Using COVID-19 data to investigate the use of early warning signs to identify epidemic peaks and areas of concern (preprint)Link opens in a new window

Joshua Looker, Kat Rock, Louise Dyson

The SARS-CoV-2 (COVID-19) pandemic has had catastrophic effects on public health and economies. Around the world, many countries employed modelling efforts to help guide pharmaceutical and non-pharmaceutical measures designed to reduce the spread of the virus. Modelling efforts for future pandemics could use the theory of early warning signals (EWS), which aims to predict 'critical transitions' in complex dynamical systems. In infectious disease systems, such transitions correspond to (re-)emergence, peaks and troughs in infections which can be indirectly observed through the reported case data. There is increasing evidence that including EWS in modelling can help improve responses to upcoming increases or decreases in case reporting. Here, we present both theoretical and data-driven analyses of the suitability of EWS to predict critical transitions in reported case data. We derive analytical statistics for a variety of infectious disease models and show, through stochastic simulations of different modelling scenarios, the applicability of EWS in such contexts. Using the COVID-19 reported case dataset from the United Kingdom, we demonstrate the performance of a range of temporal and spatial statistics to anticipate transitions in the case data. Finally, we also investigate the applicability of using EWS analysis of hospitalisation data to anticipate transitions in the corresponding case data. Together, our findings indicate that EWS analysis could be a vital addition to future modelling analysis for real-world infection data.

Modelling Immunity in Agent-based Models (preprint)Link opens in a new window

Gray Manicolm, Emily Harvey, Joshua Looker, David Wu, Oliver Maclaren, Dion O'Neale

Vaccination policies play a central role in public health interventions and models are often used to assess the effectiveness of these policies. Many vaccines are leaky, in which case the observed vaccine effectiveness depends on the force of infection. Within models, the immunity parameters required for agent-based models to achieve observed vaccine effectiveness values are further influenced by model features such as its transmission algorithm, contact network structure, and approach to simulating vaccination. We present a method for determining parameters in agent-based models such that a set of target immunity values is achieved. We construct a dataset of desired population-level immunity values against various disease outcomes considering both vaccination and prior infection from COVID-19. This dataset incorporates immunological data, data collection methodologies, immunity models, and biological insights. We then describe how we choose minimal parameters for continuous waning immunity curves that result in those target values being realized in simulations. We use simulations of the household secondary attack rates to establish a relationship between the protection per infection attempt and overall immunity, thus accounting for the dependence of protection from acquisition on model features and the force of infection.

Introducing a framework for within-host dynamics and mutations modelling of H5N1 influenza infection in humans (preprint)Link opens in a new window

Daniel Higgins, Joshua Looker, Robert Sunnucks, Jonathan Carruthers, Thomas Finnie, Matt J. Keeling, Edward M. Hill

We present a mechanistic within-host infection model for influenza A(H5N1), novel for its explicit consideration of the biological differences between the upper and lower respiratory tracts. These developments enable us to estimate a distribution of viral lifespans and effective replication rates in human H5N1 influenza cases. We combine our within-host model with a viral mutation model to determine the probability of an infected individual generating a droplet transmissible strain of influenza A(H5N1) through mutation. For three required mutations, we found a peak probability of approximately 10⁻³ that a human case of H5N1 influenza produces at least one virion during the infectious period. Our findings provide insights into the risk of differing infectious pathways of influenza A(H5N1) (namely the avian-human vs the avian-mammal-human routes), demonstrating the three-mutation pathway being a cause of concern in human cases. Additionally, our framework - combining a within-host infection model with a branching process model for viral mutation - is generalisable to other pathogens, allowing mutation probabilities to be more easily ascertained. Our findings are a starting point for further modelling of influenza A(H5N1) and other pathogens where differing tissue susceptibilities and human-to-human transmission is of concern.

Articles and software packages (with Covid-19 Modelling AotearoaLink opens in a new window)

Covid-19 Modelling Aotearoa modelled Covid-19 and its projected impacts from January 2020 until July 2023. We built a cross-organisation, transdisciplinary, dedicated, and committed group of academic researchers and scientists to help Aotearoa New Zealand face the pandemic.

Covid-19 Modelling Aotearoa arose under the leadership of Te Pūnaha Matatini investigators, and was a standalone programme hosted by the University of Auckland from January 2021. Early in the pandemic we were funded by the Ministry of Business, Innovation and Employment, followed by the Department of the Prime Minister and Cabinet, and then Manatū Hauora Ministry of Health.

I was part of the network- and individual-based modelling team that simulated the spread of Covid-19 in Aotearoa New Zealand. We developed a synthetic population of 4.5 million individuals each modelled within a bipartite interaction network representing New Zealand.

Synthetic population and network building Python package (repository: PAINLink opens in a new window, documentation: pages)

Novel simulation methods for contagion on a bipartite network Python package (repository: cobinLink opens in a new window, documentation: pages)

Published articles and reports can be found on the CMA website.Link opens in a new window