Ebola is a cool disease. It transmits among fruit bats in the area in and around the Central African Republic. Apes live in and under the trees the bats live in and ingest their feces. Humans who ingest the apes pick up the virus when slaughtering the animal, or so some think. The truth is that no one really knows for sure.
Contacts between humans is increasing as settlements expand and a demand for meat increases. Lacking access to formal methods of employment, individual sellers happily take advantage of market demand and a thinly profitable trade in bushmeat profulgates. Meat equals success and in the place of professionally or pastorally raised beef, which is mostly unavailable to poor people in countries like Liberia and Sierre Leone, people eat the monkeys, chimps and many other of our cousins which are able to harbor the many of the same pathogens we do.
One person gets sick. He or she has no access to formal care because his or her government can’t or won’t provide it so he remains at home. The family consults the local herbalist who provides some medications which offer temporary psychological relief but nothing more. As time ticks on, the victim becomes even sicker until the situation becomes so serious that the family has no choice but to carry their dying loved one to a health clinic 20 km away from their house. Along the way, everyone carrying him or her touches infected feces and vomit and three weeks later the process is repeated.
This could have all been avoided if rural economies were developed enough so that a mass migration to urban areas wasn’t necessary, had there been safer sources of meat available for an affordable price, were there sufficient jobs which wouldn’t necessitate the bushmeat trade, were the governments of Liberia and Sierre Leone effective enough to place a proper health facility close by to patient 0’s house and if health care was dependable enough to be able to spot and deal with an Ebola case.
Ebola is a conflation of ecology, economics, sociology, culture and politics, all mixed together to create conditions for one of the worst health crises the African continent has seen since HIV. It’s going to erase any of the gains of the past decade and collapse the already struggling health systems of some of the poorest places on the planet.
Meanwhile, the United States is having another 9/11 moment and this is where I’m starting to get quite concerned. Panic is about to become policy. Fears of global terrorism prompted our entry into Afghanistan, which might have been justified. But it also paved the way for the invasion of Iraq, which, from the beginning, was a disaster waiting to happen. Out of 9/11, we got the Patriot Act, a massive expansion in government powers to search, seize and detain and America stood by and allowed it to happen with little debate.
I am not a Libertarian, though keep getting accused of being one. I believe in public schools, public health care and government oversight of dangerous industries. So there. John Galt wouldn’t be much into me (but I guess from the far, far left anyone looks like a Libertarian).
I am, however, despite my leftist pedigree, quite concerned with the rights of individuals and the potential for panic and ignorance to lead to a rhetoric that can quickly spiral out of control and veer seemingly caring people away from the direction that the moral compass would normally point us in. I am remembering how many Americans supported torture during Bush II and wondered how many of them would support torture were it to be practiced on their own children. Though seemingly alarmist, I think that we need to be extremely careful.
Enough about me. The reality of Ebola is that it is a man-made crisis. Forest dwelling locals have eaten bushmeat for as long as humans have lived there but there is little evidence that there has ever been a large scale outbreak like the one we are currently experiencing (though history in Africa is often obscure). As I noted earlier, many forces are at play, all of which are associated with the rapid social change that Sub-Saharan African states are currently experiencing.
Some of these forces are inevitable. Population growth, as it did in Europe and Asia before, has led to the creation of mega-cities. The connections, however, between the rural and the urban, however have not been severed. People are going to do what they do, regardless of risk, particularly if they can make a buck meeting some market demand.
Some forces, though, are avoidable. While health care did not initiate the crisis, it helped drag it along. Liberia and Sierre Leone can boast to have two of the worst health systems in the world, but their poor capabilities are hardly unique in Sub-Saharan Africa. NGOs and missionary groups work to plug some of the gaps, but the reality is that without a concerted and proactive effort from the governments of those countries, the system will never improve. International funding is too poor and weak national economies and top heavy tax structures can’t adequately fund these systems domestically. Poor funding leaves many clinics, particularly those in rural areas where these outbreaks begin, without supplies, trained staff and diagnostic equipment. In Kenya, Malawi and Tanzania, I’ve seen more than one rural clinic without power or clean water. Worse yet, Ebola outbreaks, though devastating, are infrequent so that more pressing needs like malaria, diarrheal disease and HIV eat up the brunt of the already scarce funds clinics receive. Pathogens not only compete in the wild, but also for funding and support. This leaves many rural health workers without the protective gear they need, so that they work are the highest risk for death from diseases like Ebola.
What can we do? First, we can calm down. In the United States, the reality is that one of far more likely to be killed by an oncoming car than from Ebola and the probability of sustained transmission extremely low. Though people like to view domestic transmission events such as the one in Texas as failure, the reality is that public health and medical resources move much more quickly and effectively in Texas than in troubled Liberia. Much is made over Ebola’s lethality, but a patient who is found to be infected in the United States has a vastly higher likelihood of surviving than one in Liberia.
Second, leaders can stop spreading and capitalizing on misinformation. While attractive, promoting hysteria only leads to bad policy. The tendency in America is to view as some kind of apocalyptic movie scenario. While fun (not to me), the reality is that there are people in the world who are dying who shouldn’t be. Moreover, closing schools because someone knows someone who knows a Liberian is just simply unwise and counterproductive in the long term.
Third, the international community needs to engage the governments of Liberia and Sierre Leone to improve their public health infrastructure. This is not an easy task. The histories of working relationships of international health bodies and developing countries governments are fraught with failure. Mutual distrust, corruption and indifference of political leaders to the plight of their constituencies has created a mostly untenable system. However, providing supplies and training come at little cost is a mostly uncontroversial affair.
How long will this last? No one knows but it is inevitable that, even if this epidemic is brought under control, it certainly won’t be the last of its kind. We don’t have time to waste.
I’m reading through news about the American rights hijacking of the Ebola crisis for their own political gain. Did this outbreak have to occur right before the midterms, and right before a Senate election? The awful toll it will take on West African states aside, the virus couldn’t have picked a worse time (or a better, depending on how you look at it).
Ebola is a scary virus, assuming that one ever has the misfortune to come into contact with it. “Contact” in this case, means that you have to have direct contact with the blood, feces or vomit of a person infected and symptomatic with Ebola. Unfortunately for the virus, people don’t really live that long once they become symptomatic with the disease and the people who survive appear to be immune to it
This is a terrible model for an infectious pathogen. The symptoms are so severe that all around the person will immediately run away (except health workers, who bear the brunt of the risk) and the host doesn’t live very long providing only a short window with which to infect other hosts.
So the duration of infectiousness is short, the pathways are really awful and repeat infections are unlikely.
To put this into perspective, looks at the most successful pathogens out there, pathogens like influenza. Influenza transmits easily, nearly two thirds of those infected show no symptoms and thus can happily shed viral particles to everyone they know undetected. When symptoms do occur, they aren’t so bad as to keep every outside of a 5 miles radius of you. Influenza mutates at an incredible rate, so that a single infection doesn’t provide much protection against later infections. Even better, though its rapid mutation rate sometimes leads to horribly virulent strains like the 1918 flu pandemic which killed millions, in most cases influenza spares a healthy host.
It has developed an incredibly efficient and effective survival strategy (and for this reason is far scarier than Ebola).
So I’ve been thinking of how a virus like Ebola might persist in the wild, given it’s odd mode of transmission.
Now, we know that Ebola is a zoonotic disease, that is, it is transmitted from animal to humans. Since humans have not developed genetic resistance to the disease, we are at particular risk for its worst effects. Many of the scariest diseases out there are zoonoses. Examples would include HIV, SARS and, of course, influenza. While not always true, we tend to make peace with pathogens that are old and exclusively human. Many of the bacteria which live happily in your gut would be examples. As we haven’t had sufficient time to make peace with Ebola or HIV, the outcomes can be far worse than those seen in their normal hosts.
Thus, it is possible that Ebola is far less serious in whatever host it is adapted for. Nipa virus, which has a case fatality rate (the percentage of all infections of a pathogen which result in death) of more than 90% does nothing to the fruit bats it happily resides in. It is possible that Ebola is also harmless to whatever host it depends on.
However, it is possible that Ebola might be harmless in some hosts, while deadly in others, and this difference might be the result of a successful evolutionary adaptation.
Ebola has been pegged as residing in bats possibly explaining its wide range over central Africa. [1-6] Bats are a pathogens dream. They multiply quickly, providing ample opportunities for transmission and for evolutionary adaptations to the pathogen which might insure its long term survival. Better yet, they fly so that pathogens can disperse themselves quickly over a large geographic space. This is particularly useful if the pathogens wants to maintain healthy genetic diversity (though the creation of multiple sub-populations) and if it can infect multiple hosts which may or may not be all that mobile.
Apes would be a good example of the latter. Apes, being fairly sensitive to environmental changes, don’t like to move around a whole lot (unlike humans which are highly adaptable to just about any environment on the planet) but still might be important to the survival of the pathogen.
Ebola has been found in apes and the disease is currently devastating local populations.[4, 7-10]
And this is where I get stuck. In nature, plenty of things happen for no reason at all, but with pathogens, even accidental occurrences can have implications for survival and are often part of the tool box with which diseases evolve and persist.
A bleeding ape on a forest floor will likely kill all of its relatives in quick fashion, assuming its family doesn’t just hightail it out in which case transmission is over anyway. But the dead ape might serve an important purpose. Predators and scavengers will quickly arrive to feast on the infected corpse, transmitting the virus to carnivorous animals all around the forest. This could provide ample opportunities for transmission to other species. Even though many of these species could be poor hosts for the disease, they could also represent new opportunities for survival.
HIV would be an example of this. From HIV’s standpoint (assuming a collective viral consciousness), the jump to humans was extremely fortuitous. Humans love to have sex with multiple people, often even after having already reproduced, and physiologically they proved resistant enough to allow the virus to hang out for a few years before dying, allowing for years of transmission possibilities.
Thus, while on the surface, blood based modes of transmission seem pretty useless, they could serve a larger purpose of insuring a pathogens survival on a macro-level. In the case of HIV, humans didn’t turn into a dead end host (as they are with diseases like Brucella) but rather a new opportunity for survival.
The deadly nature of the virus in apes and humans, then might be like an insurance policy. Like a retirement portfolio, a diversified package of stocks will keep you alive in retirement much better than a portfolio with a single stock. Work has been done on pathogens which infect multiple species, and, depending on the nature of the pathogen, species diversity can either work for or against the survival of the pathogen.[11-13]
In the case of Ebola, there is no real evidence that humans play a role in sustaining transmission, but blood and predation could be sustaining something like Brucella or Q Fever in the wild.
Now, in this article, I have rambled on and bored you to death (and bless you if you made it this far) but I have to point out that I am under no illusions that pathogens act consciously, though I have like many of my colleagues present it as such. Actually, no living thing really does have a long term plan outside of its narrow goals of producing offspring. But new opportunities for transmission do present new opportunities for the long term evolutionary survival or a biological entity. These lucky occurrences are not consciously sought out, but rather enable the pathogen to do what it does successfully.
It must be said that the ecology of Ebola is somewhat of a mystery. Not much work has been done on the subject, as the pathogen hides out in some of the most inaccessible areas of the planet, and conflict and political instability in places like the Central African Republic and Northern Uganda prevent researchers from doing extensive work on the pathogen.
1. Stan D: Ebola and Fruit Bats. Clinical Infectious Diseases 2006, 42(5):V.
2. Olival KJ, Islam A, Yu M, Anthony SJ, Epstein JH, Khan SA, Khan SU, Crameri G, Wang L-F, Lipkin WI et al: Ebola virus antibodies in fruit bats, bangladesh. Emerging infectious diseases 2013, 19(2):270.
3. Hayman DTS, Yu M, Crameri G, Wang L-F, Suu-Ire R, Wood JLN, Cunningham AA: Ebola virus antibodies in fruit bats, Ghana, West Africa. Emerging infectious diseases 2012, 18(7):1207.
4. Kumulungui B, Leroy EM, Swanepoel R, Gonzalez J-P, Pourrut X, Rouquet P, Yaba P, Paweska JT, Délicat A, Hassanin A: Fruit bats as reservoirs of Ebola virus. Nature 2005, 438(7068):575.
5. Vogel G: Infectious disease. Are bats spreading Ebola across sub-Saharan Africa? Science (New York, NY) 2014, 344(6180):140.
6. Hayman DTS, Emmerich P, Yu M, Wang L-F, Suu-Ire R, Fooks AR, Cunningham AA, Wood JLN: Long-term survival of an urban fruit bat seropositive for Ebola and Lagos bat viruses. PloS one 2010, 5(8):e11978.
7. Groseth A, Feldmann H, Strong JE: The ecology of Ebola virus. Trends in microbiology 2007, 15(9):408.
8. Vogel G: Ecology. Tracking Ebola’s deadly march among wild apes. Science (New York, NY) 2006, 314(5805):1522.
9. Leroy EM, Rouquet P, Formenty P, Souquière S, Kilbourne A, Froment J-M, Bermejo M, Smit S, Karesh W, Swanepoel R et al: Multiple Ebola Virus Transmission Events and Rapid Decline of Central African Wildlife. Science 2004, 303(5656):387.
10. Walsh PD, Biek R, Real LA: Wave-like spread of Ebola Zaire. PLoS biology 2005, 3(11):e371.
11. Renwick AR, White PCL, Bengis RG: Bovine tuberculosis in southern African wildlife: a multi-species host–pathogen system. Epidemiology and Infection 2007, 135(4):529.
12. Dobson A, Meagher M: The population dynamics of brucellosis in the Yellowstone National Park. Ecology 1996, 77(4):1026.
13. Dobson A: Population Dynamics of Pathogens with Multiple Host Species. The American Naturalist 2004, 164(S5):S64.
Mostly what we’re left with is a convenience sample of some kind, usually determined by introductions from the survey workers themselves. It is absolutely the worst way to run a survey and the data is usually crap, but, worse yet, unverifiable crap.
Ideally, in a household level survey, we’d run in establish target areas for sampling, do a complete census on target areas and then perhaps take a random sample within those areas. At the minimum this would be a relatively decent approach.
Unfortunately, I often encounter one of two situations. The first is the convenience sample I mentioned above, which is inherently biased toward the social connections and thus the demographic of the survey workers themselves. If you want to do a sample of someone’s friends and family, this might be a good start, otherwise its completely awful.
The second is the “school based survey,” a design I think I hate more than all others. This travesty of sample design depends on the good graces of families which send their children to school, being lucky that the kids you are interested in show up to school the day of the survey and reasonable connections with school administrations. Worse yet, if you’re doing a survey on health, the chances that you’ll the kids you’re really interested in at school is really low. People love this awful design because it’s convenient, cheap, can be done in a short time and has the added benefit of providing one with warm feelings.
I’ve resolved myself to do neither of these again. As the manager of a Health and Demographic Surveillance System based in Kenya which monitors more than 100,000 people in two regions of Kenya, I decided I have a unique opportunity to do something a little more interesting.
In gearing up for a pilot survey to improve measurement of socio-economic status in developing country contexts, I realized that I had an incredible set of resources at my disposal. I have a full sampling frame on two sets of 50,000 people in two areas of Kenya, basic demographic information and a competent staff with sufficient time to do a project which otherwise would interfere with their regular duties.
With some help from a friend (well, much more than a friend), I maneuvered the basic of complex survey design and came up with something that might work relatively well for my purposes.
The DSS of the area of Kwale, Kenya I’m working in is divided into nine areas, each delegated to a single field interviewer who visits each of the households three times a year. Each field interviewer area is then divided into a number of subgrids, the number of which arbitrarily follows the population surveyed and the logistics of the survey rounds. Some areas are easier to survey than others. Each grid then has a number of households within them, the number of which varies depending on population density.
I want to target three areas, each of which ostensibly will represent different levels of economic development, but in reality represent different types of economic activities and lifestyles. One is relatively urbanized, another is purely agricultural and the third is occupied by agro-pastoralists who keep larger herds of large animals.
I then decided to choose 20 grids in each area at random, and then want to select up to 10 households from each selected grid again at random. The reason for choosing this strategy was purely a logistic one. Survey workers can do about 10 households in a day and I’ve given them a month (20 working days) to do it before they have to start on their next round of regular duties. Normally, I’d like to do something fancier, but without any previous data on the variables I’m interested in, it just wasn’t possible.
I have discovered that this design is called a stratified two stage cluster design which makes it all sound fancier that I really believe it to be. The advantage to using this design is that I’m able to control for the selection probabilities, which can bias the results when doing statistical tests. I have no doubt that the piss poor strategies I’ve used in the past and the dreaded “school based survey” I mentioned above are horribly biased and don’t really tell us a whole lot about whatever it is we’re trying to find out.
I used the survey package in R to determine the selection probabilities and, as I suspected, found that the probability of selection is not uniform across the sampling frame. Some households are more likely to be included in the survey, biasing the data in favor of, for example, people in more densely populated areas.
Alright, enough for now….
I was just screwing around with some data we collected a bit ago. In a nutshell, I’m working to try to improve the way we measure household wealth in developing countries. For the past 15 years, researchers have relied on a composite index based up easily observable household assets (a la Filmer/Pritchett, 2001).
Enumerators enter a households and quickly note the type of house construction, toilet facilities and the presence of things like radios, TVs, cars, bicycles, etc. Principal Components Analysis (PCA) is then used to create a single continuous measure of household wealth, which is then often broken into quantiles to somewhat appeal to our sense of class and privilege or lack thereof.
It’s a quick and dirty measure that’s almost universally used in large surveys in developing countries. It is the standard for quantifying wealth for Measure DHS, a USAID funded group which does large surveys in developing countries everywhere.
First, I take major issue with the use of PCA to create the composite. PCA assumes that inputs are continuous and normally distributed but the elements of the asset index are often dichotomous (yes/no) or categorical. Further, PCA is extremely sensitive to variations in the level of normality of the elements used, so that results will vary wildly depending on whether you induce normality in your variables or not.
It’s silly to use PCA on this kind of data, but people do it anyway and feel good about it. I’m sure that some of the reason for this is the inclusion of PCA in SPSS (why would anyone ever use SPSS (or PASW or whatever it is now)? a question for another day…)
So… we collected some data. I created a 220 question survey which asked questions typical of the DHS surveys, in addition to non-sensitive questions on household expenditures, income sources, non-observable assets like land and access to banking services and financial activity.
The DHS focuses exclusive on material assets mainly out of convenience, but also of the assumption that assets held today represent purchases in the past, which can act as pretty rough indicators of household income. So I started there and collected what they collect in addition to all my other stuff.
This time, however, I abandoned PCA and opted for Multiple Correspondence Analysis, a technique similar to PCA but intended for categorical data. The end result is similar. You get a set of weights for each item, which (in this case) are then tallied up to create a single continuous measure of wealth (or something like it) for each household in the data set.
Like PCA, the results are somewhat weak. The method only captured about 12% of the variation in the data set, which sort of begs the question as to what is happening with the other 88%. However, we got a cool graph which you can see up on the left. If you look closely, you can see that the variables used tend to follow an intuitive gradient of wealth, running from people who don’t have anything at all and shit in the shrubs to people who have cars and flush toilets.
We surveyed three areas, representing differing levels of development. Looking at how wealth varies by area. we can see that there is one very poor area, which very little variation to the others which have somewhat more spread, and a mean level of wealth that is considerably higher. All of this agreed with intuition.
“Area A” is known to be very rural, isolated and quite poor. Areas B and C are somewhat better though they are somewhat different contextually.
My biggest question, though, was whether a purely asset based index can truly represent a household’s financial status. I wondered if whether large expenditures on things like school fees and health care might actually depress the amount of money available to buy material items.
Thus, we also collected data on common expenditures such schools fees and health care, but also on weekly purchases of cell phone airtime. Interetinngly we found that over all the two were positively correlated with one another, suggesting that higher expenses do not depress the ability for households to make purchases, but found that this relationship does not hold among very poor households.
There is nothing to suggest that high expenses are having a negative effect on material assets among extremely poor households located in Area A at all. It might be the case that there is no relationship at all. This could indicate something else. Though overall there might not be a depressive effect of health care and school costs on material purchases, they might be preventing households from improving their situation. It might only be after a certain point that the two diverge from one another and households are then able to handle paying for both effectively.
Also of interest were the similar patterns found in the three areas.