Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic

Warne, David J.; Ebert, Anthony; Drovandi, Christopher; Mira, Antonietta; Mengersen, Kerrie

doi:10.1186/s12889-020-09972-z

Cited by 16 publications

(22 citation statements)

References 46 publications

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“…Recently, COVID-19 mRNA vaccines have been the first licensed for use and are rapidly being administered as supplies are provided ( 28 , 36 ). However, given the time required for adequate immunization coverage in the population at large, subsequent pandemic waves are anticipated ( 31 , 37 – 39 ). Therefore, detection methods for SARS-CoV-2 remain a crucial part of containment and mitigation strategies, and lessons learned from this pandemic may help prepare against future pandemics.…”

Section: Introductionmentioning

confidence: 99%

Tools and Techniques for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)/COVID-19 Detection

et al. 2021

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SUMMARY The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory disease coronavirus 2 (SARS-CoV-2), has led to millions of confirmed cases and deaths worldwide. Efficient diagnostic tools are in high demand, as rapid and large-scale testing plays a pivotal role in patient management and decelerating disease spread. This paper reviews current technologies used to detect SARS-CoV-2 in clinical laboratories as well as advances made for molecular, antigen-based, and immunological point-of-care testing, including recent developments in sensor and biosensor devices. The importance of the timing and type of specimen collection is discussed, along with factors such as disease prevalence, setting, and methods. Details of the mechanisms of action of the various methodologies are presented, along with their application span and known performance characteristics. Diagnostic imaging techniques and biomarkers are also covered, with an emphasis on their use for assessing COVID-19 or monitoring disease severity or complications. While the SARS-CoV-2 literature is rapidly evolving, this review highlights topics of interest that have occurred during the pandemic and the lessons learned throughout. Exploring a broad armamentarium of techniques for detecting SARS-CoV-2 will ensure continued diagnostic support for clinicians, public health, and infection prevention and control for this pandemic and provide advice for future pandemic preparedness.

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Section: Introductionmentioning

confidence: 99%

Tools and Techniques for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)/COVID-19 Detection

et al. 2021

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“…We have also focussed our attention on ABC inference for partially observed discrete-state Markov processes since ABC methods are widely applied for such models in systems biology [20,85,89,90], epidemiology [91,92,93,94,95], ecology [96,97], and physics [98,99,100]. Consequently, the high-fidelity and low-fidelity simulations within the MF-ABC framework are, respectively, assumed to be the natural choices of Gillespie's direct method and the tau-leaping method parameterised by τ .…”

Section: Discussionmentioning

confidence: 99%

Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes

Warne,

Prescott,

Baker

et al. 2021

Preprint

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Models of stochastic processes are widely used in almost all fields of science. Theory validation, parameter estimation, and prediction all require model calibration and statistical inference using data. However, data are almost always incomplete observations of reality. This leads to a great challenge for statistical inference because the likelihood function will be intractable for almost all partially observed stochastic processes. This renders many statistical methods, especially within a Bayesian framework, impossible to implement. Therefore, computationally expensive likelihood-free approaches are applied that replace likelihood evaluations with realisations of the model and observation process. For accurate inference, however, likelihood-free techniques may require millions of expensive stochastic simulations. To address this challenge, we develop a new method based on recent advances in multilevel and multifidelity methods for parameter inference using partially observed Markov processes. Our novel approach combines the multilevel Monte Carlo telescoping summation, applied to a sequence of approximate Bayesian posterior targets, with a multifidelity rejection sampler that learns from computationally inexpensive model approximations to minimise the number of computationally expensive exact simulations required for accurate inference. We present the derivation of our new algorithm for likelihood-free Bayesian inference, discuss practical implementation details, and demonstrate substantial performance improvements. Using examples from systems biology, we demonstrate improvements of more than two orders of magnitude over standard rejection sampling techniques. Our approach is generally applicable to accelerate other sampling schemes, such as sequential Monte Carlo, to enable feasible Bayesian analysis for realistic practical applications in physics, chemistry, biology, epidemiology, ecology and economics. We provide source code implementations of our methods and demonstrations (available at https://github.com/davidwarne/MLMCandMultifidelityForABC).

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“…The kind of data available for the epidemic model differs significantly from that for the experiment-based models we have considered thus far: we are interested in a practical identifiability problem where data from only a single time-series is available, which mirrors data available from an actual epidemic [152]. We first consider practical identifiability using data from the early part of the epidemic, before the number royalsocietypublishing.org/journal/rsif J. R. Soc.…”

Section: Epidemic Modelmentioning

confidence: 99%

Identifiability analysis for stochastic differential equation models in systems biology

Browning

Warne

Burrage

et al. 2020

J. R. Soc. Interface.

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Mathematical models are routinely calibrated to experimental data, with goals ranging from building predictive models to quantifying parameters that cannot be measured. Whether or not reliable parameter estimates are obtainable from the available data can easily be overlooked. Such issues of parameter identifiability have important ramifications for both the predictive power of a model, and the mechanistic insight that can be obtained. Identifiability analysis is well-established for deterministic, ordinary differential equation (ODE) models, but there are no commonly adopted methods for analysing identifiability in stochastic models. We provide an accessible introduction to identifiability analysis and demonstrate how existing ideas for analysis of ODE models can be applied to stochastic differential equation (SDE) models through four practical case studies. To assess structural identifiability , we study ODEs that describe the statistical moments of the stochastic process using open-source software tools. Using practically motivated synthetic data and Markov chain Monte Carlo methods, we assess parameter identifiability in the context of available data. Our analysis shows that SDE models can often extract more information about parameters than deterministic descriptions. All code used to perform the analysis is available on Github .

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Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic

Cited by 16 publications

References 46 publications

Tools and Techniques for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)/COVID-19 Detection

Tools and Techniques for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)/COVID-19 Detection

Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes

Identifiability analysis for stochastic differential equation models in systems biology

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