Can Machines Learn Respiratory Virus Epidemiology?: A Comparative Study of Likelihood-Free Methods for the Estimation of Epidemiological Dynamics

Tessmer, Heidi L.; Ito, Kimihito; Omori, Ryosuke

doi:10.3389/fmicb.2018.00343

Cited by 10 publications

(5 citation statements)

References 27 publications

(42 reference statements)

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“… ➢ Between 2014 and 2018, three studies applied fuzzy logic to forecast effects of non-pharmaceutical interventions or optimal vaccination strategies using historical H1N1 data 92 , 93 , and allocation of hospital personnel during a hypothetical pandemic 94 . ➢ In 2018, various types of neural networks were used to estimate the reproductive number for a compartmental model using H1N1 data 95 . ➢ In 2012, a support vector machine was used to analyze Tweets posted during the H1N1 pandemic to inform forecasts of influenza-like illness 96 .…”

Section: Resultsmentioning

confidence: 99%

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

et al. 2021

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Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

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Section: Resultsmentioning

confidence: 99%

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

et al. 2021

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“…Model tuning is critical and continues to be discussed as a salient concept in epidemiology. Tessmer and colleagues (61) showcase this with respect to improving R 0 calculations in infectious disease epidemiology and dynamics.…”

Section: Pretraining/hyperparameter Optimizationmentioning

confidence: 99%

Machine Learning in Epidemiology and Health Outcomes Research

Wiemken

Kelley

2020

Annu. Rev. Public Health

156

107

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Machine learning approaches to modeling of epidemiologic data are becoming increasingly more prevalent in the literature. These methods have the potential to improve our understanding of health and opportunities for intervention, far beyond our past capabilities. This article provides a walkthrough for creating supervised machine learning models with current examples from the literature. From identifying an appropriate sample and selecting features through training, testing, and assessing performance, the end-to-end approach to machine learning can be a daunting task. We take the reader through each step in the process and discuss novel concepts in the area of machine learning, including identifying treatment effects and explaining the output from machine learning models.

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“…While CNNs are not a typical choice for solving problems involving time-series data, they are applied here owing to the complex nature of the SEIR modeling data. [31][32][33][34][35] The CNN model comprised three one-dimensional convolutional layers with a single dense hidden layer before the output. The CNN model outputs are parameter values (R 0 , 1/γ, and 1/ε).…”

Section: Reconstructed Modelsmentioning

confidence: 99%

Anticipating the transmissibility of the 2022 mpox outbreak

Liu

Yang

Luo

et al. 2023

Journal of Medical Virology

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An ongoing outbreak of monkeypox virus (MPXV) was first reported in the United Kingdom on 6 May 2022. As of 17 November, there had been a total of 80 221 confirmed MPXV cases in over 110 countries. Based on data reported between 6 May and 30 June 2022 in the United Kingdom, Spain, and Germany, we applied a deep learning approach using convolutional neural networks to evaluate the parameters of the 2022 MPXV outbreak. The basic reproduction number (R0) of MPXV was estimated to be 2.32 in the United Kingdom, which indicates the active diffusion of MPXV since the beginning of the outbreak. The data from Spain and Germany produced higher median R0 values of 2.42 and 2.88, respectively. Importantly, the estimated R0 of MPXV in the three countries tends to the previously calculated R0 of smallpox (3.50 to 6.00). Furthermore, the incubation (1/ε) and infectious (1/γ) period was predicted between 9 and 10 days and 4−5 days, respectively. The R0 value derived from MPXV is consistent with the significantly increasing number of cases, indicating the risk of a rapid spread of MPXV worldwide, which would provide important insights for the prevention and control of MPXV epidemic.

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Can Machines Learn Respiratory Virus Epidemiology?: A Comparative Study of Likelihood-Free Methods for the Estimation of Epidemiological Dynamics

Cited by 10 publications

References 27 publications

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

Machine Learning in Epidemiology and Health Outcomes Research

Anticipating the transmissibility of the 2022 mpox outbreak

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