Out and about
NEW RESEARCH — LINK TO FULL PDF HERE
22 May 2020
Effective reproduction number for COVID-19 in Aotearoa New Zealand
- 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.
- 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 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.
NEW RESEARCH — LINK TO FULL PDF HERE
15 May 2020
A structured model for COVID-19 spread: Modelling age and healthcare inequities
- We develop a structured model to look at the spread of COVID-19 in different groups within the population. We examine two case studies: the effect of control scenarios aimed at particular age groups (e.g. school closures) and the effect of inequitable access to healthcare and testing. These scenarios illustrate how such evidence could be used to inform specific policy interventions.
- An increase in contact rates among children, which might result from reopening schools, is on its own unlikely to significantly increase the number of cases. However, if this change in turn causes a change in adult behaviour, for example increased contacts among parents, it could have a much bigger effect.
- We also consider scenarios where outbreaks occur undetected in sectors of the community with less access to healthcare. We find that the lower the contact rate between groups with differing access to healthcare, the longer it will take before any outbreaks are detected in any groups who experience unequitable access to healthcare, which in Aotearoa New Zealand includes Māori and Pacific peoples.
- Well-established evidence for health inequities, particularly in accessing primary healthcare and testing, indicates that Māori and Pacific communities in Aotearoa New Zealand are at higher risk of undetected outbreaks. The government should ensure that the healthcare needs of Māori and Pacific communities with respect to COVID-19 are being met equitably.
A New Zealand-specific interactive epidemic simulation app developed by Dr Audrey Lustig, Associate Investigator at Te Pūnaha Matatini, and hosted by the University of Auckland’s Centre for eResearch, has just been released.
Called the COVID-19 Take Control simulator, the app illustrates the effects of hygiene and physical distancing measures that all Kiwis are undertaking to control the spread of COVID-19. One of the app’s key features is that it allows the user to see the effects higher and lower collective cooperation with policies aimed at breaking the chain of transmission.
Try it out now! Check out the app here.
NEW RESEARCH — LINK TO FULL PDF HERE
22 April 2020
Effect of Alert Level 4 on Reff : review of international COVID-19 cases
- In response to the COVID-19 pandemic, countries around the world are implementing a range of intervention measures, such as population-wide social distancing and case isolation, with the goal of reducing the spread of the virus.
- Reff, the effective reproduction number, 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.
- Comparing Reff in an early outbreak phase (no or low-level interventions implemented) with a later phase (moderate to high interventions) indicates how effective these measures are for reducing Reff.
- We estimate early-phase and late-phase Reff values for COVID-19 outbreaks in 25 countries (or provinces/states). Results suggest interventions equivalent to NZ’s Alert Level 3-4 can successfully reduce Reff below the threshold for outbreak.
NEW RESEARCH — LINK TO FULL PDF HERE
17 April 2020
Estimated inequities in COVID-19 infection fatality rates by ethnicity for Aotearoa New Zealand
- 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.
- If age is the dominant factor determining IFR, estimated IFR for Māori is around 50% higher than non-Māori.
- If underlying health conditions are more important than age per se, then estimated IFR for Māori 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 Māori 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 Māori and Pasifika as a result of crowded housing and higher inter-generational contact rates. These factors should be included in future disease incidence modelling.
NEW RESEARCH — LINK TO FULL PDF HERE
9 April 2020
A stochastic model for COVID-19 spread and the effects of Alert Level 4 in Aotearoa New Zealand
- While New Zealand case numbers remain low, tracing, testing, and rapid case isolation, combined with population-wide control methods, offer an opportunity for the country to contain and eliminate COVID-19.
- Simulations using our model suggest that the current population-wide controls (Alert Level 4) have already had a significant effect on new case numbers (see figure below).
- We also find that fast case isolation, whether as a result of contact tracing, rapid testing, or otherwise, can lead to containment and possibly even elimination, when combined with strong population-wide controls.
- Slow case isolation can also lead to containment, but only as long as strong population wide controls remain in place. It is unlikely to lead to elimination.
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 potential harm to at risk communities and essential workers.
NEW RESEARCH — LINK TO FULL PDF HERE [MINOR REVISIONS MADE 30 MARCH 2020]
25 March 2020
Suppression and Mitigation Strategies for Control of COVID-19 in New Zealand
- Suppression strategies aim to keep the number of cases to an absolute minimum for as long as possible. This requires early and effective control interventions.
- Suppression can only delay an epidemic, not prevent it, but may buy enough time for a vaccine or treatment to become available.
- Mitigation strategies aim to control an epidemic so that herd immunity is acquired by the population without overwhelming healthcare systems.
- Mitigation strategies are likely to be very high risk: they are unproven internationally, potentially sensitive to uncertainty, and it may take years for herd immunity to be acquired.
- Strategy can be switched from suppression to mitigation. For example, once successful mitigation strategies have been tested in other countries. It is likely to be difficult or impossible to switch from a mitigation to a suppression strategy.
- A combination of successful suppression, strong border measures, and widespread contact tracing and testing resulting in containment could allow periods when control measures can be relaxed, but only if we can reduce cases to a handful.
A standard SEIR-type compartment model, parameterised for New Zealand, was used to simulate the spread of Covid19 in New Zealand and to test the effectiveness of various control strategies. Control aims can be broadly categorised as either suppression or mitigation. Suppression aims to keep cases to an absolute minimum for as long as possible. Mitigation aims to allow a controlled outbreak to occur, with the aim of preventing significant overloads on healthcare systems and gradually allowing the population to develop herd immunity.
Both types of strategy are fraught with uncertainty. Suppression strategies can succeed in delaying an outbreak, but only for as long as such control measures can be sustained. Once controls are eased or restricted, an epidemic is likely to follow as no herd immunity has been acquired. The success or failure of mitigation strategies can depend sensitively on the timing and efficacy of control measures, and require the ability to bring rapidly growing outbreaks under immediate control when needed. This is as yet untested even for a combination of national interventions including case isolation, household quarantine, population-wide social distancing and closure of schools and universities.
Although there are disadvantages to both types of approach, suppression has the advantage of buying time until a vaccine and/or treatment become available and allowing NZ to learn from rapidly unfolding events in other countries. A combination of successful suppression, strong border measures, and widespread contact tracing and testing resulting in containment could allow periods when control measures can be relaxed, but only if cases are reduced to a handful.
The appendix, containing the model specification, AVAILABLE HERE [MINOR REVISIONS MADE 30 MARCH 2020].
Te Pūnaha Matatini intern Shih-Hao (Samuel) Chen talks about his work with Nebula Data over the 2019-20 summer developing a tool that provides enhanced network analysis capabilities.
By Shih-Hao (Samuel) Chen
Networks arise in all shapes and forms in our everyday lives, and their features can provide new information to its respective topic. However, a list of nodes and edges alone is challenging to interpret, and transforming the dataset into a useful visualisation relies on inflexible third-party applications. We wanted to build a supportive, customisable tool that would enable data analysts to uncover new observations.
How does one analyse networks?
Networks are a group of nodes (things) and edges (relationships). The analysis of these networks provides answers to questions that otherwise would be difficult to answer, such as which bus stops have the most traffic, or which characters appear the most in a movie.
When presented with a list of nodes and edges, a computer has no intuitive method of drawing the network. After all, the visualisation is purely aesthetic. The solution is to implement a force-directed algorithm that transforms the nodes into a “natural” layout. We can see the result in Figure 1:
Figure 1. Demonstration of a force-directed network in action.
The network in this visualisation is slowly untangling, allowing us to identify central nodes and clusters.
We created our application hoping for flexibility and customisation. Upon further reading, we found that the attraction/repulsion ratio between the nodes determines the resultant network shape. As shown in Figure 2 below, we can see how the visualisation algorithm can dramatise clustering by changing the forces alone.
Figure 2. Three different attraction/repulsion force ratios, and its resultant shapes.
Although these observations are informative, metrics and values help form reliable claims on these networks. By colouring the nodes based on some measure, the application presents the information in a reader-friendly way. Figure 3 below illustrates two examples of analyses that the app supports:
Figure 3. Katz (left) and Betweenness (right) Centrality, highlighted by the colouring of the nodes.
Finally, our application also provides a time-lapse feature, where we can observe how graphs change over time. The insertion/removal of nodes and edges will change the shape of the network and analysts can use these changes to make observations.
Where to next?
Although these features are incredibly useful for uncovering hidden stories of networks, there are plenty of features that could provide further insight into the relationships around us. Community detection, for example, is an exciting field that groups nodes of high connectivity together. There are also visual features, such as nodes-overlap prevention, that would improve readability.
Shih-Hao (Samuel) Chen is currently studying a Bachelors of Engineering, specialising in Software Engineering. Samuel enjoys problem-solving and is incredibly fascinated with the complex nature of algorithms.
University of Auckland student Tamara Craigen on her summer research experience with Te Pūnaha Matatini, 3D modelling archaeological sites on Ahuahu (Great Mercury Island).
Ahuahu (Great Mercury Island) has been an area of archaeological interest for many years. The island is part of the Mercury Islands, off the coast of the Coromandel Peninsula. A multidisciplinary team from the University of Auckland and the Auckland War Memorial Museum has been involved in a long-term project in coordination with the island’s landowners Sir Michael Fay and David Richwhite and the local iwi, Ngāti Hei.
A component of this project directed by Professor Thegn Ladefoged has and continues to develop understanding of how Māori interacted with the land, especially through the ideas of kaitiakitanga and ecodynamics. Kaitiakitanga is a term that encompasses various facets of guardianship, including practices and values, while ecodynamics refers to how humans and ecological systems interact.
One area of interest on the island centres around a swamp and nearby alignments of rocks. This location resembles known Māori taro and kumara garden sites in other parts of New Zealand, prompting investigation.
Various archaeological methods have been undertaken, including excavations, pollen analysis and coring. The latter is the primary source for my project. D-section coring involves inserting a tool into the ground that takes a sample of the various sediments and dirt layers that make up the landscape, called stratigraphy. These samples can be taken in transects across an area of interest to develop a stratigraphic profile.
Over my ten-week contribution to the ongoing project, I have been working to develop the stratigraphic profiles from this garden site into 3D models of the underlying geomorphology and archaeological deposits. This has involved researching how other projects have used 3D modelling for analytical use instead of for visualisation as well as learning to use the relevant modelling and mapping software.
Digitising data from last year’s field work has also been an important aspect in preparing the models. The models my project generates will be used to understand the formation of this landscape and garden and help plan future archaeological work in the area.
Tamara Craigen is a Natalie Blair Memorial Summer Scholar from the University of Auckland who has been supported by Te Pūnaha Matatini over her ten-week project. Tamara is currently working towards a BA/BSc in Anthropology and Biological Sciences. This project has been an excellent opportunity for her to develop her skills both in her field and within a wider research community.
Three Te Pūnaha Matatini interns report on their 2019-20 summer placement with the Ministry for the Environment, where they piloted a complex systems approach to modelling policy problems.
Shnece Duncan, Ellena Black and Quyen Nguyen
During our internship, we looked at how various aspects of the real economy, the financial system and the environment could be more effectively modelled in order to improve the Ministry for the Environment’s (MfE’s) ability to analyse certain policy issues.
Using a complex systems approach, the models we developed aimed to better understand the cumulative impacts of multiple policies and stressors on the environment and people.
As an example, we developed a simulation model that explored the on-farm adoption of new practices in New Zealand. Each farmer was modelled as a separate agent within neighbourhood and social networks. The farmers were designed to be at different life-cycle stages, producing either sheep, beef, dairy or forestry products, with different decision-making strategies (environmentally friendly or profit-oriented).
During our 10-week internship at MfE, we gained invaluable insights about complex systems and complexity economics. We also gained a better understanding of agent-based models (ABMs), the benefits of ABMs over standard CGE models, and how to code them.
We would like to extend a huge thank you to Te Pūnaha Matatini for this opportunity and also to the MfE, especially senior analyst Jack Bisset, for their support and guidance throughout our internship.
Shnece Duncan is studying towards a Master of Commerce in Economics at the University of Canterbury. She is excited to apply her background in economics in a real-world situation.
Ellena Black has recently completed an Honours degree in Applied Mathematics at the University of Auckland. Her project involved creating an Agent-Based Model of gas particles that could move around in space and react on a catalytic surface.
Quyen Nguyen is in her second year Finance PhD programme at the University of Otago. Her research interest focuses on the impact of climate change on US loan portfolio valuations. She is interested in applying data science to climate finance.
By Ebba Olsen and Kahu Te Kani
Te Pūnaha Matatini (TPM) plans to introduce carbon emission reductions as a KPI from 2021 onwards. To prepare, we have analysed our carbon emissions (CO2e) from staff air travel over the past few years to better understand the centre’s past and current performance.
We calculated emissions using the Toitū carbon calculator, from all flight itineraries paid for by TPM between the years 2017 to 2019. This indicates that TPM’s air travel produced 2.04, 2.99 and 3.77 tonnes of CO2e per full-time equivalent (FTE) staff member in 2017, 2018 and 2019, respectively.
To put these numbers in perspective, our emissions were equivalent to each FTE staff member flying from Auckland to Brisbane and back twice in 2017, Auckland to Honolulu and back in 2018, and Auckland to Tokyo and back in 2019.
In 2017, TPM produced similar levels of emissions per FTE staff member compared with Victoria University of Wellington, which reported 2.00 tonnes of carbon per FTE emitted due to air travel. Yet Victoria’s emissions grew very little in 2018, sitting at 2.24 tonnes of CO2e per FTE.
TPM’s rising carbon emissions can be explained after a detailed examination of all the different reasons for staff flying (see figure below).
This analysis shows a significant increase in 2019, largely due to the “Other” category, which grew dramatically in 2019 because of the need for our investigators to attend a higher number of events related to our reapplication for TEC funding throughout the year.
As TPM is a young organisation (founded in 2015), these increases from year to year can be attributed in part to our growth, as a result of TPM attending/holding more and more events across the country. In turn, our demand for air travel as an organisation has risen, increasing our CO2 emissions.
Minimising our Annual Hui emissions
As one might expect, the largest contributing factor to total carbon emissions each year has been our Annual Hui, when the majority of our staff from around the country gather in one location. This event contributed 0.566, 0.917 and 0.748 tonnes of CO2e per FTE in 2017, 2018 and 2019, respectively.
These changes in emissions may be credited to the changes in location for the Annual Hui, as it was held in Auckland in 2017 and 2019, but was held in Christchurch in 2018.
Does this mean we should consider giving up our Hui altogether and opting for a Skype instead? Perhaps not. The Annual Hui provides an opportunity for TPM staff to come together and meet face to face, to update, discuss, and plan our research projects in a conference like manner. It is an important aspect of cultivating research excellence throughout the organisation.
Since Auckland has always been the largest hub for TPM investigators, this has meant there was an increase in the number of flights needed in 2018 compared to other years, as more investigators had to travel across the country. The distribution of our investigators as of 2019 is shown in the diagram below.
Assuming all TPM investigators attend the Annual Hui via aeroplane, we have compiled data to show the total carbon emissions produced from air travel due to this event, if it were to be held in each residing city of our investigators (see graph below).
What is quite clear is that Dunedin and Christchurch are the least efficient places to host our Annual Hui in terms of our total carbon emission measure, further reinforcing our hypothesis for such a high emissions figure for the 2018 Annual Hui.
Also in support of our theory, Hamilton and Auckland turned out to be the two lowest-carbon cities to host our Annual Hui, totalling 5.532 and 5.543 tonnes of CO2e, respectively. However, hosting in Hamilton requires 29 more investigators to travel than hosting in Auckland does, and with such a small difference between the two city’s corresponding air travel emissions, Auckland is still likely to be the most practical option.
However, we will further investigate whether Hamilton is a preferred choice for holding our Annual Hui, especially once, where feasible, other forms of transport with lower associated carbon emissions, such as train or bus, have been factored in.
It is also important to note the downsides to consistently hosting the Annual Hui and other affairs in Auckland or Hamilton, as it requires some investigators to travel far more and further than others which could result in increased absenteeism at events.
So, how can we do better?
Our analyses suggest it is important to consider holding our Annual Hui in Auckland or Hamilton. Other analyses may reveal similar insights related to other events.
As with any organisation we could lower our CO2 emissions if we simply flew less. We could hold more meetings remotely, for example over Skype, or use other more environmentally-friendly modes of transportation, if travelling is absolutely necessary. However, an obvious hurdle in using these other modes of transportation is the lack of efficient inter-city transport options across New Zealand, as highlighted in a recent article written by our director Shaun Hendy.
With the ever increasing need to address our own impacts on climate change, TPM would like to encourage other organisations to analyse their own CO2 emissions and to make improvements where possible.
Ebba Olsen is currently studying a Bachelor of Science, with a double major in Mathematics and Logic and Computation, at the University of Auckland. Ebba is excited to be able to use her analytical skills in a real world circumstance with Te Pūnaha Matatini.
Kahu Te Kani, with her passion for numbers, hopes to develop her data analysis skills during her internship with Te Pūnaha Matatini. Kahu recently completed a Bachelor of Science, majoring in mathematics and economics, at the University of Canterbury.
Te Pūnaha Matatini provided much of the impetus for Nebula Data, an innovative new data visualisation company set up by physicists with the help of the University of Auckland Business School’s Centre for Innovation and Entrepreneurship.
Nebula Data, which aims to transform how we understand the media landscape, was co-founded by University of Auckland physics student Georgia Nixon, and Shaun Hendy, professor of physics and director of Te Pūnaha Matatini, in partnership with physics students Toby Bi and Nickolas Morton.
Origins traced to Te Pūnaha Matatini research project
“The idea for Nebula grew out of a Te Pūnaha Matatini research project for the BioHeritage National Science Challenge,” said Georgia.
“They were interested in analysing the concept of “predator free” in New Zealand and wanted to devise a new method to explore the nature of this conversation in the media.
“For this project, we did a large-scale search of organisations and people who were influencing the “predator free” media landscape and built a network to reflect those who were central, those who were peripheral and how this was changing.
“After the success of this project, we were approached by a number of other organisations looking for a similar analysis.”
Team benefits from university’s entrepreneurship programme
Being involved in the University of Auckland Business School’s Centre for Innovation and Entrepreneurship’s Velocity programme in 2018 gave the team the practical business skills they needed to turn their idea into a viable venture. It provided opportunities for mentorship and introductions for support from other organisations such as ATEED and the Icehouse.
“Velocity really sparked my interest in entrepreneurship and helped me imagine what our venture could look like,” Georgia said.
“I think that often scientists aren’t in academia because they are avoiding the commercial world, but because academia offers them the freedom to research what they love. Also, a scientists’ career pathway in academia has been largely determined by the number of publications they’re able to produce.
“Commercialisation has, therefore not been given equal spotlight. Recently, there has been an encouraging rise in getting scientists to not only come up with the research ideas but to also guide it through to an end product.
“It’s great to be part of this process and see your work contributing to a bigger solution by having a positive application. Rather than treating the science as independent to commercialisation, entrepreneurship combines the two and we have been fortunate to find that middle ground with Nebula.”
Several successful projects now completed
Since being involved in the Velocity programme, the team have completed seven major projects spanning a number of industries but all rooted in network visualisations of data, natural language processing text analysis or surveys.
Types of question they have answered include:
- How has the discussion around global warming changed in New Zealand over time?
- What biases in language are used in the media when discussing nutrition vs. agriculture?
- Who the main influencers are in New Zealand’s political media landscape.
Why are Nebula data visualisations useful?
Nebula’s analytical techniques have a huge number of potential applications, in particular deciphering the impact of specific actions such as product market launches in the private sector and new policy initiatives in the public sector.
The type of analysis in critically important for any organisation interested in ways to profile issues and influence behaviours. Their compelling value proposition includes being able to translate complex data sets into visuals that can be more readily understood by decision-makers in organisations.
Interest in Nebula’s data visualisation outputs has increased rapidly since they started. The team has grown to four, but they all continue to wear multiple hats. Georgia is currently pursuing a PhD in Physics at the University of Cambridge, Toby is currently at the University of Auckland as a postgraduate researcher in Physics, and Nickolas is working as a data scientist at Arkturus Business Research.
Nebula’s future plans
In the next year, Nebula plans to expand operations.
“We are starting to pick up clients that give us reoccurring work which gives us more consistency,” says Georgia. “We’re in no rush though; we would like to keep growing at a sustainable level and enjoy the journey”.