Bayesian approach for modelling the dynamic of COVID-19 outbreak on the Diamond Princess Cruise Ship

Abstract

AbstractThe outbreaks of acute respiratory infectious disease with high attack rates on cruise ships were rarely studied. The outbreak of COVID-19 on the Diamond Princess Cruise Ship provides an unprecedented opportunity to estimate its original transmissibility with basic reproductive number (R0) and the effectiveness of containment measures. The traditional deterministic approach for estimating R0 is based on the outbreak of a large population size rather than that a small cohort of cruise ship. The parameters are therefore fraught with uncertainty. To tackle this problem, we developed a Bayesian Susceptible-Exposed-Infected-Recovery (SEIR) model with ordinary differential equation (ODE) to estimate three parameters, including transmission coefficients, the latent period, and the recovery rate given the uncertainty implicated the outbreak of COVID-19 on cruise ship with modest population size. Based on the estimated results on these three parameters before the introduction of partial containment measures, the natural epidemic curve after intervention was predicted and compared with the observed curve in order to assess the efficacy of containment measures. With the application of the Bayesian model to the empirical data on COVID-19 outbreak on the Diamond Princess Cruise Ship, the R0 was estimated as high as 5.71(95% credible interval: 4.08-7.55) because of its aerosols and fomite transmission mode. The simulated trajectory shows the entire epidemic period without containment measurements was approximately 47 days and reached the peak one month later after the index case. The partial containment measure reduced 34% (95% credible interval: 31-36%) infected passengers. Such a discovery provides an insight into timely evacuation and early isolation and quarantine with decontamination for containing other cruise ships and warship outbreaks.