Why I don’t believe that 2.5-4% of people in Santa Clara county have had COVID19

Originally posted on April 19th. Small edits on April 21st. Thanks to Scott Roy and Dmitri Petrov for comments. 

This week a study was published by researchers from Stanford about how many people in Santa Clara county have been infected with the new coronavirus SARSCoV2.

You may not have heard of Santa Clara county, but it’s the heart of Silicon Valley and its most famous residents are Stanford, Google, Apple and IBM. I lived there too for a few years when I was a postdoc at Stanford.

The researchers used a new test to detect antibodies against the virus. Antibody testing is going to be super important in the near future, but I have serious concerns about this study and its conclusions.

The main result from the paper is that they estimate that between 2.5 – 4% of people in Santa Clara have had COVID19. That would mean between 48,000 and 81,000 people. If this is correct, it would mean that the virus has infected many more people in Santa Clara than the official numbers suggest. (50-85 fold more).

If 50-85 times sounds hard to believe, that’s because it is. Even though most experts agree that the real number of infected people is higher than the reported numbers, 50-85 fold higher than reported would be quite crazy. In research, we like to say that “extraordinary claims require extraordinary evidence” (https://en.wikipedia.org/wiki/Sagan_standard). Here the claim is extraordinary but the evidence isn’t. Also, we learn that even if a study comes from a great university – this is no guarantee that the study is good.

2.5-4% seroprevalence is unlikely in Santa Clara county

Why is 2.5-4% positive in Santa Clara county an extraordinary claim? This is because in the European countries where seroprevalence is around 3%, many more people have died (relative to the size of the population) than in Santa Clara county. It would be very unlikely that the infection fatality rate (how likely you die when you catch the virus) is significantly lower in Santa Clara then in other parts of the world. For example, The Netherlands also reports a 3% seroprevalence, but has 5.5 times as many deaths per 100,000 people compared to Santa Clara county.

Two issues: biased sample and false positives

In my opinion, there are two main issues with this study. Both make that the Stanford researchers overestimate the number of people who were infected with SARS-CoV2.

One is that this was probably NOT a random sample. And two is that the false positive rate for this kind of test is high. This means that we don’t know if the people who had a positive test result have really been infected with the virus.

1. Why is this not a random sample? 

They asked people to volunteer for this study using Facebook ads. Now, I think there is nothing wrong -in principle- with using Facebook ads to recruit people. But I do think that people who have been sick with a fever and cough recently are more likely to volunteer for this study to test whether they’ve had COVID19!

If people who actually had COVID19 were twice as likely to volunteer for the study, it would mean 2x as many positive tests in the sample and thus the conclusion that 2x as many people in the county of Santa Clara have had the disease.

This is why it is so important in statistics to have what we call “unbiased samples.”

2. What is the “false positive rate” and why does it matter? 

Whenever you do an antibody test to see if someone has had a disease, you need to consider two kinds of mistakes that could happen. The test could come back negative even if someone had the disease – this is called a false negative – and the test could come back positive even if someone didn’t have the disease – this is called a false positive.

If a disease is rare (such as COVID19 in Santa Clara county) we need to worry mostly about the false positives. Using test data from the manufacturer, the authors estimate the specificity to be between 98.1- 99.9. (When they include their own data, the range becomes 98.3 – 99.9). This means that the false positive rate is somewhere between 0.1 and 1.9%. In other words, even if you test only people who have never had the disease, between 0.1 and 1.9% of people would still test positive.

What does all of this mean? 

Imagine we are testing 1000 people in an imaginary Santa Clara county.

Now imagine that 1% of the population has had COVID19. That would be around 10 people out of 1000. But, because people who were recently sick are more likely to volunteer for the study, maybe instead of 10, 20 people out of 1000 are positive. That’s 2% of the sample.

The other 98% of the sample should have a negative test. But, we know that the false positive rate of this test is between 0.1 and 1.9%, which means you’ll get another 1-19 people who test positive even if they never had the disease! Let’s assume for now that we get 10 false positives. Now we have in total 30 positive tests out of 1000 people tested. That could lead you to think that 3% of the sample of 1000 people has had COVID19 and thus 3% of Santa Clara county has had COVID19. Even though the real rate in our imaginary Santa Clara example was only 1%!

In the real Santa Clara study, 50 out of 3300 tests were positive (1.5%). In principle, these could all be false positives!

A lot of experts (here, here and here) are worried that the Stanford researchers have underestimated the false positive rate and have not corrected for their biased sample. And because they didn’t deal well with these two issues, they overestimate the percentage of people in Santa Clara who have had the disease.

How could this be done better?

1. Get a more random sample. Dr Natalie Dean from Univerisity of Florida explains why household testing is the gold standard.
2. Get a better sense of the false positive rate. Between 0.1 and 1.9 % is too wide a range if the number you are trying to measure is likely in the same range.

Why are these numbers in Santa Clara important? 

Why does it matter so much whether 0.5, 1, or 3% of people in Santa Clara have had COVID19?

Well, as of today, 73 people have died of COVID19 in Santa Clara county. If that is 73 out of 40,000 – 80,000 infected – as the Stanford researchers suggest – then the chance of dying of COVID19 is relatively low (infection fatality rate 0.1-0.2%). But if that is 73 out of, say, 10,000 – 20,000 which is more realistic, the chance of dying from COVID19 is higher (infection fatality rate 0.3-0.7%).

Because the Stanford researchers suggest that there is a ton of people in Santa Clara county who have had the disease and only relatively few who died, they suggest that the disease is maybe not so lethal. Others are taking these results and saying: “Stanford says it’s just like the flu, we can stop the lock-downs and open up the economy!”

Many public health experts think it is way too early to open up the economy and a lot more people will die if we do so now.

In fact, the reason that Santa Clara county has a very low number of people who have had the disease (probably around 1% or lower), is probably that Santa Clara county was one of the first counties in the country to issue a Stay-At-Home order and Stanford University (which is in Santa Clara county) was one of the first universities to close its campus. In many ways, Santa Clara county and Stanford have been an example in how to deal with this epidemic effectively.

I hope that if you read about more studies that use antibody tests, you read critically to determine whether their sample was random and how high the false positive rate is compared to the real positive rate they are trying to estimate.

#StayHomeStaySafe

 

 

New plans and new HIV stories

I love making plans and I love the beginning of the new year and the new semester. I actually think that the yearly rhythm of semesters and breaks is a huge benefit of being in academia.

Today I spent some time thinking about the writing I plan to do in the coming semester and the talks I will be giving in the near future. The first talk that’s coming up is an invited talk at Stanford. I am very honored to be an invited speaker at the CEHG symposium alongside Anne Stone (ASU) and Graham Coop (UC Davis). I want to try and use the opportunity of the talk to think about what stories I want to tell and I plan to write the story up for publication in addition to talking about it at Stanford.

So what are the stories I want to tell? There are many! But here are some thoughts:

“The evolution of HIV evolution.”

Recently I’ve given seminars in which the overarching storyline was “The evolution of HIV evolution.” I focused on the evolution of drug resistance within patients and explained how drug resistance evolution used to be very fast, but became slower over time. When people were treated with a single drug (AZT) in the late 80s and early 90s, the virus would evolve drug resistance very quickly and the treatment would quickly become useless. Freddy Mercury probably died because of a very fast evolving virus. Over time, treatments improved (using combinations of drugs and using better drugs) such that it became harder for the virus to evolve and nowadays, drug resistance evolution is so slow in patients on treatment, that it is no longer a big worry, and people can stay on the same drugs for many years.

A slide from a talk I gave at SMBE and ESEB in the summer of 2017.

“HIV does it all”

Here is another storyline. Any field in science needs some systems that are looked at in detail. In evolutionary genetics, these systems are fruitflies, yeast, humans, mice etc. HIV is a great system as well in part because we know so much about it and data is abundant. One of the things we have learned in recent years, thanks to work by people like Richard Neher and Kathryna Lythgoe, is that HIV evolution can surprise us again and again. For example, HIV evolution, even in absence of drugs, can be fast within patients and slow at the epidemic level. It can be happening with a lot of recombination, or showing clonal interference (unpublished, Kadie-Ann Williams and PSP), and sweeps can be hard or soft. Within host populations can be panmictic or structured. So if everything can happen, how can we make sense of this all?

“Drugs to prevent HIV”

I like the story of how drugs were and are used to prevent HIV infection. In the 90s and well into the 2000s, drugs were used to prevent mother-to-child-transmission of the virus during child birth. In fact, this was one of the big successes in the world of HIV before treatment was really working to keep infected people alive. Nowadays, drugs are available for HIV-negative people who are at increased risk of HIV infection. Pre-exposure-prophylaxis (Prep, marketed as Truvada) is probably contributing to the shrinking of the HIV epidemic in San Francisco as many of the HIV-negative gay men in the city are taking Prep. When drugs were used to save babies, they were uncontroversial, but when they are used to save gay men, they continue to be controversial and there are many places where Prep is not available (for example, in my home country, The Netherlands).

How is this story linked to evolutionary genetics? When someone is taking drugs to prevent HIV, but they end up getting infected anyways, there may be a high risk of drug resistance evolution (this happened in the babies, in their already infected mothers, and it is happening occasionally in Prep users). Also, at an epidemic level, if a large part of the population is on Prep, this may lead to sub-epidemics of drug-resistant viral strains. There is some interesting modeling work by Sally Blower on these questions.

———————————————-

OK, that’s enough brainstorming for today. I’ll develop one of these stories into a presentation for CEHG and for an article to be published somewhere. If you have any questions or suggestions, let me know!

 

 

How a collaboration on imperfect drug penetration got started

Almost three years ago, in early 2012, I attended a talk by Martin Nowak. He talked about cancer and one of the things he said was that treatment with multiple drugs at the same time is a good idea because it helps prevent the evolution of drug resistance. Specifically, he explained, when treatment is with multiple drugs, the pathogen (tumor cells in the case of cancer) needs to acquire multiple resistance mutations at the same time in order to escape drug pressure.

As I listened to Martin Nowak’s talk, I was thinking of HIV, not cancer. At that time, I had already spent about two years working on drug resistance in HIV. Treatment of HIV is always with multiple drugs, for the same reason that Martin Nowak highlighted in his talk: it helps prevent the evolution of drug resistance.

However, as I read the HIV drug resistance literature and analyzed sequence data from HIV patients, I found evidence that drug resistance mutations in HIV tend to accumulate one at a time. This is contrary to the generally accepted idea that the pathogen must acquire resistance mutations simultaneously.

There seemed to be a clear mismatch between data and theory. Data show mutations are acquired one at a time, and theory says mutations must be acquired simultaneously. One of the two must be wrong, and it can’t be the data![1]

Interesting!

After Martin Nowak’s talk, I went up to him and told him how I thought data didn’t fit the theory. Martin’s response: “Oh, that is interesting!” (Imagine this being said with an Austrian accent). “Let’s meet and talk about it.”

So, we met. Logically, Alison Hill and Daniel Rosenbloom, then grad students in Martin’s group, were there too. I had already met with Alison and Daniel many times, since they were also working on drug resistance in HIV.  John Wakeley (my advisor at Harvard) came to the meeting too.

Between the five of us, we brainstormed and fairly quickly realized that one solution to the conundrum was to assume that a body’s patient consisted of different compartments and that each drug may not penetrate into each compartment. Maybe we found this solution quickly because Alison and Daniel had already been thinking of the issue of drug penetration in the context of another project. A body compartment that has only one drug instead of two or three would allow a pathogen that has acquired one drug resistance mutation to replicate. If a pathogen with just one mutation has a place to replicate, this makes it possible for the pathogen to acquire resistance mutations one at a time.

We decided to start a collaboration to analyze a formal model to see whether our intuition was correct. Over the following three years, there were some personnel changes and several moves, graduations and new jobs. Stefany Moreno joined the project as a student from the European MEME Master’s program when she spent a semester in Martin’s group. When I moved to Stanford, Dmitri Petrov became involved in the project. Next, Alison and Daniel each got their PhD and started postdocs (Alison at Harvard, Daniel at Columbia), Stefany got her MSc and started a PhD in Groningen, I had a baby and became an assistant professor at SFSU. No one would have been surprised if the project would never have been finished! But we stuck with it and after many hours of work, especially by the first authors Alison and Stefany, and uncountable Google Hangout meetings, we can now confidently say that our initial intuition from that meeting in 2012 was correct. Compartments with imperfect drug penetration indeed allow pathogens to acquire drug resistance one mutation at a time. And, importantly, the evolution of multi-drug resistance can happen fast if mutations can be acquired one at a time, much faster than when simultaneous mutations are needed.

Our manuscript can be found on the BioRxiv (link). It is entitled “Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multi-drug resistance.” We hope you find it useful!

[1]Of course, it could be my interpretation of the data!

Stefany Moreno (in large window), Alison Hill, Daniel Rosenbloom and myself in one of the many Google Hangout meetings we had.

(Almost) 100 things we did to make the BAPGX conference a success

(Also posted on http://stanfordcehg.wordpress.com/)
I love working with a team to organize an event. The tenth Bay Area Population Genomics conference (BAPGX) was a fun event to organize. It is part of a successful series of conferences and there was plenty of support, both at Stanford and from the community, to organize it. Five Stanford postdocs volunteered to help out and did a great job.

I think the conference was a success. Here are some of the things we did to make it that success.

How it got started

1.     Dmitri Petrov (who initiated the BAPG series) asked me (Pleuni) if I could organize the conference.
2.     Dmitri and I picked a date (not realizing it was Memorial day weekend!) and after that I was free to organize it the way I wanted.
3.     I asked the CEHG mailing list for volunteers and –within 10 minutes!– found five postdocs who were willing to help me organize the event. We were ready to get started!

The BAPGX committee, Pleuni Pennings (@pleunipennings), Maria Avila (@maricugh), Carlo Artieri (@Carlo_Artieri), David Enard (@DavidEnard), Dave Yuan (@13bee_slurpee), Bridget Algee-Hewitt (@BridgetAH), Dmitri Petrov (@PetrovADmitri, not in the picture).

The BAPGX committee

4.     The BAPGX committee met 3 times. The first meeting was mostly to brainstorm, the other meetings were more focused on logistics.
5.     We sent many (many!) emails within the committee.
6.     We kept notes and files in a shared folder on Google Drive.
7.     We split tasks: Bridget was in charge of communication with speakers and participants.
8.     Carlo was in charge booking the location, catering, and poster boards.
9.     Maria was in charge of mugs and name stickers.
10. David was in charge of the logo and photography during the event.
11. Dave was in charge of wine and cheese and printing the schedules and signs.
12. Pleuni was in charge of the website, Facebook, twitter and the money.
13. We decided on the program (and many other things) together.

There was a BAPGX mug for every participant.

Logistics

14. We looked at several lecture halls at Stanford and chose M106 in the Alway building, even though it wasn’t fancy, because it had the right size (140 seats) and was adjacent to a courtyard (great for registration, breaks and posters). It was also good because people could take their coffee with them into the room and because we could order food from an outside vendor.
15. We decided to start the conference at 10AM, so that it was convenient for people who wanted to take the Caltrain (the first arrived at 9:17 in Palo Alto).
16. We put travel instructions on the website and also sent them by e-mail:http://evolgenomecehg.wordpress.com/bapgx-map-and-travel-instructions/
17. We put up some signs to make it easier for people to find the Alway building.
18. We encouraged people to ride share and included a column in the registration Google doc with ride share information.
19. We had two of our committee members (Maria and David) in and at the Caltrain to guide people to the lecture hall.
20. We made sure the lecture hall would be open on the day of the conference.
21. Two of us washed all the mugs and four of us went to Costco two days before the conference.
    

    Dave and David are preparing the registration area.

Registration

22. Registration was free and open to all.
23. We decided to cap participation at 150 (even though we had only 140 seats) under the assumption that some people would cancel or not show.
24. To sign up, people simply added their name, email, affiliation, food preference, whether they’d bring a poster and ride shareride share info to a Google doc.
25. A few days before the conference we “locked” the Google doc and asked people to email us instead.
26. We reached 150 registrations around one week before the conference. After that approximately 10 people canceled and around 5 additional people were admitted. A few people didn’t show up and a few crashed the conference, but this was no problem as we had enough seats and food.

A full lecture hall.

Using social media to build momentum

One of the great things about working with an active community and a motivated committee is that we could build a lot of momentum before the conference.

27. We had a simple website (http://evolgenomecehg.wordpress.com/bapgx/)
28. The committee communicated with the community through emails (to individual people, to the speakers and the poster presenters and to the BAPG Google group).
29. We hoped for participation from many different universities and made additional efforts to encourage people from SFSU, Santa Clara and UCSC to sign up. We also had participants from UCSF, UC Davis, UC Berkeley, the Cal Academy of Sciences, Ancestry.com, Stanford and a few other institutions.
30. We used twitter (all committee members are on twitter).
31. We decided on a twitter hashtag early on (#BAPGX).
32. We used Facebook (through the CEHG Facebook page).
33. We tried to keep everyone updated on the program and everything else we were working on (logo, mugs, cheese etc.) to show that we were working hard and that we were excited about the conference.
34. We asked three active tweeters from the community to live-tweet the conference (@razibkhan, @mwilsonsayres and @JeremyJBerg)
    

    One of many tweets on the day of #BAPGX

Money

35. We received financial support from Ancestry.com and from CEHG (thank you!!).
36. For the CEHG money we had to write a short proposal, but basically used the same text as we already had on the website.
37. We spent money on food and coffee ($2,266), water, juice, soda, wine and grapes ($711), cheese ($430), mugs ($715), and name stickers ($76).
38. We saved money by buying water, juice, soft drinks, wine, crackers, grapes, paper plates and plastic cups at Costco.
39. We also saved money by using stickers as badges (instead of more fancy badges).
40. We saved money by not video-recording anything.
41. We decided not to spend any money on inviting an outside speaker or gifts for speakers.
42. We got help with the financial administration from CEHG (Cody Sam) and from Elena Yujuico (Dmitri’s admin).

(Almost) glitches

43. We didn’t realize until fairly late that we planned the conference in Memorial Day weekend.
44. We didn’t think about Wi-Fi access for guests until the night before the conference. On the day itself we created a guest login for the Stanford network and that worked.
45. We were not consistent about the length of the normal talks. We originally announced that they’d be 12 minutes (+3 minutes for questions), but then this somehow became 15 minutes (+5 minutes for questions).

Talks

46. We allowed for normal talks (15 min) and mini talks (5 min) and got an equal number of abstracts for both.
47. We set a deadline for talk submissions (4 weeks before conference) and another deadline for registration (one week before conference).
48. We asked people to submit abstracts (this was not done for previous BAPG conferences. At previous BAPG conferences, people were encouraged to add their name in a Google doc to sign up for a talk, but we thought that making the process a little more formal would get us different speakers and potentially more well-prepared speakers).
49. We accepted all talks that were submitted before the deadline (but none that were submitted after).
50. Our program was a bit longer than previous BAPG programs, because we decided to accept all submitted abstracts and because we wanted to be sure there was ample time for questions.
51. We asked speakers to send us their slides (only Powerpoint or PDF allowed) a few days before the conference (in the end we had all files before the start of the conference!)
52. We organized a practice-your-talk session for the speakers from Stanford.

Posters

53. Participants were encouraged to bring a poster. No titles or abstracts needed, just a “yes” in the right column in the Google doc.
54. 11 People signed up to bring a poster, in the end there were 9 posters.
55. We borrowed simple poster boards from the Beckman center and brought pins and tape (We considered higher quality poster boards, but they would have cost around $500 rental fee at Stanford).
56. Posters were up the whole day (from the coffee break in the morning).
    

The day itself

57. All of the committee (except Maria who was on the Caltrain) met at 7:30 to set up everything for the day.
58. We made sure we each had all the other phone numbers.
59. We brought several computers, VGA cables, thumb drives etc (but in the end all worked from the computer that was already in the lecture hall).
60. We brought plates and knives for the cheese and grapes.
61. We asked Dmitri Petrov to say something at the start of the conference.
62. We split chairing between three members of the organizing committee (Carlo, Maria, David).
63. Bridget and Pleuni “manned” the registration table from 9 till 10:30 and explained to everyone how to indicate their affiliation on their name sticker.

Name sticker with associations indicated (a yellow dot at the approximate location of Stanford and a green dot at the location of SFSU).

64. We had fairly long breaks and allowed plenty of time for questions. Each session ran a few minutes late, but we caught up during the breaks.
65. Each of the members of the organizing committee missed at least one of the sessions to be outside to handle food.
66. Our three designated twitter volunteers did a great job live-tweeting the conference and many others joined in. The result was storified here: https://storify.com/mwilsonsayres/bay-area-population-genomics-x
67. David brought his camera (photo’s will follow!).
68. Originally we had someone (not the chair) assigned the task of keeping track of time during the talks, but in the end we decided that it worked better if the chair did it him/herself.

After the conference

69. We all stayed till the end and cleaned up the mess.
70. We found a home for the leftover mugs & the leftover wine and cheese.
71. We wrote this document.
72. We sent an email to all participants to thank them for coming & for great talks and posters & to tell them about the storified tweets.
73. We plan to have a nice lunch or dinner with the committee to celebrate the success of the conference.
74. We’ll publish the photos.
75. We’ll round up the financial administration.

Other notes

76. The talks were of very high quality (thank you, speakers!!). The fact that we asked presenters to send us their slides beforehand may have contributed to that.
77. We got a lot of positive feedback about the mini talks (lightning talks), so it may be a good idea to keep that as part of BAPG.
78. Many people stayed for the posters and cheese. The cheese may have helped with that; we had a Toma (cow’s milk cheese from Pointe Reyes), Comte (cow’s milk cheese from France), Brabander (goat’s milk Gouda from Holland) and Casatica (buffalo milk soft cheese from Italy).

10 reasons why I am thrilled about my new job at SF State University

A few days ago I signed a contract with SF State University to become an assistant professor in their Biology Department. I am soooo happy about this!

In case you are not an academic biologist, you may not realize that jobs as assistant professor, especially in nice places such as San Francisco, are very hard to get. I sent out many applications (not just in SF, but all over the US and in Europe) before I got this job. But now I feel like I hit the jackpot! Here are some of the reasons why I am so excited.

1. I will not have to apply for jobs next year. During the last three winters I have spent a lot of time and energy applying for jobs and flying to interviews (if I was lucky to get invited). Next year I won’t even be reading the ads!
2. I don’t have to move to the other side of the country (or globe) or leave beautiful California, or choose between my job and my husband’s job!
3. As an assistant professor, I will be running my own independent group (tradition says that this group will be referred to as the “Pennings Lab”). Even though I have had amazing advisors during my scientific career, I look forward to being my own boss.
4. As part of the job, I will be teaching again, and my teaching will count for my evaluations and for getting tenure[1]. I really like teaching, but in the last couple of years I didn’t teach at all, knowing that only more publications on my CV would really help me to get the job I wanted to get.
5. I will be teaching students from a wide variety of backgrounds, which will be exciting. I expect to learn a lot about the US from these students.
6. I will be in the same department as Kimberley Tanner, who does research on biology education and who convinced almost everyone in the department to take a one-week HHMI funded course on scientific teaching. She also wrote this useful paper on equity in the classroom (http://www.sfsusepal.org/wp-content/uploads/2013/08/CBE-13-06-0115-Revised.pdf).
7. I will have great colleagues to collaborate with. For example, there is someone in the SFSU Biology Department who is interested in evolution of HIV (Joseph Romeo), there is also someone who is interested in molecular evolution and who runs a “dry lab[2]” just like I will (Scott Roy), and there are several people in the department who are interested in adaptation in natural populations. Plenty of opportunities for new collaborations!
8. I’ll still be close to Stanford and look forward to continuing my collaborations with Dmitri Petrov and Bob Shafer and their groups. I will also continue to be part of the Bay Area Population Genomics community.
9. At SFSU I will get help with writing grant proposals (for example, they pay other scientists to pre-review my grants if I have them ready on time), but I don’t necessarily need to get an R01 or similarly big grant to get tenure.  Given the current funding situation, this may save me from a lot of pre-tenure stress.
10. I will be part of a department where 38% of the professors are female. I don’t know of any biology department with that many women.

——

[1] I will come up for tenure after 6 years. If I get tenure, I will become associate professor and later I can be promoted to full professor.

[2] A “dry lab” is a group of biologists working only with computers. The opposite, a “wet lab” is mostly used to refer to molecular or cell biology labs. One of the first questions I always got in interviews was: “Will you be running a wet lab, or just a dry lab?” I recently learned that mathematicians call our “dry labs” “computer labs” in stead.

On the current situation of HIV drug resistance

I recently published a review paper on HIV drug resistance. It was the first time I wrote a review paper and I enjoyed it thoroughly! It is also my first paper with Stanford as my affiliation: I walked the beautiful Stanford campus and enjoyed the sun in the breaks from writing.

Many of you probably know that the majority of the world’s HIV patients live in poor countries, especially in southern Africa. You may not know that many of these people are now receiving very good treatment. I had the unique opportunity to see the effects of improved access to treatment “on the ground” in Africa.

Last summer I was in Ethiopia for a few weeks and stayed in the Medhen social center in one of the slums in Addis Ababa. I knew that, for many years, much of the efforts of the Medhen center had focused on HIV/AIDS, and I was expecting to see the devastating effects of HIV/AIDS during my visit. However, the situation had changed a lot. The Ethiopian government now provides HIV drugs free of cost and most HIV infected people in the slum were doing well. In fact, there was no way for us to know who was infected, unless one of the nurses told us. The social center still supports some HIV patients, such as infected orphans who need financial and social support, but most of the center now focuses on “normal” poverty relief: housing, education, employment etc.

The World Health Organization also reports that access to treatment has improved dramatically in low- and middle income countries: from 300 000 people in 2002 to 9.7 million in 2012 (see here).
HIV treatment almost always consists of a combination of three drugs, often in a single pill. However, treatment only works well as long as the virus is not resistant against the drugs used. Fortunately, there is good news about drug resistance too: HIV treatments have become better and better at slowing down the evolution of drug resistance. Thanks to powerful drugs and close monitoring, many patients are now treated for many years without having any resistance problems.

In my review paper I describe that drug resistance is virtually solved in rich countries, but still a problem in poor countries. One reason for this is that poor countries often lack the possibilities to monitor the viral load of patients and to sequence the patient’s virus.
The paper also describes what is know about the relevance of pre-existing mutations for the evolution of drug resistance in a patient (also known as standing genetic variation or minority variants, depending on which field you’re from). Finally, I write about pre-exposure prophylaxis (taking HIV drugs to prevent infection) and how this is related to drug resistance.

I hope you enjoy reading the review paper as much as I enjoyed writing it!

You can download the paper here:  2013PenningsHIVReviewIDR