it will spread and whom it will effect models can give scientists government officials and doctors previews of how to stop the virus and safe lives. welcome to our coverage 101 k. jones and to some extent i've contributed to prediction models today by getting tested just like all the anchors here it's a precaution but those tests on individuals are costly a researcher in wonder came up with an efficient alternative in the battle against the pandemic rwanda's coronavirus task force relies on comprehensive contact tracing. to get an up to date picture of how much the corona virus has spread rwanda tests a cross-section of the population regularly using an ingenious strategy called pool testing. the brains behind the strategy is wilfred defun a professor at the african institute for mathematical science. if you go to the

community and test people it doesn't tell you how many people actually infected right because everybody so you always some people and so to go from the sample to the truth to reality you know. the calculations involve complex algorithms defun has translated them for us into an example from everyday life. the idea for testing is really simple so imagine that you have 9 cups of beans and you know to order 2 of those bins. and you know that teacher which corps positive by being as you was cool beans and based so you might find. time to get. to avoid this effort the mathematician resorts to a simple yet efficient trick he combines bean samples from different parts if all the beans in this pool are good he no longer has to test each pot individually only

if there are better beans in the sample must he perform it dition all tests the principle can be applied any time groups are tested and it can be used to combat covert 19. in rwanda sets of 10 and 20 samples are combined and tested similar taney asli if the pool test result is negative all the subjects in the pool sample are indicated as not having coded 19 but if the pooled result is positive each sample is retested individually the advantages of their pulling up approach is just to reduce the cost spends to the for the agence to do the turnaround time. and also to test a massively in the community also the group just. for the. advantages for these if the virus has spread dramatically pooled

samples test positive too often and retesting becomes costly and time consuming so far however africa has low number of cases compared to western countries and the method is proving useful there. this is the purple for the african countries but not only african countries even for the roping countries since we do love this and the problem is the. pulling up close tonight a pre-planned so we have been approached by the civil countries ghana and kenya are now applying the strategy to mass testing in greece have also come from the u.s. and in britain the university of edinburgh is testing students with this method. all about is a lecturer in statistic in the department of mathematics at imperial college london welcome to our program and before we get into the nitty gritty of mathematical modeling just very very briefly what exactly is it that you do at your department.

thanks for having me today so i am part of the empirical on the code 19 response team and we've looked at you know there is kinds of infectious disease models to inform public health responses to the epidemic i guess the 1st and probably one of the most important pieces was weary and out scenarios on what would prevent hospitals to collapse and prevent hundreds of thousands of deaths in the population said i was the 1st one and we showed it so as resistance in general knocked out capping off non-essential services and will be fundamental to that later on we looked at the age groups as a state and yet the demick showing that it's primary to 20 to 49 year olds to drive spread 78 percent of all transmission is occurring from those individuals with limited. contributions from school age children and more recently we looked at the

spread of the war once. in england and p. want results and we show a cop you were 17 and its. effect of 1.5 to 1.6 more transmissible the current variance their harvests in a 2nd and when you say you look at all these things what exactly does this mean i mean talk us through the prices for example of what you've been doing in brazil. so we start by you know the lies in the real are real time data as it comes in and is reported so in this case it's a case data initially it was a case of data from from analysis hospital dates are just number of admissions are a number of deaths burial dates are it sounds as you know it's been horrific only in and the amazon. and then you try to interpret it as data with mathematical models so these are. not indian wells it's

a little bit unintuitive to to work with them it's not like i throw you a ball and you know where it lands so we really need a small dose to help us understand. and how do you translate those models that are not linea for policymakers to understand what's going on so that they can decide on specific missions what we show you know the fits to the data itself along with 95 percent credible and the world's 2 presidents certainty we show forecasts you know various what if scenarios and there's no single forecast right. but a difference in areas what would happen if you know if mobility were to reduce in a population what you know and other things. and then based on that you know based on that communication verification from several teams different analysis

from different regions alternately decisions being made at a policy level i mean let's take a concrete example because here in germany and there's a lot of discussions right now politicians are talking about how to in the lock down now if german chancellor angela merkel came to you and asked you what happens to the infection rate if we were to open schools and restaurants next week could you provide her with a model. you know. yes so that is what we've been doing that they are not they are out for the past year for the british government it is just one aspect more than 10 of our more legal teams and institutions across the u.k. who come together and he back into this and so it's a. it's a very busy it's been a very busy period. i bet they actually do and taking you not giving advice you give them a model so they still have to sort of figure out what it means and make the right policy decision can you support them somehow to. maybe try to make the sorts of

every try to really make the best transparent and easily accessible as possible so you know we give them graphs and they give timelines and projections and conferences by adding very similar incidents from very different times of models and be also transparent to some sense that undergo i mean when it underlined all these models all right so if you're expensive bill these days certainly but when the pandemic is over if it's over one day what models would you like to work on. what you know i could 19 is not the only infectious disease out there in the world that i'd like and you know primarily working on an age i'd be. you know many other diseases are you've heard about 8 and friends on the 1st case there's no christian the transmission. already happened this year so there's no shortage of problems to twerk. i'm afraid you're right yes all of that happening from imperial college in

london thank you so much for your time. searching cognitive gears now you'll see what i mean what derek. how much of the population has to be vaccinated before we reach herd immunity. everyone wants to know this number which from the start has generally been estimated at roughly 70 percent of the population the problem is that pinning it down exactly isn't possible yet because there are still so many moving parts and this pandemic 2 of the most important factors that can have a major impact on herd protection are still big question marks the 1st is the extent to which people who've been vaccinated can still potentially catch and transmit the disease even if they never develop symptoms themselves the data we

have so far on this aspect is quite positive evidence from a couple of larger studies now in peer review indicates that at least some approved vaccines appear to help lower the recipients chances of being a transmitter after being vaccinated may be dramatically the 2nd factor that plays a key role in how soon we reach herd protection involves variants when the virus mutates to become more transmissible people who get it grow more contagious so they give it to more people on average and that makes achieving her protection more difficult another potentially problematic facet of this problem is that variants can also mutate in ways that lower existing immune protection to sars kovi to

whether for those who've been vaccinated or for those who have had the disease so variants could turn people we thought were protected back into potential and factors to. spike the imponderables many see falling case numbers and countries with high vaccination rates as evidence that herd protection is already beginning to influence the course of the pandemic but we won't really know we have until we get there. that's it thanks for watching.

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