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. 2022 Jan 12;10(1):139.
doi: 10.3390/healthcare10010139.

A Network Dynamics Model for the Transmission of COVID-19 in Diamond Princess and a Response to Reopen Large-Scale Public Facilities

Yuchen Zhu  1 , Ying Wang  2 , Chunyu Li  1 , Lili Liu  1 , Chang Qi  1 , Yan Jia  1 , Kaili She  1 , Tingxuan Liu  1 , Huaiping Zhu  3 , Xiujun Li  1
Affiliations

A Network Dynamics Model for the Transmission of COVID-19 in Diamond Princess and a Response to Reopen Large-Scale Public Facilities

Yuchen Zhu et al. Healthcare (Basel). .

Abstract

Background: The current epidemic of COVID-19 has become the new normal. However, the novel coronavirus is constantly mutating. In public transportation or large entertainment venues, it can spread more quickly once an infected person is introduced. This study aims to discuss whether large public facilities can be opened and operated under the current epidemic situation. Methods: The dual Barabási-Albert (DBA) model was used to build a contact network. A dynamics compartmental modeling framework was used to simulate the COVID-19 epidemic with different interventions on the Diamond Princess. Results: The effect of isolation only was minor. Regardless of the transmission rate of the virus, joint interventions can prevent 96.95% (95% CI: 96.70-97.15%) of infections. Compared with evacuating only passengers, evacuating the crew and passengers can avoid about 11.90% (95% CI: 11.83-12.06%) of infections; Conclusions: It is feasible to restore public transportation services and reopen large-scale public facilities if monitoring and testing can be in place. Evacuating all people as soon as possible is the most effective way to contain the outbreak in large-scale public facilities.

Keywords: COVID-19; dynamics model; network; public facilities; the Diamond Princess.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results.

Figures

Figure 1
Figure 1
The structure of SEIAR model.
Figure 2
Figure 2
The epidemic curves of COVID-19 when the control measure was isolation only. (a,b) The change in the number of infections according to (a) the start time or (b) the intensity of isolation; (c) the number of infections when the social intention was changed; (d) the number of asymptomatic infections. The number of cases represents the people in state I on that day, not the number of newly reported cases each day.
Figure 3
Figure 3
The epidemic curves of COVID-19 when the control measure was evacuation. (a) Results of changing the start time of evacuation when the number of evacuees was low; (b,c) results of changing (b) the number of evacuees and (c) the identity of evacuees; (d) results of changing the social intentions of passengers when the number of evacuees was high. The number of cases represents people in state I on that day, not the number of newly reported cases each day.
Figure 4
Figure 4
(a) The epidemic curves of COVID-19 when implementing joint interventions; (b–d) prevalence of infectious diseases after (b) isolation, (c) evacuation, and (d) joint intervention measures when the transmission rate increased by 50%. The number of all cases represents the sum of people infected that day, not the number of new cases.
Figure 5
Figure 5
(a,b) Results of the (a) isolation and (b) joint interventions under four transmission rates; (c) transmission rate of COVID-19 and (d) sensitivity analysis to asymptomatic infections when the transmission rate increased by 50%; (c) results of COVID-19 and (d) theoretical infectious disease when the transmission rate increased by 50%. Baseline, Isolation, and Joint denote an epidemic of infectious diseases when there were no asymptomatic infections. Baseline_A, Isolation_A, and Joint_A denote an epidemic of infectious diseases when the proportion of asymptomatic infections increased to 50%. Isolation refers to Isolation 6 in Table 1 and Joint refers to Joint 4 in Table 1.
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