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The Structural Identifiability of a Humidity-Driven Epidemiological Model of Influenza Transmission.
Zhang, Chunyang; Zhang, Xiao; Bai, Yuan; Lau, Eric H Y; Pei, Sen.
  • Zhang C; School of Mathematics, Changchun Normal University, Changchun 130032, China.
  • Zhang X; WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
  • Bai Y; Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong, China.
  • Lau EHY; WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
  • Pei S; Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong, China.
Viruses ; 14(12)2022 12 15.
Article in English | MEDLINE | ID: covidwho-2216897
ABSTRACT
Influenza epidemics cause considerable morbidity and mortality every year worldwide. Climate-driven epidemiological models are mainstream tools to understand seasonal transmission dynamics and predict future trends of influenza activity, especially in temperate regions. Testing the structural identifiability of these models is a fundamental prerequisite for the model to be applied in practice, by assessing whether the unknown model parameters can be uniquely determined from epidemic data. In this study, we applied a scaling method to analyse the structural identifiability of four types of commonly used humidity-driven epidemiological models. Specifically, we investigated whether the key epidemiological parameters (i.e., infectious period, the average duration of immunity, the average latency period, and the maximum and minimum daily basic reproductive number) can be uniquely determined simultaneously when prevalence data is observable. We found that each model is identifiable when the prevalence of infection is observable. The structural identifiability of these models will lay the foundation for testing practical identifiability in the future using synthetic prevalence data when considering observation noise. In practice, epidemiological models should be examined with caution before using them to estimate model parameters from epidemic data.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Influenza, Human / Epidemics Type of study: Observational study / Prognostic study Limits: Humans Language: English Year: 2022 Document Type: Article Affiliation country: V14122795

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Influenza, Human / Epidemics Type of study: Observational study / Prognostic study Limits: Humans Language: English Year: 2022 Document Type: Article Affiliation country: V14122795