infectious | Reed-Solomon forward error | Architecture library

This is certainly possible. All you need is an if statement in your gradient function:

In your code, N extends the Object class (see error), which happens when no explicit superclass is defined for your generic. That means that it only has access to the functions and variables of that class. As far as I know, the Object class does not define a method called getLabel() so you should probably restrict the type parameter to something like

This update is to fulfil the following requirement mentioned by the OP as a comment below the answer:

I would like each word as a separate element in the List. With your solution, all the elements are in the same List entry. For example, I would like 10000=[sub-Saharan, Africa, and, Australia.]

In order to achieve this, you should not split the string of words.

Demo:

As I mentioned in the comments, the Video Intelligence transcripts are splits with roughly 50-60 seconds from the video.

I have created a Public Issue Tracker case, link, so the product team can clarify this information within the documentation. Although, I do not have an eta for this request, I encourage you to follow the case's thread.

This can be done with rowwise() and c_across()

Here is a freshcode Base R approach (no libraries) using a very basic for loop. If the patient checked in while there was an infection (started_during), or left while there was an infection (ended_during), or was inpatient while a infectious period began and ended (in_during) it should flag the overlap as TRUE.

In the Models Library Virus model with default parameter settings at other values, and those high infectiousness values, what I see when I run the model is a periodic variation in the numbers three classes of person. Look at the plot in the lower left corner, and you'll see this. What is happening, I believe, is this:

1. When there are many healthy, non-immune people, that means that there are many people who can get infected, so the number of infected people goes up, and the number of healthy people goes down.

2. Soon after that, the number of sick, infectious people goes down, because they either die or become immune.

3. Since there are now more immune people, and fewer infectious people, the number of non-immune healthy grows; they are reproducing. (See "How it works" in the Info tab.) But now we have returned to the situation in step 1, ... so the cycle continues.

If your model is sufficiently similar to the Models Library Virus model, I'd bet that this is part of what's happening. If you don't have a plot window like the Virus model, I recommend adding it.

Also, you didn't say how many ticks you are running the model for. If you run it for a short number of ticks, you won't notice the periodic behavior, but that doesn't mean it hasn't begun.

What this all means that increasing infectiousness wouldn't necessarily increase the maximum number infected: a faster rate of infection means that the number of individuals who can infected drops faster. I'm not sure that the maximum number infected over the whole run is an interesting number, with this model and a high infectiousness value. It depends what you are trying to understand.

One of the great things about NetLogo and some other ABM systems is that you can watch the system evolve over time, using various tools such as plots, monitors, etc. as well as just looking at the agents move around or change states over time. This can help you understand what is going on in a way that a single number like an average won't. Then you can use this insight to figure out a more informative way of measuring what is happening.

Another model where you can see a similar kind of periodic pattern is Wolf-Sheep Predation. I recommend looking at that. It may be easier to understand the pattern. (If you are interested in mathematical models of this kind of phenomenon, look up Lotka-Volterra models.)

(Real virus transmission can be more complicated, because a person (or other animal) is a kind of big "island" where viruses can reproduce quickly. If they reproduce too quickly, this can kill the host, and prevent further transmission of the virus. Sometimes a virus that reproduces more slowly can harm more people, because there is time for them to infect others. This blog post by Elliott Sober gives a relatively simple mathematical introduction to some of the issues involved, but his simple mathematical models don't take into account all of the complications involved in real virus transmission.)

EDIT: You added a comment Lawan, saying that you are interested in modeling COVID-19 transmission. This paper, Variation and multilevel selection of SARS‐CoV‐2 by Blackstone, Blackstone, and Berg, suggests that some of the dynamics that I mentioned in the preceding remarks might be characteristic of COVID-19 transmission. That paper is about six months old now, and it offered some speculations based on limited information. There's probably more known now, but this might suggest avenues for further investigation.

If you're interested, you might also consider asking general questions about virus transmission on the Biology Stackexchange site.

Element wise operations are the same for matrices and vectors, so the as.matrix conversions are redundant, as no true matrix multiplication is used. Same with the rep: the zero is recycled anyway.

In effect, CPU time reduces already to 50%. In contrast, use of an external forcing with approxTime instead of the inner if and for made the model slower (not shown).

I can't really say if this works, because you example is not fully reproducible, but according to this answer it can be solved using a data-container="body" attribute.

Luckily bsplus make this very easy: just add container='body' to bs_embed_tooltip()