CMMID Repository

Test to release from isolation after testing positive for SARS-CoV-2

2021-12-23T00:00:00+00:00

Figure: Comparison of policy outcomes for vaccinated populations. A) Number of days saved vs. a 10-day isolation policy per individual and B) days infectious in the community per 10,000 infected individuals following release from isolation for 3, 5, and 7 days wait after an initial positive test to initiate testing and number of consecutive days of negative tests required for release. Points indicate median and error bars represent the 95% uncertainty interval. For days saved, the first day testing positive is considered the minimum mandatory isolation, so for example, a “3 day wait” is equal to 1 day + 3 days wait to then test again on day 4.

Summary

The rapid spread and high transmissibility of the Omicron variant of SARS-CoV-2 is likely to lead to a significant number of key workers testing positive simultaneously.
Under a policy of self-isolation after testing positive, this may lead to extreme staffing shortfalls at the same time as e.g. hospital admissions are peaking.
Using a model of individual infectiousness and testing with lateral flow tests (LFT), we evaluate test-to-release policies against conventional fixed-duration isolation policies in terms of excess days of infectiousness, days saved, and tests used.
We find that the number of infectious days in the community can be reduced to almost zero by requiring at least 2 consecutive days of negative tests, regardless of the number of days’ wait until testing again after initially testing positive.
On average, a policy of fewer days’ wait until initiating testing (e.g 3 or 5 days) results in more days saved vs. a 10-day isolation period, but also requires a greater number of tests.
Due to a lack of specific data on viral load progression, infectivity, and likelihood of testing positive by LFT over the course of an Omicron infection, we assume the same parameters as for pre-Omicron variants and explore the impact of a possible shorter proliferation phase.

Read the pre-print here. Accompanying code can be found here.

Modelling the potential consequences of the Omicron SARS-CoV-2 variant in England

2021-12-11T00:00:00+00:00

We model the potential consequences of the Omicron SARS-CoV-2 variant on transmission and health outcomes in England, with scenarios varying the extent of immune escape; the effectiveness, uptake and speed of COVID-19 booster vaccinations; and the reintroduction of control measures. These results suggest that Omicron has the potential to cause substantial surges in cases, hospital admissions and deaths in populations with high levels of immunity, including England. The reintroduction of additional non-pharmaceutical interventions may be required to prevent hospital admissions exceeding the levels seen in England during the previous peak in winter 2020–2021.

Read the full report:

23rd Dec 2021: Updated version 2 of report.

16th Dec 2021: Preprint on medRxiv.

11th Dec 2021: Original report.

Population disruption: estimating changes in population distribution in the UK during the COVID-19 pandemic

2021-06-22T00:00:00+00:00

Background

Mobility data have demonstrated major changes in human movement patterns in response to COVID-19 and associated interventions in many countries. This can involve sub-national redistribution, short-term relocations as well as international migration.

Methods

In this paper, we combine detailed location data from Facebook measuring the location of approximately 6 million daily active Facebook users in 5km² tiles in the UK with census-derived population estimates to measure population mobility and redistribution. We provide time-varying population estimates and assess spatial population changes with respect to population density and four key reference dates in 2020 (First lockdown, End of term, Beginning of term, Christmas).

Results

We show how the timing and magnitude of observed population changes can impact the size of epidemics using a deterministic model of COVID-19 transmission. We estimate that between March 2020 and March 2021, the total population of the UK has declined and we identify important spatial variations in this population change, showing that low-density areas have experienced lower population decreases than urban areas. We estimate that, for the top 10% highest population tiles, the population has decreased by 6.6%. Further, we provide evidence that geographic redistributions of population within the UK coincide with dates of non-pharmaceutical interventions including lockdowns and movement restrictions, as well as seasonal patterns of migration around holiday dates.

Conclusions

The methods used in this study reveal significant changes in population distribution at high spatial and temporal resolutions that have not previously been quantified by available demographic surveys in the UK.

Read the pre-print here. Population estimates for 2019 Local Authority Districts are available here.

Model fitting of early 2020 increase in burials in Mogadishu (Somalia) suggests possible early introduction of SARS-CoV-2

2021-06-17T00:00:00+00:00

Background

In countries with weak surveillance systems confirmed COVID-19 deaths are likely to underestimate the death toll of the pandemic. Many countries also have incomplete vital registration systems, hampering excess mortality estimation, necessitating the use of alternative data sources of mortality. We obtained satellite imagery data of the main cemeteries of Mogadishu (Somalia), which showed a sustained rise in burials above the pre-pandemic baseline in the period February-July 2020. We fitted a dynamic transmission model to this indirect measure of excess mortality to estimate the date of introduction and transmissibility of SARS-CoV-2, as well as the effect of non-pharmaceutical interventions in this low-income, crisis-affected setting.

Methods

We performed Markov chain Monte Carlo (MCMC) fitting with an age-structured compartmental COVID-19 model to provide median estimates and credible intervals for the date of introduction, the basic reproduction number (R₀) and the effect of non-pharmaceutical interventions in Mogadishu up to September 2020.

Results

Under the assumption that excess deaths in Mogadishu February-September 2020 were directly attributable to SARS-CoV-2 infection we arrived at median estimates of October-November 2019 for the date of introduction and low R₀ estimates (1.3-1.5) stemming from the early and slow rise of excess deaths and their long plateau. The effect of control measures on transmissibility appeared small or moderate (below 30%).

Conclusions

Subject to study assumptions, a very early SARS-CoV-2 introduction event may have occurred in Somalia. Estimated transmissibility in the first epidemic wave was lower than observed in European settings.

a. Quality of fits for different infection fatality ratios and seed sizes
Goodness of fit as measured by DIC (deviance information criterion) at different values of seed size and population-wide IFR. The labels above the colored lines show median estimates for R0 and the date of introduction, and the NPI-induced reduction in transmissibility during the first NPI period below.
b. Estimates of date of introduction, R0 and scaling factor for NPI stringency index
Median values and credible intervals for the fitting parameters (introduction date, NPI_scale, R0) and quality of fits at different assumed values of the infection fatality ratio (x-axis) and seed size (colors). In the top panel, labels below the lines show median estimates of the date of introduction. Shaded areas around the median (black) are 50% (darker) and 95% credible intervals.

Simulated deaths compared to burial data
Dynamics generated by sampling the posterior distributions of fitting parameters, at a seed size of 200 and four IFR values from 0.15% to 1.13%. The best fit (lowest DIC value) is at IFR=0.36%. The dashed black line and circles show the daily number of excess burials. Only the period from 23 February to 24 August was used for fitting.

Read the pre-print here. Accompanying code can be found here.

Quarantine and testing strategies to reduce transmission risk from imported SARS-CoV-2 infections: a global modelling study

2021-06-11T00:00:00+00:00

Figure: Change in R_s of infectious arrivals entering the community compared to symptomatic self-isolation only with full adherence (top row of plots) or adherence values from literature (28% of individuals adhering to quarantine and 86% adhering to post-positive test isolation, bottom row of plots), and with or without pre-flight tests. A) Quarantine of varying durations with or without testing with LFTs and PCR. B) Daily testing without quarantine with lateral flow tests, with self-isolation only upon a positive test result. Vertical lines represent 95% (outer) and 50% (inner) uncertainty intervals around medians (points). Note discrete x-axis values for quarantine duration and number of days of testing.

Background: Many countries require incoming air travellers to quarantine on arrival and/or undergo testing to limit importation of SARS-CoV-2.

Methods: We developed mathematical models of SARS-CoV-2 viral load trajectories over the course of infection to assess the effectiveness of quarantine and testing strategies. We consider the utility of pre and post-flight Polymerase Chain Reaction (PCR) and lateral flow testing (LFT) to reduce transmission risk from infected arrivals and to reduce the duration of, or replace, quarantine. We also estimate the effect of each strategy relative to domestic incidence, and limits of achievable risk reduction, for 99 countries where flight data and case numbers are estimated.

Results: We find that LFTs immediately pre-flight are more effective than PCR tests 3 days before departure in decreasing the number of departing infectious travellers. Pre-flight LFTs and post-flight quarantines, with tests to release, may prevent the majority of transmission from infectious arrivals while reducing the required duration of quarantine; a pre-flight LFT followed by 5 days in quarantine with a test to release would reduce the expected number of secondary cases generated by an infected traveller compared to symptomatic self-isolation alone, R_s, by 85% (95% UI: 74%, 96%) for PCR and 85% (95% UI: 70%, 96%) for LFT, even assuming imperfect adherence to quarantine (28% of individuals) and self-isolation following a positive test (86%). Under the same adherence assumptions, 5 days of daily LFT testing would reduce R_s by 91% (95% UI: 75%, 98%).

Conclusions: Strategies aimed at reducing the risk of imported cases should be considered with respect to: domestic incidence, transmission, and susceptibility; measures in place to support quarantining travellers; and incidence of new variants of concern in travellers’ origin countries. Daily testing with LFTs for 5 days is comparable to 5 days of quarantine with a test on exit or 14 days with no test.

Read the pre-print here. Accompanying code can be found here.

CoMix - Changes in social contacts as measured by the contact survey during the COVID-19 pandemic in England between March 2020 and March 2021

2021-06-02T00:00:00+00:00

Click here to read our full preprint.

Background

During the COVID-19 pandemic, the UK government imposed public health policies in England to reduce social contacts in hopes of curbing virus transmission. We measured contact patterns weekly from March 2020 to March 2021 to estimate the impact of these policies, covering three national lockdowns interspersed by periods of lower restrictions.

Methods:

Data were collected using online surveys of representative samples of the UK population by age and gender. We calculated the mean daily contacts reported using a (clustered) bootstrap and fitted a censored negative binomial model to estimate age-stratified contact matrices and estimate proportional changes to the basic reproduction number under controlled conditions using the change in contacts as a scaling factor.

Results

The survey recorded 101,350 observations from 19,914 participants who reported 466,710 contacts over 53 weeks. Contact patterns changed over time and by participants’ age, personal risk factors, and perception of risk. The mean of reported contacts among adults have reduced compared to previous surveys with adults aged 18 to 59 reporting a mean of 2.39 (95% CI 2.20 - 2.60) contacts to 4.93 (95% CI 4.65 - 5.19) contacts, and the mean contacts for school-age children was 3.07 (95% CI 2.89 - 3.27) to 15.11 (95% CI 13.87 - 16.41). The use of face coverings outside the home has remained high since the government mandated use in some settings in July 2020.

Conclusions

The CoMix survey provides a unique longitudinal data set for a full year since the first lockdown for use in statistical analyses and mathematical modelling of COVID-19 and other diseases. Recorded contacts reduced dramatically compared to pre-pandemic levels, with changes correlated to government interventions throughout the pandemic. Despite easing of restrictions in the summer of 2020, mean reported contacts only returned to about half of that observed pre-pandemic.

Dynamics of B.1.617.2 in the UK from importations, traveller-linked and non-traveller-linked transmission

2021-05-23T00:00:00+00:00

Read the 24th May report here. Accompanying code is available here.

Earlier versions of this analysis: 12th May report; 18th May report.

Plot below shows results for 4th June 2021. These results would suggest 44% (95% CrI: 37–51%) higher transmission for B.1.617.2 compared to non-B.1.617.2 variants circulating.

Figure 1: A) Reported cases in India. B) Proportion of reported sequences in India that are B.1.617.2, with black line showing moving average (constrained to end at 100%). C) Estimated imported cases of B.1.617.2 into the UK that contribute to onwards transmission (orange line, with 95% shaded CrI interval), reported traveller cases of B.1.617.2 as described in PHE Technical Report 12 (black dots); simulated imported cases and onwards transmission using maximum a posteriori (MAP) model estimate (red line with 95% negative binomial CrI). D) Reported cases in the UK. Black dots show data, black line shows 7 day centred moving average; green line shows estimated non-B.1.617.2 cases with 95% CrI; red line as in (C); blue line and shaded region shows predicted total cases in UK with negative binomial 95% CrI. E) Black dots show number of non-B.1.617.2 sequences in COG-UK data up to 11th May 2021; green line shows fitted model with 95% negative binomial CrI. Grey region shows data in the past week, which is likely subject to reporting delays. F) Black dots show number of B.1.617.2 sequences in COG-UK data up to 11th May 2021; red line shows fitted model with 95% negative binomial CrI. G) Black dots show proportion of B.1.617.2 sequences in COG-UK data up to 11th May 2021; blue line shows MAP model estimate. H) Estimated change in R among non-travellers over time, assuming a step-change at some point during the observed period. Line shows median and shaded region 95% CrI; as in other panels, dashed grey line shows date B.1.617.2 was declared VOC in UK. I) Estimate of Rtraveller , Rnon-traveller and Rrecent in model, with thick line showing 50% CrI and thin line showing 95% CrI. Dots show implied R based on contact tracing data in PHE Technical Report 12.

SARS-CoV-2 infection and reinfection in a seroepidemiological workplace cohort in the United States

2021-03-23T00:00:00+00:00

Read the full report here.

Figure: A) PCR positivity in the cohort between 5th April 2020 and 31st January 2021. B) Percentage ever seropositive in the cohort (number ever seropositive/ cumulative number enrolled) between 29th March 2020 and 23rd August 2020. Note that the percentage ever positive decreases initially as participants continue to be enrolled in the study. C) Number of possible reinfections in cohort over time (defined as a new positive PCR test more than 30 days after initial seropositive result). D) Odds ratio estimates comparing odds of reinfection in the seropositive group with odds of primary infection in the seronegative group, estimated using logistic regression and adjusted for race, ethnicity, state, job category and BMI. The estimates are presented with their associated 95% confidence intervals and with the cut-off week used to define baseline seroprevalence on the x-axis.

The potential for vaccination-induced herd immunity against SARS-CoV-2

2021-03-19T00:00:00+00:00

Read the full report here. The R code to reproduce this analysis can be found here.

Initial reports of vaccine effectiveness against SARS-CoV-2 suggests a substantial reduction in the risk of infection, but it remains unclear whether a large-scale immunisation programme against SARS-CoV-2 is able to generate lasting herd immunity, particularly in the presence of the highly transmissive B.1.1.7 variant. Our comment piece considers the feasibility of reaching the herd immunity threshold against SARS-CoV-2 in a fully vaccinated population and draws comparisons with other vaccine-preventable pathogens, including influenza and common immunising childhood infections. Our observations suggest that if highly transmissive variants become dominant in low seroprevalence regions, elimination in the absence of non-pharmaceutical interventions may only be possible with very high vaccine effectiveness and coverage among children as well as adults.

Figure: Comparison of vaccine impact and herd immunity thresholds for different vaccine-preventable viral diseases. A) Comparison of the effectiveness of currently available vaccines against the herd immunity threshold for different viruses. The black line shows the minimum vaccine effectiveness needed to achieve herd immunity for given R0 values. Colour points represent samples from available effectiveness and transmissibility estimates (see Appendix), with large points showing medians. If sampled points are above the line, vaccination of the entire population could in theory lead to epidemic control; the more samples that are above the line, the higher the probability of control. B) Vaccination coverage required to reach herd immunity for pre-B.1.1.7-like transmission and different levels of vaccine effectiveness. Line shows median and shaded region 95% Credible Interval. Blue, 90% effectiveness in reducing transmission; green, 70%; red 50%. C) Vaccination coverage required to reach herd immunity for B.1.1.7-like transmission. Data sources are provided in the supplementary appendix.

Confirmatory testing with a second lateral flow test may mitigate false positives at low levels of SARS-CoV-2 prevalence in English schools

2021-03-12T00:00:00+00:00

Read the full report here. The R code to reproduce this analysis can be found here.

There is currently concern over the possibility of false-positive lateral flow test (LFT) results in the mass asymptomatic testing programme in English schools, with calls for positive LFTs to be confirmed by Polymerase Chain Reaction (PCR) tests.
However, delays in isolating cases and their contacts due to PCR test delays may lead to increased transmission risk and should be avoided unless strictly necessary.
Here we show that, at current levels of prevalence in schoolchildren (~0.43%), the chance of a positive test being a true positive (Positive Predictive Value, PPV) is high (88%) and prevalence would need to decrease to below 0.05% in order for the number of false-positive test results to outnumber true positives.
Were prevalence to decrease below 0.05%, a confirmatory LFT would increase PPV to >99.97%, similar to that of a confirmatory PCR (>99.99%).
Following up an initial positive LFT with a second LFT provides a high PPV, minimises disruption, and enables faster case isolation and contact tracing than a confirmatory PCR test.
This analysis makes the assumption that LFT test results are independent of each other, which may overestimate the joint specificity and underestimate the joint sensitivity.

Figure: Positive and Negative Predictive Value for different testing strategies at varying levels of prevalence. Coloured lines and bands indicate the median and 95% CI for PPV and NPV for sampled values of prevalence, assuming the same coefficient of variation as current prevalence estimates. Solid and dashed vertical lines are approximate median and 95% CIs of current SARS-CoV-2 prevalence in school children.