==edited by Prasiantoro Tusono and Rio Swarawan Putra==

Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.

With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.

We start with an SIR model, such as that featured in the MAA model featured in

Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-4, we reproduce their figure

With a death rate of .005 (one two-hundredth of the infected per day), an infectivity rate of 0.5, and a recovery rate of .145 or so (takes about a week to recover), we get some pretty significant losses -- about 3.2% of the total population.

Resources:

This is a clone of "Fast Fashion ISCI 360 Solutions Final submission" created by user "V B" which we are using as the foundation for an exercise in the DTU course 12100 "Quantitative sustainability".

Please refer to the notes for each variable and stock to see which links were hidden from the model.

Part 2: Our solution for the issue surrounding "Fast Fashion" focuses on increasing individuals education about sustainability and how they can help reduce negative impacts on the environment by shopping less, recycling and donating. This effect of education on sustainability is seen in the "Online Shopping" equation where the impact of "Education on Sustainability" is increased by x1.5 which impacts the entire model. Furthermore, components of the feedback loop on the right are also influenced by increasing education on sustainability and thus, those values were altered accordingly. These values were chosen arbitrarily by taking into account that doubling any value is not realistic so the change should be between x1.0 and x2.0.

Another possibility though is the extent to which those who are to be helped feel besieged by the situation in which they find themselves making them extremely wary about trying something new. 

This model is not realistic, at least it is hoped that there isn't anyplace where things are this bad. Different communities will be better or worse off in different categories and some will be succeeding in all areas. Those are the communities we need to learn from.

More explanation can be found under the information icons associated with each of the elements.

Interventions and leverage points added in IM-1400 (complex!)

This model demonstrates sustainable recycling and the effects it has on the environment as well as us. We modelled this using realistic statistics and estimates from gridwatch.ca and the Ontario Baseline and Waste & Recycling Report (2023). 

[Purple]: Metal demands on a region and the associated environmental and economic factors of production and recycling.

[Pink]: Demand of total residential household and business waste and energy demands on the system.

[Green]: Physical waste produced by human activity in the region.

[Teal]: The outflow of energy produced through waste recycling and its impact of energy production and demand in the region. The Durham-York Energy Center (DYEC) is a facility that combusts garbage into energy which is highlighted in teal, which accumulates with energy produced.

[Orange]: Total energy produced through all means of power generation including modelling of the impact that recycling waste has.

[Yellow]: Carbon emissions of energy generation from energy production methods. (Excluding Wind & Hydro)

1 theoretical foundations abstract

2 empirical foundations abstract

3 causal landscapes abstract

4 deep nets abstract 

See also 2018 Gary Klein podcast and the process of explaining insight

Allison Zembrodt's Model

equations I used in kill rate :

power model - 12*0.1251361120909615*([Moose]/[Wolves])^.44491970277839954*[Wolves]

Kill rate sqrt = 12*(0.0933207+.0873463*([Moose]/[Wolves])^.5)*[Wolves]

Holling Type III - ((0.986198*([Moose]/[Wolves])^2)/ (601.468 +([Moose]/[Wolves])^2))*[Wolves]*12

linear - 12*[Wolves]*(.400271+.00560299([Moose]/[Wolves]))