Tag Archives: Ebola

15 papers on contemporary evolution in human viruses

29 May

In the fall semester of 2014 I taught a reading seminar for master students at SF State on contemporary evolution in human viruses. This blog post contains a list of the papers we read in the seminar.

I posted about this seminar previously here (about the seminar format) and here (no powerpoint allowed), and here (about being nervous for a talk).

The students’ work can be read and seen here (about H1N5), here (polio outbreak), here (Dengue), here (Ebola), here (HIV in court), here (doing my own homework), here (the origin of HIV), here (on bad small things) and here (Hep B).

These are the papers we read:

1. Fast evolution of drug resistance in HIV patient the 1980s

ReissLangeLancet

Resumption of HIV antigen production during continuous zidovudine treatment. Lancet. 1988 Feb 20;1(8582):421.
Reiss P, Lange JM, Boucher CA, Danner SA, Goudsmit J.

2. HIV: Doctor infects his ex-girlfriend, phylogenetic evidence in court

Metzker_HIV_criminalcase

Metzker, Michael L., et al. “Molecular evidence of HIV-1 transmission in a criminal case.” Proceedings of the National Academy of Sciences 99.22 (2002): 14292-14297.

3. Very contemporary: the genomics of the West-African Ebola epidemic

Gire_Ebola

Gire, Stephen K., et al. “Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak.” Science 345.6202 (2014): 1369-1372.

4. Using phylogenetics to determine origin of Dengue-3 outbreak in Australia

RitchieDENV
An explosive epidemic of DENV-3 in Cairns, Australia. PLoS One. 2013 Jul 16;8(7):e68137. doi: 10.1371/journal.pone.0068137. Print 2013. Ritchie SA1, Pyke AT, Hall-Mendelin S, Day A, Mores CN, Christofferson RC, Gubler DJ, Bennett SN, van den Hurk AF.

5. Classic paper from Beatrice Hahn’s lab on origin of HIV-1

Gao_HIV

Gao, Feng, et al. “Origin of HIV-1 in the chimpanzee Pan troglodytes troglodytes.” Nature 397.6718 (1999): 436-441.

6. Timing the start of the HIV-1 pandemic

Korber_HIVTiming

Korber, Bette, et al. “Timing the ancestor of the HIV-1 pandemic strains.”Science 288.5472 (2000): 1789-1796.

7. Where did the polio outbreak in Dominican Republic and Haiti come from?

KewEtAlPolio

Kew, Olen, et al. “Outbreak of poliomyelitis in Hispaniola associated with circulating type 1 vaccine-derived poliovirus.” Science 296.5566 (2002): 356-359.

8. Within-patient evolution of vaccine-derived polio virus

Martin_Polio

Martín, Javier, et al. “Evolution of the Sabin strain of type 3 poliovirus in an immunodeficient patient during the entire 637-day period of virus excretion.”Journal of Virology 74.7 (2000): 3001-3010.

 9. Hepatitis B within-patient evolution

LimRodrigo

Lim, Seng Gee, et al. “Viral quasi-species evolution during hepatitis Be antigen seroconversion.” Gastroenterology 133.3 (2007): 951-958.

10. Permissive mutations and the evolution of drug resistance in Influenza

Bloom_Influenza

Bloom JD, Gong LI, Baltimore D. Permissive Secondary Mutations Enable the Evolution of Influenza Oseltamivir Resistance. Science (New York, NY). 2010;328(5983):1272-1275. doi:10.1126/science.1187816.

11. Controversial experiments on H5N1 Influenza

HerfstInfluenza

Airborne transmission of influenza A/H5N1 virus between ferrets. Science. 2012 Jun 22;336(6088):1534-41. doi: 10.1126/science.1213362.
Herfst S1, Schrauwen EJ, Linster M, Chutinimitkul S, de Wit E, Munster VJ, Sorrell EM, Bestebroer TM, Burke DF, Smith DJ, Rimmelzwaan GF, Osterhaus AD, Fouchier RA.

12. Influential study on treatment to prevent HIV

GrantEtAlHIV

Grant, Robert M., et al. “Preexposure chemoprophylaxis for HIV prevention in men who have sex with men.” New England Journal of Medicine 363.27 (2010): 2587-2599.

 13. HIV drug resistance in women in Africa who were treated to prevent mother-to-child transmission

Eshleman_NVPHIV

Eshleman, Susan H., et al. “Nevirapine (NVP) resistance in women with HIV-1 subtype C, compared with subtypes A and D, after the administration of single-dose NVP.” Journal of Infectious Diseases 192.1 (2005): 30-36.

 14. Evolution of Acyclovir resistance in Varicalla-Zoster Virus

Morfin_VZV

Morfin, Florence, et al. “Phenotypic and genetic characterization of thymidine kinase from clinical strains of varicella-zoster virus resistant to acyclovir.”Antimicrobial agents and chemotherapy 43.10 (1999): 2412-2416.

 

15. Soft and hard sweeps during evolution of drug resistance in HIV

Pennings2014
Loss and recovery of genetic diversity in adapting populations of HIV. PLoS Genet. 2014 Jan;10(1):e1004000. doi: 10.1371/journal.pgen.1004000. Epub 2014 Jan 23.
Pennings PS1, Kryazhimskiy S2, Wakeley J3.

A different Ebola plot shows that number of cases is stable since one month

14 Oct

Note 2 (also Oct 14): Christophe Fraser (@ChristoPhraser) pointed out that a short article in the Lancet in 2003 about SARS made exactly the same point as I do here. 

Note (added Oct 14): several people on Twitter told me that just now, the fraction of cases that is reported is going down, meaning that the data become more and more unreliable. I don’t know if this is true, but if it is, then no matter how we plot the data, they may not mean much.

The other day, when I was writing about Ebola and airport checks, I noticed that one of the graphs on Wikipedia showed the number of new cases per week in stead of the total number of reported cases. Suddenly, the epidemic looked very different to me! Even though new cases are being found each week, the Wikipedia graph showed that the number of cases per week had been stable for a while. This is good news! Of course, we need the number of new cases per week to go down (and maybe it has just done that … says at least a Dutch newspaper), but even if the number is no longer going up, that is already a great improvement. I decided to get some data from Healthmap using code by Ebolastats from their Github repository, and plot the number of new cases per day, averaged over roughly weekly periods.   To me it looks like the number of new cases per day has been fairly stable since the week ending with September 9th.

CasesPerWeek

At the same time, if we use the same data to plot the total number of cases, it looks like the number is still increasing exponentially. However, if you just focus on the last 5 points, you may agree with me that you could fit a straight line through it.

Note that the total number of cases cannot go down. When the epidemic is over, this curve will flatten to a horizontal line, it will not go back to the x-axis. I think most of us are used to looking at plots that show the number of cases per week or per month and so, even without realizing it, when we see a curve that is going up and up, we think that it means that the epidemic is getting worse, even if it is really stable. Therefore I don’t like the kind of plot that shows the total number of cases, because I think it looks much worse than it is.

TotalCases

How many travelers need to be checked in order to prevent one case of Ebola in the United States?

11 Oct

Note (added on Oct 12th): several of my colleagues think that my analysis is too optimistic. Specifically, they think that the epidemic is currently larger than the reported numbers and they think that the epidemic will keep on growing. 

I was planning to spend this week’s class entirely on discussing a polio outbreak in Hispaniola in 2000, but the students asked whether we could spend a little more time on Ebola. Of course I was happy to comply.

I decided to ask the students to work in groups of three to estimate how many travelers would have to be screened at American airports to prevent one case of Ebola in the United States. The goal was to get a ballpark answer. I hoped that the exercise would give my students a feeling for the numbers in the Ebola epidemic. The answers they came up with varied widely. I think that for each Ebola patient that may be found by airport screenings, we’d need to screen at least 26,000 travelers. But if the epidemic grows much bigger than it is now, this number will go down.

Here is a description of how this number could be calculated.

How many people currently have Ebola, but are not yet too sick to travel?

(Note that those that have died cannot travel and let’s assume for now that those that have recovered don’t travel either)

In the last couple of weeks there have been approximately 900 new Ebola cases per week, or 129 per day (link Wikipedia).

"West Africa Ebola 2014 Reported Cases per Week Total" by Malanoqa - Own work.

“West Africa Ebola 2014 Reported Cases per Week Total” by Malanoqa – Own work.

The average incubation period of Ebola is 8 to 10 days (link CDC), so I’ll use 9 days for now. Each of the patients who get infected will therefore, on average, be asymptomatic for 9 days, meaning that on any given day, 129 x 9 = 1161 patients are infected but asymptomatic. After the incubation time, patients may show symptoms, such as fever, but may still be able to travel. I haven’t found any good information on how long this period lasts on average. In the case of Thomas Duncan, I understand that he went to the hospital for the first time on September 25th, and by September 28th he was vomiting all over the ambulance. I’d assume that he may have gotten a fever one or two days before the 25th, which means that he may have had symptoms for 5 days before he became too sick to travel.

In the case of the Spanish nurse Theresa Romero Ramos, it seems that there were seven days between the moment she started feeling ill and the day that she was so sick she called an ambulance (link CNN). So, if we take the average from those two cases, it means that patients may feel sick and have a fever for six days before they become too sick to travel. This means that at any one point in time, 129 x 6 = 774 patients may have a fever because of Ebola, but could still travel.

I therefore estimate the total number of people with Ebola, but still able to travel to be around 1161 + 774 = 1935.

How does this number of infected people compare to the number of people in the affected countries?

OK, so I now have a rough idea of how many people have Ebola at any one time, with or without symptoms, who could still travel. Next, let’s see how these numbers compare to the total number of people who live in the countries that are affected (Sierra Leone, Liberia, Guinea). From Wikipedia I learned that there are approximately 20 million people in the three countries combined. This means that, if you’d pick a person at random from one of the three countries, the probability that they have Ebola is around 1935/20,000,000 = 0.0001 (one in 10,000). The probability that they have Ebola with no symptoms would be 1161/20,000,000 = 0.00006 (one in 17 thousand) and with symptoms 774/20,000,000 = 0.00004 (one in 26 thousand).

How many infected people could fly from West Africa to the United States?

The next step is to look at how many people travel to the United States from the three affected countries. Several news outlets reported that around 150 or 160 people travel to the US from the three countries every day. If one in 10,000 people in infected, then we may expect that one infected person would travel to the US in the time that 10,000 people in total travel to the US. With 160 travelers per day, 10,000 people travel to the US in 10,000/160 = 62.5 days, or roughly two months. In other words, we should expect about one traveler with Ebola to arrive in the US every two months.

If we split this up again in asymptomatic and symptomatic Ebola patients, we find that we should expect an asymptomatic patient (like Thomas Duncan) every 17000/160 = 106 days (3.5 months) and a symptomatic patient every 26000/160 = 161 days (5.5 months) arriving in a US airport.

If the airports in the United States would check every traveler from West Africa for Ebola symptoms, I’d expect them to find one such patient every 5.5 months, in the time they screen 26,000 travelers. During that time it is quite likely that an asymptomatically infected person enters the US, because we should expect one every 3.5 months.

It seems to me that a screening for fever or Ebola symptoms at US airports is not very useful. In fact, I assumed in my calculations that everyone is equally likely to travel, whether they have a fever or not, unless they become too sick to travel. This is probably not true, especially because everyone is already screened at the airport when they leave one of the affected countries. If someone already has a fever, they will not be allowed to board a plane. This means that the screenings at the US airports are even less likely to find Ebola patients.

My conclusion: simply screening for fever and other symptoms is not useful

Screening may be useful for other reasons though. For example, it may the a good opportunity to provide information to travelers who have arrived from West Africa.

The effect of the size of the epidemic

I’ve assumed that the epidemic will stay at the size it is now (900 cases per week). I am not an epidemiologist and know little about Ebola, but there have been a steady 900 cases per week since four weeks, so I think it could stay this way. However, if the epidemic still grows, then there will also be more infected people traveling. However, no matter how big the epidemic gets, I think it will always be more likely that asymptomatically infected people arrive at an airport than then symptomatically infected people. There are two reasons for this: first, once symptoms start, people get quite sick quite quickly and may be unlikely to travel, secondly, everyone is already checked at the African airports.

Checks at the airports of the African countries

According to the New York Times, already more than 36,000 people have been screened as they left West Africa in the last two months. 77 of these people were not allowed to fly, but none of them turned out to have Ebola.

Because everyone is already checked in West Africa before boarding the flight and, possibly again when they have a lay-over in Europe, it becomes even more unlikely than I initially calculated that someone with symptoms arrives in the USA.

Comparison of my calculations with data

If 36,000 people have left the three affected countries by air in the last two months (I am not sure if this number is accurate as I have only seen it in the NYT, but let’s assume), then my calculations suggest that the expected number of patients among them would have been 3.6 (of course there can never be 3.6 patients, rather an integer number fairly close to it. I assume here that the epidemic was the same size over the last two months, even though it was a bit smaller in the first month). If we assume that only asymptomatic people travel, then the expected number of exported cases would be 2.1.

As far as I know, there have been only two cases of people taking the virus with them on a plane: Thomas Duncan and the patient who took Ebola to Nigeria (link Wikipedia). Two is not significantly lower than the expected 3.6, and really close to 2.1. Wow, I did all this handwaving and made a ton of assumptions, and the real number is not too different from my calculated prediction! 

The reasonable fit between reality (2 cases) and the prediction (3.6 or 2.1) suggests that the data we have on the epidemic may be quite accurate. For example, if the epidemic really would be much bigger than reported because of unreported cases, then we should have seen more exported cases of Ebola. Of course, it is also possible that the epidemic really is much bigger, but one of my assumptions is not correct. For example, it may be that people who are infected with Ebola are not as likely to travel as other people.

Corrections?

Please let me know if you find any mistakes in this post.

A missed opportunity

3 Oct

[Note: I originally wrote this article in Dutch for a newspaper for biologists (201409EbolaBionieuws) and translated it to publish here as well.]

"Ebola virus virion" by CDC/Cynthia Goldsmith - Public Health Image Library, #10816

“Ebola virus virion” by CDC/Cynthia Goldsmith – Public Health Image Library, #10816

Randomized placebo-controlled clinical trials are often large and very complex, but they don’t have to be, according to professor Joanna Masel. In an article for Scientia Salon, Masel argues that the few doses of ZMapp that were available to treat ebola patients should have been used for a small clinical trial. Even a very small trial is better than no trial at all. That this didn’t happen is a missed opportunity.

Ebola is a horrible disease and, as you can’t have missed, there is an epidemic going on in West Africa. About two-and-a-half thousand people already died of ebola in the last couple of months. There is no drug to treat ebola patients. At least, there is no drug of which we know that it works. There are a few experimental drugs, of which ZMapp is the best known. ZMapp consists of a mixture of three monoclonal antibodies. The press wrote a lot about whether ZMapp should be used and who should get the first available doses.

Because Ebola is associated with a very high mortality (>50%), experts agreed quickly that ZMapp should be made available to patients, even though it hasn’t gone through all the usual tests. Another question was who should get the first doses. If Africans would be the first to be treated, then the risk would be that it would be seen as using Africans as guinea pigs for a new drug. However, if non-Africans would be treated first, it could look as if whites were given priority. This is not easy decision.

ZMapp in a small trial?

Another discussion received less attention. ZMapp has never been tested in a clinical trial. But if we are going to give ZMapp to people, shouldn’t we use the opportunity to do a randomized trial right away? In such a study we can find out whether ZMapp is saving lives and we can potentially save lives at the same time. Professor Joanna Masel from the University of Arizona suggested the following: if you only have six doses , and many more sick people , then obviously most of the sick will not get the medicine. That’s terrible. But, she says, let us try to make the best of this bad situation. Let’s not pick six, but twelve people who qualify for the medications. And then lets give a placebo to half of the twelve and the real medicine to the other half. In this way we do a very small randomized, placebo-controlled clinical trial. And if we are lucky, the results of that trial will tell us whether the drug works.

But wait, we can hear the critics say, isn’t a study of twelve people far too small? In her article, Masel uses standard calculations (see also here) to determine whether a trial with twelve patients would make sense. It turns out that if the drug works extremely well, so that everyone gets the drug also survives, then in a study with twelve people we have an 80% chance that we find a significant difference between the placebo and ZMapp. And 80% is pretty decent for a clinical trial.

By now, the available doses have all been used. It is said that ZMapp has saved lives (link), but no one knows for sure. The patients who received ZMapp were probably in better condition and were treated in better hospitals than the average ebola patient. We don’t know what the real effect of ZMapp was. That’s a missed an opportunity.

EbolaTrialCalculation

There are simple tools that calculate the power of clinical trials. The screenshot is from a website http://www.sealedenvelope.com .

Genomics of the Ebola outbreak in Sierra Leone

14 Sep

I am teaching a graduate seminar at SF State on contemporary evolution of human viruses. Colleagues advised me to pick the papers for the entire semester beforehand, to reduce work during the semester. I didn’t do that, however, because I wanted to be flexible and choose (partly) based on what the students liked or what the students had trouble with. The result was that in the second week of class, I could hand out a brand new paper on the 2014 Ebola outbreak. Now that is contemporary!

The only trouble is that from now on, every other paper I choose will seem old; a Dengue outbreak in 2008? How ancient!

Here is some of the homework by the students in my class. I hope you enjoy reading it.

The context and main question of the paper

This paper focused on identifying the transmission route of the Ebola virus disease (EVD) outbreak throughout West Africa, whether the outbreak continues to be supplied by new vectors, and how the virus has changed to infect humans. The scientists used parallel viral sequencing and they ended up generating 99 EBOV genome sequences from 78 confirmed EVD patients. Phylogenetic comparison of all genomes from earlier outbreaks, suggests that the 2014 EBOV likely spread from Middle Africa within 10 years. Patients sharing intrahost variation showed specific transmission patterns in West Africa, and this suggests that transmission of viral genetics may be common.

Something new found in this study was that in contrast to previous EVD outbreaks, human-reservoir exposure is unlikely to have contributed to the growth of this epidemic. In addition, the EBOV catalog of mutations will aid in future studies. One main question that this paper addresses is whether or not future studies can monitor viral changes and adaptation, and understand how to contain this expanding epidemic.

Ryan Marder

The main conclusion of the paper

As this paper was largely descriptive in nature, I am wary to try to define the main scientific conclusion. With regard to concrete discoveries, however, their data suggests quite strongly a single point of origin for the outbreak of Ebola virus disease (EVD) in Sierra Leone, involving two different strains of the virus introduced simultaneously. Additionally, they document with high fidelity possible transmission links between groups of patients.

More important is the demonstration of the utility and information density available through the types of rapid sequencing and analysis employed in this work. Although not a protocol paper, the authors have produced a technical tour de force with a great deal of insight into the disease dynamics involved in the recent Ebola outbreak. I am sure that, as sequencing costs continue their steep decline techniques of this sort will only become more common, and the community will begin to adopt standard practices for these types of studies.

This sort of adoption and standardization will have broad implications for the future of disease mitigation. Tempered by the human genome project’s underwhelming applicability to medical breakthroughs, I remain optimistic that as genetic data is more readily applied to patient treatment, it is likely that information of this kind will contribute to tangible medical interventions which will directly benefit patients around the world.

Graham Larue

The devil’s advocate

The paper mentioned that when the first Sierra Leone case of Ebola virus disease (EVD) was confirmed, the tracing led to 13 more sick females who attended the burial of a traditional healer. It was misleading to seem the females are more prone to contract the disease than the males because the gender ratio of the funeral attendees wasn’t provided.

It was informative but boring to read when a bunch of numbers were given like single nucleotide polymorphisms (SNPs) between the 2014 EBOV genome sequences and the previous EBOV outbreak, and the numbers of intrahost single nucleotide variant (iSNV) in Sierra Leone patients. The wording was a bit confusing sometimes. One ethical issue could be sequencing for other pathogens when the 35 EDV suspected cases turned out negative for EBOV.

Emily Chang

Make a graphical abstract of the paper

GraphicalAbstractNicolas

Nicolas Cole

Two tweets about the paper

ArturoTweets

Arturo Altamirano (@articluateartie)