Speaker
Description
Epidemiological models are key tools for understanding infectious disease dynamics. Traditional SIR models assume that all individuals in a host population are initially susceptible, limiting their ability to predict complex outbreak patterns observed in real-world epidemics. Here, we introduce the USIR model, which incorporates the concept of evolutionary niche expansion—an adaptation of Malthusian principles to epidemiology. This framework recognizes that initially unsusceptible individuals may become susceptible through pathogen adaptation. By including an "unsusceptible" compartment, the USIR model captures dynamics overlooked by classical models, such as bimodal incidence curves separated by years.
We demonstrate the model's applicability using the example of Monkeypox (Mpox) in Australia, which exhibited a small peak in September 2022 followed by a resurgence in November 2024. The USIR model accurately simulated this behavior, providing insights into how pathogen-host interactions evolve over time. Key findings highlight that the model’s ability to extend peak predictions years ahead stems from its representation of unsusceptible individuals transitioning into susceptibility through pathogen adaptation.
Our study illustrates the USIR model’s potential for long-term epidemic prediction, after an initial/previous peak has occurred and its relevance to public health strategies, particularly for emerging zoonotic pathogens. By incorporating pathogen evolution and resource accessibility, these models provide a robust framework for forecasting and managing infectious disease outbreaks.
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