Quantifying the effect of climate on respiratory viruses: From seasonality to interannual variability

Morales Miranda, Adriana

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https://hdl.handle.net/2142/120298 Copy

Description

Title

Quantifying the effect of climate on respiratory viruses: From seasonality to interannual variability

Author(s)

Morales Miranda, Adriana

Issue Date

2023-04-21

Director of Research (if dissertation) or Advisor (if thesis)

• Martinez, Pamela P.
• Kirkpatrick, Kay

Doctoral Committee Chair(s)

DeVille, Lee

Committee Member(s)

Rapti, Zoi

Department of Study

Mathematics

Discipline

Mathematics

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• mathematical modeling
• respiratory viruses
• partially observed Markov process
• infectious disease dynamics

Abstract

Environmental factors have been shown to partially explain the seasonality and interannual variability of infectious diseases. Despite the wide amount of research in this area, the link between climatic factors and the transmission of respiratory diseases at different temporal scales is still poorly understood, especially in the Southern Hemisphere. In this thesis, we focus on the transmission dynamics of Respiratory Syncytial Virus (RSV) and Influenza A, two of the leading pathogens responsible for substantial morbidity and mortality worldwide. Recently available data of weekly lab-confirmed cases from Chile provide a unique opportunity to understand the effect of temperature and specific humidity on the seasonality and interannual variability of RSV and Influenza A. Using statistical models and techniques we perform an in depth data analysis to explore the association of disease incidence and environmental drivers across all of Chile. In addition, we quantify the effect of climate covariates on the transmission dynamics by formulating multiple mechanistic compartmental models that take into account intrinsic and extrinsic factors that may impact disease transmission. Using a partially observed Markov process modeling framework, we construct Susceptible-Infected-Recovered stochastic transmission models that mimic the dynamics of disease spread and perform iterated filtering to calculate the maximum log likelihood estimate of model parameters using data from the capital of Chile. Our statistical analyses show consistent annual seasonality, with cases prevalent during the winter months across all of Chile. Specific humidity is significantly associated with the mean timing onset of RSV, where a 5 g/kg increase in mean annual specific humidity and a 1 ºC increase in mean annual temperature shift the timing of the RSV epidemic back by one week. These associations are weaker for Influenza A. Furthermore, even though both viruses are sensitive to climate their onset patterns are very different, with RSV starting in the North and Influenza A in the South, highlighting the inherent differences in pathogen virology. Moreover, we observe a latitudinal gradient with respect to the climate covariates suggesting that both viruses can survive during very different winter conditions. When looking at the results from both statistical and mechanistic models, we found no significant association between climate covariates and the year-to-year variation in amplitude of epidemics for RSV and Influenza A. Nevertheless, the mechanistic epidemiological models presented in this study are able to capture the timing and seasonality of disease onset. This thesis provides a platform for understanding the impact of environmental factors on the transmission dynamics of RSV and Influenza A. Determining the spatiotemporal transmission patterns of infectious diseases and the environmental factors impacting transmission is one of the key steps in infectious disease modeling and control. While both RSV and Influenza A exhibit strong seasonal epidemics during the winter months, more research is needed to better understand the associations between climate drivers, pathogen survival, human mobility, age structure, and disease transmission. Identifying specific factors relevant to a particular virus in a certain geographic location can help public health professionals make informed decisions, effectively respond to outbreaks, and prioritize the allocation of resources.

Graduation Semester

2023-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2023 Adriana Morales Miranda

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