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Finding R0 Value of Covid-19

Jesse Osnes Author

04/14/2021 Added

54 Plays

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UCARE 2020-21 Project Presentation

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[00:00:02.399]Jesse Osnes: Alright, so my.
[00:00:04.440]Jesse Osnes: You care project was finding the are not value of kernel code 19 pandemic i'm just justice and I worked on this project with Dr Dane from the mathematics department.
[00:00:19.529]Jesse Osnes: Software the background i'm sure you know, coordinate teen pandemic that started early January, February 2020.
[00:00:30.420]Jesse Osnes: And it didn't really hit us till march here in nebraska and we didn't that's when the lockdowns happened and we didn't.
[00:00:41.190]Jesse Osnes: seem that cases until or a significant amount of cases until.
[00:00:48.000]Jesse Osnes: late summer early fall.
[00:00:50.460]Jesse Osnes: and
[00:00:52.620]Jesse Osnes: start off with the definition of are not value.
[00:00:56.760]Jesse Osnes: or not values the expected number of secondary cases produced by single infection.
[00:01:03.510]Jesse Osnes: That is susceptible in a susceptible population and then the definition the mathematical formula for are not value based stuff but si our model.
[00:01:17.010]Jesse Osnes: Which is a special kind of system with differential equations is beta overcame a divided by divided by gamma will give us are not such as one quick definition that we're going to use through out.
[00:01:32.970]Jesse Osnes: A goal for research was to find that are not value for.
[00:01:39.180]Jesse Osnes: Using differential equation modeling specifically so our model.
[00:01:44.130]Jesse Osnes: And we wanted to look only at nebraska is at open cases for this.
[00:01:51.780]Jesse Osnes: As the processes that we did fall semester we gathered gathered a bunch of data.
[00:01:58.140]Jesse Osnes: And we made sure to.
[00:02:00.480]Jesse Osnes: use data from an open source.
[00:02:03.780]Jesse Osnes: That way, we can see if it's reliable or not.
[00:02:09.030]Jesse Osnes: Which Open Source data would make it reliable and.
[00:02:15.840]Jesse Osnes: Then we also did a literary review of the theory behind what we were doing so.
[00:02:23.430]Jesse Osnes: studied different differential equations how they model diseases, just in general, the theory behind it and, specifically, we did this for the SI our model because this works generally the best for modeling outbreaks.
[00:02:42.090]Jesse Osnes: The semester spring semester we wrote better matlab code specifically did si our model solver.
[00:02:51.600]Jesse Osnes: which solves our system of differential equations and code that would optimize our.
[00:03:00.750]Jesse Osnes: parameters meaning really fits best to our data that we gathered back in the fall.
[00:03:09.120]Jesse Osnes: Further about our methods so that's our model is is given in the middle there it's.
[00:03:16.560]Jesse Osnes: DST T is the rate of susceptible as at is changing the ITT the rate of infectious that is changing.
[00:03:26.340]Jesse Osnes: Dr dt is removed so rid of the removed people changing some.
[00:03:34.200]Jesse Osnes: basic idea we want to solve the system of equations it's going to give us our beta in our game and we divide beta and give it to give us are not value this stuff remember that definition I started with are not as faded faded vacuum so that's why.
[00:03:54.750]Jesse Osnes: And then talk a little bit more about about matlab that we did.
[00:03:59.220]Jesse Osnes: Once again we didn't si or solver.
[00:04:02.490]Jesse Osnes: Specific name of it was run to kind of method.
[00:04:10.080]Jesse Osnes: We specifically looked at first 200 days of confirm cases in nebraska.
[00:04:16.620]Jesse Osnes: which started about mid March and then whatever 250 days from that is.
[00:04:26.250]Jesse Osnes: And then we use her the code that we wrote.
[00:04:30.960]Jesse Osnes: to fit the.
[00:04:34.920]Jesse Osnes: System of differential equations.
[00:04:38.430]Jesse Osnes: That was solved for the best to our data, and that would give us our beta and gamma that best explains the data.
[00:04:49.290]Jesse Osnes: Once we have to beat it and did it beta gamma once again we just divide those two and that's how we get the best are not value.
[00:05:00.360]Jesse Osnes: So the results so based off of what we found first 250 days in nebraska there are not value, which is 1.66625.
[00:05:12.360]Jesse Osnes: And that comes from beta being 1.33 gamma been to your point 08 and that's once again from solving the differential equations with matt lab and fitting it best to the data that we found.
[00:05:31.890]Jesse Osnes: So further steps we can do enough what we're currently working on right now is is dividing up.
[00:05:41.490]Jesse Osnes: we're building upon the code to make it so that we can divide up the SEC our data into sections, so that way we can choose any section of days and find the arca are not value.
[00:05:57.030]Jesse Osnes: So what we have right now we have to start at zero and we can go to anywhere in between zero and 250.
[00:06:07.080]Jesse Osnes: But we want to build our code, so that we can start anywhere so let's say if we wanted to find middle hundred days with our value we could make it so it goes from 100 to 200 and find the arm about you so that's our next step.
[00:06:27.630]Jesse Osnes: And the other, this is, this is something that will be very useful in give us more accurate or not, as approximation.
[00:06:40.110]Jesse Osnes: Here are the resources that I have.
[00:06:44.610]Jesse Osnes: Where I found my data in the some of the articles that I read over the fall.
[00:06:53.610]Jesse Osnes: And that is all.

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