Investigators' Blog

Making a global impact in predicting and preventing pandemics

Making a global impact in predicting and preventing pandemics

11 October 2021

Professor David Hayman made a global impact in 2020 with his contributions to the report on biodiversity and pandemics by the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES).

David Hayman is an epidemiologist and Principal Investigator at Te Pūnaha Matatini who uses multidisciplinary approaches to address how infectious diseases are maintained within their hosts and how the process of emergence occurs.

Dave has spent a long time working on emerging infectious diseases and bats, making him a natural candidate for Aotearoa New Zealand to put forward when the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) put out a call for nominations for an expert panel to produce a report on the interactions between biodiversity and human drivers of disease emergence.

The report on escaping the ‘era of pandemics’ was produced at pace during a week-long virtual workshop to review the scientific evidence on the origin, emergence and impact of COVID-19 and other pandemics, as well as on options for controlling and preventing pandemics.

Dave now sits on the One Health High Level Expert Panel (OHHLEP), a high-level expert panel that gives advice across four international agencies: the World Health Organization, the World Organization for Animal Health, the Food and Agriculture Organisation and the United Nations Environmental Programme.

He says that the IPBES report has been influential across these agencies, and is often referred to. “We are a high level expert panel that provides expertise and advice to these major global organisations about how they can work better together,” he says. “And I think the IPBES report has actually influenced that.”

The IPBES report has been an important step in these four agencies coming to terms with the complexity and interrelatedness of disease and the environment, and they are recognising the need to address these issues in a transdisciplinary way.

“There’s a lot of things from Te Pūnaha Matatini and working in Aotearoa New Zealand that influenced my contributions to the report. There’s lots I’ve Iearned from Te Pūnaha Matatini about style of working and things like respect for Māori and Indigenous knowledge.”

Dave describes himself as both a pessimist and an optimist as we face a future of increasing pandemics and the effects of climate change.

“It can seem all bad,” he says. “But on the plus side a lot of the drivers for climate change, biodiversity crises and extinction crises are the same as the things that are driving disease emergence. So we can potentially have win, win solutions.”

“We can look at things like reducing industrial-scale trafficking of wildlife or agricultural encroachment into rainforest, both of which are bad for the environment and may also be bad for human health.”

“You can potentially reduce one risk and improve things in another way.”

Dave concludes that tackling these issues will require quite big societal changes, but “what COVID-19 did show is that you can do large-scale stuff. You can shut down whole countries. I’m not saying that’s a good thing, but it showed us the scale and pace at which societies can change and adapt.”


A biosecurity risk framework for forestry in Aotearoa New Zealand

A biosecurity risk framework for forestry in Aotearoa New Zealand

8 October 2021

In her role with Scion, Dr Rebecca Turner is working with stakeholders in Aotearoa New Zealand and internationally using data to predict biosecurity risk.

Dr Rebecca Turner joined Scion as a postgraduate fellow in 2018, co-funded by the Biological Heritage National Science Challenge and Te Pūnaha Matatini. In 2020 she was promoted to a full-time biosecurity scientist.

Scion is a Crown research institute that specialises in research, science and technology development for the forestry, wood product, wood-derived materials, and other biomaterial sectors.

“My postdoc led directly into this role,” says Rebecca. “It created the opportunity for me to get to know Scion systems, plus New Zealand researchers and international collaborators through Te Pūnaha Matatini.

“Crown research institutes focus on applied science for the sectors they serve, so publishing reports for industry use is important. Having the postdoc and the TPM funding helped me build academic credibility by publishing papers.”

Rebecca’s background is in ecology, molecular biology and mathematical modelling. She is interested in research using mathematical techniques to understand biology and other applications.

Rebecca was involved with Te Pūnaha Matatini Whānau during her postdoctoral fellowship, and appreciated building her network at our Annual Hui each year. She also contributed to our Mycoplasma bovis response, working with data from the National Animal Identification and Tracing (NAIT) system that tracks cattle movement around New Zealand.

At Scion, Rebecca’s initial project explored the potential of using border interception data to predict arrivals and establishments of invasive pests in New Zealand. She says that the team hopes to be able to use border interception data to warn people what invasive species to look out for in orchards and forests.

The project quickly became complicated and grew to include international interception data, and Rebecca is now working with the United States Forest Service on an extension of this project looking at data about beetles, which include large groups of potential forestry pests.

“Although we’re really good at biosecurity in New Zealand and we’ve got a really good rate of interceptions and getting them down to species level relative to our population size, we’re still a small country, and we can only collect a certain amount of data. So we then started working with international stakeholders to get interception data from other countries as well.”

Three years in, they are now in a place where they have all the data, have started analysing it, and are starting to see where some of that data is useful for predicting establishments.

“In New Zealand forestry the major plant species that we have is Pinus radiata, so we’re looking for insects that are associated with Pinus radiata, and trying to predict which are going to establish in New Zealand, using things like interception data and climate matching.”

“We’re creating a biosecurity risk framework specifically for the forestry industry. I’m also collaborating with AgResearch and Plant & Food Research through Better Border Biosecurity (B3) to create frameworks for the agricultural industry and the pasture industry.”

Find out more about Rebecca

Modelling to support a future COVID-19 strategy for Aotearoa New Zealand

23 September 2021

Updated modelling exploring how high rates of vaccine coverage might reduce the health burden from COVID-19 if combined with moderate public health measures to reduce transmission of the virus.

Aotearoa New Zealand is on track to vaccinate upwards of 80% of those aged over 12 against COVID-19 with the Pfizer/BioNTech mRNA vaccine. Recent announcements by Pfizer and BioNTech suggest that the vaccine may soon be approved for use in children aged 5-11 years. This means it is possible that Aotearoa could achieve vaccine coverage across the total population of more than 90%.

We consider how these high rates of coverage might reduce the health burden from COVID-19 if combined with moderate public health measures to reduce transmission of the virus. Scenarios are evaluated using the Te Pūnaha Matatini vaccine model, using current data about vaccine effectiveness with respect to the Delta variant. As the effects of the vaccine on transmission remain uncertain, we consider three levels of vaccine effectiveness: high, central, and low to illustrate a range of possibilities.

The results suggest that a combination of high levels of vaccination within the community, a strong test-trace-isolate-quarantine system (assuming case numbers are kept sufficiently low) and moderate public health measures may be enough to attain population immunity, greatly reducing the need for strong public health measures, such as stay-at-home orders and workplace closures.


FluTracking incidence calculation methods

20 September 2021

This report details the methods used for calculating the estimated weekly incidence of COVID-19-like and influenza-like illness in Aotearoa New Zealand, using data from the FluTracking weekly survey.

FluTracking is a participatory health surveillance system for Australia and New Zealand that uses an online, voluntary survey to detect the potential spread of influenza, and more recently COVID-19. Volunteer participants from the general public receive a weekly email prompting them to fill out a survey which asks whether they have experienced any cold, flu, or COVID-19-like symptoms in the previous week.

FluTracking data is an incredibly useful resource for identifying patterns in symptom onset across the New Zealand population. It allows us to report on how symptom onset differs over time and between seasons, as well as across different regions in Aotearoa. However, there are limitations in this data that can reduce the utility of some results.

This report details a methodology that can be used to mitigate some of the limitations in the FluTracking data, and provides case definitions that can be used to inform responses to outbreaks of infectious disease, such as COVID-19. This report also includes a brief summary of FluTracking estimates for May 2020 to April 2021 as an example of applying this new methodology.

Contagion network modelling in the first weeks of the August 2021 outbreak

In the first weeks of the 2021 Auckland August COVID-19 outbreak, the contagion network team provided a number of reports to officials with estimates of the likely size of the outbreak and updates on the estimated effect of Alert Level interventions on curtailing spread.

The report ‘Modelling Estimates of Expected Size: the August 2021 COVID-19 Outbreak in Aotearoa’ describes the modelling approaches used in these estimates, including both a Contagion Network Model and a Branching Process Model. Output from both of these models was conditioned to real-world case data as it became available, using a Gaussian Process Model.

The reports ‘Preliminary modelling of a new community case of COVID-19 as of 17 August 2021’ and ‘Impact of wastewater testing results on preliminary estimates of COVID-19 community transmission as of 18 August 2021’ were compiled in the first two days of the outbreak when very little was known about the details of the outbreak. The first of these was produced when all that was known was that a single case of COVID-19 had been discovered in the community, linked to a man living in Devonport. The second included consideration of the information of a negative wastewater test from the weeks before the detection of the first case. Both of these reports significantly underestimate the size of the outbreak at detection, though both did predict that the outbreak was likely to be significantly larger than was known at the time of writing.

As the outbreak progressed, daily updates were produced to incorporate the effect of new cases discovered over the previous 24 hour period. These semi-automated daily updates used the same methods as described in the report of 26th August, with both a Contagion Network Model and a Branching Process Model treated with Gaussian Process conditioning. As the outbreak progressed, and new daily cases decreased, detailed information from contact tracing became relatively more important for estimating near-term risk and outbreak progression. Hence, the example shown here from 7th September is the last daily update provided for the initial phase of the outbreak.

Read the reports

Inter-regional movement and contagion risk analysis August 2021

15 September 2021

We use a range of data sources and analytic approaches to estimate the number of movements between regions of Aotearoa and to give some estimates of the risk of transmission of COVID-19 to regions outside of Auckland, during the early stages of the August 2021 outbreak.

We also use one of these approaches – the Aotearoa Co-incidence Network – to generate some regional boundaries that are optimal for preventing inter-regional spread of COVID-19, in the sense that they partition the country into regions that minimise the potential spread between regions while maximising the number of links that are allowed to remain within regions.

We find that:

  • The regions at greatest risk of onward transmission from Auckland in the period before 17 August 2021 were Hamilton, Wellington, and Christchurch and their surrounding Territorial Authorities (TAs), along with Queenstown Lakes.
  • The movement-based risk assessment does not account for current epidemiological evidence, i.e. confirmed cases of COVID-19 in Wellington, but not in e.g. Christchurch.
  • Other than a brief initial surge of travel north from Auckland on 17 August 2021, there are very low levels of vehicle movements in or out of Auckland during the current AL4 intervention – similar to previous elevated alert levels. Similarly, traffic movements within Auckland are reduced to those seen in previous periods at AL4.
  • Potential interactions through work or education, in the period prior to 17 August 2021, for individuals living in those SA2s where cases of COVID-19 had been confirmed by 21 August 2021 extend beyond the Auckland region. Within Auckland, the pattern of SA2s with large numbers of potential interactions is heterogeneous and is widely spread, spatially.
  • It is possible to partition the country into regions with high numbers of connections through work or education within the region and low numbers of interaction between regions. Some possible partitionings are presented.

Modelling the potential spread of COVID-19 during the August 2021 outbreak

7 September 2021

Here are two technical reports in which we consider the epidemic course for the August 2021 cluster, detected on 17 August in Auckland, following the shift to Alert Level 4 restrictions in New Zealand, using a stochastic branching process model.

On 17 August 2021 a case of COVID-19 was identified in Auckland, ending an extended period with no community transmission of SARS-CoV-2 in Aotearoa New Zealand. This was subsequently confirmed to be the Delta variant of SARS-CoV-2. In response, the government moved the entire country to Alert Level 4 – the most stringent form of restrictions. It was uncertain at the time how large the outbreak could be, how far it had spread and how effective the response would be in controlling the outbreak.

The purpose of these technical reports is to describe the mathematical modelling that was used in the days following detection of the outbreak to provide situational awareness and inform the government’s high-level outbreak response. We present initial estimates for the number of people infected at the time the outbreak was first detected and outline plausible scenarios for the outbreak dynamics in the following weeks. We also describe a method for assessing the risk of hidden outbreaks in parts of New Zealand with no detected cases, based on community testing rates and the results of wastewater testing.

Read the reports

Exploring COVID-19 transmission risk and vulnerability through the Aotearoa Co-incidence Network (ACN)

3 September 2021

The Aotearoa Co-incidence Network (ACN) provides a highly insightful tool to explore the manner in which the regions of Aotearoa New Zealand are connected to each other through co-incidence of individuals at workplaces and schools.

We used data from Aotearoa New Zealand’s Integrated Data Infrastructure (IDI) to create a co-incidence network of workplace employment and school enrolment. In this study we summarise the methods used to create the ACN, and detail the ways in which it can be used to inform the Aotearoa New Zealand response to disease outbreaks, such as COVID-19.

Specifically, we show how analysis of the network can be used to inform the strategy of mitigating existing outbreaks (“stamp-it-out”) by revealing those sets of areas between which an outbreak is likely to spread most quickly. We also show how analysis of the network structure can reveal spatially limited communities which can inform regional responses to disease outbreak (i.e., regional based interventions) should they need to occur, as well as specific areas of high transmission risk – both of these results can be used to aid a “prepare-for-it” strategy.

Finally, we cross-reference our findings with data on disease vulnerabilities (i.e., long-term health conditions, ethnicity, and deprivation) to highlight specific areas with a combination of high risk of contagious disease transmission and elevated vulnerability to such diseases. A web-based app, developed alongside this publication allows for visualisation and exploration of transmission risk and vulnerability and is presented as a useful tool for decision and policy makers to inform more equitable responses to diseases such as COVID-19.

Key points


  • The Aotearoa Co-incidence Network (ACN) represents the connections induced by interactions between individuals from dwellings in different regions of Aotearoa. The ACN details the number of connections made through shared workplaces and schools, which gives an indication of likely transmission spread should an outbreak of disease (e.g., COVID-19) occur.
  • The ACN makes no assumptions about the likely point of occurrence for an initial outbreak. That is, the transmission risk represented is an estimate of the risk of onward transmission to a region, for an initial outbreak that occurs at an arbitrary location in the country. Clearly, some regions (e.g. those with MIQ facilities and international airports) are at higher risk of being locations for seeding an outbreak. Such additional information should be considered alongside the ACN.
  • We identify several spatially contiguous communities in the ACN based on the patterns of connections. Interestingly, these communities are similar to the Territorial Authority (TA) boundaries, with some exceptions. The communities tend the cover multiple TAs, and in some cases extend boundaries. For example, the community detected in the Auckland region covers Auckland TA but also extends further south. This community more closely reflects the actual region covered when Alert Level 3 was implemented in the Auckland region in August 2020.
  • We use PageRank centrality to highlight geospatial areas that have the highest transmission risk based on the structure of connections in the ACN. Cross-referencing transmission risk with data on vulnerability allows us take an equity- focused approach to determining areas most in need of support (be that from governmental, iwi, or community sources).
  • We find that the regions with highest risk for transmission are located in urban areas, especially Hamilton, Wellington, and Palmerston North. Areas of low-transmission risk include Thames-Coromandel, Mackenzie, and Waitomo.
  • When we consider the intersection of transmission risk and vulnerability, we find that the most at risk regions include places such as South Auckland, Invercargill, Whanga ̄rei, New Plymouth, and Napier, as well as Wellington and Hamilton.

COVID-19 network modelling trilogy

Elimination, Alert Level 2.5 and other non-pharmaceutical interventions

26 July 2021

The reports ‘Network modelling of elimination strategy pillars: Prepare for it, Stamp it out’; ‘Alert Level 2.5 is insufficient for suppression or elimination of COVID-19 community outbreak’; and ‘Contagion network modelling of effectiveness for a range of non-pharmaceutical interventions for COVID-19 elimination in Aotearoa New Zealand’ were sent to the New Zealand government on 9 December 2020, 15 February 2021, and 16 November 2020 respectively. The reports are presented here as a connected trilogy that are intended to be read in the order above.

Here is some background and context that might be of use to readers:

All three reports deal with the wild type variant of SARS-CoV2 that was common around the world (and in many cases in Aotearoa New Zealand) in the latter half of 2020. The transmissibility of SARS-CoV2 is commonly communicated via an R0 value. This value was around 2-3 for the wild type variant considered in this report.1 More recently a number of more transmissible variants of concern have emerged. The alpha and delta variants that were first noted in the United Kingdom and India, respectively, have R0 values that are around two and four times higher again than the wild type strain of SARS-CoV2. Hence, any control measures and results discussed in these reports will typically no longer apply with the current prevalence of these more transmissible strains.

The ‘Elimination strategy’ report considers the policy settings and possible interventions that can be used to prepare for the emergence of COVID-19 in the community in Aotearoa New Zealand. It then looks at the effectiveness of different Alert Level 3-like interventions in eliminating community transmission once it is detected. Pre-detection, the report focuses primarily on testing rates of symptomatic individuals in the community. Post-detection, a range of measures that could reduce transmission (e.g. closing workplaces, mask wearing) are considered, along with the effect of elevated community testing rates and contact tracing of exposed individuals. Many of the parameters for this report were estimated from the behaviour observed during the August 2020 outbreak in Auckland.

At the end of August 2020, at the tail end of the outbreak, Auckland dropped from Alert Level 3 (AL3) to Alert Level 2.5 (AL2.5), while there were still new cases of COVID-19 being found in the community.2 Despite this, the outbreak remained under control and was eventually eliminated. This raises the question of whether or not the less restrictive AL2.5 could have been used to eliminate a similar outbreak in future, rather than the more disruptive AL3.

The ‘Alert Level 2.5’ report addresses precisely this question. It finds that for a similar community outbreak, AL2.5 is most likely to result in suppression, but not elimination-like behaviour. The probability of elimination under AL2.5 is strongly linked to the outbreak size at initial detection. Outbreaks with ten or fewer total cases (including unknown cases) at the time of alert level elevation have an approximately 60% chance of being eliminated within 150 days of detection, while if the outbreak size is 11 or more at the point when alert levels are elevated, the probability of elimination falls to under 12%.

The fact that a period of AL2.5 did lead to elimination while community cases were still being discovered at the tail-end of the Auckland August outbreak may be explained either by good luck (i.e. a very low probability but successful elimination event) or, more likely, by the preceding period of AL3. During AL3, high levels of contact tracing and testing may have essentially ring-fenced the outbreak. In such a scenario, individuals in the vicinity of the existing cluster would effectively remain at a higher Alert Level while the rest of Auckland moved to level 2.5.

Both the ‘Elimination strategy’ and the ‘Alert Level 2.5’ reports consider a number of different variations of the AL3 and AL2.5 interventions, such as improved contact tracing processes due to increased rates of QR code scanning or adoption of Bluetooth contact tracing.

The final report of this trio, ‘Modelling of effectiveness for a range of non-pharmaceutical interventions’, looks at a large number of combinations of transmission reduction interventions at four levels of strictness (none, partial, increased, and strict) for workplace and community, and three levels (none, partial, and closure) for schools. For example, closing schools but not workplaces, while mandating mask-wearing indoors would approximately match the closure of schools, and ‘partial’ control in workplaces and communities.

The purpose of this report is not so much to design alternative Alert Levels, but rather to provide a sensitivity analysis. This analysis can be used to infer some coarse bounds on the types of behaviour that might be expected in the previous two reports if parameters were adjusted, or if assumptions were modified. Even though this report was produced in advance of its two companions, it is presented here as an add-on that may help to quantify any uncertainty or variation in the more realistic scenarios of the first two reports.



  1. We do not directly use R0 to parameterise infectivity or transmissibility in our contagion model. Rather, we characterise transmissibility with the parameter beta – the average number of infections per person per unit time, which is calibrated against the generation time. R0 is related to beta by: R0 = beta/gamma, where gamma is the rate per unit time at which individuals are removed from the infectious state(s).
  2. Covid-19: What happened in New Zealand on 31 August – Radio New Zealand


Read the reports


PhD scholarship on system change to reverse health inequality and environmental degradation – Ki te toi o te ora

Applications are invited for a PhD scholarship to explore system change to reverse health inequality and environmental degradation.

Health, wellbeing and the environment are intertwined through complex relationships, but research, policy and governance systems tend to treat them as separate areas of knowledge and action. Recognition of these fundamental interrelationships is becoming more widespread in Aotearoa New Zealand as Māori continue to revive and promote traditional knowledge and cultural practices, and as global systems are tested by the COVID-19 pandemic.

The social processes and relationships involved in producing inequality affect both health (such as life expectancy) and environmental outcomes (such as climate change). This research will explore what the common influences are, and what this means for producing change to reverse trends in both.

You will work with diverse stakeholders to map the key factors, actors and influences contributing to inequality, health and the environment in Aotearoa New Zealand. You will then work with a research team using the results to identify levers for system change and develop better ways to prioritise and evaluate actions.


This scholarship is open to anyone who can be in New Zealand and meets the requirements to enrol in a PhD at the relevant institution. We are happy to consider students from a diverse range of fields. An interest in systems methods will be an advantage for this project, but prior knowledge is not required.

The successful candidate will hold, or expect to complete soon, a masters degree, or similar, in a relevant discipline. Experience and knowledge in one or more of the areas of health and its determinants, inequality and environment would be helpful.

Applicants from all backgrounds are actively encouraged to apply and we especially encourage Māori and Pasifika to apply.


You will be part of an excellent, passionate and experienced team of researchers who will be available to co-supervise, or act as advisors to the research, depending on your needs and skills. You will be enrolled with the School of Health at Te Herenga Waka, Victoria University of Wellington but there is flexibility both in supervision and where you can be based, such as an action partner within an iwi/hapū or community organisation, or within the academic community of any of your supervisor’s institutions.

You will be part of Te Pūnaha Matatini, the Aotearoa New Zealand Centre of Research Excellence for Complex Systems. Te Pūnaha Matatini brings together different disciplines, ways of thought, methods, and people to define and solve society’s thorny interconnected problems.

Te Pūnaha Matatini has an active whānau group which supports early career researchers, committed to the Te Pūnaha Matatini values of manaakitanga and whakawhanaungatanga, offering supportive tuakana / teina learning environments.


Informal enquiries are welcome by email:

Financial details

  • Full tuition fees
  • Stipend of NZ$28,800 per year (tax free)

Start date

Start date is flexible but would preferably be between November 2021 and March 2022.

How to apply

Send an email expressing your interest, along with a CV, academic record, and list of three potential referees to Dr Anna Matheson at

Due date

Applications will be considered until the position is filled. Applications received by 31 October 2021 will receive full consideration.

Selection process

  1. After the due date shortlisted candidates will be invited for an interview and follow-up discussion to establish mutual fit.
  2. At your interview you can meet some of the project team and ask questions.
  3. Following these interviews, and a reference check, we will offer the scholarship to the most suitable candidate.