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Specification testing for ordinary differential equation models with fixed design and applications to COVID-19 epidemic models

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  • Liu, Ran
  • Zhu, Lixing

Abstract

Checking the models about the ongoing Coronavirus Disease 2019 (COVID-19) pandemic is an important issue. Some famous ordinary differential equation (ODE) models, such as the SIR and SEIR models have been used to describe and predict the epidemic trend. Still, in many cases, only part of the equations can be observed. A test is suggested to check possibly partially observed ODE models with a fixed design sampling scheme. The asymptotic properties of the test under the null, global and local alternative hypotheses are presented. Two new propositions about U-statistics with varying kernels based on independent but non-identical data are derived as essential tools. Some simulation studies are conducted to examine the performances of the test. Based on the available public data, it is found that the SEIR model, for modeling the data of COVID-19 infective cases in certain periods in Japan and Algeria, respectively, maybe not be appropriate by applying the proposed test.

Suggested Citation

  • Liu, Ran & Zhu, Lixing, 2023. "Specification testing for ordinary differential equation models with fixed design and applications to COVID-19 epidemic models," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    • Handle: RePEc:eee:csdana:v:180:y:2023:i:c:s0167947322001967
    DOI: 10.1016/j.csda.2022.107616
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    References listed on IDEAS

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