Publications
114 results found
Wardle J, Bhatia S, Kraemer MUG, et 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.
Imai N, Rawson T, Knock E, et 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
Bhatia S, Imai N, Watson OJ, et al., 2023, Lessons from COVID-19 for re-scalable data collection, Lancet Infectious Diseases, 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.
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
Unwin H, Cori A, Imai N, et 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%).
Geismar C, Nguyen V, Fragaszy E, et 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
Probert WJM, Sauter R, Pickles M, et 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 &
Geismar C, Nguyen V, Fragaszy E, et 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 Suppl 1
BACKGROUND: The serial interval is a key epidemiological measure that quantifies the time between an infector's and an infectee's onset of symptoms. This measure helps investigate epidemiological links between cases, and is an important parameter in transmission models used to estimate transmissibility and inform control strategies. The emergence of multiple variants of concern (VOC) during the SARS-CoV-2 pandemic has led to uncertainties about potential changes in the serial interval of COVID-19. We estimated the household serial interval of multiple VOC using data collected by the Virus Watch study. This online, prospective, community cohort study followed-up entire households in England and Wales since mid-June 2020. METHODS: This analysis included 5842 symptomatic individuals with confirmed SARS-CoV-2 infection among 2579 households from Sept 1, 2020, to Aug 10, 2022. SARS-CoV-2 variant designation was based upon national surveillance data of variant prevalence by date and geographical region. We used a Bayesian framework to infer who infected whom by exploring all transmission trees compatible with the observed dates of symptoms, given assumptions on the incubation period and generation time distributions using the R package outbreaker2. FINDINGS: We characterised the serial interval of COVID-19 by VOC. The mean serial interval was shortest for omicron BA5 (2·02 days; 95% credible interval [CrI] 1·26-2·84) and longest for alpha (3·37 days; 2·52-4·04). The mean serial interval before alpha (wild-type) was 2·29 days (95% CrI 1·39-2·94), 3·11 days (2·28-3·90) for delta, 2·72 days (2·01-3·47) for omicron BA1, and 2·67 days (1·90-3·46) for omicron BA2. We estimated that 17% (95% CrI 5-26) of serial interval values are negative across all variants. INTERPRETATION: Most methods estimating the reproduction number from incidence time series do no
Bosse N, Abbott S, Bracher J, et al., 2022, Comparing human and model-based forecasts of COVID-19 in Germany and Poland, PLOS COMPUTATIONAL BIOLOGY, Vol: 18, ISSN: 1553-734X
Abbas M, Cori A, Cordey S, et 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
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.
Bosse NI, Gruson H, Cori A, et al., 2022, Evaluating Forecasts with scoringutils in R
Evaluating forecasts is essential in order to understand and improveforecasting and make forecasts useful to decision-makers. Much theoretical workhas been done on the development of proper scoring rules and other scoringmetrics that can help evaluate forecasts. In practice, however, conducting aforecast evaluation and comparison of different forecasters remainschallenging. In this paper we introduce scoringutils, an R package that aims togreatly facilitate this process. It is especially geared towards comparingmultiple forecasters, regardless of how forecasts were created, and visualisingresults. The package is able to handle missing forecasts and is the first Rpackage to offer extensive support for forecasts represented through predictivequantiles, a format used by several collaborative ensemble forecasting efforts.The paper gives a short introduction to forecast evaluation, discusses themetrics implemented in scoringutils and gives guidance on when they areappropriate to use, and illustrates the application of the package usingexample data of forecasts for COVID-19 cases and deaths submitted to theEuropean Forecast Hub between May and September 2021
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.
Lenggenhager L, Martischang R, Sauser J, et 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.
Wardle J, Bhatia S, Kraemer MUG, et 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>
Lindsey BB, Villabona-Arenas CJ, Campbell F, et 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
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Abbas M, Cori A, Cordey S, et al., 2022, Reconstructing transmission chains of SARS-CoV-2 amid multiple outbreaks in a geriatric acute-care hospital
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>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, in order to determine who infected whom. We evaluated general transmission patterns according to type of case (HCWs working in dedicated Covid-19 cohorting wards: HCW<jats:sub>covid</jats:sub>; HCWs working in non-Covid-19 wards where outbreaks occurred: HCW<jats:sub>outbreak</jats:sub>; patients with nosocomial Covid-19: patient<jats:sub>noso</jats:sub>) by deriving the proportion of infections attributed to each type of case across all posterior trees and comparing them to random expectations.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>During the study period (March 1 to May 7, 2020) we included 180 SARS-CoV-2 positive cases: 127 HCWs (91 HCW<jats:sub>covid</jats:sub>, 36 HCW<jats:sub>outbreak</jats:sub>) and 53 patients. The attack rates ranged from 10-19% for patients, and 21% for HCWs. We estimated that there were 16 importation events (3 patients, 13 HCWs) th
Shanaube K, Gachie T, Hoddinott G, et 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
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
Ferguson N, Ghani A, Cori A, et al., 2021, Report 49: Growth, population distribution and immune escape of Omicron in England
To estimate the growth of the Omicron variant of concern (1) and its immune escape (2–9) characteristics, we analysed data from all PCR-confirmed SARS-CoV-2 cases in England excluding those with a history of recent international travel. We undertook separate analyses according to two case definitions. For the first definition, we included all cases with a definitive negative S-gene Target Failure (SGTF) result and specimen dates between 29/11/2021 and 11/12/2021 inclusive. For the second definition, we included cases with a positive genotype result and specimen date between 23/11/2021 and 11/12/2021 inclusive. We chose a later start date for the SGTF definition to ensure greater specificity of SGTF for Omicron.We used logistic and Poisson regression to identify factors associated with testing positive for Omicron compared to non-Omicron (mostly Delta) cases. We explored the following predictors: day, region, symptomatic status, sex, ethnicity, age band and vaccination status. Our results suggest rapid growth of the frequency of the Omicron variant relative to Delta, with the exponential growth rate of its frequency estimated to be 0.34/day (95% CI: 0.33-0.35) [2.0 day doubling time] over the study period from both SGTF and genotype data. The distribution of Omicron by age, region and ethnicity currently differs markedly from Delta, with 18–29-year-olds, residents in the London region, and those of African ethnicity having significantly higher rates of infection with Omicron relative to Delta.Hospitalisation and asymptomatic infection indicators were not significantly associated with Omicron infection, suggesting at most limited changes in severity compared with Delta.To estimate the impact of Omicron on vaccine effectiveness (VE) for symptomatic infection we used conditional Poisson regression to estimate the hazard ratio of being an Omicron case (using SGTF definition) compared with Delta, restricting our analysis to symptomatic cases and matching by da
Bosse NI, Abbott S, Bracher J, et al., 2021, Comparing human and model-based forecasts of COVID-19 in Germany and Poland
<jats:label>1</jats:label><jats:title>Abstract</jats:title><jats:p>Forecasts based on epidemiological modelling have played an important role in shaping public policy throughout the COVID-19 pandemic. This modelling combines knowledge about infectious disease dynamics with the subjective opinion of the researcher who develops and refines the model and often also adjusts model outputs. Developing a forecast model is difficult, resource- and time-consuming. It is therefore worth asking what modelling is able to add beyond the subjective opinion of the researcher alone. To investigate this, we analysed different real-time forecasts of cases of and deaths from COVID-19 in Germany and Poland over a 1-4 week horizon submitted to the German and Polish Forecast Hub. We compared crowd forecasts elicited from researchers and volunteers, against a) forecasts from two semi-mechanistic models based on common epidemiological assumptions and b) the ensemble of all other models submitted to the Forecast Hub. We found crowd forecasts, despite being overconfident, to outperform all other methods across all forecast horizons when forecasting cases (weighted interval score relative to the Hub ensemble 2 weeks ahead: 0.89). Forecasts based on computational models performed comparably better when predicting deaths (rel. WIS 1.26), suggesting that epidemiological modelling and human judgement can complement each other in important ways.</jats:p>
Limbada M, Macleod D, Situmbeko V, et al., 2021, Rates of viral suppression in a cohort of people with stable HIV from two community models of ART delivery versus facility-based HIV care in Lusaka, Zambia: a cluster-randomised, non-inferiority trial nested in the HPTN 071 (PopART) trial, The Lancet HIV, Vol: 9, Pages: E13-E23, ISSN: 2405-4704
BackgroundNon-facility-based antiretroviral therapy (ART) delivery for people with stable HIV might increase sustainable ART coverage in low-income and middle-income countries. Within the HPTN 071 (PopART) trial, two interventions, home-based delivery (HBD) and adherence clubs (AC), which included groups of 15–30 participants who met at a communal venue, were compared with standard of care (SoC). In this trial we looked at the effectiveness and feasibility of these alternative models of care. Specifically, this trial aimed to assess whether these models of care had similar virological suppression to that of SoC 12 months after enrolment.MethodsThis was a three-arm, cluster-randomised, non-inferiority trial, done in two urban communities in Lusaka, Zambia included in the HPTN 071 trial. The two communities were split into zones, which were randomly assigned (1:1:1) to the three treatment strategies: 35 zones to the SoC group, 35 zones to the HBD group, and 34 zones to the AC group. ART and adherence support were delivered once every 3 months at home for the HBD group, in groups of 15–30 people in the AC group, or in the clinic for the SoC group. Adults with HIV who were receiving first-line ART for at least 6 months, virally suppressed using national HIV guidelines in the last 12 months, had no other health conditions requiring the clinicians attention, live in the study catchment area, and provided written informed consent, were eligible for inclusion. The primary endpoint was viral suppression at 12 months (with a 6 month final measurement window [ie, 9–15 months]), defined as less than 1000 HIV RNA copies per mL, with a non-inferiority margin of 5%.FindingsBetween May 5 and Dec 19, 2017, 9900 individuals were screened for inclusion, of whom 2489 (25·1%) participants were enrolled into the trial: 781 (31%) in the SoC group, 852 (34%) in the HBD group, and 856 (34%) in the AC group. A higher proportion of participants had viral load measurem
Bhatia S, Wardle J, Nash R, et al., 2021, Report 47: A generic method and software to estimate the transmission advantage of pathogen variants in real-time : SARS-CoV-2 as a case-study
Recent months have demonstrated that emerging variants may set back the global COVID-19 response.The ability to rapidly assess the threat of new variants in real-time is critical for timely optimisation ofcontrol strategies.We extend the EpiEstim R package, designed to estimate the time-varying reproduction number (Rt),to estimate in real-time the e ective transmission advantage of a new variant compared to a referencevariant. Our method can combine information across multiple locations and over time and was validatedusing an extensive simulation study, designed to mimic a variety of real-time epidemic contexts.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 Francerespectively. We further estimate that Beta and Gamma combined are 1.25 (95% CrI 1.24-1.27) timesmore transmissible than the wildtype (France data). All results are in line with previous estimates fromliterature, but could have been obtained earlier and more easily with our o -the-shelf open-source tool.Our tool can be used as an important rst step towards quantifying the threat of new variants inreal-time. Given the popularity of EpiEstim, this extension will likely be used widely to monitor theco-circulation and/or emergence of multiple variants of infectious pathogens.
Sonabend R, Whittles LK, Imai N, et al., 2021, Non-pharmaceutical interventions, vaccination, and the SARS-CoV-2 delta variant in England: a mathematical modelling study, The Lancet, Vol: 398, Pages: 1825-1835, ISSN: 0140-6736
Background:England's COVID-19 roadmap out of lockdown policy set out the timeline and conditions for the stepwise lifting of non-pharmaceutical interventions (NPIs) as vaccination roll-out continued, with step one starting on March 8, 2021. In this study, we assess the roadmap, the impact of the delta (B.1.617.2) variant of SARS-CoV-2, and potential future epidemic trajectories.Methods:This mathematical modelling study was done to assess the UK Government's four-step process to easing lockdown restrictions in England, UK. We extended a previously described model of SARS-CoV-2 transmission to incorporate vaccination and multi-strain dynamics to explicitly capture the emergence of the delta variant. We calibrated the model to English surveillance data, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data using a Bayesian evidence synthesis framework, then modelled the potential trajectory of the epidemic for a range of different schedules for relaxing NPIs. We estimated the resulting number of daily infections and hospital admissions, and daily and cumulative deaths. Three scenarios spanning a range of optimistic to pessimistic vaccine effectiveness, waning natural immunity, and cross-protection from previous infections were investigated. We also considered three levels of mixing after the lifting of restrictions.Findings:The roadmap policy was successful in offsetting the increased transmission resulting from lifting NPIs starting on March 8, 2021, with increasing population immunity through vaccination. However, because of the emergence of the delta variant, with an estimated transmission advantage of 76% (95% credible interval [95% CrI] 69–83) over alpha, fully lifting NPIs on June 21, 2021, as originally planned might have led to 3900 (95% CrI 1500–5700) peak daily hospital admissions under our central parameter scenario. Delaying until July 19, 2021, reduced peak hospital admissions by three fol
Desai A, Nouvellet P, Bhatia S, et al., 2021, Data journalism and the COVID-19 pandemic: opportunities and challenges, The Lancet Digital Health, Vol: 3, Pages: e619-e621, ISSN: 2589-7500
Pickles M, Cori A, Probert WJM, et 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 COMPUTATIONAL BIOLOGY, Vol: 17, ISSN: 1553-734X
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.
Miller KJ, Mueller K-R, Hermes D, 2021, Basis profile curve identification to understand electrical stimulation effects in human brain networks, PLOS COMPUTATIONAL BIOLOGY, Vol: 17, ISSN: 1553-734X
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Mishra S, Scott JA, Laydon DJ, et 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.
Knock ES, Whittles LK, Lees JA, et 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.
O'Driscoll M, Harry C, Donnelly CA, et 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.
Risher K, Cori A, Reniers G, et 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
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