientists based at academic institutions, public health agencies, or Ministries of Health, and public health decision makers. The workshop was conducted in four interactive group activities. The dialogue among participants led to the identification of potential opportunities for support and actions to strengthen the use of outputs from advanced analytics for decision making. These opportunities could be actions to strengthen processes and structures, improve workflows, find consensus on ways of working together, establish a knowledge foundation for support, and jointly drive evidence-based decision-making priorities for epidemic and pandemic preparedness. Workshop participants highlighted further need to capture additional perspectives held by actors from diverse geographical areas, contexts, and roles who were not present at the workshop, and political decision makers to enrich the understanding of the different priorities for advanced analytics for decision making in different regional and local contexts. The workshop highlighted the benefit in bringing together experts from around the globe to share experience and lessons learned to identify priority activities to tackling challenges and improve the way advanced analysis is perceived and used for policy and response decision making. Overall, this workshop has contr

Report

Hall M, Golubchik T, Bonsall D, Abeler-Dörner L, Limbada M, Kosloff B, Schaap A, de Cesare M, MacIntyre-Cockett G, Otecko N, Probert W, Ratmann O, Bulas Cruz A, Piwowar-Manning E, Burns DN, Cohen MS, Donnell DJ, Eshleman SH, Simwinga M, Fidler S, Hayes R, Ayles H, Fraser C, HPTN 071 PopART Phylogenetics protocol team, PANGEA consortiumet al., 2024, Demographics of sources of HIV-1 transmission in Zambia: a molecular epidemiology analysis in the HPTN 071 PopART study, The Lancet Microbe, Vol: 5, Pages: E62-E71, ISSN: 2666-5247

BACKGROUND: In the last decade, universally available antiretroviral therapy (ART) has led to greatly improved health and survival of people living with HIV in sub-Saharan Africa, but new infections continue to appear. The design of effective prevention strategies requires the demographic characterisation of individuals acting as sources of infection, which is the aim of this study. METHODS: Between 2014 and 2018, the HPTN 071 PopART study was conducted to quantify the public health benefits of ART. Viral samples from 7124 study participants in Zambia were deep-sequenced as part of HPTN 071-02 PopART Phylogenetics, an ancillary study. We used these sequences to identify likely transmission pairs. After demographic weighting of the recipients in these pairs to match the overall HIV-positive population, we analysed the demographic characteristics of the sources to better understand transmission in the general population. FINDINGS: We identified a total of 300 likely transmission pairs. 178 (59·4%) were male to female, with 130 (95% CI 110-150; 43·3%) from males aged 25-40 years. Overall, men transmitted 2·09-fold (2·06-2·29) more infections per capita than women, a ratio peaking at 5·87 (2·78-15·8) in the 35-39 years source age group. 40 (26-57; 13·2%) transmissions linked individuals from different communities in the trial. Of 288 sources with recorded information on drug resistance mutations, 52 (38-69; 18·1%) carried viruses resistant to first-line ART. INTERPRETATION: HIV-1 transmission in the HPTN 071 study communities comes from a wide range of age and sex groups, and there is no outsized contribution to new infections from importation or drug resistance mutations. Men aged 25-39 years, underserved by current treatment and prevention services, should be prioritised for HIV testing and ART. FUNDING: National Institute of Allergy and Infectious Diseases, US President's Emergency Plan for AI

Journal article

Perez-Guzman PN, Knock E, Imai N, Rawson T, Elmaci Y, Alcada J, Whittles LK, Thekke Kanapram D, Sonabend R, Gaythorpe KAM, Hinsley W, FitzJohn RG, Volz E, Verity R, Ferguson NM, Cori A, Baguelin Met al., 2023, Author Correction: Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England., Nat Commun, Vol: 14

Journal article

Cuomo-Dannenburg G, McCain K, McCabe R, Unwin HJT, Doohan P, Nash RK, Hicks JT, Charniga K, Geismar C, Lambert B, Nikitin D, Skarp J, Wardle J, Kont M, Bhatia S, Imai N, van Elsland S, Cori A, Morgenstern Cet al., 2023, Marburg virus disease outbreaks, mathematical models, and disease parameters: a systematic review, Lancet Infectious Diseases, ISSN: 1473-3099

Recent Marburg virus disease (MVD) outbreaks in Equatorial Guinea and Tanzania highlighted the importance of better understanding this highly lethal infectious pathogen. We conducted a systematic review (PROSPERO CRD42023393345), reported according to PRISMA guidelines, of peer-reviewed papers reporting historical outbreaks, modelling studies and epidemiological parameters focused on MVD. We searched PubMed and Web of Science until 31/03/2023. Two reviewers evaluated all titles and abstracts, with consensus-based decision-making. To ensure agreement, 31% (13/42) of studies were double-extracted and a custom-designed quality assessment questionnaire was used for risk of bias assessment. We present detailed information on 478 reported cases and 385 deaths from MVD. Analysis of historical outbreaks and seroprevalence estimates suggests the possibility of undetected MVD outbreaks, asymptomatic transmission and/or cross-reactivity with other pathogens. Only one study presented a mathematical model of MVD transmission. We estimate an unadjusted, pooled total random effect case fatality ratio for MVD of 61.9% (95% CI: 38.8-80.6%, I^2=93%). We identify important epidemiological parameters relating to transmission and natural history for which there are few estimates. This review and the accompanying database provide a comprehensive overview of MVD epidemiology, and identify key knowledge gaps, contributing crucial information for mathematical models to support future MVD epidemic responses.

Journal article

Michalow J, Jahn A, Cori A, Boily M-C, Chimpandule T, Mbiriyawanda S, Ozituosauka W, Nyirenda R, Imai-Eaton JWet al., 2023, Burden and trends of symptomatic sexually transmitted infections in Malawi from 2000 to 2021: comparative analysis of survey and case report data, Sexually Transmitted Diseases, ISSN: 0148-5717

Background: In settings without aetiologic testing for sexually transmitted infections (STIs), programmes rely on STI symptom data to inform priorities. To evaluate whether self-reported STI symptoms in household surveys consistently represent the STI burden, we compared symptomatic infection rates between survey self-reporting and health facility case reporting in Malawi.Methods: We analysed self-reported symptoms and treatment seeking in the past year among sexually active adults from four Malawi Demographic and Health Surveys between 2000-2015. Bayesian mixed-effects models were used to estimate temporal trends, spatial variation, and sociodemographic determinants. Survey reporting was compared with health facility syndromic diagnoses between 2014-2021. Results: In surveys, 11.0% (95% CI:10.7-11.4%) of adults reported STI or STI-related symptoms in the last year, of whom 54.2% (52.8-55.7%) sought treatment. In facilities, the mean annual symptomatic case diagnosis rate was 3.3%. Survey-reported treatment in the last year was 3.8% (95% CrI:2.3-6.1%) for genital ulcer, 3.8% (2.0-6.7%) for vaginal discharge, and 2.6% (1.2-4.7%) for urethral discharge. Mean annual diagnosis rates at facilities were 0.5% for genital ulcer, 2.2% for vaginal discharge, and 2.0% for urethral discharge. Both data sources indicated a higher burden of symptoms among women, individuals above 25 years, and in Southern Malawi. Conclusion: Survey and facility case reports indicated similar spatial and demographic patterns of STI symptom burden and care seeking, but implied large differences in the magnitude and relative burden of symptoms, particularly genital ulcer, which could affect programme priorities. Targeted aetiologic surveillance would improve interpretation of these data to enable more comprehensive STI surveillance.

Journal article

Bhatia S, Parag KV, Wardle J, Nash RK, Imai N, Elsland SLV, Lassmann B, Brownstein JS, Desai A, Herringer M, Sewalk K, Loeb SC, Ramatowski J, Cuomo-Dannenburg G, Jauneikaite E, Unwin HJT, Riley S, Ferguson N, Donnelly CA, Cori A, Nouvellet Pet al., 2023, Retrospective evaluation of real-time estimates of global COVID-19 transmission trends and mortality forecasts, PLOS ONE, Vol: 18, ISSN: 1932-6203

Journal article

Bhatia S, Imai N, Watson OJ, Abbood A, Abdelmalik P, Cornelissen T, Ghozzi S, Lassmann B, Nagesh R, Ragonnet-Cronin ML, Schnitzler JC, Kraemer MUG, Cauchemez S, Nouvellet P, Cori Aet al., 2023, Lessons from COVID-19 for re-scalable data collection, Lancet Infectious Diseases, Vol: 23, Pages: E383-E388, ISSN: 1473-3099

Novel data and analyses have played an important role in informing the public health response to the COVID-19 pandemic. Existing surveillance systems were scaled up, and in some instances, new systems developed to meet the challenges posed by the magnitude of the pandemic. Here, we describe the routine and novel data that were used to address urgentpublic health questions during the pandemic, underscore challenges in sustainability and equity in data generation, and highlight key lessons learnt for designing scalable data collection systems to support decision-making during a public health crisis.As countries emerge from the acute phase of the pandemic, COVID-19 surveillance systems are being scaled down. However, as SARS-CoV-2 resurgence remains a threat to global health security, it is important that a minimal cost-effective system remains active that can be rapidly scaled up if necessary. We propose that a retrospective evaluation to identify the cost-benefit profile of the various data streams collected during the pandemic should be on the scientific research agenda.

Journal article

Geismar C, Nguyen V, Fragaszy E, Shrotri M, Navaratnam AMD, Beale S, Byrne TE, Fong WLE, Yavlinsky A, Kovar J, Hoskins S, Braithwaite I, Aldridge RW, Hayward AC, White PJ, Jombart T, Cori Aet al., 2023, Bayesian reconstruction of SARS-CoV-2 transmissions highlights substantial proportion of negative serial intervals, Epidemics: the journal of infectious disease dynamics, Vol: 44, ISSN: 1755-4365

BACKGROUND: The serial interval is a key epidemiological measure that quantifies the time between the onset of symptoms in an infector-infectee pair. It indicates how quickly new generations of cases appear, thus informing on the speed of an epidemic. Estimating the serial interval requires to identify pairs of infectors and infectees. Yet, most studies fail to assess the direction of transmission between cases and assume that the order of infections - and thus transmissions - strictly follows the order of symptom onsets, thereby imposing serial intervals to be positive. Because of the long and highly variable incubation period of SARS-CoV-2, this may not always be true (i.e an infectee may show symptoms before their infector) and negative serial intervals may occur. This study aims to estimate the serial interval of different SARS-CoV-2 variants whilst accounting for negative serial intervals. METHODS: This analysis included 5 842 symptomatic individuals with confirmed SARS-CoV-2 infection amongst 2 579 households from September 2020 to August 2022 across England & Wales. We used a Bayesian framework to infer who infected whom by exploring all transmission trees compatible with the observed dates of symptoms, based on a wide range of incubation period and generation time distributions compatible with estimates reported in the literature. Serial intervals were derived from the reconstructed transmission pairs, stratified by variants. RESULTS: We estimated that 22% (95% credible interval (CrI) 8-32%) of serial interval values are negative across all VOC. The mean serial interval was shortest for Omicron BA5 (2.02 days, 1.26-2.84) and longest for Alpha (3.37 days, 2.52-4.04). CONCLUSIONS: This study highlights the large proportion of negative serial intervals across SARS-CoV-2 variants. Because the serial interval is widely used to estimate transmissibility and forecast cases, these results may have critical implications for epidemic control.

Journal article

Bhatia S, Wardle J, Nash R, Nouvellet P, Cori Aet al., 2023, Extending EpiEstim to estimate the transmission advantage of pathogen variants in real-time: SARS-CoV-2 as a case-study, Epidemics: the journal of infectious disease dynamics, Vol: 44, Pages: 1-8, ISSN: 1755-4365

The evolution of SARS-CoV-2 has demonstrated that emerging variants can set back the global COVID-19 response. The ability to rapidly assess the threat ofnew variants is critical for timely optimisation of control strategies.We present a novel method to estimate the effective transmission advantage of a new variant compared to a reference variant combining information across multiple locations and over time. Through an extensive simulation study designed to mimic real-time epidemic contexts, we show that our method performs well across a range of scenarios and provide guidance on its optimal useand interpretation of results. We also provide an open-source software implementation of our method. The computational speed of our tool enables users torapidly explore spatial and temporal variations in the estimated transmission advantage.We estimate that the SARS-CoV-2 Alpha variant is 1.46 (95% Credible Interval 1.44-1.47) and 1.29, (95% CrI 1.29-1.30) times more transmissible than the wild type, using data from England and France respectively. We furtherestimate that Delta is 1.77 (95% CrI: 1.69-1.85) times more transmissible than Alpha (England data).Our approach can be used as an important first step towards quantifying the threat of emerging or co-circulating variants of infectious pathogens in real-time.

Journal article

Nash RK, Bhatt S, Cori A, Nouvellet Pet al., 2023, Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool, PLOS COMPUTATIONAL BIOLOGY, Vol: 19, ISSN: 1553-734X

Journal article

Bosse NI, Abbott S, Cori A, van Leeuwen E, Bracher J, Funk Set al., 2023, Scoring epidemiological forecasts on transformed scales., PLoS Comput Biol, Vol: 19

Forecast evaluation is essential for the development of predictive epidemic models and can inform their use for public health decision-making. Common scores to evaluate epidemiological forecasts are the Continuous Ranked Probability Score (CRPS) and the Weighted Interval Score (WIS), which can be seen as measures of the absolute distance between the forecast distribution and the observation. However, applying these scores directly to predicted and observed incidence counts may not be the most appropriate due to the exponential nature of epidemic processes and the varying magnitudes of observed values across space and time. In this paper, we argue that transforming counts before applying scores such as the CRPS or WIS can effectively mitigate these difficulties and yield epidemiologically meaningful and easily interpretable results. Using the CRPS on log-transformed values as an example, we list three attractive properties: Firstly, it can be interpreted as a probabilistic version of a relative error. Secondly, it reflects how well models predicted the time-varying epidemic growth rate. And lastly, using arguments on variance-stabilizing transformations, it can be shown that under the assumption of a quadratic mean-variance relationship, the logarithmic transformation leads to expected CRPS values which are independent of the order of magnitude of the predicted quantity. Applying a transformation of log(x + 1) to data and forecasts from the European COVID-19 Forecast Hub, we find that it changes model rankings regardless of stratification by forecast date, location or target types. Situations in which models missed the beginning of upward swings are more strongly emphasised while failing to predict a downturn following a peak is less severely penalised when scoring transformed forecasts as opposed to untransformed ones. We conclude that appropriate transformations, of which the natural logarithm is only one particularly attractive option, should be considered when as

Journal article

Perez Guzman PN, Knock ES, Imai N, Rawson T, Elmaci Y, Alcada J, Whittles LK, Thekke Kanapram D, Sonabend R, Gaythorpe KAM, Hinsley W, Fitzjohn RG, Volz E, Verity R, Ferguson NM, Cori A, Baguelin Met al., 2023, Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England, Nature Communications, Vol: 14, Pages: 1-9, ISSN: 2041-1723

As the SARS-CoV-2 pandemic progressed, distinct variants emerged and dominated in England. These variants, Wildtype, Alpha, Delta, and Omicron were characterized by variations in transmissibility and severity. We used a robust mathematical model and Bayesian inference framework to analyse epidemiological surveillance data from England. We quantified the impact of non-pharmaceutical interventions (NPIs), therapeutics, and vaccination on virus transmission and severity. Each successive variant had a higher intrinsic transmissibility. Omicron (BA.1) had the highest basic reproduction number at 8.3 (95% credible interval (CrI) 7.7-8.8). Varying levels of NPIs were crucial in controlling virus transmission until population immunity accumulated. Immune escape properties of Omicron decreased effective levels of immunity in the population by a third. Furthermore, in contrast to previous studies, we found Alpha had the highest basic infection fatality ratio (2.9%, 95% CrI 2.7-3.2), followed by Delta (2.2%, 95% CrI 2.0–2.4), Wildtype (1.2%, 95% CrI 1.1–1.2), and Omicron (0.7%, 95% CrI 0.6-0.8). Our findings highlight the importance of continued surveillance. Long-term strategies for monitoring and maintaining effective immunity against SARS-CoV-2 are critical to inform the role of NPIs to effectively manage future variants with potentially higher intrinsic transmissibility and severe outcomes.

Journal article

Martoma RA, Washam M, Martoma JC, Cori A, Majumder MSet al., 2023, Modeling vaccination coverage during the 2022 central Ohio measles outbreak: a cross-sectional study, LANCET REGIONAL HEALTH-AMERICAS, Vol: 23, ISSN: 2667-193X

Journal article

Bhatia S, Imai N, Watson OJ, Abbood A, Abdelmalik P, Cornelissen T, Ghozzi S, Lassmann B, Nagesh R, Ragonnet-Cronin ML, Schnitzler JC, Kraemer MU, Cauchemez S, Nouvellet P, Cori Aet al., 2023, Lessons from COVID-19 for rescalable data collection (May, 10.1016/S1473-3099(23)00121-4, 2023), LANCET INFECTIOUS DISEASES, Vol: 23, Pages: E227-E227, ISSN: 1473-3099

Journal article

Cori A, Lassmann B, Nouvellet P, 2023, Data needs for better surveillance and response to infectious disease threats, EPIDEMICS, Vol: 43, ISSN: 1755-4365

Journal article

Wardle J, Bhatia S, Kraemer MUG, Nouvellet P, Cori Aet al., 2023, Gaps in mobility data and implications for modelling epidemic spread: a scoping review and simulation study, Epidemics: the journal of infectious disease dynamics, Vol: 42, Pages: 1-11, ISSN: 1755-4365

Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases.We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest.Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.

Journal article

Imai N, Rawson T, Knock E, Sonabend R, Elmaci Y, Perez-Guzman P, Whittles L, Thekke Kanapram D, Gaythorpe K, Hinsley W, Djaafara B, Wang H, Fraser K, Fitzjohn R, Hogan A, Doohan P, Ghani A, Ferguson N, Baguelin M, Cori Aet al., 2023, Quantifying the impact of delaying the second COVID-19 vaccine dose in England: a mathematical modelling study, The Lancet Public Health, Vol: 8, Pages: e174-e183, ISSN: 2468-2667

Background: The UK was the first country to start national COVID-19 vaccination programmes, initially administering doses 3-weeks apart. However, early evidence of high vaccine effectiveness after the first dose and the emergence of the Alpha variant prompted the UK to extend the interval between doses to 12-weeks. In this study, we aim to quantify the impact of delaying the second vaccine dose on the epidemic in England.Methods: We used a previously described model of SARS-CoV-2 transmission, calibrated to English COVID-19 surveillance data including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data using a Bayesian evidence synthesis framework. We modelled and compared the epidemic trajectory assuming that vaccine doses were administered 3-weeks apart against the real reported vaccine roll-out schedule. We estimated and compared the resulting number of daily infections, hospital admissions, and deaths. Scenarios spanning a range of vaccine effectiveness and waning assumptions were investigated.Findings: We estimate that delaying the interval between the first and second COVID-19 vaccine doses from 3- to 12-weeks prevented an average 58,000 COVID-19 hospital admissions and 10,100 deaths between 8th December 2020 and 13th September 2021. Similarly, we estimate that the 3-week strategy would have resulted in more infections and deaths compared to the 12-week strategy. Across all sensitivity analyses the 3-week strategy resulted in a greater number of hospital admissions. Interpretation: England’s delayed second dose vaccination strategy was informed by early real-world vaccine effectiveness data and a careful assessment of the trade-offs in the context of limited vaccine supplies in a growing epidemic. Our study shows that rapidly providing partial (single dose) vaccine-induced protection to a larger proportion of the population was successful in reducing the burden of COVID-19 hospitalisations and deaths. Ther

Journal article

Nash RK, Cori A, Nouvellet P, 2022, Estimating the epidemic reproduction number from temporally aggregated incidence data: a statistical modelling approach and software tool

<jats:sec><jats:title>Background</jats:title><jats:p>The time-varying reproduction number (R<jats:sub>t</jats:sub>) is an important measure of epidemic transmissibility; it can directly inform policy decisions and the optimisation of control measures. EpiEstim is a widely used software tool that uses case incidence and the serial interval (SI, time between symptoms in a case and their infector) to estimate R<jats:sub>t</jats:sub>in real-time. The incidence and the SI distribution must be provided at the same temporal resolution, which limits the applicability of EpiEstim and other similar methods, e.g. for pathogens with a mean SI shorter than the frequency of incidence reporting.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We use an expectation-maximisation algorithm to reconstruct daily incidence from temporally aggregated data, from which R<jats:sub>t</jats:sub>can then be estimated using EpiEstim. We assess the validity of our method using an extensive simulation study and apply it to COVID-19 and influenza data. The method is implemented in the opensource R package EpiEstim.</jats:p></jats:sec><jats:sec><jats:title>Findings</jats:title><jats:p>For all datasets, the influence of intra-weekly variability in reported data was mitigated by using aggregated weekly data. R<jats:sub>t</jats:sub>estimated on weekly sliding windows using incidence reconstructed from weekly data was strongly correlated with estimates from the original daily data. The simulation study revealed that R<jats:sub>t</jats:sub>was well estimated in all scenarios and regardless of the temporal aggregation of the data. In the presence of weekend effects, R<jats:sub>t</jats:sub>estimates from reconstructed data were more successful at recovering the true value of R<jats:sub>t</jats:sub>than those

Journal article

Shanaube K, Gachie T, Hoddinott G, Schaap A, Floyd S, Mainga T, Bond V, Hayes R, Fidler S, Ayles H, HPTN071 PopART Study Teamet al., 2022, Depressive symptoms and HIV risk behaviours among adolescents enrolled in the HPTN071 (PopART) trial in Zambia and South Africa, PLoS One, Vol: 17, ISSN: 1932-6203

BACKGROUND: Mental health is a critical and neglected public health problem for adolescents in sub-Saharan Africa. In this paper we aim to determine the prevalence of depressive symptoms and the association with HIV risk behaviours in adolescents aged 15-19 years in Zambia and SA. METHODS: We conducted a cross-sectional survey from August-November 2017 in seven control communities of HPTN 071 (PopART) trial (a community-randomised trial of universal HIV testing and treatment), enrolling approximately 1400 eligible adolescents. HIV-status was self-reported. Depressive symptoms were measured with the Short Mood and Feelings Questionnaire (SMFQ), with a positive screen if adolescents scored ≥12. We fitted a logistic regression model to identify correlates of depressive symptoms with subgroup analyses among those who self-reported ever having had sex, by gender and country. RESULTS: Out of 6997 households approached, 6057 (86.6%) were enumerated. 2546 adolescents were enumerated of whom 2120 (83.3%) consented to participate and were administered the SMFQ. The prevalence of depressive symptoms was 584/2120 (27.6%) [95%CI: 25.7%-29.5%]. Adolescents in SA were less likely to experience depressive symptoms (Adjusted Odds Ratio [AOR] = 0.63 (95% CI: 0.50, 0.79), p-value<0.0001). Female adolescents (AOR = 1.46 (95% CI: 1.19, 1.81), p-value<0.0001); those who reported ever having sex and being forced into sex (AOR = 1.80 (95% CI: 1.45, 2.23), p-value<0.001) and AOR = 1.67 (95% CI: 0.99, 2.84); p-value = 0.057 respectively) were more likely to experience depressive symptoms. Among 850 (40.1%) adolescents who self-reported to ever having had sex; those who used alcohol/drugs during their last sexual encounter were more likely to experience depressive symptoms (AOR = 2.18 (95% CI: 1.37, 3.47); p-value = 0.001), whereas those who reported using a condom were less likely to experience depressive symptoms (AOR = 0.74 (95% CI: 0.55, 1.00); p-value = 0.053). CONCLUSION: Th

Journal article

Unwin H, Cori A, Imai N, Gaythorpe K, Bhatia S, Cattarino L, Donnelly C, Ferguson N, Baguelin Met al., 2022, Using next generation matrices to estimate the proportion of infections that are not detected in an outbreak, Epidemics: the journal of infectious disease dynamics, Vol: 41, ISSN: 1755-4365

Contact tracing, where exposed individuals are followed up to break ongoing transmission chains, is a key pillar of outbreak response for infectious disease outbreaks. Unfortunately, these systems are not fully effective, and infections can still go undetected as people may not remember all their contacts or contacts may not be traced successfully. A large proportion of undetected infections suggests poor contact tracing and surveillance systems, which could be a potential area of improvement for a disease response. In this paper, we present a method for estimating the proportion of infections that are not detected during an outbreak. Our method uses next generation matrices that are parameterized by linked contact tracing data and case line-lists. We validate the method using simulated data from an individual-based model and then investigate two case studies: the proportion of undetected infections in the SARS-CoV-2 outbreak in New Zealand during 2020 and the Ebola epidemic in Guinea during 2014. We estimate that only 5.26% of SARS-CoV-2 infections were not detected in New Zealand during 2020 (95% credible interval: 0.243 – 16.0%) if 80% of contacts were under active surveillance but depending on assumptions about the ratio of contacts not under active surveillance versus contacts under active surveillance 39.0% or 37.7% of Ebola infections were not detected in Guinea (95% credible intervals: 1.69 – 87.0% or 1.70 – 80.9%).

Journal article

Probert WJM, Sauter R, Pickles M, Cori A, Bell-Mandla NF, Bwalya J, Abeler-Dörner L, Bock P, Donnell DJ, Floyd S, Macleod D, Piwowar-Manning E, Skalland T, Shanaube K, Wilson E, Yang B, Ayles H, Fidler S, Hayes RJ, Fraser C, Hayes R, Fidler S, Beyers N, Ayles H, Bock P, El-Sadr W, Cohen M, Eshleman S, Agyei Y, Piwowar-Manning E, Bond V, Hoddinott G, Donnell D, Floyd S, Wilson E, Emel L, Noble H, Macleod D, Burns D, Fraser C, Cori A, Sista N, Griffith S, Moore A, Headen T, White R, Miller E, Hargreaves J, Hauck K, Thomas R, Limbada M, Bwalya J, Pickles M, Sabapathy K, Schaap A, Dunbar R, Shanaube K, Yang B, Simwinga M, Smith P, Vermund S, Mandla N, Makola N, van Deventer A, James A, Jennings K, Kruger J, Phiri M, Kosloff B, Mwenge L, Kanema S, Sauter R, Probert W, Kumar R, Sakala E, Silumesi A, Skalland T, Yuhas Ket al., 2022, Projected outcomes of universal testing and treatment in a generalised HIV epidemic in Zambia and South Africa (the HPTN 071 [PopART] trial): a modelling study, The Lancet HIV, Vol: 9, Pages: e771-e780, ISSN: 2352-3018

BackgroundThe long-term impact of universal home-based testing and treatment as part of universal testing and treatment (UTT) on HIV incidence is unknown. We made projections using a detailed individual-based model of the effect of the intervention delivered in the HPTN 071 (PopART) cluster-randomised trial.MethodsIn this modelling study, we fitted an individual-based model to the HIV epidemic and HIV care cascade in 21 high prevalence communities in Zambia and South Africa that were part of the PopART cluster-randomised trial (intervention period Nov 1, 2013, to Dec 31, 2017). The model represents coverage of home-based testing and counselling by age and sex, delivered as part of the trial, antiretroviral therapy (ART) uptake, and any changes in national guidelines on ART eligibility. In PopART, communities were randomly assigned to one of three arms: arm A received the full PopART intervention for all individuals who tested positive for HIV, arm B received the intervention with ART provided in accordance with national guidelines, and arm C received standard of care. We fitted the model to trial data twice using Approximate Bayesian Computation, once before data unblinding and then again after data unblinding. We compared projections of intervention impact with observed effects, and for four different scenarios of UTT up to Jan 1, 2030 in the study communities.FindingsCompared with standard of care, a 51% (95% credible interval 40–60) reduction in HIV incidence is projected if the trial intervention (arms A and B combined) is continued from 2020 to 2030, over and above a declining trend in HIV incidence under standard of care.InterpretationA widespread and continued commitment to UTT via home-based testing and counselling can have a substantial effect on HIV incidence in high prevalence communities.FundingNational Institute of Allergy and Infectious Diseases, US President's Emergency Plan for AIDS Relief, International Initiative for Impact Evaluation, Bill &

Journal article

Geismar C, Nguyen V, Fragaszy E, Shrotri M, Navaratnam AMD, Beale S, Byrne TE, Fong WLE, Yavlinsky A, Kovar J, Braithwaite I, Aldridge RW, Hayward AC, White P, Jombart T, Cori Aet al., 2022, Bayesian reconstruction of household transmissions to infer the serial interval of COVID-19 by variants of concern: analysis from a prospective community cohort study (Virus Watch), LANCET, Vol: 400, Pages: 40-40, ISSN: 0140-6736

Journal article

Bosse N, Abbott S, Bracher J, Hain H, Quilty BJ, Jit M, van Leeuwen E, Cori A, Funk Set al., 2022, Comparing human and model-based forecasts of COVID-19 in Germany and Poland, PLOS COMPUTATIONAL BIOLOGY, Vol: 18, ISSN: 1553-734X

Journal article

Abbas M, Cori A, Cordey S, Laubscher F, Robalo Nunes T, Myall A, Salamun J, Huber P, Zekry D, Prendki V, Iten A, Vieux L, Sauvan V, Graf C, Harbarth Set al., 2022, Reconstruction of transmission chains of SARS-CoV-2 amidst multiple outbreaks in a geriatric acute-care hospital: a combined retrospective epidemiological and genomic study, eLife, Vol: 11, ISSN: 2050-084X

Background:There is ongoing uncertainty regarding transmission chains and the respective roles of healthcare workers (HCWs) and elderly patients in nosocomial outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in geriatric settings.Methods:We performed a retrospective cohort study including patients with nosocomial coronavirus disease 2019 (COVID-19) in four outbreak-affected wards, and all SARS-CoV-2 RT-PCR positive HCWs from a Swiss university-affiliated geriatric acute-care hospital that admitted both Covid-19 and non-Covid-19 patients during the first pandemic wave in Spring 2020. We combined epidemiological and genetic sequencing data using a Bayesian modelling framework, and reconstructed transmission dynamics of SARS-CoV-2 involving patients and HCWs, to determine who infected whom. We evaluated general transmission patterns according to case type (HCWs working in dedicated Covid-19 cohorting wards: HCWcovid; HCWs working in non-Covid-19 wards where outbreaks occurred: HCWoutbreak; patients with nosocomial Covid-19: patientnoso) by deriving the proportion of infections attributed to each case type across all posterior trees and comparing them to random expectations.Results:During the study period (March 1 to May 7, 2020) we included 180 SARS-CoV-2 positive cases: 127 HCWs (91 HCWcovid, 36 HCWoutbreak) and 53 patients. The attack rates ranged from 10-19% for patients, and 21% for HCWs. We estimated that 16 importation events occurred with high confidence (four patients, 12 HCWs) that jointly led to up to 41 secondary cases; in six additional cases (five HCWs, one patient), importation was possible with a posterior between 10-50%. Most patient-to-patient transmission events involved patients having shared a ward (95.2%, 95% credible interval [CrI] 84.2-100%), in contrast to those having shared a room (19.7%, 95%CrI 6.7-33.3%). Transmission events tended to cluster by case type: patientnoso were almost twice as likely to be infected by oth

Journal article

Nash RK, Nouvellet P, Cori A, 2022, Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges, PLOS Digital Health, Vol: 1, Pages: e0000052-e0000052, ISSN: 2767-3170

The time-varying reproduction number (Rt) is an important measure of transmissibility during outbreaks. Estimating whether and how rapidly an outbreak is growing (Rt > 1) or declining (Rt < 1) can inform the design, monitoring and adjustment of control measures in real-time. We use a popular R package for Rt estimation, EpiEstim, as a case study to evaluate the contexts in which Rt estimation methods have been used and identify unmet needs which would enable broader applicability of these methods in real-time. A scoping review, complemented by a small EpiEstim user survey, highlight issues with the current approaches, including the quality of input incidence data, the inability to account for geographical factors, and other methodological issues. We summarise the methods and software developed to tackle the problems identified, but conclude that significant gaps remain which should be addressed to enable easier, more robust and applicable estimation of Rt during epidemics.

Journal article

Green WD, Ferguson NM, Cori A, 2022, Inferring the reproduction number using the renewal equation in heterogeneous epidemics, Journal of the Royal Society Interface, Vol: 19, ISSN: 1742-5662

Real-time estimation of the reproduction number has become the focus ofmodelling groups around the world as the SARS-CoV-2 pandemic unfolds.One of the most widely adopted means of inference of the reproductionnumber is via the renewal equation, which uses the incidence of infectionand the generation time distribution. In this paper, we derive a multi-typeequivalent to the renewal equation to estimate a reproduction numberwhich accounts for heterogeneity in transmissibility including throughasymptomatic transmission, symptomatic isolation and vaccination. Wedemonstrate how use of the renewal equation that misses these heterogeneitiescan result in biased estimates of the reproduction number. While thebias is small with symptomatic isolation, it can be much larger with asymptomatictransmission or transmission from vaccinated individuals if thesegroups exhibit substantially different generation time distributions to unvaccinatedsymptomatic transmitters, whose generation time distribution isoften well defined. The bias in estimate becomes larger with greater populationsize or transmissibility of the poorly characterized group. We applyour methodology to Ebola in West Africa in 2014 and the SARS-CoV-2 inthe UK in 2020–2021.

Journal article

Lenggenhager L, Martischang R, Sauser J, Perez M, Vieux L, Graf C, Cordey S, Laubscher F, Nunes TR, Zingg W, Cori A, Harbarth S, Abbas Met al., 2022, Occupational and community risk of SARS-CoV-2 infection among employees of a long-term care facility: an observational study, Antimicrobial Resistance and Infection Control, Vol: 11, ISSN: 2047-2994

BackgroundWe investigated the contribution of both occupational and community exposure for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among employees of a university-affiliated long-term care facility (LTCF), during the 1st pandemic wave in Switzerland (March–June 2020).MethodsWe performed a nested analysis of a seroprevalence study among all volunteering LTCF staff to determine community and nosocomial risk factors for SARS-CoV-2 seropositivity using modified Poison regression. We also combined epidemiological and genetic sequencing data from a coronavirus disease 2019 (COVID-19) outbreak investigation in a LTCF ward to infer transmission dynamics and acquisition routes of SARS-CoV-2, and evaluated strain relatedness using a maximum likelihood phylogenetic tree.ResultsAmong 285 LTCF employees, 176 participated in the seroprevalence study, of whom 30 (17%) were seropositive for SARS-CoV-2. Most (141/176, 80%) were healthcare workers (HCWs). Risk factors for seropositivity included exposure to a COVID-19 inpatient (adjusted prevalence ratio [aPR] 2.6; 95% CI 0.9–8.1) and community contact with a COVID-19 case (aPR 1.7; 95% CI 0.8–3.5). Among 18 employees included in the outbreak investigation, the outbreak reconstruction suggests 4 likely importation events by HCWs with secondary transmissions to other HCWs and patients.ConclusionsThese two complementary epidemiologic and molecular approaches suggest a substantial contribution of both occupational and community exposures to COVID-19 risk among HCWs in LTCFs. These data may help to better assess the importance of occupational health hazards and related legal implications during the COVID-19 pandemic.

Journal article

Wardle J, Bhatia S, Kraemer MUG, Nouvellet P, Cori Aet al., 2022, Gaps in mobility data and implications for modelling epidemic spread: a scoping review and simulation study

<jats:p>Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases.We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and, in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest.Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.</jats:p>

Journal article

Lindsey BB, Villabona-Arenas CJ, Campbell F, Keeley AJ, Parker MD, Shah DR, Parsons H, Zhang P, Kakkar N, Gallis M, Foulkes BH, Wolverson P, Louka SF, Christou S, State A, Johnson K, Raza M, Hsu S, Jombart T, Cori A, Evans CM, Partridge DG, Atkins KE, Hue S, de Silva TIet al., 2022, Characterising within-hospital SARS-CoV-2 transmission events using epidemiological and viral genomic data across two pandemic waves (vol 13, pg 1013, 2022), NATURE COMMUNICATIONS, Vol: 13

Journal article

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