Early Warning Signal Detection for Ross River Virus in Queensland, Australia

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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.


Type of resource text
Date modified December 5, 2022
Publication date May 5, 2022; May 2022


Author Abraham, Ann
Thesis advisor Mordecai, Erin
Thesis advisor Petrov, Dmitri
Degree granting institution Stanford University, Department of Biology


Subject Ross River Virus
Subject infectious disease
Subject temperature
Genre Text
Genre Thesis

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This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International license (CC BY-NC).

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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


Undergraduate Theses, Department of Biology, 2021-2022

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