Abstract
The world is becoming increasingly vulnerable to infectious diseases, creating a global health security issue. Over the last 2 decades, many global and national health crises have emerged such as SARS, H5N1, H1N1, and now COVID-19. The recent COVID-19 pandemic reflects how unexpected events often audit our resilience (Weick and Sutclifffe [10]. The mortality and morbidity statistics associated with COVID-19 has become a key impact metric. At the time of publication, in Canada, upwards of 675,000 cases of COVID 19 have been reported and 17,500 deaths (https://health-infobase.canada.ca/covid-19/epidemiological-summary-covid-19-cases.html?stat=num&measure=total&map=pt#a2). The pandemic has tested and left wanting the global ability to respond to such a threat. Heyman et al. [3] argue that “the world is ill-prepared” to handle any “sustained and threatening public-health emergency.” Such public health emergencies stemming from infectious disease outbreaks are creating a serious threat to societal well-being and national security. The inherent interconnectivity and interdependency within societal public health systems require analysis that provides a deep understanding regarding the potential impact of COVID-19 on populations in response to intervention strategies. In dynamic systems, the effects of an intervention are only evident after a time delay. Understanding the system and its inherent dynamics is a key requirement for sensemaking and is a game-changer in supporting crisis management of complex issues such as a global pandemic. This chapter examines a case study of early sensemaking about the COVD-19 pandemic in Canada through the application of System Dynamics.
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Notes
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Albright, S. Christian, "VBA for Modelers: Develo** Decision Support Systems with Microsoft Excel", 5th Edition, South-Western College Publications, 2015.
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We assumed it took a minimum of 1 day to implement the distancing regulations.
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Assuming the average duration of the illness is 21 days.
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Appendices
ANNEX A: COVID-19 System Dynamics Model Equations
Action Start Time = 30
Units: day
Confirmed Cases = Deaths + Infected + Recovered
Units: people
Controlled Reproduction Rate = Reproduction Rate*Infection Duration
Units: people/person
Death Rate = Infected*Fatality Rate/Infection Duration
Units: people/day
Deaths = INTEG (Death Rate, 1)
Units: people
Desired Effectiveness of Distancing = 0.94
Units: dmnl
Distancing Change Time = 2
Units: days
Distancing Duration = 30
Units: days
Distancing Effectiveness Change = IF THEN ELSE(Time < Action Start Time,0, IF THEN ELSE(Time <=(Action Start Time + Distancing Duration),(Desired Effectiveness of Distancing-Effectiveness of Distancing)/Distancing Change Time, (Long Term Distancing Effectiveness-Effectiveness of Distancing)/Distancing Change Time))
Units: dmnl/day
Effect of Distancing([(0,0)-(1,1)], (0,1), (0.5,0.5), (1,0))
Units: dmnl
Effect of Isolation([(0,0)-(1,1)], (0,1), (0.5,0.5), (1,0))
Units: dmnl
Effectiveness of Distancing = INTEG (Distancing Effectiveness Change,0)
Units: dmnl
Effectiveness of Isolation = INTEG (Isolation Effectiveness Change,0)
Units: dmnl
Fatality Rate = 0.036
Units: fraction
FINAL TIME = 365
Units: day
The final time for the simulation.
Infected = INTEG (Infecting-Death Rate-Recovery Rate, Initial Infected)
Units: people
Infecting = (Susceptible/Initial Susceptible)*Infected*Reproduction Rate
Units: people/day
Infection Duration = 21
Units: day
Initial Infected = 1
Units: people
Initial Susceptible = 1360396
Units: people
INITIAL TIME = 0
Units: day
The initial time for the simulation.
Isolation Effectiveness Change = (Maximum Isolation Effectiveness-Effectiveness of Isolation)/Isolation Effectiveness Change Time
Units: dmnl/day
Isolation Effectiveness Change Time = 60
Units: days
Long Term Distancing Effectiveness = 0.3
Units: dmnl
Maximum Isolation Effectiveness = IF THEN ELSE(Time <=Action Start Time, 0, (Infection Duration-Time to Get Tested)/Infection Duration)
Units: dmnl
Recovered = INTEG (Recovery Rate, 0)
Units: people
Recovery Rate = Infected*(1-Fatality Rate)/Infection Duration
Units: people/day
Reproduction Rate = Uncontrolled Infection Rate*Effect of Distancing(Effectiveness of Distancing)*Effect of Isolation(Effectiveness of Isolation)
Units: people/person/day
SAVEPER = TIME STEP
Units: day [0,?]
The frequency with which output is stored.
Susceptible = INTEG (-Infecting, Initial Susceptible)
Units: people
TIME STEP = 0.125
Units: day [0,?]
The time step for the simulation.
Time to Get Tested = 2
Units: days
Uncontrolled Infection Rate = Uncontrolled Reproduction Rate/Infection Duration
Units: people/person/day
Uncontrolled Reproduction Rate = 6.41
Units: dmnl
ANNEX B: Fitting the Model to Canadian Provinces
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Taylor, I., Masys, A.J. (2021). A System Dynamics Model of COVID-19 in Canada: A Case Study in Sensemaking. In: Masys, A.J. (eds) Sensemaking for Security. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-71998-2_11
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DOI: https://doi.org/10.1007/978-3-030-71998-2_11
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