Accurately Estimating Unreported Infections using Information Theory

  1. Jiaming Cui
  2. Bijaya Adhikari
  3. Arash Haddadan
  4. A S M Ahsan-Ul Haque
  5. Jilles Vreeken
  6. Anil Vullikanti
  7. B. Aditya Prakash

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Abstract

One of the most significant challenges in combating against the spread of infectious diseases was the difficulty in estimating the true magnitude of infections. Unreported infections could drive up disease spread, making it very hard to accurately estimate the infectivity of the pathogen, therewith hampering our ability to react effectively. Despite the use of surveillance-based methods such as serological studies, identifying the true magnitude is still challenging. This paper proposes an information theoretic approach for accurately estimating the number of total infections. Our approach is built on top of Ordinary Differential Equations (ODE) based models, which are commonly used in epidemiology and for estimating such infections. We show how we can help such models to better compute the number of total infections and identify the parametrization by which we need the fewest bits to describe the observed dynamics of reported infections. Our experiments on COVID-19 spread show that our approach leads to not only substantially better estimates of the number of total infections but also better forecasts of infections than standard model calibration based methods. We additionally show how our learned parametrization helps in modeling more accurate what-if scenarios with non-pharmaceutical interventions. Our approach provides a general method for improving epidemic modeling which is applicable broadly.

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  1. Version published to 10.1101/2021.09.14.21263467v4 on medRxiv
  2. Version published to 10.1101/2021.09.14.21263467v3 on medRxiv
  3. Version published to 10.1101/2021.09.14.21263467v2 on medRxiv
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    Please note, not all rigor criteria are appropriate for all manuscripts.

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    One of the limitations of our work is that the benefits of using MdlInfer depends on the suitability of the base epidemiological model. If the base epidemiological model is not expressive enough for the observed data, then the gains from MdlInfer may not be significant. As future work, it may be useful to adapt MdlInfer to give a measure of the quality of the base epidemiological model. We also note that MdlInfer is built on ODE-based epidemiological models; other kinds of epidemic models, e.g., agent-based models [25, 52, 34, 55, 23, 46], are more suitable in some settings. It would be interesting to extend MdlInfer to incorporate such models. Finally, there is significant population heterogeneity in disease outcomes, e.g., there are differences in severity rate or mortality rate, when infected with COVID-19, for different age group [44, 33], which has not been considered in our work. To summarize, MdlInfer is a robust data-driven method to accurately estimate unreported infections, which will help data scientists, epidemiologists, and policy makers to further improve existing ODE-based epidemiological models, make accurate forecasts, and combat the ongoing COVID-19 pandemic. More generally, MdlInfer opens up a new line of research in epidemiology using information theoretic methods.

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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  5. Version published to 10.1101/2021.09.14.21263467v1 on medRxiv