Early Warning Signal Detection for Ross River Virus in Queensland, Australia
Abstract/Contents
- Abstract
- Over the past few decades, there have been resurgences in vector-borne diseases that may have been driven by factors such as climate change, drug resistance, and urbanization. To predict these changes, forecasting methods and simulation models have been developed, but they are difficult to apply to vector-borne diseases due to limited case data available and the complex dynamics of arthropod vectors. Having the ability to predict imminent outbreaks before cases increase is imperative to mitigate the consequences of early exponential growth. One promising approach applies the theory of critical slowing down, which predicts that as a system approaches a critical transition (for example, from disease elimination to resurgence), its ability to return to stability following minor disturbances diminishes. This phenomenon results in particular patterns in disease incidence that are detectable as characteristic changes in summary statistics prior to the critical transition. Additionally, temperature strongly influences mosquito traits and life cycles, which in turn affect disease transmission. Detection of thermal thresholds, temperature moving from an inhibitory range to a suitable range for transmission, can allow us to take timely preventative actions prior to a potential outbreak. We tested for early warning signals of critical slowing down of Ross River virus in Queensland, Australia, by calculating nine summary statistics across a rolling window. The summary statistics were then compared to the temperature suitability for transmission (defined as relative R0(T) from a previous model) to determine whether the system crossed a thermal threshold. Five of the 15 HHS regions exhibited critical slowing down, primarily in variance, skewness, kurtosis, autocorrelation, and index of dispersion. Gold Coast exhibited the strongest evidence for critical slowing down occurring when temperatures crossed a threshold between 18.2-18.3 degrees Celsius. This shows that early warning signal detection can aid in predicting disease outbreaks and determining possible influential variables such as temperature.
Description
| Type of resource | text |
|---|---|
| Date modified | December 5, 2022 |
| Publication date | May 5, 2022; May 2022 |
Creators/Contributors
| Author | Abraham, Ann |
|---|---|
| Thesis advisor | Mordecai, Erin |
| Thesis advisor | Petrov, Dmitri |
| Degree granting institution | Stanford University, Department of Biology |
Subjects
| Subject | Ross River Virus |
|---|---|
| Subject | infectious disease |
| Subject | temperature |
| Genre | Text |
| Genre | Thesis |
Bibliographic information
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- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International license (CC BY-NC).
Preferred citation
- Preferred citation
- Abraham, A. and Mordecai, E. (2022). Early Warning Signal Detection for Ross River Virus in Queensland, Australia. Stanford Digital Repository. Available at https://purl.stanford.edu/qy260fm4817
Collection
Undergraduate Theses, Department of Biology, 2021-2022
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- Contact
- ann5@stanford.edu
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