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1.
SSRN; 2022.
Preprint in English | SSRN | ID: ppcovidwho-333480

ABSTRACT

Background: Paediatric Multisystem Inflammatory Syndrome (PIMS-TS) is a rare life-threatening complication that typically occurs several weeks after SARS-CoV-2 infection in children and young people (CYP). We used national and regional-level data from the COVID-19 pandemic wave in England to develop and optimise a model to predict PIMS-TS cases in subsequent waves. Methods: SARS-CoV-2 infections in CYP aged 0-15 years in England were estimated using the PHE-Cambridge real-time model. PIMS-TS cases were identified through the British Paediatric Surveillance Unit during the first pandemic wave (March-June 2020). Since November 2020, cases were identified through Secondary Uses Services (SUS), a national healthcare activity dataset. A predictive model was developed to estimate PIMS-TS risk and lag times after SARS-CoV-2 infection for the Alpha (weeks 1-10, 2021) and Delta (weeks 22-30, 2022) waves. Findings: During the Alpha wave, the model accurately predicted PIMS-TS cases (506 (95% CI: 491-531) vs 502 observed cases), with a median estimated the risk of 0·038% (IQR, 0·037-0·041%;38/100,000 infections) of paediatric SARS-CoV-2 infections. For the Delta wave, the median risk of PIMS-TS was significantly lower at 0·026% (IQR, 0·025-0·029%;27/100,000 infections) , with 212 observed PIMS-TS cases compared to 450 predicted by the model during June-October 2021. Interpretation: We developed a model that accurately predicted national and regional PIMS-TS cases in CYP during the Alpha wave. PIMS-TS cases were, however, 53% lower than predicted during the Delta wave. Further studies are needed to understand the mechanisms of the observed lower risk with the Delta variant.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-318473

ABSTRACT

During an infectious disease outbreak, biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy. Motivated by the ongoing response to COVID-19, we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions. In particular, we focus on parameter estimation in the presence of known biases in the data, and the effect of non-pharmaceutical interventions in enclosed subpopulations, such as households and care homes. We illustrate these methods by applying them to the COVID-19 pandemic.

3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-307507

ABSTRACT

Background: Paediatric Multisystem Inflammatory Syndrome (PIMS-TS) is a rare life-threatening complication that occurs in some children two to four weeks after SARS-CoV-2 infection. Although the precise causal mechanisms underpinning the relationship between SARS-CoV-2 and PIMS-TS are unclear, several recent studies have confirmed a strong temporal association. This study provides further evidence in support of a causal and temporal link. A novel methodology is presented whereby PIMS-TS incidence parameters estimated from data published on SARS-CoV-2 in the first wave of the COVID-19 pandemic in England were used to make accurate projections of PIMS-TS cases in the second wave. Methods: Case classifications and data on PIMS-TS cases were obtained from the British Paediatric Surveillance Unit (BPSU) in an endeavour initiated by Public Heath England (PHE). The dataset contained all PIMS-TS cases presenting as symptomatic in England in the first wave of the pandemic. PIMS-TS incidence rates in children aged <15 years were estimated for the first wave and expressed as a fraction of SARS-CoV-2 cases. Data on SARS-CoV-2 cases were extracted from the PHE-Cambridge real-time model. Temporal analysis was performed to estimate the lag-time between peak SARS-CoV-2 incidence and peak PIMS-TS. The incidence and lag-time parameters estimated during the first wave were used to produce weekly projections of PIMS-TS cases in the second wave. These projections were then employed operationally in a clinical setting. Statistical analyses were performed to assess the accuracy of the forecasts once data on PIMS-TS cases were published by the BPSU approximately three months after the PIMS-TS forecasts were generated. Findings: Statistical analyses show that the PIMS-TS parameters estimated from the first wave produced accurate projections of PIMS-TS incidence in the second wave. Results at the aggregated national level showed there were no statistically significant differences observed between the PIMS-TS admission data and forecasts in England. Forecasts generated at the disaggregated regional level were also accurate, with no statistically significant differences observed between the PIMS-TS admissions data and forecasts in five of the nine Public Health England Centres (PHECs). However, a statistically significant divergence was observed between the PIMS-TS admissions data and the second wave forecasts in the regions of London and in the East, North West, and South West of England.Interpretation: This study provides further evidence in support of a causal and temporal association between SARS-CoV-2 and PIMS-TS, since data on SARS-CoV-2 incidence in the first wave of the COVID-19 pandemic in England have been shown to be a good baseline from which to generate forecasts of PIMS-TS incidence in the second wave, at both aggregated national and disaggregated regional levels.Funding Information: : Department of Health and Social Care (DHSC) Grant-in-aid funding to Public Health England (PHE).Declaration of Interests: None;this study did not receive any specific grant funding from external agencies in the public, commercial or not-for-profit sectors.Ethics Approval Statement: : PHE has legal permission under Regulation 3 of The Health Service (Control of Patient Information) Regulations 2002, to conduct national surveillance of communicable diseases in England and, as such, individual patient consent is not required. Public Health Wales, through the established order legislation, is required to conduct surveillance of communicable diseases in Wales and, as such, individual patient consent is not required. The surveillance protocol was approved by the Public Benefit and Privacy Panel for Health and Social Care in Scotland (Ref: 20210041, 19 May 2020).

4.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-327147

ABSTRACT

In a recent paper, we described how lateral flow device (LFD) testing might be used to reduce the amount of excess time individuals spend in isolation following confirmation of a COVID-19 infection. Through the work presented here, we look to expand upon this and explore in more detail the benefit that such an approach might provide. We use our previously described model to study scenarios through the metrics "proportion released still infectious", "excess time spent in isolation" (time isolated while no longer infectious), and "time spent infectious after early release". We also look to consider the effect on these metrics by comparing values obtained when a single negative LFD test is required for early release, versus requiring two and three sequential negative LFD tests. Results show that jointly employing self-isolation and LFD testing may deliver sizeable reductions to the proportion of individuals being release while still infection, the average amount of excess time spent in isolation by those no longer a public health threat, and the average amount of time spent infectious by those released early. These effects considered in conjunction could provide a considerable decrease in the public health risk posed by still infectious individuals being released back into the population by actively monitoring their infection status throughout their isolation period. Such an approach could also help lighten the impact incurred on the individual by reducing the amount of time spent in isolation while posing no further public health risk, in addition to alleviating pressures on the economy and in healthcare settings caused by mass isolation in times of high prevalence.

5.
Clin Infect Dis ; 74(3): 407-415, 2022 02 11.
Article in English | MEDLINE | ID: covidwho-1684538

ABSTRACT

BACKGROUND: How severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity varies with viral load is incompletely understood. Whether rapid point-of-care antigen lateral flow devices (LFDs) detect most potential transmission sources despite imperfect clinical sensitivity is unknown. METHODS: We combined SARS-CoV-2 testing and contact tracing data from England between 1 September 2020 and 28 February 2021. We used multivariable logistic regression to investigate relationships between polymerase chain reaction (PCR)-confirmed infection in contacts of community-diagnosed cases and index case viral load, S gene target failure (proxy for B.1.1.7 infection), demographics, SARS-CoV-2 incidence, social deprivation, and contact event type. We used LFD performance to simulate the proportion of cases with a PCR-positive contact expected to be detected using 1 of 4 LFDs. RESULTS: In total, 231 498/2 474 066 (9%) contacts of 1 064 004 index cases tested PCR-positive. PCR-positive results in contacts independently increased with higher case viral loads (lower cycle threshold [Ct] values), for example, 11.7% (95% confidence interval [CI] 11.5-12.0%) at Ct = 15 and 4.5% (95% CI 4.4-4.6%) at Ct = 30. B.1.1.7 infection increased PCR-positive results by ~50%, (eg, 1.55-fold, 95% CI 1.49-1.61, at Ct = 20). PCR-positive results were most common in household contacts (at Ct = 20.1, 8.7% [95% CI 8.6-8.9%]), followed by household visitors (7.1% [95% CI 6.8-7.3%]), contacts at events/activities (5.2% [95% CI 4.9-5.4%]), work/education (4.6% [95% CI 4.4-4.8%]), and least common after outdoor contact (2.9% [95% CI 2.3-3.8%]). Contacts of children were the least likely to test positive, particularly following contact outdoors or at work/education. The most and least sensitive LFDs would detect 89.5% (95% CI 89.4-89.6%) and 83.0% (95% CI 82.8-83.1%) of cases with PCR-positive contacts, respectively. CONCLUSIONS: SARS-CoV-2 infectivity varies by case viral load, contact event type, and age. Those with high viral loads are the most infectious. B.1.1.7 increased transmission by ~50%. The best performing LFDs detect most infectious cases.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19 Testing , Child , Family Characteristics , Humans , Viral Load
6.
2021.
Preprint in English | Other preprints | ID: ppcovidwho-294889

ABSTRACT

As the SARS-CoV-2 virus mutates, mutations harboured in patients become increasingly diverse. Patients classified into two strains may have overlapping non-variant-defining mutations. Mutation calling by sequencing is relative to a reference genome . As SARS-CoV-2 mutates, tracking emerging mutant strains may become increasingly problematic if the reference genome remains Wuhan-Hu-1, because the comparison then becomes indirect : current dominant strain relative to Wuhan-Hu-1 versus emerging strain relative to Wuhan-Hu-1. The original Thermo Fisher’s TaqPath PCR test, on which the UK has standardized national testing of SARS-CoV-2 primarily, targets Wuhan-Hu-1. PCR targets appear readily updated, as TaqPath 2.0 now targets both currently known and future SARS-CoV-2 mutations, probing the N gene and ORF1ab but not the S gene, with 8 probes instead of the original 3 probes. Going forward, our statistical method can more directly compare current wildtype versus emerging mutants, since our new method can use any pair of probes updated to probe the current wildtype and anticipated mutations. The fact that patients harbour mixtures of mutations allows our statistical methods to potentially catch emerging mutants. Given a PCR test which targets the current dominant strain (current wildtype), our statistical method can potentially directly differentiate the current wildtype from an emerging strain.

7.
Lancet Respir Med ; 9(12): 1450-1466, 2021 12.
Article in English | MEDLINE | ID: covidwho-1483032

ABSTRACT

Many nations are pursuing the rollout of SARS-CoV-2 vaccines as an exit strategy from unprecedented COVID-19-related restrictions. However, the success of this strategy relies critically on the duration of protective immunity resulting from both natural infection and vaccination. SARS-CoV-2 infection elicits an adaptive immune response against a large breadth of viral epitopes, although the duration of the response varies with age and disease severity. Current evidence from case studies and large observational studies suggests that, consistent with research on other common respiratory viruses, a protective immunological response lasts for approximately 5-12 months from primary infection, with reinfection being more likely given an insufficiently robust primary humoral response. Markers of humoral and cell-mediated immune memory can persist over many months, and might help to mitigate against severe disease upon reinfection. Emerging data, including evidence of breakthrough infections, suggest that vaccine effectiveness might be reduced significantly against emerging variants of concern, and hence secondary vaccines will need to be developed to maintain population-level protective immunity. Nonetheless, other interventions will also be required, with further outbreaks likely to occur due to antigenic drift, selective pressures for novel variants, and global population mobility.


Subject(s)
COVID-19 Vaccines/administration & dosage , COVID-19 , Immunologic Memory , COVID-19/immunology , COVID-19/prevention & control , Humans , Reinfection , SARS-CoV-2 , Vaccination
8.
Philos Trans R Soc Lond B Biol Sci ; 376(1829): 20200279, 2021 07 19.
Article in English | MEDLINE | ID: covidwho-1309696

ABSTRACT

England has been heavily affected by the SARS-CoV-2 pandemic, with severe 'lockdown' mitigation measures now gradually being lifted. The real-time pandemic monitoring presented here has contributed to the evidence informing this pandemic management throughout the first wave. Estimates on the 10 May showed lockdown had reduced transmission by 75%, the reproduction number falling from 2.6 to 0.61. This regionally varying impact was largest in London with a reduction of 81% (95% credible interval: 77-84%). Reproduction numbers have since then slowly increased, and on 19 June the probability of the epidemic growing was greater than 5% in two regions, South West and London. By this date, an estimated 8% of the population had been infected, with a higher proportion in London (17%). The infection-to-fatality ratio is 1.1% (0.9-1.4%) overall but 17% (14-22%) among the over-75s. This ongoing work continues to be key to quantifying any widespread resurgence, should accrued immunity and effective contact tracing be insufficient to preclude a second wave. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.


Subject(s)
COVID-19/epidemiology , Models, Statistical , Pandemics , SARS-CoV-2/pathogenicity , Basic Reproduction Number/statistics & numerical data , COVID-19/transmission , COVID-19/virology , Communicable Disease Control/trends , Contact Tracing/trends , England/epidemiology , Forecasting , Humans , London/epidemiology
9.
Philos Trans R Soc Lond B Biol Sci ; 376(1829): 20200269, 2021 07 19.
Article in English | MEDLINE | ID: covidwho-1309688

ABSTRACT

The number of COVID-19 outbreaks reported in UK care homes rose rapidly in early March of 2020. Owing to the increased co-morbidities and therefore worse COVID-19 outcomes for care home residents, it is important that we understand this increase and its future implications. We demonstrate the use of an SIS model where each nursing home is an infective unit capable of either being susceptible to an outbreak (S) or in an active outbreak (I). We use a generalized additive model to approximate the trend in growth rate of outbreaks in care homes and find the fit to be improved in a model where the growth rate is proportional to the number of current care home outbreaks compared with a model with a constant growth rate. Using parameters found from the outbreak-dependent growth rate, we predict a 73% prevalence of outbreaks in UK care homes without intervention as a reasonable worst-case planning assumption. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.


Subject(s)
COVID-19/epidemiology , Pandemics , SARS-CoV-2/pathogenicity , Aged , COVID-19/virology , Cost of Illness , Female , Humans , Male , Nursing Homes/statistics & numerical data , United Kingdom/epidemiology
10.
Clin Infect Dis ; 74(3): 407-415, 2022 02 11.
Article in English | MEDLINE | ID: covidwho-1223330

ABSTRACT

BACKGROUND: How severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity varies with viral load is incompletely understood. Whether rapid point-of-care antigen lateral flow devices (LFDs) detect most potential transmission sources despite imperfect clinical sensitivity is unknown. METHODS: We combined SARS-CoV-2 testing and contact tracing data from England between 1 September 2020 and 28 February 2021. We used multivariable logistic regression to investigate relationships between polymerase chain reaction (PCR)-confirmed infection in contacts of community-diagnosed cases and index case viral load, S gene target failure (proxy for B.1.1.7 infection), demographics, SARS-CoV-2 incidence, social deprivation, and contact event type. We used LFD performance to simulate the proportion of cases with a PCR-positive contact expected to be detected using 1 of 4 LFDs. RESULTS: In total, 231 498/2 474 066 (9%) contacts of 1 064 004 index cases tested PCR-positive. PCR-positive results in contacts independently increased with higher case viral loads (lower cycle threshold [Ct] values), for example, 11.7% (95% confidence interval [CI] 11.5-12.0%) at Ct = 15 and 4.5% (95% CI 4.4-4.6%) at Ct = 30. B.1.1.7 infection increased PCR-positive results by ~50%, (eg, 1.55-fold, 95% CI 1.49-1.61, at Ct = 20). PCR-positive results were most common in household contacts (at Ct = 20.1, 8.7% [95% CI 8.6-8.9%]), followed by household visitors (7.1% [95% CI 6.8-7.3%]), contacts at events/activities (5.2% [95% CI 4.9-5.4%]), work/education (4.6% [95% CI 4.4-4.8%]), and least common after outdoor contact (2.9% [95% CI 2.3-3.8%]). Contacts of children were the least likely to test positive, particularly following contact outdoors or at work/education. The most and least sensitive LFDs would detect 89.5% (95% CI 89.4-89.6%) and 83.0% (95% CI 82.8-83.1%) of cases with PCR-positive contacts, respectively. CONCLUSIONS: SARS-CoV-2 infectivity varies by case viral load, contact event type, and age. Those with high viral loads are the most infectious. B.1.1.7 increased transmission by ~50%. The best performing LFDs detect most infectious cases.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19 Testing , Child , Family Characteristics , Humans , Viral Load
11.
Lancet Reg Health Eur ; 3: 100075, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1144857

ABSTRACT

BACKGROUND: Paediatric Multisystem Inflammatory Syndrome temporally associated with SARS-CoV-2 (PIMS-TS), first identified in April 2020, shares features of both Kawasaki disease (KD) and toxic shock syndrome (TSS). The surveillance describes the epidemiology and clinical characteristics of PIMS-TS in the United Kingdom and Ireland. METHODS: Public Health England initiated prospective national surveillance of PIMS-TS through the British Paediatric Surveillance Unit. Paediatricians were contacted monthly to report PIMS-TS, KD and TSS cases electronically and complete a detailed clinical questionnaire. Cases with symptom onset between 01 March and 15 June 2020 were included. FINDINGS: There were 216 cases with features of PIMS-TS alone, 13 with features of both PIMS-TS and KD, 28 with features of PIMS-TS and TSS and 11 with features of PIMS-TS, KD and TSS, with differences in age, ethnicity, clinical presentation and disease severity between the phenotypic groups. There was a strong geographical and temporal association between SARS-CoV-2 infection rates and PIMS-TS cases. Of those tested, 14.8% (39/264) children had a positive SARS-CoV-2 RT-PCR, and 63.6% (75/118) were positive for SARS-CoV-2 antibodies. In total 44·0% (118/268) required intensive care, which was more common in cases with a TSS phenotype. Three of five children with cardiac arrest had TSS phenotype. Three children (1·1%) died. INTERPRETATION: The strong association between SARS-CoV-2 infection and PIMS-TS emphasises the importance of maintaining low community infection rates to reduce the risk of this rare but severe complication in children and adolescents. Close follow-up will be important to monitor long-term complications in children with PIMS-TS. FUNDING: PHE.

12.
Cell ; 184(8): 2201-2211.e7, 2021 04 15.
Article in English | MEDLINE | ID: covidwho-1086820

ABSTRACT

SARS-CoV-2 has caused over 2 million deaths in little over a year. Vaccines are being deployed at scale, aiming to generate responses against the virus spike. The scale of the pandemic and error-prone virus replication is leading to the appearance of mutant viruses and potentially escape from antibody responses. Variant B.1.1.7, now dominant in the UK, with increased transmission, harbors 9 amino acid changes in the spike, including N501Y in the ACE2 interacting surface. We examine the ability of B.1.1.7 to evade antibody responses elicited by natural SARS-CoV-2 infection or vaccination. We map the impact of N501Y by structure/function analysis of a large panel of well-characterized monoclonal antibodies. B.1.1.7 is harder to neutralize than parental virus, compromising neutralization by some members of a major class of public antibodies through light-chain contacts with residue 501. However, widespread escape from monoclonal antibodies or antibody responses generated by natural infection or vaccination was not observed.


Subject(s)
Antibodies, Monoclonal/immunology , Antibodies, Neutralizing/immunology , Antibodies, Viral/immunology , COVID-19/immunology , SARS-CoV-2/immunology , Spike Glycoprotein, Coronavirus/immunology , Animals , Antibodies, Neutralizing/blood , Antibodies, Viral/blood , CHO Cells , COVID-19/epidemiology , Chlorocebus aethiops , Cricetulus , HEK293 Cells , Humans , Pandemics , Protein Binding , Structure-Activity Relationship , Vero Cells
14.
Infect Dis Model ; 5: 409-441, 2020.
Article in English | MEDLINE | ID: covidwho-632576

ABSTRACT

During an infectious disease outbreak, biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy. Motivated by the ongoing response to COVID-19, we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions. In particular, we focus on parameter estimation in the presence of known biases in the data, and the effect of non-pharmaceutical interventions in enclosed subpopulations, such as households and care homes. We illustrate these methods by applying them to the COVID-19 pandemic.

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