A majority of the epidemiological articles on SARS were submitted after the epidemic had ended, although the corresponding studies had relevance to public health authorities during the epidemic.  … although the academic response to the SARS epidemic was rapid, most articles on the epidemiology of SARS were published after the epidemic was over even though SARS was a major threat to public health. ¹

(My emphasis) This is an analysis of the published response to the SARS epidemic in 2003.   The conclusion is basically that, although SARS papers were clearly fast-tracked by journals, most papers didn’t see publication until after the epidemic was over.

They suggest that journals could speed up their fast-track systems for this sort of thing, and cite some of the subsequent attempts to speed up availability of publications (Nature Proceedings, Pandemic Flu Updates from the BMJ, and PLoS Current: Influenza), but point out that it’s not solely up to the journals: Most of the articles they looked at weren’t even submitted until the epidemic was over.  They suggest that authors could speed up data-collection and analysis:

This bottleneck could be reduced by developing a series of ready-to-use information technologies, to improve timeliness and, thus relevance, and further, to improve standardization, and thus comparability across studies in the event of an outbreak.¹

That presumably means that epidemiologists should, right now, be preparing tools for the next pandemic.  I assume some are, but I don’t know how widespread that is.

I’d be very interested to compare the 2003 SARS response to the 2009/2010 pandemic flu response.  My impression was that the response was much faster — not only through the fast-tracked sites mentioned above, but through semi-formal channels as well.  Though it wasn’t a target of this paper (which focused on peer-reviewed papers) I’d also be interested to see how much (if any) impact blogs had for the flu response.

1. Xing, W., Hejblum, G., Leung, G., & Valleron, A. (2010). Anatomy of the Epidemiological Literature on the 2003 SARS Outbreaks in Hong Kong and Toronto: A Time-Stratified Review PLoS Medicine, 7 (5) DOI: 10.1371/journal.pmed.1000272

This study confirms on a national scale that the 2008 rotavirus season among children aged <5 years was dramatically reduced compared to pre-RV5 seasons.  …  Based on the observed decrease during the 2008 season, we estimated that ~55,000 acute gastroenteritis hospitalizations were prevented during the 2008 rotavirus season in the United States. A decrease of this magnitude would translate into the elimination of 1 in every 20 hospitalizations among US children aged <5 years.¹

(My emphasis)

Here’s what that looks like:

Interestingly, the reduction in gastroenteritis wasn’t only in vaccinated children:

In 2008, acute gastroenteritis hospitalization rates decreased for all children aged <5 years, including those who were either too young or too old to be eligible for RV5 vaccination. …These findings … raise the possibility that vaccination of a proportion of the population could be conferring indirect benefits (ie, herd immunity) to nonvaccinated individuals through reduced viral transmission in the community¹

(My emphasis, again)

Assuming this continues to hold up (and similar studies² have found similar large reductions) it’s a striking example of herd immunity.

(Added later: The vaccine this paper looked at was RotaTeq.  This is not the vaccine that was recently found to be contaminated with porcine circovirus genomic fragments; that was the other rotavirus vaccine, Rotarix.)³

(Second update: RotaTeq apparently also is contaminated with porcine circovirus genomic fragments.)

1. Curns, A., Steiner, C., Barrett, M., Hunter, K., Wilson, E., & Parashar, U. (2010). Reduction in Acute Gastroenteritis Hospitalizations among US Children After Introduction of Rotavirus Vaccine: Analysis of Hospital Discharge Data from 18 US States The Journal of Infectious Diseases DOI: 10.1086/652403
2. For references see
   Weinberg, G., & Szilagyi, P. (2010). Vaccine Epidemiology: Efficacy, Effectiveness, and the Translational Research Roadmap The Journal of Infectious Diseases DOI: 10.1086/652404
3. I haven’t talked about the Rotarix withdrawal because I think it’s been widely and very well covered on other blogs.  (I have 536 papers in my list of things I want to talk about here some time, so I usually don’t bother blogging about findings other places cover in detail.)  Vincent Racaniello at the Virology Blog has his usual high-quality commentary on it here.  He also made an important point on his podcast, This Week In Virology (either number 75 or number 77, I don’t remember which), which I don’t see explicitly on the post: The circovirus-containing vaccine went through all the safety trials, and no problems were seen.

   Obviously circovirus genomes aren’t supposed to be in the vaccine and they’ve got to go.  But (1) we don’t know if the genomes are infectious, or just fragments; (2) there’s no evidence, in spite of centuries of exposure to porcine circovirus, that it has any effects in humans; (3) the vaccines were shown to be safe, at least in the short term.

   On a larger scale, we’re entering a new era of analysis.  I suspect more of this sort of contamination will turn up as the sensitivity of our screening techniques improve, much like chemical detection: As we improve chemical detection to the parts-per-billion and parts-per-trillion level there needs to be better understanding of safety levels. Is this true for biologics? There are good arguments that there may be no safe level for some biologics, and any detection should lead to withdrawal, but on the other hand there clearly is a safe level for other biologics.  Human poop is loaded with vast amounts of viruses of peppers, for example; now that we know that should we regulate pepper mottle virus?

   I don’t have answers, which is why I relegate this to a footnote, albeit a long a rambling footnote.

I’ve been expecting a resurgence of swine-origin influenza virus (SOIV) in North America for a while now, and it hasn’t happened. The virus is still out there, still infecting a few thousand people per week, but there hasn’t been a third large-scale wave of virus transmission. That’s different from the 1918 and 1957 pandemics (see here for details). What’s different this year?

Of course there are tons of things that are different this year, but I recently got around to doing something I should have done a while ago: Trying to estimate what proportion of Americans are now immune to SOIV. I’ve seen estimates that around 40% of the population should now be immune. I get roughly the same number (slightly higher, closer to 50%, because those estimates didn’t take into account pre-existing immunity to SOIV), but I think there’s an important point that might be missed in this: Most of the immunity might be clustered in the two most susceptible populations (children and the elderly), with two-thirds to three-quarters of them being immune.

There are three ways someone could be immune to SOIV. They could have been exposed to a related virus, some time in the past, and have developed a long-term immunity. They could have been infected with SOIV, somewhere in the first or second wave. Or, of course, they could have been vaccinated.

Numbers for each of those are available. They’re more or less approximate; not all the sources break down age groups in exactly the same way, for one thing, and I don’t have precise numbers for everything.¹ I’ll try to flag places where I’m especially guessing, and it’s entirely possible that this I’ve made some obvious, basic mistakes in here, since this has been written in the interstices of cleaning our house for Chinese New Year (a Herculean task) and hosing down the kids to get them ready for the party at YongHui’s this evening.  If so, let me know and I’ll try to correct them.

We need to break immunity down by age, because pre-existing immunity to SOIV was strongly age-dependent. (That’s presumably why the virus was strongly biased to infecting younger people this year.) For the demographics of the US I’m using the 2008 census data. All this is summarized in the table below, and my explanations follow.

1. Pre-existing immunity. A paper in New England Journal of Medicine last year² found that 4% of children, 6% of young adults, and 34% of older adults (born before 1950; I’m going with 65 years old as the dividing line just to make it easier to compare to other data) were already immune to H1N1. That’s more or less consistent with other studies I’ve seen, so let’s go with that.

2. Infection. The CDC estimates that somewhere over 55 million people in the US were infected in the first and second wave of SOIV, and gives approximate age distribution here. I’ve used the mid-point of their range estimates, so it’s possible that significantly more people were infected.  This works out to a quarter of US children being infected with SOIV,  which is broadly consistent with measures elsewhere — for example, a recent Lancet paper³ that estimated that about a third of children in the UK were infected.

3. Vaccination. The CDC’s Anne Schuchat’s Feb 5 press conference was very useful for this. The CDC has estimated that somewhere over 70 million people in the US have been vaccinated. Schuchat gave two further figures: Some 37% of children, and about 21% of adults have been vaccinated. These figures come from two different sources — a CDC survey and a Harvard survey respectively — so they may not be directly comparable, and I don’t know the breakdown in adults (young adults vs. elderly) but these figures do work out to about the right total, around 80 million people. Probably a little high, but not by much.

Another source of fuzziness is how much overlap there is between infected people and vaccinated people. It’s probably safe to say that most vaccinated people were not subsequently infected, but it’s quite possible that people were infected (perhaps with no symptoms, which we know happened quite frequently) and were subsequently vaccinated. ⁴ I’ve tried to adjust for this by assuming that half of infected people didn’t know it, and went on to get vaccinated, ⁵ as well as subtracting the proportion of people who were already immune (who presumably had no way of knowing that).  That’s the “Vaccinated uninfected” column.  Or, I’ve assumed  no overlap (just plain “Vaccinated”), to get an approximate range of immunity out there.

Including “Vaccinated uninfected” gives the “Number immune (low)”; assuming that all vaccinated were not infected gives the “Number immune (high)”.

And my conclusions are that:

• Over half the US population as a whole is now immune to the new SOIV.
• As many as three-quarters of the elderly and two-thirds of the children — the most important populations as far as flu is concerned — may be immune.
• Between a third and about half of this immunity was due to vaccination.

That level of immunity is probably enough to impact virus transmission drastically. In the early waves, if a child was infected then virtually every child she contacted in school or on the playground would be susceptible. Now only one in three of them are potentially infectable. I’ll have to spend some time looking at the models of influenza spread but I think that considering that the SOIV was not spectacularly infectious anyway, this level of population immunity is easily enough to prevent the third winter wave of disease I was expecting to see.

(I am particularly curious about modeling the impact of vaccination. Without vaccination would there have been a third wave? My guess is that there would have been, but that’s just a guess.  Update for clarification: Vaccination rates were highest in children. Without vaccination only about 25% of children would be immune — vaccination therefore doubled or tripled the amount of immunity in this critical population, and I think SOIV would have resurged in schools in winter without this intervention.)

What’s more, this level of immunity, especially in the apparent absence of the usual seasonal flu strains, has important implications about influenza over the next few years, but this post is already too long, so maybe I’ll talk about that some other time.

1. Probably quite accurate numbers are available, but not to me. Or, at least, not without a lot more work.
2. Hancock, K., Veguilla, V., Lu, X., Zhong, W., Butler, E., Sun, H., Liu, F., Dong, L., DeVos, J., Gargiullo, P., Brammer, T., Cox, N., Tumpey, T., & Katz, J. (2009). Cross-Reactive Antibody Responses to the 2009 Pandemic H1N1 Influenza Virus New England Journal of Medicine, 361 (20), 1945-1952 DOI: 10.1056/NEJMoa0906453
3. Miller, E., Hoschler, K., Hardelid, P., Stanford, E., Andrews, N., & Zambon, M. (2010). Incidence of 2009 pandemic influenza A H1N1 infection in England: a cross-sectional serological study The Lancet DOI: 10.1016/S0140-6736(09)62126-7
4. This is another significant potential source of error, I think.
5. That is, I’ve subtracted that proportion of people from the vaccinated totals.

Note the outlining of the Intermittent Fever districts, including Lansing, MI, where I live. Note the intense yellow rim of Yellow Fever.  Note the Small Pox Measles Scarlatina Consumption Endemic region along the Eastern seaboard, the large-case TYPHUS, the DYSENTERY, the casual “And many epidemics” tacked on to the main Yellow Fever, the serpentine red band tracing cholera. ¹ There’s goitre in the Midwest and Mexico, elephantiasis down in South America, “Dia. & Dys. (severe)” in tiny writing down in the Bahamas, and the Bermudas are “generally healthy: Influenza, Rheumatism, Dysentery, Yellow Fever”.  And so much more.  (Compare to the map of Malaria in the USA, 1870.)

This amazing map is a mere afterthought, an inset of a map whose awesomeness goes up to 11.  The US map to the right² (again, click for a larger version) is still just a small fraction of the whole, and that’s not even mentioning the jaw-dropping charts and graphs, also inset, showing “Consumption: Proportion of Deaths in the different quarters of the Globe”, “Comparative Value of Life in Different Countries”, “Proportionate Mortality of European Residents in Foreign Countries” and still more and more.

This map is “The geographical distribution of health & disease, in connection chiefly with natural phenomena. (with) Fever districts of United States & W. Indies, on an enlarged scale,” and it’s from:

The physical atlas of natural phenomena
by Alexander Keith Johnston, F.R.S.E., F.R.G.S., F.G.S.
William Blackwood and Sons, Edinburgh and London, MDCCCLVI ³

I’d run across reverent mentions of this map — especially the Fever Districts inset — here and there in old books, and I just stumbled across it in downloadable form.  You must go at once to The David Rumsey Collection and pore over it for several hours, at the highest resolution.

1. Lansing seems to have been just barely cholera-free, at least in 1856.
2. The colors refer to “zones of disease” – Torrid (brown), Sub-torrid & temperate (green), sub-temperate & arctic (blue)
3. That’s 1856, for those of you who, like me, need to pause a while in thought when confronted with years in Roman numerals

I don’t have the original data that were used to make the 1918 data¹ (black traces; these are mortality data, rather than cases; some sources suggest that this would underestimate the case number for the spring/summer peak of 1918, if the virus increased its virulence before the fall outbreak) , but I think I have the chart aligned with the CDC’s weeklies (red) for this year’s pandemic. ² They’re not identical, but they’re similar so far.  In particular, there was a surge in the summer, a big peak early in the fall — much earlier than standard seasonal influenza, which doesn’t usually get going until January or February — and then the fall cases dropped dramatically.  The 1918 influenza was followed by a third, smaller, peak, in winter, around the usual period for influenza.

In 1957, when a new pandemic influenza struck the US, the pattern of three waves — smallish in summer, very large in fall, followed by a slightly smaller but still major wave in early winter — was also broadly similar.³ Again, there was a spring/summer wave throughout the US, starting in June and peaking, maybe, in August.⁴  The fall outbreak started in September and peaked in late October, there was a lull, and then there was a new surge early in 1958, this time perhaps peaking a little later in the winter than the 1918 version.

1957/1958 influenza cases, by week5

The next pandemic was 1968/1969, when H3N2 moved in and supplanted the H2N2.  The pattern here seems different: There was little or no spring/summer wave,⁶ and while the outbreak did start a little earlier than usual, it was only by a few weeks:

The first outbreaks in the civilian population developed in Puerto Rico and Alaska in late September and early October. The first outbreak in  civilian population in the continental USA did not develop until the third week of October, when the small desert city of Needles, Calif., reported an influenza-like illness involving 35%-40% of the of the population. ⁷

(By comparison, by the third week in October this year, the pandemic seems to have just about peaked.)  Along with the late start in 1968, there was no lull; the influenza kept building to its peak, in December, and then quickly dropped down and stayed down. It was really more like a very large seasonal epidemic, very different from the patterns of 1918, 1957, or 2009.  (There’s actually a more impressive bump in the following summer, 1969, so this might be simply because the virus didn’t reach the US in time, and carried over into the summer rather than presaging the pandemic.)

1968/1969 influenza cases, by week, compared to previous years7

The next pandemic was 1977-1978, when H1N1 returned.  I don’t think we have good data for that, because the main measure was death, and that pandemic strain didn’t impact mortality significantly. ^{8 ,9}  However, as far as I can tell, that was mainly a winter-only wave. So the common claim that pandemics come in several waves is not universally true — just two of the four 20th century pandemics acted that way — but it does seem to be true for the 2009 pandemic.

In any case, based on previous pandemic patterns where the disease started this early, I’m guessing that we’re already past the very worst of the 2009 SOIV outbreak, and we’re going to enter a bit of a pause; but it’s going to come back early in 2010 — perhaps not to the same levels as we’ve been seeing in late October/early November, but not too far off.

1. Taubenberger JK, & Morens DM (2006). 1918 Influenza: the mother of all pandemics. Emerging infectious diseases, 12 (1), 15-22 PMID: 16494711
2. http://www.cdc.gov/flu/weekly/index.htm
3. This was the introduction of an H2N2 strain that replaced the previously-circulating H1N1 virus
4. I haven’t seen the data for the summer of 1957, so this is based on comments in the descriptive papers, and I don’t know how big the summer wave was
5. D. A. Henderson, Brooke Courtney, Thomas V. Inglesby, Eric Toner, & Jennifer B. Nuzzo (2009). Public Health and Medical Responses to the 1957-58 Influenza Pandemic Biosecurity and Bioterrorism, 7 (3), 1-9
6. You can see a little bump in the summer of 1968, which could conceivably have been the summer wave, but it’s definitely much less dramatic than the 1957 or the 2009 summer.
7. Robert G. Sharrar (1969). National Influenza Experience in the USA, 1968-69 Bull Wld Hlth Org, 41, 361-366
8. Lui, K., & Kendal, A. (1987). Impact of influenza epidemics on mortality in the United States from October 1972 to May 1985. American Journal of Public Health, 77 (6), 712-716 DOI: 10.2105/AJPH.77.6.712
9. The 1977-78 H1N1 was almost certainly a laboratory accident, a release of an earlier strain from pre-1957.  Accordingly, most people over their mid-20s were already immune to the pandemic virus.  Since older people are usually the ones with the highest mortality rates, the 1977-78 pandemic didn’t trtanslate into increased mortality, and doesn’t show up in mortality rates.

–Patterson KD, & Pyle GF (1991). The geography and mortality of the 1918 influenza pandemic. Bulletin of the history of medicine, 65 (1), 4-21 PMID: 2021692

(Click on the image for a larger version)

Noroviruses cause an unpleasant, but rarely serious, diarrhea and vomiting-type disease — “cruise ship flu”¹ is one term for it.  As well as cruise ships, nursing homes and hospitals and other more or less closed systems also see outbreaks of norovirus disease fairly regularly, and as you’d expect the elderly and immune-compromised are more at risk from the disease.

Noroviruses have been around for a long time (they were first identified in the early 1970s, as “Norwalk Viruses”), but it’s in the last ten years or less that they really exploded; in 2002 there was an abrupt, worldwide upsurge of norovirus outbreaks, and more epidemics have followed almost every winter. Those outbreaks were all different mutant variants of norovirus; I talked about this earlier.² Each outbreak³ was associated with a new variant of norovirus, that is no longer controlled by the immunity that controlled the previous outbreak.

A couple of recent papers look at norovirus epidemics more closely. One⁴ analyzed the different strains involved in global outbreaks. They found that there were eight distinct variants of the GII.4 noroviruses, three of which caused global epidemics. Other strains did become epidemic, but on a more local scale (countries or continents, rather than everywhere).

My first thought was that that’s essentially the strategy that influenza viruses have used, also very effectively; each new flu season sees new variants of influenza virus, and each season’s most abundant viruses are the ones that are less well controlled by the immunity among their target population. This is the notorious “antigenic shift” that beginning virologists learn to parrot in their first class. The parallel to influenza epidemics was also noted by the authors, and they pointed out another parallel: Most of the global norovirus epidemics seem to have originated in Asia, as with influenza A.

What surprised me originally about the norovirus equivalent of antigenic shift was that at the time, conventional wisdom had it that immunity doesn’t play a big part in controlling seasonal norovirus outbreaks; immunity to noroviruses is weak and short-lived, and I had not thought that immunity from the previous winter would be a factor in controlling outbreaks this winter. The previous paper I talked about showed evidence, though, that immunity is a major factor in determining norovirus epidemics, and the other paper I have here⁵ looks at this in much more detail. I won’t go into their work in any detail⁶ but what they’re doing is building predictive models for norovirus epidemics. Very briefly, their overall conclusion is:

These results point to a complex interplay between host, viral and climatic factors driving norovirus epidemic patterns. Increases in norovirus are associated with cold, dry temperature, low population immunity and the emergence of novel genogroup 2 type 4 antigenic variants.⁵

The “new variant” part matches the first paper’s description of epidemics — mostly, but not always, they’re driven by a new version of the virus, but new variants don’t necessarily explode globally. It seems that a new variant may often be necessary for an outbreak, but isn’t sufficient; and in some cases, other factors may mean new variants aren’t absolutely necessary. Cool, dry weather supports an epidemic (and this is probably a big part of the highly seasonal pattern of norovirus infections, as well; it’s charmingly called “Winter vomiting disease” by some). And epidemics are possible when population immunity to a particular strain of norovirus drops under a certain level.

“Daily norovirus laboratory reports (grey circles) and predicted values (red line) from full model including temperature, relative humidity, immunity, new variants and autoregressive terms and other confounders.“

The authors point out that new variants are selected by population immunity, so two of these factors are not strictly independent. However, “Despite this, these two factors had significant effects after controlling for the other”;⁵ perhaps there’s some immunity even to variant strains of norovirus. Since immunity to norovirus does drop very quickly,⁷ perhaps a year is enough to open a window for new strains, but not for the same one; particularly if the weather cooperates. Or perhaps the arrow is going the other way — population immunity at the end of one season chokes out the prevalent strain, and only new strains that are relatively resistant survive to cause the next epidemic once the weather cooperates.

At any rate, from these parameters the authors derived a predictive model. Applied retrospectively, it looks pretty impressive (see the figure to the right; click for a larger version). ⁸  It will be interesting to see how well it actually predicts new outbreaks.

By the way, regular readers may have noticed that this is two weeks in a row with only one new post — I usually aim for two or three per week, but what with my kids starting their school this week, and my teaching schedule⁹ kicking in, I’m scrambling some to keep up.  Hopefully I’ll be in more control soon, but I make no promises.

1. It’s not flu!
2. Referring to this paper: Lindesmith, L.C., Donaldson, E.F., LoBue, A.D., Cannon, J.L., Zheng, D., Vinje, J., Baric, R.S. (2008). Mechanisms of GII.4 Norovirus Persistence in Human Populations . PLoS Medicine, 5(2), e31. DOI: 10.1371/journal.pmed.0050031
3. Except for the 2007/08 outbreak, which was mainly the same strain as the previous year’s
4. Siebenga, J., Vennema, H., Zheng, D., Vinjé, J., Lee, B., Pang, X., Ho, E., Lim, W., Choudekar, A., Broor, S., Halperin, T., Rasool, N., Hewitt, J., Greening, G., Jin, M., Duan, Z., Lucero, Y., O’Ryan, M., Hoehne, M., Schreier, E., Ratcliff, R., White, P., Iritani, N., Reuter, G., & Koopmans, M. (2009). Norovirus Illness Is a Global Problem: Emergence and Spread of Norovirus GII.4 Variants, 2001–2007 The Journal of Infectious Diseases, 200 (5), 802-812 DOI: 10.1086/605127
5. Lopman, B., Armstrong, B., Atchison, C., & Gray, J. (2009). Host, Weather and Virological Factors Drive Norovirus Epidemiology: Time-Series Analysis of Laboratory Surveillance Data in England and Wales PLoS ONE, 4 (8) DOI: 10.1371/journal.pone.0006671
6. There are intimidating equations and everything
7. Though it’s not known exactly how quickly
8. By the way, while looking around for images to illustrate this norovirus post, I came across a lot of images of people hurling, and worse.  I decided to stick with obscure graphs instead.  No need to thank me.
9. A half-dozen classes in graduate immunology, a half dozen veterinary virology, and a dozen in veterinary immunology this year; plus a couple of guest lectures, where I’ll talk about immunity to viruses, probably focusing on swine-origin influenza virus as a particularly topical example

Hepatitis C virus (HCV), one of the classic intravenous-spread viruses, was only identified about 20 years ago.  Where and when did it originate, and how did it spread?

A recent paper¹ estimates that the common ancestor of the present world-wide HCV strains was in Guinea-Bissau, around 1470.  From there:

… infections moved to the New World via Benin–Ghana, even when they originated from Guinea–Gambia. … It is therefore likely that the slave trade has played a historical role in the global dissemination of HCV genotype 2. A similar role has previously been proposed for the transcontinental transmission of yellow fever virus prior to mass global travel. ¹

The pattern of HCV spread matches the flow of the slave trade.

There’s another very interesting historical finding from this epidemiology.  HCV epidemiology is very different in Cameroon vs. Guinea-Bissau.  In Cameroon, HCV exploded in the early to mid-20th century; whereas in Guinea-Bissau, HCV spread in the 20th century was slower.  The authors here suggest that this reflects different styles of health care in the two countries — aggressive treatment vs. limited treatment.  But it’s an indirect consequence of treatment of other diseases, and the effects on HCV were the opposite of what you’d expect:

We suggest that the differential epidemic histories of HCV genotype 2 in the two countries probably result from historical differences in the large-scale administration of intravenous antimicrobial drugs, decades before the risk of transmission of blood-borne viruses was understood. After World War I, medical care in Cameroun Français was provided mostly by military doctors, and public-health interventions aimed to cover the whole population … In contrast, the health system before the mid-1940s in Portuguese Guinea (now Guinea-Bissau) was more directed towards protecting the health of the European colonists and their Guinean employees. …Thus, the 25 year delay in organizing public-health interventions in Portuguese Guinea, combined with a lower incidence of yaws and trypanosomiasis in this drier land, resulted in a much lower proportion of the population receiving intravenous injections than in Cameroun Français, and a reduced opportunity for iatrogenic HCV transmission. ¹

In other words, the aggressive treatment of diseases in Cameroon probably dramatically reduced the frequency of many diseases, but because it involved injections with non-sterile needles, the treatment also managed to spread HCV.  The more lackadaisical attitude in Portuguese Guinea may have let other diseases flourish, but accidentally restricted the spread of IV contaminants like HCV as well.

1. Markov, P., Pepin, J., Frost, E., Deslandes, S., Labbe, A., & Pybus, O. (2009). Phylogeography and molecular epidemiology of hepatitis C virus genotype 2 in Africa Journal of General Virology, 90 (9), 2086-2096 DOI: 10.1099/vir.0.011569-0

Five Google.com scientists, and one from the CDC, have published evidence in Nature¹ that Google search terms are accurate ways of measuring influenza epidemics.  Their influenza tool is available at http://www.google.org/flutrends/ (and has an explanation of the techniques involved).  Their accuracy seems pretty decent, as the figure below shows — red traces are CDC-recorded cases, black is the cases as predicted from Google searches.  

A comparison of model estimates for the mid-Atlantic region (black) against CDC-reported ILI percentages (red)1

There are a surprising number of on-line maps for influenza and avian influenza, although as though far as I know they’re all much more descriptive (all based on reported cases) than Google’s version, which is (sort of) predictive.  For example, there’s the avian influenza outbreak map, various maps from the WHO , and the CDC’s set of maps.  (There’s also Bird Flu Breaking News, which occasionally links to my posts, but the site seems to be broken; too bad, because if I remember correctly, it had an interesting variant on maps that was conceptually related to Google’s — showing where new discussion on avian flu was located.)  

1. Jeremy Ginsberg, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski, Larry Brilliant (2009). Detecting influenza epidemics using search engine query data Nature, 457 (7232), 1012-1014 DOI: 10.1038/nature07634

Probably most of you remember the outbreaks of foot-and-mouth disease in Britain in 2001, and again last year. FMD is a virus that affects many hooved animals; it’s not usually fatal, but causes productivity loss. FMD outbreaks are economically devastating, because aside from the productivity loss many countries, that are free of the disease, will refuse to take meat or other agricultural products from outbreak areas. The goal of FMD management, then, is to keep it away, and if it ever hit, to contain it and slaughter all infected and potentially-infected animals.

The 2001 outbreak in Great Britain came from outside the country. The 2007 outbreak, though, was clearly from a local source: The FMD research lab in the Institute for Animal Health (IAH), Pirbright, Surrey. The latest paper discusses the epidemiology of that outbreak, and how they used whole-genome sequencing to track and predict sites of FMD.

(This is timely, because the US is planning to move the sole American FMD research center, now on Plum Island, to the mainland. There’s obvious concern that the virus could escape from containment within research labs and infect neighboring animals, causing the first American FMD outbreak since 1929. I am not particularly knowledgeable about the field, but I have to think that, at best, the timing of the planned move is unfortunate.)

FMD is caused by a picornavirus, the same broad family as polio and cold viruses. Like those viruses, FMD mutates rapidly, traveling around as a quasispecies cloud. The clouds can be easily divided into 7 broad groups, and within the most common serotype (O) there are 8 distinct subgroups (see the map² to the right [click for a larger version] for their geographical distribution).

The FMD genome is 8134 nucleotides long, and the sequence analysis that has been used for epidemiology like the 7 different topotypes has been based on no more than 8% of that length — the VP1 gene, usually. That’s enough to track high-level changes, because of FMD’s rapid mutation rate:²

the rate of evolution is approximately 1% per year …. If the concept of a constant evolutionary rate is accepted and there are no constraints on virus evolution then it would expected that new topotypes could arise in approximately 15 years. In reality, this extent of evolution probably takes much longer. For example, FMD viruses belonging to the Asia 1 serotype, first identified in samples from Pakistan in 1954 … have not yet exceeded 15% nucleotide difference …

But 8% of the genome is not nearly enough to track changes within a single epidemic, like the one in Surrey last year; it simply isn’t long enough to pick up the handful of variations. It was known in the previous outbreak, in 2001, that the information was there in the genome (“virus recovered from closely housed animals can differ by 1 to 2 nucleotides and is likely to pass through a “bottleneck” on passage between farms”).³ The issue was a practical, technological one — being able to sequence entire virus genomes quickly enough to pass back information to people in the field.

By 2007, the technology was there. The people at the IAH were able to sequence genomes from viruses isolated in the outbreak with a fine enough comb to track changes throughout the spread, and fast enough pass information back to the field within 24-48 hours. Their sequencing confirmed that the virus was in fact a lab escapee, because it was almost identical to a couple of lab strains but was different from circulating viruses. ⁴

The 40-odd viral genomes yielded a fair bit of useful information (see the figure to the left for a summary). For example,

The small number of nucleotide substitutions observed between viruses from source and recipient IP suggests that there has been direct transmission without the involvement of other susceptible species, e.g. sheep or deer.

It’s obviously useful to know if there’s a wild-animal reservoir of disease, but an even more important insight came from this work as well.

the virus from IP3b was nine nucleotides different from the virus from IP1b … This is a high number of changes for a single farm-to-farm transmission … and we predicted that there were likely to be intermediate undetected infected premises between the first outbreaks in August and IP3b. … Serosurveillance of all sheep within 3 km of the September outbreaks revealed another infected premises (IP5), on which it was estimated that disease had been present for at least two, and possibly up to five weeks. As Figure 2B shows, IP5 is a likely link between the August and September outbreaks.

I would be interested in hearing from the people on the ground just how useful this information was — for example, were they impelled to search more for an intermediate source based on this information, or did they already suspect it from other, classical ways? But in any case, it’s clear that genomics is capable of pushing epidemiology a lot further in the future.

1. Cottam, E.M., Wadsworth, J., Shaw, A.E., Rowlands, R.J., Goatley, L., Maan, S., Maan, N.S., Mertens, P.P., Ebert, K., Li, Y., Ryan, E.D., Juleff, N., Ferris, N.P., Wilesmith, J.W., Haydon, D.T., King, D.P., Paton, D.J., Knowles, N.J. (2008). Transmission Pathways of Foot-and-Mouth Disease Virus in the United Kingdom in 2007. PLoS Pathogens, 4(4), e1000050. DOI: 10.1371/journal.ppat.1000050
2. Samuel, A. R., and Knowles, N. J. (2001). Foot-and-mouth disease type O viruses exhibit genetically and geographically distinct evolutionary lineages (topotypes). J Gen Virol 82, 609-621.
3. Cottam, E. M., Haydon, D. T., Paton, D. J., Gloster, J., Wilesmith, J. W., Ferris, N. P., Hutchings, G. H., and King, D. P. (2006). Molecular epidemiology of the foot-and-mouth disease virus outbreak in the United Kingdom in 2001. J Virol 80, 11274-11282.
4. As far as I know, it’s not yet known how exactly the virus escaped from the IAH. I’ve read what seems to be informed speculation that it may have come from the drains, as decontamination systems designed to prevent that weren’t properly maintained; but I don’t know if that’s true, an educated guess, or mere rumor and guesswork.