Publications
90 results found
Perez-Guzman PN, Knock E, Imai N, et al., 2023, Author Correction: Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England., Nat Commun, Vol: 14
Bhatia S, Parag KV, Wardle J, et al., 2023, Retrospective evaluation of real-time estimates of global COVID-19 transmission trends and mortality forecasts, PLOS ONE, Vol: 18, ISSN: 1932-6203
Bhatia S, Imai N, Watson OJ, et 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.
Perez Guzman PN, Knock ES, Imai N, et 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.
Bhatia S, Imai N, Watson OJ, et 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
Gaythorpe K, Fitzjohn R, Hinsley W, et al., 2023, Data pipelines in a public health emergency: the human in the machine, Epidemics: the journal of infectious disease dynamics, Vol: 43, ISSN: 1755-4365
In an emergency epidemic response, data providers supply data on a best-faith effort to modellers and analysts who are typically the end user of data collected for other primary purposes such as to inform patient care. Thus, modellers who analyse secondary data have limited ability to influence what is captured. During an emergency response, models themselves are often under constant development and require both stability in their data inputs and flexibility to incorporate new inputs as novel data sources become available. This dynamic landscape is challenging to work with. Here we outline a data pipeline used in the ongoing COVID-19 response in the UK that aims to address these issues.A data pipeline is a sequence of steps to carry the raw data through to a processed and useable model input, along with the appropriate metadata and context. In ours, each data type had an individual processing report, designed to produce outputs that could be easily combined and used downstream. Automated checks were in-built and added as new pathologies emerged. These cleaned outputs were collated at different geographic levels to provide standardised datasets. Finally, a human validation step was an essential component of the analysis pathway and permitted more nuanced issues to be captured. This framework allowed the pipeline to grow in complexity and volume and facilitated the diverse range of modelling approaches employed by researchers. Additionally, every report or modelling output could be traced back to the specific data version that informed it ensuring reproducibility of results.Our approach has been used to facilitate fast-paced analysis and has evolved over time. Our framework and its aspirations are applicable to many settings beyond COVID-19 data, for example for other outbreaks such as Ebola, or where routine and regular analyses are required.
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
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%).
Cox V, O'Driscoll M, Imai N, et al., 2022, Estimating dengue transmission intensity from serological data: a comparative analysis using mixture and catalytic models., PLoS Neglected Tropical Diseases, Vol: 16, Pages: e0010592-e0010592, ISSN: 1935-2727
BACKGROUND: Dengue virus (DENV) infection is a global health concern of increasing magnitude. To target intervention strategies, accurate estimates of the force of infection (FOI) are necessary. Catalytic models have been widely used to estimate DENV FOI and rely on a binary classification of serostatus as seropositive or seronegative, according to pre-defined antibody thresholds. Previous work has demonstrated the use of thresholds can cause serostatus misclassification and biased estimates. In contrast, mixture models do not rely on thresholds and use the full distribution of antibody titres. To date, there has been limited application of mixture models to estimate DENV FOI. METHODS: We compare the application of mixture models and time-constant and time-varying catalytic models to simulated data and to serological data collected in Vietnam from 2004 to 2009 (N ≥ 2178) and Indonesia in 2014 (N = 3194). RESULTS: The simulation study showed larger mean FOI estimate bias from the time-constant and time-varying catalytic models (-0.007 (95% Confidence Interval (CI): -0.069, 0.029) and -0.006 (95% CI -0.095, 0.043)) than from the mixture model (0.001 (95% CI -0.036, 0.065)). Coverage of the true FOI was > 95% for estimates from both the time-varying catalytic and mixture model, however the latter had reduced uncertainty. When applied to real data from Vietnam, the mixture model frequently produced higher FOI and seroprevalence estimates than the catalytic models. CONCLUSIONS: Our results suggest mixture models represent valid, potentially less biased, alternatives to catalytic models, which could be particularly useful when estimating FOI from data with largely overlapping antibody titre distributions.
Imai N, Gaythorpe K, Bhatia S, et al., 2022, COVID-19 in Japan, January – March 2020: insights from the first three months of the epidemic, BMC Infectious Diseases, Vol: 22, ISSN: 1471-2334
Background:Understanding the characteristics and natural history of novel pathogens is crucial to inform successful control measures. Japan was one of the first affected countries in the COVID-19 pandemic reporting their first case on 14 January 2020. Interventions including airport screening, contact tracing, and cluster investigations were quickly implemented. Here we present insights from the first 3 months of the epidemic in Japan based on detailed case data. Methods:We conducted descriptive analyses based on information systematically extracted from individual case reports from 13 January to 31 March 2020 including patient demographics, date of report and symptom onset, symptom progression, travel history, and contact type. We analysed symptom progression and estimated the time-varying reproduction number, Rt, correcting for epidemic growth using an established Bayesian framework. Key delays and the age-specific probability of transmission were estimated using data on exposures and transmission pairs. Results:The corrected fitted mean onset-to-reporting delay after the peak was 4 days (standard deviation: ±2 days). Early transmission was driven primarily by returning travellers with Rt peaking at 2.4 (95%CrI:1.6, 3.3) nationally. In the final week of the trusted period (16 – 23 March 2020), Rt accounting for importations diverged from overall Rt at 1.1 (95% CrI: 1.0, 1.2) compared to 1.5 (95% CrI: 1.3, 1.6) respectively. Household (39.0%) and workplace (11.6%) exposures were the most frequently reported potential source of infection. The estimated probability of transmission was assortative by age with individuals more likely to infect, and be infected by, contacts in a similar age group to them. Across all age groups, cases most frequently onset with cough, fever, and fatigue. There were no reported cases of patients <20 years old developing pneumonia or severe respiratory symptoms.Conclusions:Information collected in the early phases of an out
Imai N, Gaythorpe KAM, Bhatia S, et al., 2022, COVID-19 in Japan: insights from the first three months of the epidemic, Publisher: Cold Spring Harbor Laboratory
BackgroundUnderstanding the characteristics and natural history of novel pathogens is crucial to inform successful control measures. Japan was one of the first affected countries in the COVID-19 pandemic reporting their first case on 14 January 2020. Interventions including airport screening, contact tracing, and cluster investigations were quickly implemented. Here we present insights from the first 3 months of the epidemic in Japan based on detailed case data. MethodsWe conducted descriptive analyses based on information systematically extracted from individual case reports from 13 January to 31 March 2020 including patient demographics, date of report and symptom onset, symptom progression, travel history, and contact type. We analysed symptom progression and estimated the time-varying reproduction number, Rt, correcting for epidemic growth using an established Bayesian framework. Key delays and the age-specific probability of transmission were estimated using data on exposures and transmission pairs. ResultsThe corrected fitted mean onset-to-reporting delay after the peak was 4 days (standard deviation: ±2 days). Early transmission was driven primarily by returning travellers with Rt peaking at 2.4 (95%CrI:1.6, 3.3) nationally. In the final week of the trusted period, Rt accounting for importations diverged from overall Rt at 1.1 (95% CrI: 1.0, 1.2) compared to 1.5 (95% CrI: 1.3, 1.6) respectively. Household (39.0%) and workplace (11.6%) exposures were the most frequently reported potential source of infection. The estimated probability of transmission was assortative by age. Across all age groups, cases most frequently onset with cough, fever, and fatigue. There were no reported cases of patients <20 years old developing pneumonia or severe respiratory symptoms.ConclusionsInformation collected in the early phases of an outbreak are important in characterising any novel pathogen. Timely recognition of key symptoms and high-risk settings for transmi
Bhatia S, Imai N, Cuomo-Dannenburg G, et al., 2021, Estimating the number of undetected COVID-19 cases among travellers from mainland China, Wellcome Open Research, Vol: 5, Pages: 143-143
<ns4:p><ns4:bold>Background:</ns4:bold> As of August 2021, every region of the world has been affected by the COVID-19 pandemic, with more than 196,000,000 cases worldwide.</ns4:p><ns4:p> <ns4:bold>Methods: </ns4:bold>We analysed COVID-19 cases among travellers from mainland China to different regions and countries, comparing the region- and country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different regions and countries.</ns4:p><ns4:p> <ns4:bold>Results: </ns4:bold>Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that up to 70% (95% CI: 54% - 80%) of imported cases could remain undetected relative to the sensitivity of surveillance in Singapore. The percentage of undetected imported cases rises to 75% (95% CI 66% - 82%) when comparing to the surveillance sensitivity in multiple countries.</ns4:p><ns4:p> <ns4:bold>Conclusions: </ns4:bold>Our analysis shows that a large number of COVID-19 cases remain undetected across the world.<ns4:bold> </ns4:bold>These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China.</ns4:p>
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
Cox V, ODriscoll M, Imai N, et al., 2021, Estimating dengue transmission intensity from serological data: a comparative analysis using mixture and catalytic models
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Dengue virus (DENV) infection is a global health concern of increasing magnitude. To target intervention strategies, accurate estimates of the force of infection (FOI) are necessary. Catalytic models have been widely used to estimate DENV FOI and rely on a binary classification of serostatus as seropositive or seronegative, according to pre-defined antibody thresholds. Previous work has demonstrated the use of thresholds can cause serostatus misclassification and biased estimates. In contrast, mixture models do not rely on thresholds and use the full distribution of antibody titres. To date, there has been limited application of mixture models to estimate DENV FOI.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We compare the application of mixture models and time-constant and time-varying catalytic models to simulated data and to serological data collected in Vietnam from 2004 to 2009 (N ≥ 2178) and Indonesia in 2014 (N = 3194).</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The simulation study showed greater estimate bias from the time-constant and time-varying catalytic models (FOI bias = 1.3% (0.05%, 4.6%) and 2.3% (0.06%, 7.8%), seroprevalence bias = 3.1% (0.25%, 9.4%) and 2.9% (0.26%, 8.7%), respectively) than from the mixture model (FOI bias = 0.41% (95% CI 0.02%, 2.7%), seroprevalence bias = 0.11% (0.01%, 3.6%)). When applied to real data from Vietnam, the mixture model frequently produced higher FOI and seroprevalence estimates than the catalytic models.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Our results suggest mixture models represent valid, potentially less biased, alternatives to catalytic models, which could be particularly useful when estimating
Bhatia S, Parag K, Wardle J, et al., 2021, Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment
<jats:title>Abstract</jats:title> <jats:p>From 8th March to 29th November 2020, we produced weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for 81 countries with evidence of sustained transmission. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. We evaluated the robustness of the forecasts using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3\% and 45.6\% of the observations lying in the 50\% Credible Interval in 1-week and 4-week ahead forecasts respectively. We could accurately characterise the overall phase of the epidemic up to 4-weeks ahead in 84.9\% of country-days. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.</jats:p>
Bhatia S, Parag KV, Wardle J, et al., 2021, Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>As of July 2021, more than 180,000,000 cases of COVID-19 have been reported across the world, with more than 4 million deaths. Mathematical modelling and forecasting efforts have been widely used to inform policy-making and to create situational awareness.</jats:p></jats:sec><jats:sec><jats:title>Methods and Findings</jats:title><jats:p>From 8<jats:sup>th</jats:sup> March to 29<jats:sup>th</jats:sup> November 2020, we produced weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for countries with evidence of sustained transmission. The estimates and forecasts were based on an ensemble model comprising of three models that were calibrated using only the reported number of COVID-19 cases and deaths in each country. We also developed a novel heuristic to combine weekly estimates of transmissibility and potential changes in population immunity due to infection to produce forecasts over a 4-week horizon. We evaluated the robustness of the forecasts using relative error, coverage probability, and comparisons with null models.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>During the 39-week period covered by this study, we produced short- and medium-term forecasts for 81 countries. Both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. We could accurately characterise the overall phase of the epidemic up to 4-weeks ahead in 84.9% of country-days. The medium-term forecasts can be used in conjunction with the short-term fo
Imai N, Hogan AB, Williams L, et al., 2021, Interpreting estimates of coronavirus disease 2019 (COVID-19) vaccine efficacy and effectiveness to inform simulation studies of vaccine impact: a systematic review, Wellcome Open Research, Vol: 6, Pages: 185-185
<ns3:p><ns3:bold>Background:</ns3:bold> The multiple efficacious vaccines authorised for emergency use worldwide represent the first preventative intervention against coronavirus disease 2019 (COVID-19) that does not rely on social distancing measures. The speed at which data are emerging and the heterogeneities in study design, target populations, and implementation make it challenging to interpret and assess the likely impact of vaccine campaigns on local epidemics. We reviewed available vaccine efficacy and effectiveness studies to generate working estimates that can be used to parameterise simulation studies of vaccine impact.</ns3:p><ns3:p> <ns3:bold>Methods:</ns3:bold> We searched MEDLINE, the World Health Organization’s Institutional Repository for Information Sharing, medRxiv, and vaccine manufacturer websites for studies that evaluated the emerging data on COVID-19 vaccine efficacy and effectiveness. Studies providing an estimate of the efficacy or effectiveness of a COVID-19 vaccine using disaggregated data against SARS-CoV-2 infection, symptomatic disease, severe disease, death, or transmission were included. We extracted information on study population, variants of concern (VOC), vaccine platform, dose schedule, study endpoints, and measures of impact. We applied an evidence synthesis approach to capture a range of plausible and consistent parameters for vaccine efficacy and effectiveness that can be used to inform and explore a variety of vaccination strategies as the COVID-19 pandemic evolves.</ns3:p><ns3:p> <ns3:bold>Results:</ns3:bold> Of the 602 articles and reports identified, 53 were included in the analysis. The availability of vaccine efficacy and effectiveness estimates varied by vaccine and were limited for VOCs. Estimates for non-primary endpoints such as effectiveness against infection and onward transmission were sparse. Synthesised estimates were relatively consistent
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.
Gaythorpe K, Bhatia S, Mangal T, et al., 2021, Children’s role in the COVID-19 pandemic: a systematic review of early surveillance data on susceptibility, severity, and transmissibility, Scientific Reports, Vol: 11, Pages: 1-14, ISSN: 2045-2322
Background: SARS-CoV-2 infections have been reported in all age groups including infants, children, and adolescents. However, the role of children in the COVID-19 pandemic is still uncertain. This systematic review of early studies synthesises evidence on the susceptibility of children to SARS-CoV-2 infection, the severity and clinical outcomes in children with SARS-CoV-2 infection, and the transmissibility of SARS-CoV-2 by children in the early phases of the COVID-19 pandemic. Methods and findings: A systematic literature review was conducted in PubMed. Reviewers extracted data from relevant, peer-reviewed studies published up to July 4th 2020 during the first wave of the SARS-CoV-2 outbreak using a standardised form and assessed quality using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. For studies included in the meta-analysis, we used a random effects model to calculate pooled estimates of the proportion of children considered asymptomatic or in a severe or critical state. We identified 2,775 potential studies of which 128 studies met our inclusion criteria; data were extracted from 99, which were then quality assessed. Finally, 29 studies were considered for the meta-analysis that included information of symptoms and/or severity, these were further assessed based on patient recruitment. Our pooled estimate of the proportion of test positive children who were asymptomatic was 21.1% (95% CI: 14.0 - 28.1%), based on 13 included studies, and the proportion of children with severe or critical symptoms was 3.8% (95% CI: 1.5 - 6.0%), based on 14 included studies. We did not identify any studies designed to assess transmissibility in children and found that susceptibility to infection in children was highly variable across studies.Conclusions: Children’s susceptibility to infection and onward transmissibility relative to adults is still unclear and varied widely between studies. However, it is evident that most children e
Ferguson NM, Laydon D, Nedjati-Gilani G, et al., 2021, COVID-19 and potential global mortality - Revisited (Retraction of Vol 144, art no 105054, 2020), EARLY HUMAN DEVELOPMENT, Vol: 156, ISSN: 0378-3782
Watson O, Alhaffar M, Mehchy Z, et al., 2021, Leveraging community mortality indicators to infer COVID-19 mortality and transmission dynamics in Damascus, Syria, Nature Communications, Vol: 12, Pages: 1-10, ISSN: 2041-1723
The COVID-19 pandemic has resulted in substantial mortality worldwide. However, to date, countries in the Middle East and Africa have reported considerably lower mortality rates than in Europe and the Americas. Motivated by reports of an overwhelmed health system, we estimate the likely under-ascertainment of COVID-19 mortality in Damascus, Syria. Using all-cause mortality data, we fit a mathematical model of COVID-19 transmission to reported mortality, estimating that 1.25% of COVID-19 deaths (sensitivity range 1.00% – 3.00%) have been reported as of 2 September 2020. By 2 September, we estimate that 4,380 (95% CI: 3,250 – 5,550) COVID-19 deaths in Damascus may have been missed, with 39.0% (95% CI: 32.5% – 45.0%) of the population in Damascus estimated to have been infected. Accounting for under-ascertainment corroborates reports of exceeded hospital bed capacity and is validated by community-uploaded obituary notifications, which confirm extensive unreported mortality in Damascus.
Cracknell Daniels B, Gaythorpe K, Imai N, et al., 2021, Yellow fever in Asia - a risk analysis, Journal of Travel Medicine, Vol: 28, ISSN: 1195-1982
BackgroundThere is concern about the risk of yellow fever (YF) establishment in Asia, owing to rising numbers of urban outbreaks in endemic countries and globalisation. Following an outbreak in Angola in 2016, YF cases were introduced into China. Prior to this, YF had never been recorded in Asia, despite climatic suitability and the presence of mosquitoes. An outbreak in Asia could result in widespread fatalities and huge economic impact. Therefore, quantifying the potential risk of YF outbreaks in Asia is a public health priority.MethodsUsing international flight data and YF incidence estimates from 2016, we quantified the risk of YF introduction via air travel into Asia. In locations with evidence of a competent mosquito population, the potential for autochthonous YF transmission was estimated using a temperature-dependent model of the reproduction number and a branching process model assuming a negative binomial distribution.ResultsIn total, 25 cities across Asia were estimated to be at risk of receiving at least one YF viraemic traveller during 2016. At their average temperatures, we estimated the probability of autochthonous transmission to be <50% in all cities, which was primarily due to the limited number of estimated introductions that year.ConclusionDespite the rise in air travel, we found low support for travel patterns between YF endemic countries and Asia resulting in autochthonous transmission during 2016. This supports the historic absence of YF in Asia and suggests it could be due to a limited number of introductions in previous years. Future increases in travel volumes or YF incidence can increase the number of introductions and the risk of autochthonous transmission. Given the high proportion of asymptomatic or mild infections and the challenges of YF surveillance, our model can be used to estimate the introduction and outbreak risk and can provide useful information to surveillance systems.
van Elsland S, Imai N, on behalf of the Imperial College COVID-19 Response Team, 2021, Imperial College COVID-19 Response Team 2020-2021 Report, Publisher: Imperial College London
Unwin HJT, Cori A, Imai N, et al., 2021, Using next generation matrices to estimate the proportion of infections that are not detected in an outbreak
<jats:p>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%) but depending on assumptions 39.0% or 37.7% of Ebola infections were not detected in Guinea (95% credible intervals: 1.69 – 87.0% or 1.7 – 80.9%).</jats:p>
Gaythorpe K, Morris A, Imai N, et al., 2021, chainchecker: an application to visualise and explore transmission chains for Ebola virus disease, PLoS One, ISSN: 1932-6203
Nouvellet P, Bhatia S, Cori A, et al., 2021, Reduction in mobility and COVID-19 transmission, Nature Communications, Vol: 12, ISSN: 2041-1723
In response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts.Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world.Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27-77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49-91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12-48%]) post-relaxation.In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.
Fu H, Wang H, Xi X, et al., 2021, A database for the epidemic trends and control measures during the first wave of COVID-19 in mainland China, International Journal of Infectious Diseases, Vol: 102, Pages: 463-471, ISSN: 1201-9712
Objectives: This data collation effort aims to provide a comprehensive database to describe the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19)across main provinces in China. Methods: From mid-January to March 2020, we extracted publicly available data on the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted a descriptive analysis of the epidemics in the six most-affected provinces. Results: School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends were different across provinces. Compared to Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as local transmission of COVID-19 declined, switching the focus of measures to testing and quarantine of inbound travellers could help to sustain the control of the epidemic. Conclusions: Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database with these indicators and information on control measures provides useful source for exploring further research and policy planning for response to the COVID-19 epidemic.
Knock E, Whittles L, Lees J, et al., 2020, Report 41: The 2020 SARS-CoV-2 epidemic in England: key epidemiological drivers and impact of interventions
England has been severely affected by COVID-19. We fitted a model of SARS-CoV-2 transmission in care homes and the community to regional 2020 surveillance data. Only national lockdown brought the reproduction number below 1 consistently; introduced one week earlier in the first wave it could have reduced mortality by 23,300 deaths on average. The mean infection fatality ratio was initially ~1.3% across all regions except London and halved following clinical care improvements. The infection fatality ratio was two-fold lower throughout in London, even when adjusting for demographics. The infection fatality ratio in care homes was 2.5-times that in the elderly in the community. Population-level infection-induced immunity in England is still far from herd immunity, with regional mean cumulative attack rates ranging between 4.4% and 15.8%.
Unwin H, Mishra S, Bradley V, et al., 2020, State-level tracking of COVID-19 in the United States, Nature Communications, Vol: 11, Pages: 1-9, ISSN: 2041-1723
As of 1st June 2020, the US Centers for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available deathdata within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on therate of transmission of SARS-CoV-2. We estimate thatRtwas only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%-4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals.
Grassly NC, Pons-Salort M, Parker EPK, et al., 2020, Comparison of molecular testing strategies for COVID-19 control: a mathematical modelling study, Lancet Infectious Diseases, Vol: 20, Pages: 1381-1389, ISSN: 1473-3099
BACKGROUND: WHO has called for increased testing in response to the COVID-19 pandemic, but countries have taken different approaches and the effectiveness of alternative strategies is unknown. We aimed to investigate the potential impact of different testing and isolation strategies on transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: We developed a mathematical model of SARS-CoV-2 transmission based on infectiousness and PCR test sensitivity over time since infection. We estimated the reduction in the effective reproduction number (R) achieved by testing and isolating symptomatic individuals, regular screening of high-risk groups irrespective of symptoms, and quarantine of contacts of laboratory-confirmed cases identified through test-and-trace protocols. The expected effectiveness of different testing strategies was defined as the percentage reduction in R. We reviewed data on the performance of antibody tests reported by the Foundation for Innovative New Diagnostics and examined their implications for the use of so-called immunity passports. FINDINGS: If all individuals with symptoms compatible with COVID-19 self-isolated and self-isolation was 100% effective in reducing onwards transmission, self-isolation of symptomatic individuals would result in a reduction in R of 47% (95% uncertainty interval [UI] 32-55). PCR testing to identify SARS-CoV-2 infection soon after symptom onset could reduce the number of individuals needing to self-isolate, but would also reduce the effectiveness of self-isolation (around 10% would be false negatives). Weekly screening of health-care workers and other high-risk groups irrespective of symptoms by use of PCR testing is estimated to reduce their contribution to SARS-CoV-2 transmission by 23% (95% UI 16-40), on top of reductions achieved by self-isolation following symptoms, assuming results are available at 24 h. The effectiveness of test and trace depends strongly on coverage and the timelines
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