Using system dynamics on anylogic how can you model a simulation that will give an infectious curve of this nature(Below picture) using SEIR.
enter image description here
I have tried to simulate, however my graph goes up and down. It does not oscillate as per the attached picture.
I need to simulate something similar to the graph for my assingment.
There should be three types of events in your model.
First, lets call it "initial spread", is triggered on the start of your simulation.
Second, lets call it "winter season", is triggered annualy in November\December.
Third, lets call it "mass vaccination" - you can decide when to trigger it and for what selection of your agents.
So first two are kind of global events, and the third event is specific to some sub-population (this can make the third wave kind of "toothy" if you trigger it in slightly different moments for different populations).
That is pretty it.
Curios to see how your model will predict the fourth wave - second winter season of your simulation. So keep us updated :)
There are a number of ways to model this. One of the simplest ways is to simply use a time aspect for one of your infection rate parameters so that the infection rate increases or decreases with time.
See the example below.
I took the SIR model from the Cloud https://cloud.anylogic.com/model/d465d1f5-f1fc-464f-857a-d5517edc2355?mode=SETTINGS
And simply added an event to change the Infectivity rate using an event.
Changing the chart to only show infected people the result now looked something like this.
(See the 3 waves that were created)
You will obviously use a parameters optimization experiment to get the parameter settings as close to reality as possible
Related
I would like to predict the switching behavior of time-dependent signals. Currently the signal has 3 states (1, 2, 3), but it could be that this will change in the future. For the moment, however, it is absolutely okay to assume three states.
I can make the following assumptions about these states (see picture):
the signals repeat periodically, possibly with variations concerning the time of day.
the duration of state 2 is always constant and relatively short for all signals.
the duration of states 1 and 3 are also constant, but vary for the different signals.
the switching sequence is always the same: 1 --> 2 --> 3 --> 2 --> 1 --> [...]
there is a constant but unknown time reference between the different signals.
There is no constant time reference between my observations for the different signals. They are simply measured one after the other, but always at different times.
I am able to rebuild my model periodically after i obtained more samples.
I have the following problems:
I can only observe one signal at a time.
I can only observe the signals at different times.
I cannot trigger my measurement with the state transition. That means, when I measure, I am always "in the middle" of a state. Therefore I don't know when this state has started and also not exactly when this state will end.
I cannot observe a certain signal for a long duration. So, i am not able to observe a complete period.
My samples (observations) are widespread in time.
I would like to get a prediction either for the state change or the current state for the current time. It is likely to happen that i will never have measured my signals for that requested time.
So far I have tested the TimeSeriesPredictor from the ML.NET Toolbox, as it seemed suitable to me. However, in my opinion, this algorithm requires that you always pass only the data of one signal. This means that assumption 5 is not included in the prediction, which is probably suboptimal. Also, in this case I had problems with the prediction not changing, which should actually happen time-dependently when I query multiple predictions. This behavior led me to believe that only the order of the values entered the model, but not the associated timestamp. If I have understood everything correctly, then exactly this timestamp is my most important "feature"...
So far, i did not do any tests on Regression-based approaches, e.g. FastTree, since my data is not linear, but keeps changing states. Maybe this assumption is not valid and regression-based methods could also be suitable?
I also don't know if a multiclassifier is required, because I had understood that the TimeSeriesPredictor would also be suitable for this, since it works with the single data type. Whether the prediction is 1.3 or exactly 1.0 would be fine for me.
To sum it up:
I am looking for a algorithm which is able to recognize the switching patterns based on lose and widespread samples. It would be okay to define boundaries, e.g. state duration 3 of signal 1 will never last longer than 30s or state duration 1 of signal 3 will never last longer 60s.
Then, after the algorithm has obtained an approximate model of the switching behaviour, i would like to request a prediction of a certain signal state for a certain time.
Which methods can I use to get the best prediction, preferably using the ML.NET toolbox or based on matlab?
Not sure if this is quite what you're looking for, but if detecting spikes and changes using signals is what you're looking for, check out the anomaly detection algorithms in ML.NET. Here are two tutorials that show how to use them.
Detect anomalies in product sales
Spike detection
Change point detection
Detect anomalies in time series
Detect anomaly period
Detect anomaly
One way to approach this would be to first determine the periodicity of each of the signals independently. This could be done by looking at the frequency distribution of time differences between measurements of state 2 only and separately for each signal.
This will give a multinomial distribution. The shortest time difference will be the duration of the switching event (after discarding time differences less than the max duration of state 2). The second shortest peak will be the duration between the end of one switching event and the start of the next.
When you have the 3 calculations of periodicity you can simply calculate the difference between each of them. Given you have the timestamps of the measurements of state 2 for each signal you should be able to calculate the time of switching for all other signals.
I am using parameter variation in AnyLogic (in a system dynamics model). I am interested in how one parameter changes with the various iterations. The parameter is binary: 0 when supply of water is greater than demand and 1 when supply is lower than demand. The parameters being varied are a given percentage of decrease in outdoor irrigation, a given percentage of decrease in indoor water-use, and a given percentage of households that have rainwater harvesting systems. Visually, I need a time plot where on the x-axis is time (10,950 days; i.e. 30 years) and the binary on the y-axis. This should essentially show which iteration pushes a 1 further into the future.
I have watched videos and seen how histograms and 2D data are used to visualize the results of the iterations, but this does not show which iteration produced which output specifically. Is there a way to first, visually show the output as I have described above and second, return the data for a specific iteration?
Many thanks!
Parameter variation experiments have After Iteration and After Simulation run actions that are executed after each iteration and simulation respectively. Here, it is possible to access the values inside the simulation object after it finished but before it is destroyed. There is also a getCurrentIteration() method which can be used to control the parameter variation experiment and retrieve the data.
For more detail please consult here and see "SIR Agent Based Calibration" example model in AnyLogic example models library (Help -> Example Models).
Using the Road Traffic Library, I have created a 4-way intersection that is stop-controlled and I need to measure the average delay for each approach. Currently I am using timeMeasureStart and timeMeasureEnd blocks, showing the time taken as the car enters the road/model, until it exits the intersection.
Instead, I want to measure from the time the car slows to 40km/h, until it exits the intersection. Any suggestions?
The initial speed of all cars entering the model is 60 km/hr.
Sure, there is no pre-defined way but this custom approach should work:
make sure your cars are a custom agent type, not the default cars. Lets name it My Car
add a variable myTimeBelow40 into MyCar of type double
check the car speed regularly (every 0.1 sec?!) in an event in MyCar.checkSpeed. Use the getSpeed() function. If it is below 40 KPH, you log the current time into myTimeBelow40
log the departure time of your car: your time since 40 KPH is the difference
Finally, add a statistic across your car population or log your individual car duration into main
#Benjamin thank you for pointing me in the right direction.
Here is my solution, guided by the suggestion. I'm sure it could be refined but in the end it is what worked for me and my limited knowledge of AnyLogic.
For a 4-way intersection, I wanted the delay for each approach, so I created 4 custom car agents, with each populations starting out empty.
In each agent, I had 2 variable blocks - var_Start and var_Slow, an one event block set to Timeout, Cyclic, first read at time(), and proceeding in intervals of 0.1s. In the event action, i specidied the following:
if(getSpeed(KPH) <= 40) {
var_Slow=time();
var_Slow.suspend();
}
In main I used Histogram Data, labeled as dataDelay, and a chart with the mean showing, to see the results. I had one for each intersection.
Back in the car agent, in actions on startup:
var_Start=time();
and on destroy:
if(var_Slow = 0)
main.dataDelay.add(time()-var_Start);
else
main.dataDelay.add(time()-var_Slow);
At the car source block in main, I kept the initial and prefered speed at 60, however if there were cars backed up then new cars were often initiated at a slower speed, sometimes already below 40kph, hence the if,else code on destroy.
I had everything labeled according to its corresponding approach direction, unlike the simplified version I have here.
I have an AnyLogic simulation model using trucks and forklifts as agents (Transporter type), and among other things would like to identify each time one of them becomes within a certain distance of another one (example within 5 meters). I will record this count as a variable within the main space of the model. I am using path-guided navigation.
I have seen a method "agentsInRange" which will probably be able to do the trick, but am not sure where to call this from. I assume I should be able to use the AL functionality of "Min distance to obstacle" (TransporterFleet) and "Collision detection timeout" (TransporterControl)?
Thanks in advance!
Since there don't seem to be pre-built functions for this, afaik, the easiest way is to:
add an int variable to your transporter agent type counter
add an event to your transporter type checkCollision that triggers every second or so
in the event, loop across the entire population of transporters and count the number that are closer than X meters (use distanceTo(otherTransporter) and write your own custom code)
add that number to counter
Note that this might be very inefficient computationally as it is quite brute-force. But might be good enough :)
I would like to analyse the electrical energy consumed by a furnace in a conveyor system. Is there a method or specific function to do this using Anylogic?
I've found little material surrounding this, so pointing in the right direction would be great.
Simple answer: There is no build-in functionality. But you can easily do this yourself.
If you want very high accuracy, you should check system dynamics (but this slows your model quite a bit).
If you want a simple approach, I can think of this:
create a variable elecConsumptionPerMin in your furnace agent
create an event consumeElec in your furnace that cyclically (every minute) adds to the total furnace consumption (another variable)
on model startup, ideally set myFurnace.consumeElec.suspend()
when the furnace starts producing, call ´myFurnace.consumeElec.resume()` to start consuming enegery
Obviously, you can refine this to the nth degree and you might also want to experiment with state charts. But this is the simple approach