ALMA

ALMA, short for Aotearoa's Large Scale Multi-Agent simulator, is a model of the whole of New Zealand in which every resident is stood in for by a simulated person.^1,3 There are more than five million of them.³ Each carries over 80 attributes, including age, gender, ethnicity, household and workplace.¹ Each has a "life diary" that moves them through time and space across the country's geography.³ The population is built from more than 30 datasets supplied by government agencies and seeded from suburb-level figures, and PHF Science states that no individual's personal data is used.¹

PHF Science is the New Zealand Institute for Public Health and Forensic Science.¹ Its data science team built ALMA, which grew out of the institute's COVID-19 modelling³, and it works closely with New Zealand's Public Health Agency and other partners.¹ The data science lead, Alvaro Orsi, presented it at Digital Health Week NZ in November 2025.⁴ The institute has also built a separate twin of Tonga for studying resilience to climate change.⁴

The published use is measles. ALMA has been used to model the impact of a measles outbreak and the country's resilience to one, so that officials can try policy responses on the simulated population first.^1,4 Neither PHF Science's pages nor the January 2026 news report give results.^1,3,4 Other intended uses are evaluating vaccination campaigns and distancing, climate resilience, urban planning, the effect of economic shocks on businesses, and supply chains.¹ It describes the analysis as combining time series models with causal discovery, methods that try to work out which factors drive which.^1,3

ALMA is not open. The pages point to no public code, dataset, method paper or licence, and they offer a demonstration on request.¹ That makes it an example of what a national population twin run by a public agency looks like, rather than a tool others can pick up. Its accuracy cannot be judged from what is published.