Discrete Inverse Method for Extracting Disease Transmission Rates from Accessible Infection Data

• Author(s) / Creator(s)

  • Wang, Xiunan
  • Wang, Hao

• Abstract
  (description taken from this, pre-print version of the article)
  Accurate estimation of the transmissibility of an infectious disease is critical to understanding disease transmission dynamics and designing effective control strategies. However, it has always been difficult to estimate the transmission rates due to the unobservability and multiple contributing factors. In this paper, we develop a data-driven inverse method based on discretizations of compartmental differential equation models for estimating time-varying transmission rates of infectious diseases. By developing iteration algorithms for three typical classes of infectious diseases, namely a disease with seasonal cycles, a disease with non-seasonal cycles, and a disease with no obvious periodicity, we demonstrate that the discrete inverse method is a valuable tool for extracting information from available pandemic or epidemic incidence data. We also obtain insights for some epidemiological phenomena and issues in concern based on each application. Our method is highly intuitive and generates rapid implementation even with multiple years of data instances. In particular, it can be used in conjunction with other data-driven technologies, such as machine learning, to forecast future disease dynamics based on future policies or human mobility trends, providing guidance to public health authorities.
  Supplementary Materials
  (no description for supplementary materials available, includes diagrams, theorems, and equations.)
  Supplementary materials were not peer-reviewed in final published version as well.

• Date created

  2024-06-01

• Subjects / Keywords

  • inverse method
  • discretization
  • iteration
  • infectious disease
  • transmission rate
  • data-driven

• Type of Item

  Article (Draft / Submitted)

• DOI

  https://doi.org/10.7939/r3-6kyf-hn03

• License

  Attribution-NonCommercial 4.0 International