Modeling the Impact of Exposed Cases in a Hantavirus Outbreak on a Cruise Ship
TLDR
A stochastic model estimates hantavirus transmission on a cruise ship, revealing hidden exposed cases and a high reproduction number (2.76) before quarantine.
Key contributions
- Developed a discrete-time SEIRD model to simulate hantavirus transmission on a cruise ship.
- Used an Ensemble Adjustment Kalman Filter to infer epidemiological parameters from reported case data.
- Estimated a basic reproduction number (R0) of 2.76, indicating high potential for sustained transmission.
- Identified a hidden reservoir of exposed individuals, undetectable by symptom-based surveillance.
Why it matters
This study offers a crucial modeling framework for assessing infectious disease outbreaks in confined settings like cruise ships. It highlights the critical need for rapid, widespread testing and active monitoring to detect hidden cases, which symptom-based surveillance alone misses. This approach can inform timely intervention planning.
Original Abstract
The emergence of a hantavirus variant aboard a commercial cruise ship presents a significant public health concern. This study develops a discrete-time stochastic Susceptible-Exposed-Infectious-Recovered-Dead model to estimate transmission dynamics, hidden exposed infections, and outbreak risk among passengers and crew. Epidemiological parameters and latent disease states were inferred using an Ensemble Adjustment Kalman Filter calibrated to reported case data from WHO and ECDC situation reports. The estimated basic reproduction number was 2.76, with a 95\% confidence interval of 2.52-2.99, indicating substantial potential for sustained onboard transmission before strict quarantine measures. Simulations further suggest that several exposed individuals may remain unidentified during the early outbreak phase, creating a hidden reservoir that symptom-based surveillance alone may fail to detect. These findings highlight the importance of rapid surveillance, widespread testing, targeted quarantine, and active monitoring of exposed individuals in confined travel settings. The proposed modeling framework can support timely outbreak assessment and intervention planning for infectious-disease events in similarly dense and spatially constrained populations.
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