Covid-19 risk assessments

Last updated: 23 December 2020

How Covid-19 risk assessments are reflected in the app

Every vessel has a comparative risk rating, which is an output of our Covid-19 model. The epidemiological model takes in several factors about a vessel’s history and daily incidence rates of Covid-19 in order to mathematically simulate contagion scenarios.

The colour of the vessel on the map represents the estimated comparative risk of crew onboard being infected with Covid-19. Red means higher risk, orange and yellow lower. The risk rating can also be found in the vessel report and on the vessel details panel.

Due to the unpredictable nature of real-world outbreaks, the rating is not meant to be definitive, but rather representative of the most-likely scenario. Note that a vessel with comparatively high risk rating will still have low absolute probability of infection (i.e. <1%).

Covid-19 information is updated daily at approximately 4 pm NZST.

Figure 1. Epidemiology risk model based on vessel history

How our Covid-19 model works

The Covid-19 risk is assessed using an epidemiological model that mathematically estimates likely contagion scenarios based on a vessel’s travel history.

Epidemiological models are widely used to inform policy development 1, predict outbreaks 2, and implement controls measures 3 in response to Covid-19 both in Aotearoa New Zealand 4, 5 and abroad.

Our model is based on the work of public health experts Wilson et al. 2 and is derived from a stochastic version of the compartmental CovidSIM model 6, which assigns people to compartments based on their infection status (susceptible, exposed, infectious, and recovered/removed) and accounts for the unpredictable nature of real-world outbreaks.

The model is populated with parameters for SARS-CoV-2 transmission, historical infection rates across the globe, and shipping characteristics for each vessel. A list of parameters is found in Table 1.

For each vessel, we consider an initially uninfected crew of 20 at a time 30 days ago. At each subsequent port visit, there is the possibility that crew may become infected as a result of interaction with the community, due to either shore leave or routine contact with port staff, stevedores, maintenance workers, etc. Contagiousness for the duration a vessel was in port, as defined by the effective reproduction number, is 2.5 2. Historical infection rates at the time the vessel was in port for each port country are obtained from Johns Hopkins University 7.

During the subsequent voyage, any infected crew members can potentially infect others on board. Contagiousness aboard the vessel is set at 3.0 2. We assumed that 71% of infected Covid-19 cases develop clearly detectable symptoms 2.

This process is repeated sequentially until the present time, at which the number of infected crew members is counted. Due to the very high probability of zero infected crew members, the simulation is repeated one million times for each vessel. This results in a distribution, or histogram, showing the likelihood of different outcomes, i.e. the number of infected crew members. The very large number of simulations with zero infected crew is beyond the axis range of the plot and is therefore not shown.

The uncertainty (spread) around possible contagion scenarios is high 2, but by taking the average outcome over one million simulations, we can approximate the most likely scenario, i.e. the average number of infected crew (Figure 2).

Figure 2. The most likely contagion scenario is found by calculating the average number of infected crew over one million simulations.

The final step is to classify each vessel as high, medium or low risk. This is done by considering the vessel’s risk relative to all others calculated to date. High risk is assigned to vessels having a likelihood of infected crew in the top 10% relative to all others. Medium risk is assigned to those in the top 75–90%, and low risk is assigned to those vessels in the 0–75% bracket of risk. This is done by calculating a cumulative distribution function based on all vessel risks to date and identifying the corresponding brackets of risk.

Figure 3. High risk is assigned to vessels having a likelihood of infected crew in the top 10% relative to all others. Medium risk is assigned to those in the top 75–90%, and low risk is assigned to those vessels in the 0–75% bracket of risk.

Vessels and their estimated comparative risk are then visualised in Starboard and updated on a daily basis.

Currently we include all commercial (cargo/tanker) vessels within the vicinity of Aotearoa New Zealand. We do not currently consider crew change information, which would provide additional input to improve risk assessment. We intend to incorporate these additional features as the data become available.

Table 1

Parameter Value(s) used Further details
Incidence of SARS-CoV-2 infection Variable Based on daily incidence rates from Johns Hopkins University 7, adjusted for under-estimation by using a 10-fold difference between reported cases and infections 2, 8
Percent of infections that are asymptomatic 29% See 2, 9
Latency period 5 days See 2, 10
Prodromal period 1 day See 2
Symptomatic period 10 days (split into 2 periods of 5 days each) See 2, 11
Relative contagiousness in the prodromal period 100% See 2, 10
Contagiousness after the prodromal period 100% (first 5 days), 50% (second 5 days) See 2, 12
Effective reproduction number on board the ship 3.0 See 2
Effective reproduction number in the port 2.5 See 2, 10
Duration in port Variable Based on vessel transponder data
Voyage length Variable Based on vessel transponder data.
Crew size 20 See 2, 13.

References

  1. Thompson, R.N. Epidemiological models are important tools for guiding Covid-19 interventions. BMC Med 18, 152 (2020). https://doi.org/10.1186/s12916-020-01628-4.
  2. Wilson N, Blakely T, Baker M, Eichner M. Estimating the Risk of Outbreaks of Covid-19 Associated with Shore Leave by Merchant Ship Crews: Simulation Studies for New Zealand. N Z Med J (in press).
  3. Wilson, N., Baker, M. G., & Eichner, M. (2020). Estimating the Impact of Control Measures to Prevent Outbreaks of Covid-19 Associated with Air Travel into a Covid-19-free country: A Simulation Modelling Study. medRxiv.
  4. Jefferies, S., French, N., Gilkison, C., Graham, G., Hope, V., Marshall, J., … & Prasad, N. (2020). Covid-19 in New Zealand and the impact of the national response: a descriptive epidemiological study. The Lancet Public Health, 5(11), e612–e623.
  5. Wilson, N., Telfar Barnard, L., Kvalsvig, A., & Baker, M. (2020). Potential health impacts from the Covid-19 pandemic for New Zealand if eradication fails: report to the NZ ministry of health. Wellington: University of Otago Wellington.
  6. Schneider, K., Ngwa, G. A., Schwehm, M., Eichner, L., & Eichner, M. (2020). The Covid-19 Pandemic Preparedness Simulation Tool: CovidSIM. Available at SSRN 3578789.
  7. Dong E, Du H, Gardner L. An interactive web-based dashboard to track Covid-19 in real time. Lancet Inf Dis. 20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1.
  8. Havers FP, Reed C, Lim T, Montgomery JM, Klena JD, Hall AJ, et a. Seroprevalence of Antibodies to SARS-CoV-2 in 10 Sites in the United States, March 23-May 12, 2020. JAMA Intern Med 2020.
  9. Pollan M, Perez-Gomez B, Pastor-Barriuso R, Oteo J, Hernan MA, Perez-Olmeda M, Sanmartin JL, Fernandez-Garcia A, Cruz I, Fernandez de Larrea N, Molina M, Rodriguez-Cabrera F, Martin M, Merino-Amador P, Leon Paniagua J, Munoz-Montalvo JF, Blanco F, Yotti R, Group E-CS. Prevalence of SARS-CoV-2 in Spain (ENE-COVID): a nationwide, population- based seroepidemiological study. Lancet 2020.
  10. Centers for Disease Control and Prevention. Covid-19 pandemic planning scenarios. 2020; (20 May). https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html.
  11. WHO-China Joint Mission. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (Covid-19). 2020; (16–24 February). https://www.who.int/docs/default-source/coronavirus/who-china-joint-mission-on-covid-19-final-report.pdf.
  12. Woelfel R, Corman V, Guggemos W, Seilmaier M, Zange S, Mueller M, Niemeyer D, Vollmar P, Rothe C, Hoelscher M, Bleicker T, Bruenink S, Schneider J, Ehmann R, Zwirglmaier K, Drosten C, Wendtner C. Clinical presentation and virological assessment of hospitalized cases of coronavirus disease 2019 in a travel-associated transmission cluster. medRxiv 2020; (8 March). https://www.medrxiv.org/content/10.1101/2020.03.05.20030502v1.
  13. Wikipedia. Maritime transport. (Accessed 20 August 2020). https://en.wikipedia.org/wiki/Maritime_transport.