In the last module, we talked about other input basic information about test, trace, isolate programs and get basic metrics for evaluating these programs. But in many cases, you might not want to look at just a single scenario. For instance, you might want to see what happen if you change your protocol somewhere, and compare what's you're doing now with different strategies to improve performance. Or perhaps you'd have to make educated guess for some parameters when you filled out the first time, and now you want to see how things would change if you were wrong. In order to facilitate that, we have created the option of making a scenario B that can be compared with the original scenario. To get started, I'm going to load the information on the Shire that Lucy inputted last time. If you recall, the app doesn't save anything about our previous work, so I'll need to the load the data from a config file, from our previous run. I hit "Load", I hit "Browse", and this YAML file here has the information I want. Now, let's come up with the information from our previous run. If I navigate to the dashboard, you see we still have the results here from our previous run. I can scroll down, and you see this checkbox here that says, "I'd like to create a scenario B." When I check the checkbox, the screen changes. Now, we have down here a new section, where on the left side, we have scenario A, which lists all of the inputs we had previously and we can't edit this scenario A, but it's showing everything we input as we went through the app before. On the right, we have scenario B, and this right now looks exactly the same as the scenario A, but the differences is here I can edit the app. As an example here, let's suppose that the Shire is planning a huge expansion in testing with the aim of doubling the number of cases captured by surveillance. We want to adjust the inputs here to reflect that. I take this 500 and I put that up to 1,000 people detected over the four week period, and that isn't just 1,000 people that's bringing our percentage of all cases of infected people up from 33 percent up into 66 percent, so I bring the slider up. Then we are actually quite effective at isolating these people, or we're planning to be in our new strategy, so we think we can isolate 95 percent of them, or 950 out of the 1,000. Fifty of those people will have already been in quarantine when we isolated them, so we bring this S_4 up to 900. We also plan, as part of this expansion, to be more aggressive about how well we follow up household contacts. We can go here to this H_2 question about the percentage of household contacts we traced and quarantined and up that up to 95 percent, and let's say we are also able to reach them about a half-day quicker. We take day 5 here and we bring them down to 4.5. Now, I've adjusted these inputs in B to be different than scenario A, and this should give us different results. If I go up to the top now, you see now we have two different outputs. On the left side, we have our original outputs from scenario A, showing a reduction in R from 2.5 to 1.9, and that we're going to identify about 40 percent of our cases. But now we also have on the right, scenario B, which shows that we are reducing R from 2.5-1.1 under this very aggressive program, almost to the threshold of one where we are controlling the epidemic for contact tracing alone, and that our program is managing to identify 81 percent of cases. If we go down, you also see at the bottom we have a summary of the inputs for this scenario as well, just like we do for scenario A, so we can highlight the differences. But what you're not seeing is the plots we saw before. To see those, you have to go to this button here "Show Plots" and click on it. When we do that, it hides the inputs from scenario B, we can get back to that by clicking this Update Scenario B button. Let me just show you. Let's see there, it comes back, and we can get back to the plots by clicking "Show Plots". Now, this figure has two lines, the top line is showing the impact of isolating cases that not identified during contact tracing from scenario A, and the bottom line is showing what we have for scenario B. You can see there's no a huge change in the slope of the lines, but what has changed a lot is our position along them. Here is our percent isolated from our first run, and then down here is the percent isolated in our news scenario B, and that's not surprising because the biggest thing we changed about our contact tracing program here was to isolate more people through community surveillance. If we look below that, we see a similar results for the delay from symptom onset onto isolation and the reproductive number. While we still only see marginal increases from shortening this time in scenario B versus scenario A, you can see that the line is shifted well down. In fact, if we reduce our time to isolation to be only 1-2 days from symptom onset, we might actually bring R below one in this scenario. Let's look and see how things have changed for our household results. So here the differences are a little bit more dramatic. You can see that we have a greater slope on our line for scenario B than scenario A, reflecting the fact that since we're isolating so many of the people who we're contacting free primary surveillance, that we're getting more impact from isolating their household contacts as well, and plus our improvement here has really brought us way down in terms of the impact on reproductive number. Likewise, we can see that there's also a big impact for delay to quarantine household contacts, but that delay really doesn't start to have an effect until it gets longer than around six days. Though if it's very long, it can have a pretty dramatic effect on our performance. So that also reflects the importance given that we're doing so well on our primary case isolation of isolating those household contacts. If we look at the Community tab, we see things are not that much different than we saw before. There's just marginal impacts of increasing the time, the isolation, and the proportion isolated in our scenario B, though of course we're doing much better to start with, and that just reflects the low proportion of contacts and transmission that we think are due to community and this baseline scenario. When we look at our advanced options in the next module, we'll be able to get a sense of how those assumptions can be changed. That's how we can update scenario B. Just like before, if you save a report or generate a report, you'll get all of this information output, including both your scenario A and your scenario B results.
