The Role of the Mathematical Sciences in Supporting the Covid-19 Response in Australia and New Zealand

MCCAW, JAMES M.; PLANK, MICHAEL J.

doi:10.1017/s1446181123000123

Cited by 7 publications

(6 citation statements)

References 71 publications

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“…This example mainly concerns the beginning of the pandemic (~ rst 6 months of 2020). More nuanced policy questions were addressed later during the pandemic (e.g., 13,28 ). Participants said that the successful incorporation of models early on to address simple, pressing questions helped to show the value for uptake later on (good return on investment), and infrastructure that could be readily leveraged (accessibility).…”

Section: Effective Use Of Modelsmentioning

confidence: 99%

“…Our objective was to identify guidance for effective use of predictive models in practical decision-making within and across One Health sectors by understanding challenges and solutions in situations where this has been successful, and summarizing recommendations from experts who have experience with intelligence-to-decision work ows that involve science from models. We focused our inquiry on Aotearoa New Zealand (NZ) -a country with a reputation for effective consideration of model outcomes in policy decisions [10][11][12][13][14] to understand conditions that lead to successful use of models for informing decisions. We conducted 41 semi-structured interviews across One Health sectors (Fig.…”

Section: Introductionmentioning

confidence: 99%

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Effective use of models in intelligence-to-decision workflows within and across One Health sectors

Pepin,

Carlisle,

Chipman

et al. 2024

Preprint

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Decision-makers in public service face uncertainty. Operational management or policy decisions need to be made about system-level ecological and sociological processes that are complex, poorly understood, and change over time. Relying on intuition, evidence, and experience for robust decision-making is challenging without a formal assimilation of these elements (a model), especially when the decision needs to consider potential impacts if an action is or is not taken. Models can provide assistance to this challenge, but effective use of modeling tools in decision-making can be difficult due to lack of trust, expertise, and transparency and consistency in modeling methods and results. We conducted 41 semi-structured interviews of researchers, operational managers, and policy decision-makers with direct experience in intelligence-to-decision workflows involving models within and across human health, animal health, or environmental sectors (One Health sectors). Qualitative analysis of the interview data reveals important ingredients for effective development and use of quantitative models in informing management or policy decisions in One Health sectors. Two of the priorities we identified for implementing improved workflows include establishing different standards for development of modeling intelligence before or after decisions are made and investment in knowledge brokers with modeling expertise working in teams with decision-makers. These and other priorities we identified are important considerations for developers and users of modeling intelligence in a broad range of institutional contexts.

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Section: Effective Use Of Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effective use of models in intelligence-to-decision workflows within and across One Health sectors

Pepin,

Carlisle,

Chipman

et al. 2024

Preprint

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“…During the COVID-19 pandemic, effective and timely infectious disease surveillance was an integral part of government policy [1]. This emphasis on disease surveillance led to the development and extension of tools that can provide efficient and reliable updates on the effective reproduction number [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Joint estimation of the effective reproduction number and daily incidence in the presence of aggregated and missing data

Conway,

Mueller

2024

Preprint

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Disease surveillance is an integral component of government policy, allowing public health professionals to monitor transmission of infectious diseases and appropriately apply interventions. To aid with surveillance efforts, there has been extensive development of mathematical models to help inform policy decisions, However, these mathematical models rely upon data streams that are expensive and often only practical for high income countries. With a growing focus on equitable public health tools there is a dire need for development of mathematical models that are equipped to handle the data stream challenges prevalent in low and middle income countries, where data is often incomplete and subject to aggregation. To address this need, we develop a mathematical model for the joint estimation of the effective reproduction number and daily incidence of an infectious disease using incomplete and aggregated data. Our investigation demonstrates that this novel mathematical model is robust across a variety of reduced data streams, making it suitable for application in diverse regions.Author summaryMonitoring the transmission of infectious diseases is an important part of government policy that is often hindered by limitations in data streams. This is especially true in low and middle income countries where health sectors have less funding. In this work we develop a mathematical model to enhance disease surveillance by overcoming these data limitations, providing accurate inferences of relevant epidemiological parameters.

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“…Aotearoa New Zealand currently lacks dedicated forecasting tools for Covid-19 and other infectious diseases [21]. In this study, we present a method for forecasting Covid-19 cases, hospital admissions and hospital occupancy in Aotearoa New Zealand.…”

Section: Introductionmentioning

confidence: 99%

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

Plank,

Watson,

Maclaren

2023

Preprint

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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.Author summaryThe emergency phase of the Covid-19 pandemic has ended, but Covid-19 continues to put significant additional load on stretched healthcare systems. Forecasting the number of hospital cases caused an infectious disease like Covid-19 over the next few weeks can help with effective planning and response. The ability to forecast reliably requires timely, high-quality data and accurate mathematical models. We have developed a model for forecasting the number of Covid-19 cases and hospitalisations in Aotearoa New Zealand. The model works in two stages: firstly predicting the number of new cases and secondly estimating the proportion of those cases that will need hospital treatment. The model produces a range of likely values, which is important because is impossible to predict with 100% accuracy. We show that the model does a reasonably good job of predicting hospitalisations up to 3 weeks ahead. The model has been used by public health agencies in Aotearoa New Zealand to help with healthcare capacity planning.

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The Role of the Mathematical Sciences in Supporting the Covid-19 Response in Australia and New Zealand

Cited by 7 publications

References 71 publications

Effective use of models in intelligence-to-decision workflows within and across One Health sectors

Effective use of models in intelligence-to-decision workflows within and across One Health sectors

Joint estimation of the effective reproduction number and daily incidence in the presence of aggregated and missing data

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

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