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Background The Diamond Princess cruise ship was the site of a large outbreak of coronavirus disease 2019 (COVID-19). Of 437 Americans and their travel companions on the ship, 114 (26%) tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods We interviewed 229 American passengers and crew after disembarkation following a ship-based quarantine to identify risk factors for infection and characterize transmission onboard the ship. Results The attack rate for passengers in single-person cabins or without infected cabinmates was 18% (58/329), compared with 63% (27/43) for those sharing a cabin with an asymptomatic infected cabinmate, and 81% (25/31) for those with a symptomatic infected cabinmate. Whole genome sequences from specimens from passengers who shared cabins clustered together. Of 66 SARS-CoV-2-positive American travelers with complete symptom information, 14 (21%) were asymptomatic while on the ship. Among SARS-CoV-2-positive Americans, 10 (9%) required intensive care, of whom 7 were ≥70 years. Conclusion Our findings highlight the high risk of SARS-CoV-2 transmission on cruise ships. High rates of SARS-CoV-2 positivity in cabinmates of individuals with asymptomatic infections suggest that triage by symptom status in shared quarters is insufficient to halt transmission. A high rate of intensive care unit admission among older individuals complicates the prospect of future cruise travel during the pandemic, given typical cruise passenger demographics. The magnitude and severe outcomes of this outbreak were major factors contributing to the Centers for Disease Control and Prevention’s decision to halt cruise ship travel in U.S. waters in March 2020.

Ecological and laboratory studies have demonstrated that temperature modulates West Nile virus (WNV) transmission dynamics and spillover infection to humans. Here we explore whether inclusion of temperature forcing in a model depicting WNV transmission improves WNV forecast accuracy relative to a baseline model depicting WNV transmission without temperature forcing. Both models are optimized using a data assimilation method and two observed data streams: mosquito infection rates and reported human WNV cases. Each coupled model-inference framework is then used to generate retrospective ensemble forecasts of WNV for 110 outbreak years from among 12 geographically diverse United States counties. The temperature-forced model improves forecast accuracy for much of the outbreak season. From the end of July until the beginning of October, a timespan during which 70% of human cases are reported, the temperature-forced model generated forecasts of the total number of human cases over the next 3 weeks, total number of human cases over the season, the week with the highest percentage of infectious mosquitoes, and the peak percentage of infectious mosquitoes that on average increased absolute forecast accuracy 5%, 10%, 12%, and 6%, respectively, over the non-temperature forced baseline model. These results indicate that use of temperature forcing improves WNV forecast accuracy and provide further evidence that temperature influences rates of WNV transmission. The findings provide a foundation for implementation of a statistically rigorous system for real-time forecast of seasonal WNV outbreaks and their use as a quantitative decision support tool for public health officials and mosquito control programs.

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