# Testing for Covid-19 in the U.S.

For almost a month, on a daily basis, we are working with colleagues (Romuald, Chi and Mathieu) on modeling the dynamics of the recent pandemic. I learn of lot of things discussing with them, but we keep struggling with the tests. Paul, in Montréal, helped me a little bit, but I think we will still have to more to get a better understand. To but honest, we stuggle with two very simple questions

• how many people are tested on a daily basis ?

Recently, I discovered Modelling COVID-19 exit strategies for policy makers in the United Kingdom, which is very close to what we try to do… and in the document two interesting scenarios are discussed, with, for the first one, “1 million ‘reliable’ daily tests are deployed” (in the U.K.) and “5 million ‘useless’ daily tests are deployed”. There are about 65 millions unhabitants in the U.K. so we talk here about 1.5% people tested, on a daily basis, or 7.69% people ! It could make sense, but our question was, at some point, is that realistic ? where are we today with testing ? In the U.S. https://covidtracking.com/ collects interesting data, on a daily basis, per state.

```url = "https://raw.githubusercontent.com/COVID19Tracking/covid-tracking-data/master/data/states_daily_4pm_et.csv" download.file(url,destfile="covid.csv") base = read.csv("covid.csv")```

Unfortunately, there is no information about the population. That we can find on wikipedia. But in that table, the state is given by its full name (and the symbol in the previous dataset). So we new also to match the two datasets properly,

```url="https://en.wikipedia.org/wiki/List_of_states_and_territories_of_the_United_States_by_population" download.file(url,destfile = "popUS.html") #pas contaminé 2/3 R=3 library(XML) tables=readHTMLTable("popUS.html") T=tables[[1]][3:54,c("V3","V4")] names(T)=c("state","pop") url="https://en.wikipedia.org/wiki/List_of_U.S._state_abbreviations" download.file(url,destfile = "nameUS.html") tables=readHTMLTable("nameUS.html") T2=tables[[1]][13:63,c(1,4)] names(T2)=c("state","symbol") T=merge(T,T2) T\$population = as.numeric(gsub(",", "", T\$pop, fixed = TRUE)) names(base)[2]="symbol" base = merge(base,T[,c("symbol","population")])```

Now our dataset is fine… and we can get a function to plot the number of people tested in the U.S. (cumulated). Here, we distinguish between the positive and the negative,

```drawing = function(st ="NY"){ sbase=base[base\$symbol==st,c("date","positive","negative","population")] sbase\$DATE = as.Date(as.character(sbase\$date),"%Y%m%d") sbase=sbase[order(sbase\$DATE),] par(mfrow=c(1,2)) plot(sbase\$DATE,(sbase\$positive+sbase\$negative)/sbase\$population,ylab="Proportion Test (/population of state)",type="l",xlab="",col="blue",lwd=3) lines(sbase\$DATE,sbase\$positive/sbase\$population,col="red",lwd=2) legend("topleft",c("negative","positive"),lwd=2,col=c("blue","red"),bty="n") title(st) plot(sbase\$DATE,sbase\$positive/(sbase\$positive+sbase\$negative),ylab="Ratio of positive tests",ylim=c(0,1),type="l",xlab="",col="black",lwd=3) title(st)}```

`drawing("NY")`

As at now, 4% of the entiere population got tested… over 6 weeks…. The graph on the right is the proportion of people who tested positive… I won’t get back on that one here today, I keep it for our work. In New Jersey, we got about 2.5% of the entiere population tested, overall,

`drawing("NJ")`

Let us try a last one, Florida

`drawing("FL")`

As at today, it is 1.5% of the population, over 6 weeks. Overall, in the U.S. less than 0.1% people are tested, on a daily basis. Which is far from the 1.5% in the U.K. scenarios. Now, here come the second question,

• what are we actually testing for ?

On that one, my experience in biology is… very limited, and Paul helped me. He mentioned this morning a nice report, from a lab in UC Berkeley

One of my question was for instance, if you get tested positive, and you do it again, can you test negative ? Or, in the context of our data, do we test different people ? are some people tested on a regular basis (perhaps every week) ? For instance, with antigen tests (Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) – also called molecular or PCR – Polymerase Chain Reaction – test) we test if someone is infectious, while with antibody test (using serological immunoassays that detect viral-specific antibodies — Immunoglobin M (IgM) and G (IgG) — also called serology test), we test for immunity. Which is rather different…

I have no idea what we have in our database, to be honest… and for the past six weeks, I have seen a lot of databases, and most of the time, I don’t know how to interpret, I don’t know what is measured… and it is scary. So, so far, we try to do some maths, to test dynamics by tuning parameters “the best we can” (and not estimate them). But if anyone has good references on testing, in the context of Covid-19 (for instance on specificity, sensitivity of all those tests) I would love to hear about it !

# “Family History” and Life Insurance

Today and tomorrow, I will attend the Online International Conference in Actuarial science, data science and finance, organised by colleagues in Lyon. But I won’t be in Lyon, I will be at home, in Montréal…

I will give a talk on wednesday afternoon, on a joint paper with Ewen Gallic and Olivier Cabrignac. Slides are available here, and if I can get a copy of the video, I will share it…