The relative effects of non-pharmaceutical interventions on wave one Covid-19 mortality: natural experiment in 130 countries.

BACKGROUND: Non-pharmaceutical interventions have been implemented around the world to control Covid-19 transmission. Their general effect on reducing virus transmission is proven, but they can also be negative to mental health and economies, and transmission behaviours can also change voluntarily, without mandated interventions. Their relative impact on Covid-19 attributed mortality, enabling policy selection for maximal benefit with minimal disruption, is not well established due to a lack of definitive methods. METHODS: We examined variations in timing and strictness of nine non-pharmaceutical interventions implemented in 130 countries and recorded by the Oxford COVID-19 Government Response Tracker (OxCGRT): 1) School closing; 2) Workplace closing; 3) Cancelled public events; 4) Restrictions on gatherings; 5) Closing public transport; 6) Stay at home requirements ('Lockdown'); 7) Restrictions on internal movement; 8) International travel controls; 9) Public information campaigns. We used two time periods in the first wave of Covid-19, chosen to limit reverse causality, and fixed country policies to those implemented: i) prior to first Covid-19 death (when policymakers could not possibly be reacting to deaths in their own country); and, ii) 14-days-post first Covid-19 death (when deaths were still low, so reactive policymaking still likely to be minimal). We then examined associations with daily deaths per million in each subsequent 24-day period, which could only be affected by the intervention period, using linear and non-linear multivariable regression models. This method, therefore, exploited the known biological lag between virus transmission (which is what the policies can affect) and mortality for statistical inference. RESULTS: After adjusting, earlier and stricter school (- 1.23 daily deaths per million, 95% CI - 2.20 to - 0.27) and workplace closures (- 0.26, 95% CI - 0.46 to - 0.05) were associated with lower Covid-19 mortality rates. Other interventions were not significantly associated with differences in mortality rates across countries. Findings were robust across multiple statistical approaches. CONCLUSIONS: Focusing on 'compulsory', particularly school closing, not 'voluntary' reduction of social interactions with mandated interventions appears to have been the most effective strategy to mitigate early, wave one, Covid-19 mortality. Within 'compulsory' settings, such as schools and workplaces, less damaging interventions than closing might also be considered in future waves/epidemics.

Rethinking the Ethics of Pandemic Rationing: Egalitarianism and Avoiding Wrongs.

This paper argues that we ought to rethink the harm-reduction prioritization strategy that has shaped early responses to acute resource scarcity (particularly of intensive care unit beds) during the COVID-19 pandemic. Although some authors have claimed that "[t]here are no egalitarians in a pandemic," it is noted here that many observers and commentators have been deeply concerned about how prioritization policies that proceed on the basis of survival probability may unjustly distribute the burden of mortality and morbidity, even while reducing overall deaths. The paper further argues that there is a general case in favor of an egalitarian approach to medical rationing that has been missed in the ethical commentary so far; egalitarian approaches to resource rationing minimize wrongful harm. This claim is defended against some objections and the paper concludes by explaining why we should consider the possibility that avoiding wrongful harm is more important than avoiding harm simpliciter.

Predicting elimination of evolving virus variants (preprint)

As the SARS-CoV-2 virus spreads around the world new variants are appearing regularly. Although some countries have achieved very swift and successful vaccination campaigns, on a global scale the vast majority of the population is unvaccinated and new variants are proving more resistant to the current set of vaccines. We present a simple model of disease spread which includes the evolution of new variants and varying vaccine effectiveness to these new strains. We show that rapid vaccine updates to target new strains are more effective than slow updates and containing spread through non-pharmaceutical interventions is vital whilst these vaccines are delivered. Finally when measuring the key model inputs, e.g. the rate at which new mutations and variants of concern emerge, is difficult we show how an observable model output, the number of new variants which have been seen, is strongly correlated with the probability the virus is eliminated.

Estimating decay curves of neutralising antibodies to SARS-CoV-2 infection (preprint)

Estimating the longevity of an individual's immune response to the Sars-Cov-2 virus is vital for future planning, particularly of vaccine requirements. Neutralising antibodies (Nabs) are increasingly being recognised as a correlate of protection and whilst there are many studies which follow the response of a cohort of people, each study alone is not enough to predict the long term response. Studies use different assays to measure Nabs making them hard to combine. We present a modelling method which can combine multiple datasets and can be updated as more detailed data becomes available. Combining data from six published datasets we predict that after a short period of rapid decay the half-life of the NAb response is approximately one year giving optimism that the response will be long-lived.

Unweighted lotteries and compounding injustice: reply to Schmidt et al.

I argue that Schmidt et al, while correctly diagnosing the serious racial inequity in current ventilator rationing procedures, misidentify a corresponding racial inequity issue in alternative 'unweighted lottery' procedures. Unweighted lottery procedures do not 'compound' (in the relevant sense) prior structural injustices. However, Schmidt et al do gesture towards a real problem with unweighted lotteries that previous advocates of lottery-based allocation procedures, myself included, have previously overlooked. On the basis that there are independent reasons to prefer lottery-based allocation of scarce lifesaving healthcare resources, I develop this idea, arguing that unweighted lottery procedures fail to satisfy healthcare providers' duty to prevent unjust population-level health outcomes, and thus that lotteries weighted in favour of Black individuals (and others who experience serious health injustice) are to be preferred.

Emerging SARS-CoV-2 diversity revealed by rapid whole genome sequence typing. (preprint)

Background: Discrete classification of SARS-CoV-2 viral genotypes can identify emerging strains and detect geographic spread, viral diversity, and transmission events. Methods: We developed a tool (GNUVID) that integrates whole genome multilocus sequence typing and a supervised machine learning random forest-based classifier. We used GNUVID to assign sequence type (ST) profiles to each of 69,686 SARS-CoV-2 complete, high-quality genomes available from GISAID as of October 20th 2020. STs were then clustered into clonal complexes (CCs), and then used to train a machine learning classifier. We used this tool to detect potential introduction and exportation events, and to estimate effective viral diversity across locations and over time in 16 US states. Results: GNUVID is a scalable tool for viral genotype classification (available at https://github.com/ahmedmagds/GNUVID) that can be used to quickly process tens of thousands of genomes. Our genotyping ST/CC analysis uncovered dynamic local changes in ST/CC prevalence and diversity with multiple replacement events in different states. We detected an average of 20.6 putative introductions and 7.5 exportations for each state. Effective viral diversity dropped in all states as shelter-in-place travel-restrictions went into effect and increased as restrictions were lifted. Interestingly, our analysis showed correlation between effective diversity and the date that state-wide mask mandates were imposed. Conclusions: Our classification tool uncovered multiple introduction and exportation events, as well as waves of expansion and replacement of SARS-CoV-2 genotypes in different states. Combined with future genomic sampling the GNUVID system could be used to track circulating viral diversity and identify emerging clones and hotspots.

Maori and Pacific People in New Zealand have higher risk of hospitalisation for COVID-19 (preprint)

Aims. We aim to quantify differences in clinical outcomes from COVID-19 infection by ethnicity with a focus on risk of hospitalisation. Methods. We used data on age, ethnicity, deprivation index, pre-existing health conditions, and clinical outcomes on 1,829 COVID-19 cases reported in New Zealand. We used a logistic regression model to calculate odds ratios for the risk of hospitalisation by ethnicity. We also consider length of hospital stay and risk of fatality. Results. M[a]ori have 2.5 times greater odds of hospitalisation than non-M[a]ori, non-Pacific people, after controlling for age and pre-existing conditions. Similarly, Pacific people have 3 times greater odds. Conclusions. Structural inequities and systemic racism in the healthcare system mean that M[a]ori and Pacific communities face a much greater health burden from COVID-19. Older people and those with pre-existing health conditions are also at greater risk. This should inform future policy decisions including prioritising groups for vaccination.

MHC-II constrains the natural neutralizing antibody response to the SARS-CoV-2 spike RBM in humans (preprint)

SARS-CoV-2 antibodies develop within two weeks of infection, but wane relatively rapidly post-infection, raising concerns about whether antibody responses will provide protection upon re-exposure. Here we revisit T-B cooperation as a prerequisite for effective and durable neutralizing antibody responses centered on a mutationally constrained RBM B cell epitope. T-B cooperation requires co-processing of B and T cell epitopes by the same B cell and is subject to MHC-II restriction. We evaluated MHC-II constraints relevant to the neutralizing antibody response to a mutationally-constrained B cell epitope in the receptor binding motif (RBM) of the spike protein. Examining common MHC-II alleles, we found that peptides surrounding this key B cell epitope are predicted to bind poorly, suggesting a lack MHC-II support in T-B cooperation, impacting generation of high-potency neutralizing antibodies in the general population. Additionally, we found that multiple microbial peptides had potential for RBM cross-reactivity, supporting previous exposures as a possible source of T cell memory.

Comprehensive analysis of SARS-CoV-2 antibody dynamics in New Zealand (preprint)

ObjectivesCirculating antibodies are important markers of previous infection and immunity. Questions remain with respect to the durability and functionality of SARS-CoV-2 antibodies. This study explored antibody responses in recovered COVID-19 patients in a setting where the probability of re-exposure is effectively nil, owing to New Zealands successful elimination strategy. MethodsA triplex bead-based assay that detects antibody isotype (IgG, IgM and IgA) and subclass (IgG1, IgG2, IgG3, IgG4) responses against Nucleocapsid (N) protein, Receptor Binding Domain (RBD) and Spike (S) protein of SARS-CoV-2 was developed. After establishing baseline levels with pre-pandemic control sera (n=113), samples from PCR-confirmed COVID-19 patients with mild-moderate disease (n=189) collected up to eight months post-infection were examined. The relationship between antigen-specific antibodies and neutralising antibodies (NAbs) was explored with a surrogate neutralisation assay that quantifies inhibition of the RBD/hACE-2 interaction. ResultsWhile most individuals had broad isotype and subclass responses to each antigen shortly after infection, only RBD and S protein IgG, as well as NAbs, were stable over the study period, with 99%, 96% and 90% of samples, respectively, having responses over baseline 4-8 months post-infection. Anti-RBD antibodies were strongly correlated with NAbs at all time points (Pearsons r [≥] 0.87) and feasibility of using finger prick sampling to accurately measure anti-RBD IgG was demonstrated. ConclusionAntibodies to SARS-CoV-2 persist for up to eight months following mild to moderate infection. This robust response can be attributed to the initial exposure without immune boosting given the lack of community transmission in our setting.

Early intervention is the key to success in COVID-19 control (preprint)

New Zealand responded to the COVID-19 pandemic with a combination of border restrictions and an Alert Level system that included strict stay-at-home orders. These interventions were successful in containing the outbreak and ultimately eliminating community transmission of COVID-19. The timing of interventions is crucial to their success. Delaying interventions for too long may both reduce their effectiveness and mean that they need to be maintained for a longer period of time. Here, we use a stochastic branching process model of COVID-19 transmission and control to simulate the epidemic trajectory in New Zealand and the effect of its interventions during its COVID-19 outbreak in March-April 2020. We use the model to calculate key outcomes, including the peak load on the contact tracing system, the total number of reported COVID-19 cases and deaths, and the probability of elimination within a specified time frame. We investigate the sensitivity of these outcomes to variations in the timing of the interventions. We find that a delay to the introduction of Alert Level 4 controls results in considerably worse outcomes. Changes in the timing of border measures have a smaller effect. We conclude that the rapid response in introducing stay-at-home orders was crucial in reducing the number of cases and deaths and increasing the probability of elimination.

The relative effects of non-pharmaceutical interventions on early Covid-19 mortality: natural experiment in 130 countries (preprint)

Background Concurrent non-pharmaceutical interventions have been implemented around the world to control Covid-19 transmission. Their general effect on reducing virus transmission is proven, but they can also be negative to mental health and economies, and transmission behaviours can also change in absence of mandated policies. Their relative impact on Covid-19 attributed mortality rates, enabling policy selection for maximal benefit with minimal disruption, is not well established. Methods We exploited variations in nine non-pharmaceutical interventions implemented in 130 countries (3250 observations) in two periods chosen to limit reverse causality: i) prior to first Covid-19 death (when policymakers could not possibly be reacting to deaths in their own country); and, ii) 14-days-post first Covid-19 death (when deaths were still low, on average). We examined associations with daily deaths per million in each subsequent 24-day period (the time between virus transmission and mortality) which could only be affected by the policy period. A mean score of strictness and timeliness was coded for each intervention. Days in each country were indexed in time by first reported Covid-19 death to proxy for virus transmission rate. Multivariable linear regression models of Covid-19 mortality rates on all concurrent interventions were adjusted for seasonality, potential confounders, and potential cross-country differences in their mortality definitions. Robustness was checked by removing countries with known data reporting issues and with non-linear, negative binomial, models. Results After adjusting for multiple concurrent interventions and confounders, and accounting for both timing and strictness of interventions, earlier and stricter school (-1.23 daily deaths per million, 95% CI -2.20 -0.27) and workplace closures (-0.26, 95% CI -0.46 -0.05) were associated with lower Covid-19 mortality rates. Only controlling for strictness international travel controls, and only controlling for timing later restrictions on gatherings, were also associated with lower Covid-19 mortality. Other interventions, such as stay-at-home orders or restrictions on public transport, were not significantly associated with differences in mortality rates across countries. Findings were robust across multiple statistical approaches. Conclusions Focusing on compulsory, particularly school closing, not voluntary reduction of social interactions with mandated policies appears to have been the most effective strategy to mitigate early Covid-19 mortality.

Potential reduction in transmission of COVID-19 by digital contact tracing systems (preprint)

Digital tools are being developed to support contact tracing as part of the global effort to control the spread of COVID-19. These include smartphone apps, Bluetooth-based proximity detection, location tracking, and automatic exposure notification features. Evidence on the effectiveness of alternative approaches to digital contact tracing is so far limited. We use an age-structured branching process model of the transmission of COVID-19 in different settings to estimate the potential of manual contact tracing and digital tracing systems to help control the epidemic. We investigate the effect of the uptake rate and proportion of contacts recorded by the digital system on key model outputs: the effective reproduction number, the mean outbreak size after 30 days, and the probability of elimination. We show that effective manual contact tracing can reduce the effective reproduction number from 2.4 to around 1.5. The addition of a digital tracing system with a high uptake rate over 75% could further reduce the effective reproduction number to around 1.1. Fully automated digital tracing without manual contact tracing is predicted to be much less effective. We conclude that, for digital tracing systems to make a significant contribution to the control of COVID-19, they need be designed in close conjunction with public health agencies to support and complement manual contact tracing by trained professionals.

Effective reproduction number for COVID-19 in Aotearoa New Zealand (preprint)

The effective reproduction number, Reff, is the average number of secondary cases infected by a primary case, a key measure of the transmission potential for a disease. Compared to many countries, New Zealand has had relatively few COVID-19 cases, many of which were caused by infections acquired overseas. This makes it difficult to use standard methods to estimate Reff. In this work, we use a stochastic model to simulate COVID-19 spread in New Zealand and report the values of Reff from simulations that gave best fit to case data. We estimate that New Zealand had an effective reproduction number Reff = 1.8 for COVID-19 transmission prior to moving into Alert Level 4 on March 25 2020 and that after moving into Alert level 4 this was reduced to Reff = 0.35. Our estimate Reff = 1.8 for reproduction number before Alert Level 4, is relatively low compared to other countries. This could be due, in part, to measures put in place in early- to mid-March, including: the cancellation of mass gatherings, the isolation of international arrivals, and employees being encouraged to work from home.

Probability of elimination for COVID-19 in Aotearoa New Zealand (preprint)

On 25th March 2020, New Zealand implemented stringent lockdown measures (Alert Level 4, in a four-level alert system) with the goal of eliminating community transmission of COVID-19. Once new cases are no longer detected over consecutive days, the probability of elimination is an important measure for informing decisions on when certain COVID-19 restrictions should be relaxed. Our model of COVID-19 spread in New Zealand estimates that after 2-3 weeks of no new reported cases, there is a 95% probability that COVID-19 has been eliminated. We assessed the sensitivity of this estimate to varying model parameters, in particular to different likelihoods of detection of clinical cases and different levels of control effectiveness. Under an optimistic scenario with high detection of clinical cases, a 95% probability of elimination is achieved after 10 consecutive days with no new reported cases, while under a more pessimistic scenario with low case detection it is achieved after 22 days.

Model-free estimation of COVID-19 transmission dynamics from a complete outbreak (preprint)

New Zealand had 1499 cases of COVID-19 before eliminating transmission of the virus. Extensive contract tracing during the outbreak has resulted in a dataset of epidemiologically linked cases. This data contains useful information about the transmission dynamics of the virus, its dependence on factors such as age, and its response to different control measures. We use Monte-Carlo network construction techniques to provide an estimate of the number of secondary cases for every individual infected during the outbreak. We then apply standard statistical techniques to quantify differences between groups of individuals. Children under 10 years old are significantly under-represented in the case data. Children infected fewer people on average and had a lower secondary attack rate in comparison to adults and the elderly. Imported cases infected fewer people on average and had a lower secondary attack rate than domestically acquired cases. Superspreading is a significant contributor to the epidemic dynamics, with 20% of cases among adults responsible for 65-85% of transmission. Asymptomatic cases infected fewer individuals than clinical cases. Serial intervals are approximately normally distributed ( = 5.0 days,{sigma} = 5.7 days). Early isolation and quarantine of cases reduced secondary transmission rates. Border controls and strong social distancing measures, particularly when targeted at superspreading, play a significant role in reducing the spread of COVID-19.

The effect of border controls on the risk of COVID-19 reincursion from international arrivals (preprint)

In an attempt to maintain elimination of COVID-19, the New Zealand government has closed the border to everyone except citizens and residents. All arrivals are required to spend 14 days in government-managed isolation/quarantine and to be tested for COVID-19 on day 3 and on day 12 of their stay. We model the testing, isolation and potential transmission of COVID-19 within managed isolation facilities to estimate the risk of undetected cases and the risk of infectious cases being released into the community. We use a stochastic individual-based that includes a time-dependent probability of a false negative test result, complete isolation of confirmed and probable cases, and secondary transmission of COVID-19 between close contacts. We show that the combination of 14-day quarantine with day 3 and day 12 testing reduces risk of releasing an infectious case to around 0.1% per infected arrival. Shorter quarantine periods, or reliance on testing only with no quarantine, substantially increase this risk. It is important to avoid contacts between individuals staying in quarantine to minimise the risk of secondary transmission. We calculate the ratio of cases detected on day 3 to cases detected on day 12 in the model and show that this may be a useful indicator of the likelihood of secondary transmission occurring within quarantine. We do not explicitly model transmission of COVID-19 from individuals in quarantine to staff, but this is likely to present a significant risk. This needs to be minimised by strict infection control, use of personal protective equipment by staff at all times, and avoiding close contact between staff and hotel guests.

Successful contact tracing systems for COVID-19 rely on effective quarantine and isolation (preprint)

Background. Test, trace and isolate are the three crucial components of the response to COVID-19 identified by the World Health Organisation. Mathematical models of contact tracing often over-simplify the ability of traced contacts to quarantine or isolate. Method. We use an age-structured branching process model of individual disease transmission combined with a detailed model of symptom onset, testing, contact quarantine and case isolation to model each aspect of the test, trace, isolate strategy. We estimated the effective reproduction number under a range of scenarios to understand the importance of each aspect of the system. Findings. People's ability to quarantine and isolate effectively is a crucial component of a successful contact tracing system. 80% of cases need to be quarantined or isolated within 4 days of quarantine or isolation of index case to be confident the contact tracing system is effective. Interpretation. Provision of universal support systems to enable people to quarantine and isolate effectively, coupled with investment in trained public health professionals to undertake contact tracing, are crucial to success. We predict that a high-quality, rapid contact tracing system with strong support structures in place, combined with moderate social distancing measures, is required to contain the spread of COVID-19. Funding. This work was funded by the Ministry of Business, Innovation and Employment, New Zealand and Te Punaha Matatini, the NZ Centre of Research Excellence for Complex Systems.

A structured model for COVID-19 spread: modelling age and healthcare inequities (preprint)

We use a stochastic branching process model, structured by age and level of healthcare access, to look at the heterogeneous spread of COVID-19 within a population. We examine the effect of control scenarios targeted at particular groups, such as school closures or social distancing by older people. Although we currently lack detailed empirical data about contact and infection rates between age groups and groups with different levels of healthcare access within New Zealand, these scenarios illustrate how such evidence could be used to inform specific interventions. We find that an increase in the transmission rates amongst children from reopening schools is unlikely to significantly increase the number of cases, unless this is accompanied by a change in adult behaviour. We also find that there is a risk of undetected outbreaks occurring in communities that have low access to healthcare and that are socially isolated from more privileged communities. The greater the degree of inequity and extent of social segregation, the longer it will take before any outbreaks are detected. Well-established evidence for health inequities, particularly in accessing primary healthcare and testing, indicates that M[a]ori and Pacific peoples are at higher risk of undetected outbreaks in Aotearoa New Zealand. This highlights the importance of ensuring that community needs for access to healthcare, including early proactive testing, rapid contact tracing, and the ability to isolate, are being met equitably. Finally, these scenarios illustrate how information concerning contact and infection rates across different demographic groups may be useful in informing specific policy interventions.

Effect of Alert Level 4 on effective reproduction number: review of international COVID-19 cases (preprint)

The effective reproduction number, Reff, is an important measure of transmission potential in the modelling of epidemics. It measures the average number of people that will be infected by a single contagious individual. A value of Reff > 1 suggests that an outbreak will occur, while Reff< 1 suggests the virus will die out. In response to the COVID-19 pandemic, countries worldwide are implementing a range of intervention measures, such as population-wide social distancing and case isolation, with the goal of reducing Reff to values below one, to slow or eliminate transmission. We analyse case data from 25 international locations to estimate their Reff values over time and to assess the effectiveness of interventions, equivalent to New Zealand's Alert Levels 1-4, for reducing transmission. Our results show that strong interventions, equivalent to NZ's Alert Level 3 or 4, have been successful at reducing Reff below the threshold for outbreak. In general, countries that implemented strong interventions earlier in their outbreak have managed to maintain case numbers at lower levels. These estimates provide indicative ranges of Reff for each Alert Level, to inform parameters in models of COVID-19 spread under different intervention scenarios in New Zealand and worldwide. Predictions from such models are important for informing policy and decisions on intervention timing and stringency during the pandemic.

Estimated inequities in COVID-19 infection fatality rates by ethnicity for Aotearoa New Zealand (preprint)

There is limited evidence as to how COVID-19 infection fatality rates (IFR) may vary by ethnicity. We combine demographic and health data for ethnic groupings in Aotearoa New Zealand with international data on IFR for different age groups to estimate inequities in IFR by ethnicity. We find that, if age is the dominant factor determining IFR, estimated IFR for M[a]ori is around 50% higher than non-M[a]ori. If underlying health conditions are more important than age per se, then estimated IFR for M[a]ori is more than 2.5 times that of New Zealand European, and estimated IFR for Pasifika is almost double that of New Zealand European. IFRs for M[a]ori and Pasifika are likely to be increased above these estimates by racism within the healthcare system and other inequities not reflected in official data. IFR does not account for differences among ethnicities in COVID-19 incidence, which could be higher in M[a]ori and Pasifika as a result of crowded housing and higher inter-generational contact rates. These factors should be included in future disease incidence modelling. The communities at the highest risk will be those with elderly populations, and M[a]ori and Pasifika communities, where the compounded effects of underlying health conditions, socioeconomic disadvantage, and structural racism result in imbricated risk of contracting COVID-19, becoming unwell, and death.