The impact of Covid-19 vaccination in Aotearoa New Zealand: a modelling study (preprint)

Aotearoa New Zealand implemented a Covid-19 elimination strategy in 2020 and 2021, which enabled a large majority of the population to be vaccinated before being exposed to the virus. This strategy delivered one of the lowest pandemic mortality rates in the world. However, quantitative estimates of the population-level health benefits of vaccination are lacking. Here, we use a validated mathematical model to investigate counterfactual scenarios with differing levels of vaccine coverage in different age and ethnicity groups. The model builds on earlier research by adding age- and time-dependent case ascertainment, the effect of antiviral medications, improved hospitalisation rate estimates, and the impact of relaxing control measures. The model was used for scenario analysis and policy advice for the New Zealand Government in 2022 and 2023. We compare the number of Covid-19 hospitalisations, deaths, and years of life lost in each counterfactual scenario to a baseline scenario that is fitted to epidemiological data between January 2022 and June 2023. Our results estimate that vaccines saved 6650 (95% credible interval [4424, 10180]) lives, and prevented 74500 [51000, 115400] years of life lost and 45100 [34400, 55600] hospitalisations during this 18-month period. Making the same comparison before the benefit of antiviral medications is accounted for, the estimated number of lives saved by vaccines increases to 7604 [5080, 11942]. Due to inequities in the vaccine rollout, vaccination rates among M\=aori were lower than in people of European ethnicity. Our results show that, if vaccination rates had been equitable, an estimated 11-26% of the 292 M\=aori Covid-19 deaths that were recorded in this time period could have been prevented. We conclude that Covid-19 vaccination greatly reduced health burden in New Zealand and that equity needs to be a key focus of future vaccination programmes.

Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand (preprint)

Near-term forecasting of infectious disease incidence and consequent demand for acute healthcare services can support capacity planning and public health responses. Despite well-developed scenario modelling to support the Covid-19 response, Aotearoa New Zealand lacks advanced infectious disease forecasting capacity. We develop a model using Aotearoa New Zealand's unique Covid-19 data streams to predict reported Covid-19 cases, hospital admissions and hospital occupancy. The method combines a semi-mechanistic model for disease transmission to predict cases with Gaussian process regression models to predict the fraction of reported cases that will require hospital treatment. We evaluate forecast performance against out-of-sample data over the period from 2 October 2022 to 23 July 2023. Our results show that forecast performance is reasonably good over a 1-3 week time horizon, although generally deteriorates as the time horizon is lengthened. The model has been operationalised to provide weekly national and regional forecasts in real-time. This study is an important step towards development of more sophisticated situational awareness and infectious disease forecasting tools in Aotearoa New Zealand.

Improving estimates of epidemiological quantities by combining reported cases with wastewater data: a statistical framework with applications to COVID-19 in Aotearoa New Zealand (preprint)

Background: Timely and informed public health responses to infectious diseases such as COVID-19 necessitate reliable information about infection dynamics. The case ascertainment rate (CAR), the proportion of infections that are reported as cases, is typically much less than one and varies with testing practices and behaviours, making reported cases unreliable as the sole source of data. The concentration of viral RNA in wastewater samples provides an alternate measure of infection prevalence that is not affected by human behaviours. Here, we investigated how these two data sources can be combined to inform estimates of the instantaneous reproduction number, R, and track changes in the CAR over time. Methods: We constructed a state-space model that we solved using sequential Monte Carlo methods. The observed data are the levels of SARS-CoV-2 in wastewater and reported case incidence. The hidden states that we estimate are R and CAR. Model parameters are estimated using the particle marginal Metropolis Hastings algorithm. Findings: We analysed data from 1 January 2022 to 31 March 2023 from Aotearoa New Zealand. Our model estimates that R peaked at 2.76 (95% CrI 2.20, 3.83) around 18 February 2022 and the CAR peaked around 12 March 2022. Accounting for reduced CAR, we estimate that New Zealand's second Omicron wave in July 2022 was similar in size to the first, despite fewer reported cases. We estimate that the CAR in the BA.5 Omicron wave in July 2022 was approximately 50% lower than in the BA.1/BA.2 Omicron wave in March 2022. The CAR in subsequent waves around November 2022 and April 2023 was estimated to be comparable to that in the second Omicron wave. Interpretation: This work on wastewater-based epidemiology (WBE) can be used to give insight into key epidemiological quantities. Estimating R, CAR, and cumulative number of infections provides useful information for planning public health responses and understanding the state of immunity in the population. This model is a useful disease surveillance tool, improving situational awareness of infectious disease dynamics in real-time, which may be increasingly useful as intensive pandemic surveillance programmes are wound down.

Key challenges for the surveillance of respiratory viruses: transitioning out of the acute phase of the SARS-CoV-2 pandemic (preprint)

To support the ongoing management of viral respiratory diseases, many countries are moving towards an integrated model of surveillance for SARS-CoV-2, influenza, and other respiratory pathogens. While many surveillance approaches catalysed by the COVID-19 pandemic provide novel epidemiological insight, continuing them as implemented during the pandemic is unlikely to be feasible for non-emergency surveillance, and many have already been scaled back. Furthermore, given anticipated co-circulation of SARS-CoV-2 and influenza, surveillance activities in place prior to the pandemic require review and adjustment to ensure their ongoing value for public health. In this perspective, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies, their contribution to epidemiological assessment, forecasting, and public health decision making.

The role of the mathematical sciences in supporting the COVID-19 response in Australia and New Zealand (preprint)

Mathematical modelling has been used to support the response to the COVID-19 pandemic in countries around the world including Australia and New Zealand. Both these countries have followed similar pandemic response strategies, using a combination of strict border measures and community interventions to minimise infection rates until high vaccine coverage was achieved. This required a different set of modelling tools to those used in countries that experienced much higher levels of prevalence throughout the pandemic. In this article, we provide an overview of some of the mathematical modelling and data analytics work that has helped to inform the policy response to the pandemic in Australia and New Zealand. This is a reflection on our experiences working at the modelling-policy interface and the impact this has had on the pandemic response. We outline the various types of model outputs, from short-term forecasts to longer-term scenario models, that have been used in different contexts. We discuss issues relating to communication between mathematical modellers and stakeholders such as health officials and policymakers. We conclude with some future challenges and opportunities in this area.

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.

Losartan promotes cell survival following SARS-CoV-2 infection in vitro (preprint)

Warning: This manuscript presents an in vitro study pending validation by clinical trials. Introduction: Coronavirus disease 2019 (COVID-19) can be associated with mortality and high morbidity worldwide. There is an extensive effort to control infection and disease caused by SARS-CoV-2. This study addressed the hypothesis that angiotensin II type I receptor blocker, Losartan, may restrict pathogenesis caused by SARS-CoV-2 by decreasing viral-induced cytopathological changes by blocking angiotensin II type 1 receptor (AT1R), thus reducing the affinity of the virus for ACE2, and inhibiting papain-like protease of the virus. Method: Losartan inhibitory effect on deubiquitination and deISGylation properties of papain-like protease was investigated using a fluorescence method and gel shift analysis determining its inhibitory effects. The inhibitory effect of Losartan on SARS-CoV-2 cell replication was investigated both when losartan was added to the cell culture 1 hour before (pre-infection group) and 1 hour after (post-infection group) SARS-CoV-2 infection of Vero E6 cells. Results: Losartan treatment of Vero E6 cells prior to and after SARS-CoV-2 infection reduced SARS-CoV-2 replication by 80% and 70% respectively. Losartan was not a strong deubiquitinase and deISGylase inhibitor of PLpro. Conclusion: Losartan added pre- and post-infection to the Vero E6 cell culture significantly prevents cell destruction and replication by SARS-CoV2. Losartan has low side-effects, is readily available, and can be produced at high levels globally, all features of a promising drug in treatment of COVID-19 if validated by clinical trials.

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.

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.

A stochastic model for COVID-19 spread and the effects of Alert Level 4 in Aotearoa New Zealand (preprint)

While case numbers remain low, population-wide control methods combined with efficient tracing, testing, and case isolation, offer the opportunity for New Zealand to contain and eliminate COVID-19. We use a stochastic model to investigate containment and elimination scenarios for COVID-19 in New Zealand, as the country considers the exit from its four week period of strong Level 4 population-wide control measures. In particular we consider how the effectiveness of its case isolation operations influence the outcome of lifting these strong population-wide controls. The model is parameterised for New Zealand and is initialised using current case data, although we do not make use of information concerning the geographic dispersion of cases and the model is not stratified for age or co-morbidities. We find that fast tracing and case isolation (i.e. operations that are sustained at rates comparable to that at the early stages of New Zealand's response) can lead to containment or elimination, as long as strong population-wide controls remain in place. Slow case isolation can lead to containment (but not elimination) as long as strong Level 4 population-wide controls remain in place. However, we find that relaxing strong population-wide controls after four weeks will most likely lead to a further outbreak, although the speed of growth of this outbreak can be reduced by fast case isolation, by tracing, testing, or otherwise. We find that elimination is only likely if case isolation is combined with strong population-wide controls that are maintained for longer than four weeks. Further versions of this model will include an age-structured population as well as considering the effects of geographic dispersion and contact network structure, the possibility of regional containment combined with inter-regional travel restrictions, and the potential for harm to at risk communities and essential workers.