THE GENTLE VIRUS

THE GENTLE VIRUS 

All living organisms focus huge amounts of energy on having successful offspring. 

In humans, this means breastfeeding and constant care of babies for the first few 

years of their lives. In other organisms, like sea tortoises, the energy is spent not 

on care for existing offspring but in creating the conditions necessary to success- 

fully launch hundreds of instantly self-sufficient infants—accumulating nutrients to 

place in eggs, traveling to the right place to lay eggs, and burying eggs in sand to 

protect them from predators. Whatever they may look like, parents want their kids 

to succeed, and they deploy a range of techniques to aid them in that objective. 

Among the concerned parents out there are wasps. Two families of wasps go to 

an extraordinary measure to protect their offspring. These wasps, of the braconid 

and ichneumonid families, lay their eggs on the backs of caterpillar larvae. The 

eggs then eat the flesh of the caterpillar as they grow. This is actually a fairly

common setup on our planet, with thousands of such relationships in existence. 

There is an evolutionary tension between the caterpillar and the wasp. The cater- 

pillar’s defenses change over time to thwart the wasp eggs, and the wasp eggs de- 

velop the capacity to counteract or skirt the caterpillar’s defenses, and so on.

In their battle to win this evolutionary arms race, the female braconids and ich- 

neumonids do something not known among other wasps that live in this way: they 

coat their eggs in a special substance before they lay them on the back of a cater- 

pillar. Slowly, this potent substance kills the caterpillar, leaving the eggs to grow 

unrestricted on the bounty that remains. 

The wasp mothers’ truly amazing substance is not a plant toxin or a venom. It’s 

a concentrated dose of virus. This virus, a member of the polydnavirus family, 

harmlessly infects the wasp but unleashes a range of consequences in the cater- 

pillar. It replicates in the wasp’s ovaries and is injected, together with the wasp’s 

eggs, into the caterpillar. The virus returns the favor by suppressing the host cater- 

pillar’s immune system and causing severe disease and even death to the cater- 

pillar, thereby protecting the eggs. The wasp helps the virus, and the virus helps 

the wasp. 

Viruses operate along a continuum with their hosts: some harm their hosts, 

some benefit their hosts, and some—perhaps most—live in relative neutrality, nei- 

ther substantively harming nor benefiting the organisms they must at least tempo- 

rarily inhabit for their own survival. 

In this chapter we’ll shift gears. Rather than discuss the harm viruses can cause, 

we’ll focus on how they can assist us in the battle against infectious and other dis- 

eases. The goal of public health should not be to eradicate all viral agents; the goal 

should be to control the deadly ones. 

Perhaps the most profound way that viruses have assisted us in the fight against 

pandemics has been in the case of vaccines. And there is no better example of this 

partnership than our relationship with the cowpox virus. 

In the late eighteenth century, the noted English scientist Edward Jenner became 

fascinated with the observation that milkmaids somehow seemed to avoid

becoming infected with smallpox. On May 14, 1796, taking a bit of a leap, Jenner 

inoculated James Phipps, the eight-year-old son of his gardener, with cowpox that 

he’d scraped from the hand of a young milkmaid named Sarah Nelmes. She had 

acquired the virus from a cow named Blossom, whose hide you can apparently still 

see if you visit St. George’s medical school in London. 

Young James Phipps got mildly sick, a bit of fever and some discomfort but that 

was all. After James recovered, Jenner went on to inoculate the boy with a small 

amount of the actual smallpox virus.¹ The smallpox did nothing. The effect, which 

Jenner then replicated in others, would go on to be one of the most profound find- 

ings in human history. He had developed a vaccine to prevent smallpox, one of the 

worst scourges of humankind. The discovery is credited by some as saving more 

lives than any other discovery in history. 

The vaccines that were created as a result of Jenner’s work eventually led to the 

eradication of smallpox from the planet. I remember seeing one of the original 

documents certifying that smallpox had been eliminated. It was in the Johns 

Hopkins office of D. A. Henderson, who had led the WHO’s global smallpox eradi- 

cation campaign. D. A. had kindly lent me one of his largely unused offices at Hop- 

kins as a staging ground to accumulate the supplies I’d need to start our work 

monitoring outbreaks in central Africa. I remember thinking to myself about how 

important eradication was and how it had been accomplished. 

We credit the eradication of smallpox to a vaccine. But it’s worth examining this 

further. The vaccine that allowed us this triumph was actually an unadulterated 

virus that we harnessed and used for our benefit. In fact, even the word vaccine it- 

self derives from the Latin term for cowpox, or variolae vaccinae, where variolae 

means “pox” and vaccinae means “of cows.” In other words, at its very heart, the 

concept of a vaccine is the productive use of one virus to fight another.

becoming infected with smallpox. On May 14, 1796, taking a bit of a leap, Jenner 

inoculated James Phipps, the eight-year-old son of his gardener, with cowpox that 

he’d scraped from the hand of a young milkmaid named Sarah Nelmes. She had 

acquired the virus from a cow named Blossom, whose hide you can apparently still 

see if you visit St. George’s medical school in London. 

Young James Phipps got mildly sick, a bit of fever and some discomfort but that 

was all. After James recovered, Jenner went on to inoculate the boy with a small 

amount of the actual smallpox virus.¹ The smallpox did nothing. The effect, which 

Jenner then replicated in others, would go on to be one of the most profound find- 

ings in human history. He had developed a vaccine to prevent smallpox, one of the 

worst scourges of humankind. The discovery is credited by some as saving more 

lives than any other discovery in history. 

The vaccines that were created as a result of Jenner’s work eventually led to the 

eradication of smallpox from the planet. I remember seeing one of the original 

documents certifying that smallpox had been eliminated. It was in the Johns 

Hopkins office of D. A. Henderson, who had led the WHO’s global smallpox eradi- 

cation campaign. D. A. had kindly lent me one of his largely unused offices at Hop- 

kins as a staging ground to accumulate the supplies I’d need to start our work 

monitoring outbreaks in central Africa. I remember thinking to myself about how 

important eradication was and how it had been accomplished. 

We credit the eradication of smallpox to a vaccine. But it’s worth examining this 

further. The vaccine that allowed us this triumph was actually an unadulterated 

virus that we harnessed and used for our benefit. In fact, even the word vaccine it- 

self derives from the Latin term for cowpox, or variolae vaccinae, where variolae 

means “pox” and vaccinae means “of cows.” In other words, at its very heart, the 

concept of a vaccine is the productive use of one virus to fight another.

words, they’re just viruses we inject into people (or animals) to create an immune 

response that will protect against another more deadly virus. Others, like the oral 

polio vaccine and the measles, mumps, rubella (MMR) vaccine, are attenuated 

virus vaccines—live viruses that we have bred in the lab to make less deadly and 

used in effectively the same way. Some, like the influenza vaccines, are inactivated 

virus vaccines—viruses we have made incapable of reproducing themselves yet can 

elicit an appropriate immune response. They are still viruses. Others, like the hep- 

atitis B vaccine and human papillomavirus (HPV) vaccine, use selected parts of the 

virus. The point is that pretty much the entire contemporary science of vaccinology 

uses viruses themselves to protect against other viruses. Safe viruses are some of 

the best friends we have in fighting the deadly ones. 

The utility of using microbes to protect us against infectious diseases seems clear 

enough. But can microbes help us to control chronic diseases? The answer 

increasingly is yes. 

Introductory courses in public health make firm distinctions between infectious 

and chronic diseases. They place infectious diseases like HIV, influenza, and 

malaria on one side of the aisle and chronic diseases like cancer, heart disease, 

and mental illness on the other. Yet these distinctions do not always hold up to 

greater scrutiny. 

In 1842 Domenico Rigoni-Stern, an Italian physician, looked at the patterns of 

disease in his hometown of Verona. Among the things Rigoni-Stern noticed was 

that the rate of cervical cancer appeared to be substantially lower among nuns than 

married women. He also noted that behavioral factors like age at first sexual inter- 

course and promiscuity seemed related to the frequency of the cancer. He con- 

cluded that the cancer was caused by sex. 

While sex itself did not end up being the cause of cervical cancer, Rigoni-Stern 

was on exactly the right track. In 1911 the young scientist F. Peyton Rous injected 

tissue from a chicken tumor into healthy chickens, while he was working at the

Rockefeller Institute for Medical Research (now the Rockefeller University). Rous 

found that the injected tissue caused precisely the same type of cancer in the 

healthy chicken recipient. The cancer was transmissible! The virus that causes that 

chicken cancer—now called Rous sarcoma virus after its discoverer—was the first 

virus demonstrated to cause any cancer, and it won Rous the Nobel Prize. It would 

not be the last virus found to have a connection to cancer

In the 1970s the German physician-scientist Harald zur Hausen had a hunch about 

the cause of cervical cancer. Following the work of Rigoni-Stern and Rous, zur 

Hausen suspected it was caused by an infectious agent. Unlike the scientists of his 

time who thought that the cause was herpes simplex virus, zur Hausen believed 

that the virus that caused genital warts, the papilloma virus, was the culprit. Zur 

Hausen and his colleagues spent much of the late 1970s characterizing different 

human papillomaviruses from warts of various sorts and looking to see if they 

could be found in tissue samples that came from biopsies of women with cervical 

cancer. In the early 1980s they finally hit pay dirt. They discovered two papillo- 

maviruses, HPV-16 and HPV-18, in a high percentage of biopsy specimens. Today, 

these two viruses alone are considered to account for up to 70 percent of cervical 

cancer. 

Zur Hausen, like his predecessor Rous, received the Nobel Prize for his break- 

through. And the research they conducted went on to form the foundation for a 

vaccine against cervical cancer. In June 2006, Merck received approval from the US 

Food and Drug Administration (FDA) to market Gardasil, an HPV vaccine. Like the 

other vaccines discussed earlier, Gardasil uses elements of the human papillo- 

mavirus itself to elicit an immune response that prevents those inoculated from 

being infected if they later have contact with the actual virus. In the case of Gar- 

dasil, the vaccine utilizes virus-like particles (VLPs) that look like the actual viruses 

but have no actual genetic material so they cannot replicate themselves. And the 

vaccine works. By preventing infection from the types of human papilloma virus 

that cause cervical cancer, the vaccine effectively prevents most of the deadly can- 

cer. 

Chronic diseases are notoriously difficult to treat. Whether for cancer, heart dis- 

ease, or mental illness, treatments rarely return people to their pre-disease condi- 

tion, and in many cases there are no treatment options at all. When a chronic dis- 

ease is found to be caused by a microbe, the potential for cure and prevention im- 

proves dramatically. Cervical cancer, for example, which once required invasive, 

damaging, and only sporadically effective treatment, can suddenly be prevented by

the deployment of a vaccine. Microbes make for low-hanging fruit when it comes 

to preventing and possibly curing chronic disease. 

Cervical cancer is not the only chronic disease that is caused by a microbe. 

Liver cancer can be caused by both hepatitis B virus and hepatitis C virus. Re- 

searchers are currently exploring the possibility that prostate cancer, one of the 

leading causes of cancer death in American men, can be caused by xenotropic MLV 

related virus (XMRV). Stomach ulcers can be caused by the bacteria Helicobacter 

pylori. At least some types of lymphotropic virus, a virus family we discussed in 

chapter 9 and that we’ve discovered among the hunters we worked with in central 

Africa, are known to cause leukemia. It’s even possible that heart disease, the cul- 

prit in one-third of US deaths and countless deaths worldwide, has an infectious 

component. The innovative American evolutionary biologist Paul Ewald, who has 

written on the connection between infectious agents and chronic disease, suggests 

that the interplay between Chlamydia pneumoniae and environmental factors may 

be to blame for heart attacks, strokes, and other cardiovascular illness. 

In some cases viral causes are suspected but have not yet been confirmed— 

perfect fodder for eager scientists. The distribution of type I diabetes cases suggest 

a possible connection with an infectious agent, but none to date has been iden- 

tified. My own research team and our collaborators recently began work on a grant 

from the National Cancer Institute to screen tumor specimens from multiple types 

of cancer in search of viruses. It’s exploratory research, but the potential benefits 

as we find them could be monumental. 

Even some mental illnesses may result from infections with microbes. As we’ve 

seen, microbes can have an impact on behavior. Toxoplasma alters very specific 

neural circuits in rodent brains to decrease their fear of cats and thereby increase 

the chances that the parasite can complete its life cycle by ending up in a hungry 

cat. Rabies causes fear of water and increases aggressiveness in those infected 

with it, which helps accumulate virus in saliva and deliver it through a potentially

fatal bite. 

With these prominent examples of behavioral manipulation, it’s an obvious leap 

to suspect that microbes could play a contributing role in mental illness, a subject 

that has been the focus of a researcher at Johns Hopkins Medical School for some 

years. Robert Yolken studies a range of disorders, including bipolar disorder, 

autism, and schizophrenia, examining them closely to see if microbes might play a 

role. His primary focus is schizophrenia. 

Schizophrenia certainly seems to invite discussion on links with infectious 

agents. For years, researchers have noted a relationship between seasonality of 

birth and schizophrenia: children born in winter months are more likely to develop 

schizophrenia than those who are not. This finding has long been thought to sug- 

gest that wintertime illnesses such as influenza, infecting either the pregnant moth- 

er or infant, may predispose an individual toward schizophrenia, although the re- 

sults remain unclear for now. 

Yolken’s most recent focus has been Toxoplasma gondii, or simply toxoplasma. 

He and others in the field have put together a plausible if perhaps not fully defin- 

itive case for the parasite’s role in this devastating mental illness.³ Multiple studies 

have found a correlation between schizophrenia and the presence of antibodies to 

toxoplasma. Some adults who experience the onset of toxoplasma disease expe- 

rience psychological side effects. And antipsychotic drugs used to treat 

schizophrenia have also been seen to have an effect on toxoplasma in laboratory 

cell cultures. In a sign of the intense research that has surrounded the subject of 

schizophrenia, studies have documented that individuals with schizophrenia have 

had more exposure to cats than unaffected controls. Together these and other 

studies point to a connection. This connection still faces challenges since the para- 

site is not likely to be involved in all cases of schizophrenia, a disease that also has 

important genetic determinants. 

A virus may also be the cause of a complex, controversial, and somewhat

mysterious disorder. Chronic fatigue syndrome (CFS) is a debilitating illness with 

no known origins and a variety of nonspecific symptoms: weakness, extreme fa- 

tigue, muscle pain, headaches, and difficulty concentrating, among others. Most 

people who have stayed up all night studying for a final or pushed themselves too 

hard at the gym will recognize these symptoms as familiar and common. They are 

also common symptoms for many other medical conditions, making it difficult to 

eliminate other possible root causes. As a result, medical experts and members of 

the public have debated the authenticity of CFS as a unique disorder. However, re- 

cent studies support those who argue that CFS is a genuine disease. Following 

several studies with contradicting results, a study published in August 2010 found 

a correlation between CFS and a virus in the murine leukemia virus family. More 

research is necessary to establish a causal link between MLV and CFS, but the find- 

ing has offered hope to many.

As with cancer, a microbial cause of schizophrenia or CFS would invite quick 

and possibly important new diagnostics, therapies, and vaccines for these chronic 

disorders, which cause great pain and discomfort to victims and families. In the 

case of cervical cancer, the vast majority of the illness is ascribable to human papil- 

loma virus, so a vaccine preventing it could be developed. This is not always the

case. If only a percentage of people who suffer from schizophrenia or CFS do so 

because of a virus, it will make the associations more complicated and the dis- 

covery of links more challenging. Yet it’s worth the effort. Many chronic diseases 

lack good treatment options, and our ability to create vaccines and drugs for mi- 

crobes is legendary. Wouldn’t you want to vaccinate yourself or your children for 

schizophrenia or heart disease? Even if it only protected them from one of a hand- 

ful of causes of the illnesses? One day, we hope, you will be able to do just that. 

Using one microbe to prevent another microbe from causing disease is pretty 

amazing. But how about using a microbe to actually address the disease directly? 

This is something that’s increasingly explored in the nascent field of virotherapy. 

All viruses infect cells as part of their life cycle, and they don’t infect cells ran- 

domly. As we’ve discussed, viruses infect cells in a lock-and-key manner: they enter 

into those cells that have particular proteins, or cell receptors, on their cell surfaces 

that the virus recognizes. If a virus existed that recognized and infected only can- 

cerous cells, for example, then the virus could theoretically burn through those 

cells, killing the cancers along the way. The hope, of course, would be that when 

they were done with the cancer cells, they’d have nothing to infect and would die 

off. 

Just such a virus exists. The Seneca Valley virus is a naturally occurring virus 

that appears to specifically target tumor cells living at the interface of the nervous 

and endocrine systems. It reproduces in the tumor cells, causing lysis, or rupturing 

and death of the cells. When released, it spreads to new tumor cells to continue its 

work. Now that’s a gentle virus! 

Seneca Valley virus was discovered in a biotech company laboratory in Pennsyl- 

vania’s Seneca Valley. The virus had likely contaminated cell cultures from cattle or 

pig products commonly used in the laboratory. It was isolated and found to be a 

new virus in the picornavirus family, which includes polio. Testing showed that the 

virus had amazing selectivity to cancerous cells in the neuroendocrine system yet

failed to infect healthy cells. This is a good reminder that not all viruses that cross 

the species barrier do harm. 

The Seneca Valley virus is not alone. The small but growing group of virother- 

apy researchers use and adapt a range of viruses, including herpes virus, aden- 

ovirus (one of the viruses that causes colds), and the measles virus—to create 

viral therapies that can knock down cancer. Probably the most advanced among 

them is a herpes virus therapy developed by a biotech firm called BioVex, which is 

in the last stage of trials to determine its ability to control head and neck cancer. 

While the results of the trial have not yet been released, Amgen, a Fortune 500 

biotech company, recently entered into the final stages of a deal to acquire the 

smaller BioVex as well as its herpes virus therapy. 

What about viruses that interfere with other viruses? 

One brilliant example is a wonderful little virus called GB virus C, which 

appeared in chapter 5 and is found in a high percentage of people. This odd- 

sounding virus is in the same family as hepatitis C virus, but it certainly doesn’t kill 

us. In fact, it can save us. 

In an incredible study published in the top medical journal the New England 

Journal of Medicine in 2004, researchers showed that infection with the GB virus C 

could help prolong the lives of men who were infected with HIV. When examined 

five to six years after infection with HIV, men without detectable GB virus C were 

nearly three times more likely to die than those who had active GB virus C infec- 

tions. How GB virus C acts to save AIDS patients is still unclear, but it appears that 

it might interfere directly with HIV. Whatever the mechanism, this tiny organism 

has likely prolonged millions of lives during the course of the current pandemic. 

Viruses can also interfere with other kinds of microbes—bacteria can get sick too. 

Viruses infect all forms of cellular life, whether bacteria, parasite, or mammal. As

we discussed in chapter 1, while nonspecialists tend to see microbes as a homoge- 

nous bunch, nothing could be further from the truth. All of the cell-based life 

forms (bacteria, parasites, fungi, animals, plants, and so forth) are thought to be 

more closely related to each other than they are to viruses.⁴ Furthermore, parasites 

fall into the class of life called eukaryotes and are more closely related to us than 

either they or we are to bacteria. 

A fascinating Harvard virologist now at the Texas Biomedical Research Institute, 

Jean Patterson became interested in just this phenomenon in the mid-1980s. While 

her main focus had been viruses, she wanted to look closer at a group of parasites 

called protozoa, which includes malaria and leishmania, a harmful protozoan para- 

site transmitted to humans by the bite of the sand fly. Patterson was interested in 

how the parasites translated their genetic information into action, and she became 

fixated on discovering a virus that could infect this interesting parasite. 

In 1988 Patterson and her colleagues discovered a small virus that naturally in- 

fects leishmania parasites; they were the first to characterize a virus from this 

group of parasites. Viruses that infect parasites could provide natural systems for 

parasite virotherapy. And as with the cancer-killing viruses, parasite viruses could 

potentially be adapted for efficiency and safety. 

I’ve personally spent a reasonable portion of my professional life studying pro- 

tozoa parasites. First, as a doctoral student working in Malaysian Borneo with my 

veterinary colleagues Billy Karesh, Annelisa Kilbourn, and Edwin Bosi, we tried to 

understand malaria in wild and captive orangutans.⁵ More recently, my colleagues 

and I searched for the origin of malaria in central Africa, a subject discussed in de- 

tail in chapter 3. Could it be possible that in some of our vials holding an ape 

malaria parasite resides a new malaria-infecting virus? One that could potentially 

kill our own deadly malaria, Plasmodium falciparum? 

When most people think about microbes, they frame it as a battle of people versus 

bugs. Perhaps if they’re being a bit more creative, they’ll consider the battles

among the microbes themselves. But the reality is even more interesting than that. 

We’re part of an incredibly rich community of interacting microbes—with hugely 

complicated collaborations, battles, and wars of attrition with each other and our- 

selves. 

Consider the human body. Only about one out of every ten cells between your 

hat and shoes is human—the other nine belong to the masses of bacteria that coat 

our skin, live in our guts, and thrive in our mouths. When we consider the diversity 

of genetic information on board, only one out of every thousand bits of genetic 

information on and in us can properly be called human. The bacteria and viruses 

represented by thousands of species will outnumber the human genes every time. 

The sum total of bacteria, viruses, and other microbes present in our body is 

called the microbiota, and the sum total of their genetic information is called the 

microbiome. A new science has developed in the past five years to characterize the 

human microbiome. Empowered by new molecular techniques that bypass the 

nearly impossible task of individually culturing each of the thousands of microbes, 

scientists are rapidly figuring out exactly what the overall community of human and 

microbial cells in our bodies consists of. 

The findings coming out are fascinating. Our guts are teeming with a complex 

assemblage of microbes, many of whom are long-term residents. They are not sim- 

ply free riders. A great deal of the plant material we consume requires bacteria and 

their enzymes for digestion; human enzymes alone would not do the trick. And 

how the community of microbes is structured makes a big difference. 

In a pivotal series of studies, Jeff Gordon and his students and postdocs (many 

of whom are now successful professors themselves) showed just how important 

the communities of bugs in our guts actually are. They have demonstrated that 

obesity is associated with a decreased relative abundance of one particular group 

of bacteria—the Bacteroidetes. 

In another elegant study, Gordon and his team showed that the obese micro- 

biota increases the amount of energy that can be obtained from food. In the final 

coup de grâce, they showed that altering the gut microbiota of normal mice with

the obese microbiota results in significant weight gain. Very simply, bacteria in our 

guts play a role in obesity. Just as we saw with cervical cancer, a microbial cause of 

a chronic disease may point to an easier method to solve it. One day we may very 

well use a combination of probiotics and antibiotics to subtly alter our gut micro- 

biota and to help us maintain a healthy weight. 

Perhaps not surprisingly, the teeming masses of microbes in our guts also play 

a role in how we’re affected by deadly microbes. In the case of salmonella, a deadly 

bacteria and one of the leading causes of food-borne illness, it’s been known for 

some time that the biggest risk factors for the disease are eating eggs away from 

home and using antibiotics. Eating eggs is a risk factor since chickens infected 

with the bacteria can contaminate them. The antibiotic use, however, has long pre- 

sented a mystery. 

Recent research on gut microbiomes may shed some light. Justin Sonnenburg, 

a Stanford professor, is conducting important work to do just this. He uses an in- 

credible system for maintaining germfree mice in a laboratory. The rodents live in 

completely sterile conditions—even to the point where their food is autoclaved be- 

fore they eat it, eliminating any potential microbial contaminants. The germfree ro- 

dents provide a perfect model for picking through the exact determinants of dif- 

ferent gut microbiota on the conditions of their hosts. 

While it’s long been suspected that antibiotic use kills helpful microbes, thus 

damaging the natural shield that our gut microbes provide against new and inva- 

sive bugs like salmonella, it’s still not clear exactly how this happens. In the future, 

the work done in Sonnenburg’s lab should tell us. 

There are gentle microbes out there—bugs that help us, defend us, and live qui- 

etly within us doing no harm at all. If we could accurately determine which of the 

microbes on our bodies and in the environment were beneficial to us and which 

were rogue, we’d find something pleasantly surprising: the harmful ones are cer- 

tainly in the minority. The goal of public health should not be to have a completely 

sterile world but to find the rogue elements and control them. A key part of ad- 

dressing the nasty microbes will be to cultivate the microbes that help us. One day

soon, the way we protect ourselves may be by propping up the bugs that live within 

us rather than knocking them down.

MICROBE FORECASTING

MICROBE FORECASTING

It was a large city. And it was hit hard. The first cases emerged in late August, and 

the victims suffered terribly. The earliest symptoms were profuse diarrhea and 

vomiting. They experienced severe dehydration, increased heart rate, muscle 

cramps, restlessness, severe thirst, and the loss of skin elasticity. Some of the 

cases progressed to kidney failure, while others led to coma or shock. Many of 

those who came down with the disease died. 

Then on the night of August 31, the outbreak truly broke. Over the next three 

days, 127 people in a single neighborhood died. And by September 10 the number 

of fatalities would reach 500. The epidemic seemed to spare no one. Children and 

adults alike were killed. Few families did not have at least one member who came 

down with the disease. 

The epidemic led to intense panic. Within a week, three-quarters of the 

neighborhood’s residents fled. Stores closed. Homes were locked. And you could 

walk down a formerly bustling urban street without seeing a single person. 

Early in the outbreak, a forty-year-old epidemiologist began an investigation to 

determine its source. He consulted community leaders and methodically inter- 

viewed families of the victims and made careful maps of every single case. Fol- 

lowing his hunch about a waterborne disease, he studied the sources of the 

community’s water and determined that it came from only one of two urban water 

utilities. He conducted microscopic and chemical analyses of specimens from the 

water system, which proved inconclusive. 

In his report to the responsible officials, he presented his analysis and con- 

cluded that contaminated water was to blame. Despite the lack of definitive results 

from the analyses, the mapping of cases strongly supported his conclusion that 

one particular water outlet was the source of the outbreak. He recommended shut- 

ting down the water supply, and the officials agreed. And while the outbreak may 

have already been in decline because of the mass exodus, that investigation and

water closure proved pivotal. 

What was unusual about this outbreak was not the procedural investigation that 

followed. Modern epidemiologists in countries throughout the world conduct ex- 

actly this kind of investigation regularly. They enlist the help of local leaders, study 

the distribution of cases, conduct analyses on potential sources, and then often 

argue with officials as to the best course of action. What was unusual was that the 

outbreak was in 1854—before the field of epidemiology existed. 

As you may have guessed, the investigator responsible for cracking the outbreak 

was none other than John Snow, the now famous London physician and clergyman 

considered one of the founders of contemporary epidemiology. The culprit was, of 

course, the bacteria Vibrio cholerae, or cholera. By finding that water was the source 

rather than “foul air,” Snow contributed to the modern germ theory of infectious 

diseases—that communicable diseases are caused by microbes. To this day, you 

can see a replica of the famous Broad Street pump that Snow identified as the 

source of the 1854 outbreak, in Soho, London. 

It seems intuitive to us today, but the way that Snow used interviews, case iden- 

tification, and mapping to chart the origin of the Broad Street cholera outbreak of 

1854 was revolutionary in its time. While maps had certainly been used extensively 

prior to 1854, the map he made of Soho is considered the first of its kind, not only 

in epidemiology but also in cartography. He was the first to utilize maps to analyze 

geographically related events to make a conclusion about causality—namely, that 

the Broad Street pump was the source of the outbreak. By doing so he has been 

credited with using the first geographic information system, or GIS, a now com- 

monly used cartographic system for capturing and analyzing geographic infor- 

mation. 

In contemporary GIS, layers of information are added to maps like Snow’s to 

provide depth of geographic information and to suggest patterns of causality. 

While Snow’s map included streets, homes, locations of illness and water sources, 

a contemporary version could include many more layers—genetic information 

from cholera specimens collected in different locations, dimensions of time that 

track changes spatially with an added weather layer or social connections between 

the individuals in the various homes. 

Modern GIS is among a range of contemporary tools that is radically changing 

the way that we investigate outbreaks and understand the transmission of diseases. 

When used in a coordinated and comprehensive way, these tools have the poten- 

tial to fundamentally change the way that we monitor for outbreaks and stop them 

in their tracks. 

We now have multiple scientific and technical advantages that Snow lacked in 

the mid-nineteenth century. Among the most profound is that we have significantly 

improved our capacity to catch the bugs we’re chasing and to document their 

diversity. The revolution in molecular biology, in particular the techniques for 

capturing and sequencing genetic information, has profoundly changed our ability 

to identify the microbes that surround us.

The map of London used by John Snow to find the source of the cholera outbreak. 

Miraculous but now standard techniques like the polymerase chain reaction 

(PCR), which resulted in the Nobel Prize for its discoverer Kary Mullis, allow us to 

snip out tiny pieces of genetic information from microbes and create billions of 

identical copies, whose sequences can then be read and sorted out according to 

the family of microbes to which they belong. Yet standard PCR requires that you 

know what you’re looking for. If, for example, we want to find an unknown malaria 

parasite, we can use PCR designed to identify malaria-specific sequence, since all 

malaria parasites have genetic regions that look similar enough to each other. But 

what if we don’t know what we’re looking for?

In the early 2000s, intent on finding unknown microbes, a bright young molec- 

ular biologist, Joe DeRisi, and his colleagues adapted an interesting technique 

developed by DeRisi’s doctoral adviser, Pat Brown, a Stanford biochemist. The 

DNA microarray chip consisted of thousands of tiny bits of distinct artificial genetic 

sequence distributed in an orderly fashion across a small glass slide. Since genetic 

information sticks to its mirror image sequence, if you flush solution from a

specimen containing genetic information across a slide like this, the bits that 

match the designed sequences on the slide will fuse. You can then determine what 

was in the specimen by determining which of the sequences on the slide trapped 

their natural siblings. The technique had already provided thousands of scientists 

with a new way of characterizing the bits of genetic information that flow through 

living systems by the time DeRisi got his hands on it. 

Prior to DeRisi’s innovation, the microarray chips had been used primarily to 

help determine the internal workings of the genes of humans and animals, but De- 

Risi and his colleagues realized that the technique could be modified to create a 

powerful viral detection system. Instead of designing the chips with bits of artificial 

human genetic information, he and his colleagues designed chips with bits of viral 

genetic information. By carefully reviewing the scientific databases for genetic 

information on all of the viruses known to science, they crafted chips that had bits 

of genetic information from a whole range of viral families lined up in neat rows. If 

they introduced genetic information from a sick patient, and it contained a virus 

with a sequence similar to one on the chip, the sequence would be trapped and— 

bingo!—we’d know the bug we were dealing with. 

The viral microarray, as these specialized chips became known, have proliferated 

and spread to labs throughout the world. They’ve helped quickly identify the micro- 

bial villain responsible for new pandemics, like the coronavirus that causes SARS. 

Yet they are not perfect. These chips can only be made to capture viruses from 

families of viruses already known to science. If there are groups of viruses out 

there whose sequences we are completely unaware of, and there certainly are, then 

we have nothing with which to engineer the chips. Truly unknown viruses would 

slide right by. 

Within the past few years, viral microarrays have been supplemented with a series 

of bold new genetic sequencing approaches. New machines churn out mammoth 

amounts of sequence data from specimens—amounts of sequence that previously 

would have been prohibitively expensive or time consuming. These machines are 

permitting an entirely new form of viral discovery. 

Rather than look for particular bits of information, the approach is to take a 

specimen—say a drop of blood—and sequence every bit of genetic information it 

contains. Technically, it’s more complicated than that, but the result is similar to 

what you’d expect. We are approaching a moment when we will be able to read 

every single sequence within a given biological specimen. Every bit of DNA or RNA 

from the host specimen, and critically, every bit from the microbes that are riding 

along with them. 

One of the central problems becomes the bioinformatics—how to sort through 

all of the billions of bits of information that are produced by these incredible tech- 

nologies. Fortunately, in an enlightened move, scientists at the NIH picked up and 

nurtured an electronic repository of sequencing information developed at the 

famed Los Alamos National Laboratory and now called GenBank. Since scientists 

are required by funding sources and journals to submit sequences to GenBank 

prior to submitting academic papers, we collectively contribute billions of bits of 

genetic information each year. GenBank right now holds over a hundred billion bits 

of sequence information. And it’s growing rapidly. When a new sequence is iden- 

tified from a sequencing run, it can be rapidly compared electronically to what’s in 

GenBank to see if there’s a match. 

In late 2006 and early 2007 these techniques were used to good effect. In early 

December 2006 the organs of a patient who had died of a brain hemorrhage in 

Dandenong hospital in Australia were harvested for transplantation. A sixty-three- 

year-old grandmother received one of the kidneys, another unnamed recipient re- 

ceived the other kidney, and a sixty-four-year-old lecturer in a local university re- 

ceived the man’s liver. By early January all three had died. 

The local hospital and collaborating labs looked for all of the usual suspects. 

They utilized PCR and tried to grow up the microbe on culture media. They even 

tried one of the viral microarrays, to no avail. A virus was only found when the 

specimen was subjected to massive sequencing. The team that found it, led by Ian

Lipkin, a world-class laboratory virologist at Columbia University, had to sort 

through over a hundred thousand sequences to find the fourteen sequences be- 

longing to the mystery virus. Truly a needle in a haystack! The mystery virus ended 

up being in a group of viruses called arenaviruses that often live in rodents. With- 

out massive sequencing, the virus would not likely have been found. 

But while identifying what’s actually in a small new outbreak is vital, it’s only the 

beginning. As we get better and better at understanding what’s out there, we will 

have to start asking a tougher question: where is it going? Will it become a pan- 

demic? 

There are three primary objectives to the emerging science of pandemic preven- 

tion: 

1. We need to identify epidemics early. 

2. We need to assess the probability that they will grow into pandemics. 

3. We need to stop the deadly ones before they grow into pandemics. 

The viral microarray and sequencing techniques give us a snapshot of what is 

causing an epidemic, but more is needed to assess the possibility that a new agent 

in a limited outbreak has the right stuff to go pandemic. This is exactly the objec- 

tive of a new program being developed by DARPA, the U.S. Department of De- 

fense’s Advanced Research Projects Agency. DARPA has had a stunning impact on 

the contemporary world of technology, including sponsoring early research that 

has contributed in substantive ways to the development of modern computing, vir- 

tual reality, and the Internet itself. 

DARPA is developing a program called Prophecy, whose objective is to “suc- 

cessfully predict the natural evolution of any virus.” Prophecy seeks nothing less 

than to use technology to predict where an outbreak will go by combining it with 

the support of a team of local on-the-ground experts in hotspots around the world.

Predicting the future trajectory of a virus seems like science fiction, but DARPA 

does not shy away from high-risk/high-payoff ideas, and Prophecy falls clearly in 

that mold. Fortunately, what we know about pandemics and the technologies avail- 

able today bring the objectives it seeks within the realm of possibility. 

Cutting-edge experimental virologists like Raul Andino, at the University of Cali- 

fornia, San Francisco, works to determine rational predictions of the evolution of 

viruses. Viruses reproduce rapidly, so any viral infection, even if it’s the result of 

infection with a single viral particle, will rapidly develop into a swarm,¹ a group of 

viruses, some identical, but mostly mutants differing in one way or another from 

the parental strain that created them. By documenting and studying the way that 

the overall viral swarms respond to different environments, Andino and his col- 

leagues have worked to develop rational strategies for the production of vaccines 

that use live viruses, a subject we will return to in chapter 11. He also hopes to use 

the same information to determine the boundaries within which a swarm can 

evolve. Swarms can’t go in every direction, and getting a sense of what a swarm is 

composed of will help us get a sense of what it can evolve into. 

Another scientist working to change the ways we can forecast microbial evolu- 

tion is not a microbiologist at all but rather a physics-trained bioengineer. Steve 

Quake, an awardee of the same NIH Pioneer Program that has funded my own re- 

search, develops technology that permits us to study and manipulate life in sur- 

prising and incredibly useful ways. In the past ten years this jeans-wearing ski bum 

has spun off multiple companies, developed handfuls of patents, and published 

scores of papers in some of the highest-ranking journals—all while maintaining a 

successful teaching program at Stanford University. Among the useful innovations 

coming from Quake’s group are microfluidic platforms. Essentially, he’s produced 

entire laboratories on small laboratory chips. 

In one particularly notable application, he’s taken the tedious and complex work 

of cell culture, where cells from mammals and other organisms are grown under 

laboratory conditions, from the bench to the chip. The chips he and his team have 

created, just a few centimeters long, house ninety-six separate compartments

where cells grow for weeks at a time and can be carefully measured and manip- 

ulated. While there are many applications for having cell culture on an automated 

and compact chip, one of them is the speed and efficiency for evaluating new 

viruses from large numbers of specimens. It’s not difficult to imagine a chip-based 

system that quickly tells us in what kind of cells a new agent can survive and there- 

fore how it’s most likely to spread (e.g., by sex, blood, sneezes, and so on). 

When we see an outbreak, there are a number of questions we’d like to have an- 

swered. First, what’s the microbe behind it? Techniques like viral microarrays and 

high throughput sequencing are increasing the speed at which we can identify new 

agents and also helping us to find things that we’d have missed through older 

techniques. But once we’ve identified a microbe, we want to know where it’s going. 

We’ll return in chapter 12 to a vision of what the ultimate pandemic prevention sys- 

tem will look like, but it would certainly involve approaches like those developed by 

the Andino lab to assess the potential evolutionary directions that a virus can take. 

And the tools that Quake’s group has developed might one day form a set of 

high-speed chips that quickly evaluate how it’s likely to spread. 

Modern information and communication technology provides us with another set 

of tools that does something distinct and complementary to the biotech advances 

discussed above. In fact, some of this technology is sitting in your pocket as you 

read this sentence. 

In one of our research sites in southwest Cameroon sits a rubber plantation 

called Hevecam, where we conducted an experiment. This experiment represents 

one of the exciting new trends in public health. And it’s all based on simple cell 

phones. 

In Hevecam, a plantation with nearly a hundred thousand inhabitants, when 

individuals get sick they go to a local clinic. If they’re sufficiently ill, they then move 

from that local clinic to the referral hospital in the center of the plantation. Yet 

traditionally there has been no good way for the referral hospital to monitor what’s

happening in the local clinics. A few years ago Lucky Gunasekara, who now heads 

up our program on digital epidemiology, and his partners at the nonprofit Frontli- 

neSMS:Medic that he co-founded, set up a simple system based on text messages 

to allow the referral hospital to monitor what was occurring in the local clinics. By 

simply texting a series of preset codes, the vast majority of vital clinical information 

could be communicated up the medical hierarchy clearly, instantly, and efficiently. 

Using predetermined codes and simple text message forms, the local clinics could 

rapidly inform everyone else of how many cases of malaria, diarrhea, and other ill- 

nesses they were seeing. 

Simple technologies can have dramatic impact. With a few simple techniques, 

medical conditions at Hevecam could be monitored not only in the referral hos- 

pital but also remotely over a web dashboard for anyone with appropriate access. 

By allowing local clinicians or patients themselves the capacity to communicate, 

information can be accumulated, organized, and analyzed, leading to a much more 

rapid and localized sense of what’s going on during a health emergency. 

Something just like this occurred during the earthquake in Haiti in 2010. Im- 

mediately after the earthquake, organizations like Ushahidi² set up short, free 

codes to which people could text “help” messages. They then turned to the local 

DJs who, along with popular word of mouth, publicized the numbers. Amazingly, 

when the dust cleared, the statistical analysis of the text message distributions 

mapped accurately onto high-resolution aerial imagery of damage. Effectively, peo- 

ple’s text messages gave highly informative clues as to where the greatest damage 

occurred. More importantly for those in Haiti, the messages saved lives, with the 

critical information transmitted to the heroic rescue workers on the scene. 

Similar systems have been used during outbreaks, such as the cholera outbreak 

in Haiti in the fall of 2010. The ultimate hope is that outbreak detection can be 

crowdsourced, with small bits of information provided by sufferers that converges 

into a real-time picture of the beginnings of outbreaks and their subsequent 

spread. The short codes are only the start. As more and more countries adopt elec- 

tronic medical records, people around the world will increasingly link to them

including the critical week of February 3, 2008, when a 5.9 magnitude earthquake 

occurred in the Lake Kivu region. By establishing a baseline for the frequency of 

calls, Eagle and his team were able to see telltale clues of unusual calling patterns 

during the period immediately following the earthquake. They were able to detect 

the time of the quake through a peak in call numbers. They were also able to estab- 

lish the epicenter of the quake by using location data from cell towers, placing the 

epicenter central to the locations of the heaviest call volumes. 

The idea that using data derived from cell phones can detect an earthquake in 

space and time is amazing. It also suggests a range of different applications. Indi- 

viduals who are ill may have fundamentally different call patterns than those that 

are not, and call patterns may also alter as a new outbreak spreads. Analyses of call 

data records alone might not provide perfect early detection of a new outbreak, but 

combined with other sources of outbreak data from organizations like ours and 

other health institutions, it might help us chart early epidemic spread. 

Cell phones are growing more ubiquitous by the day and will likely be critical tools 

in helping to detect and respond quickly to outbreaks before they become pan- 

demics. Yet they are not the only technology-heavy solutions being used in the 

growing field of digital surveillance. In 2009 my colleagues at Google³ published a 

fascinating paper showing that individuals’ online search patterns also provide a 

sense of what people are becoming infected with. 

With the vast stores of search data kept by Google and US influenza surveil- 

lance data collected by the CDC, the team was able to calibrate their system to 

determine the key search words that sick people or their caregivers used to indicate 

the presence of illness. The team used searches on words related to influenza and 

its symptoms and remedies to establish a system that accurately tracked the in- 

fluenza statistics generated by the CDC. In fact, they did better. Since Google 

search data is available immediately, and CDC influenza surveillance data lags

because of time needed for reporting and posting, Google was able to beat the 

CDC in providing accurate influenza trends before the traditional surveillance sys- 

tem. 

Early data on seasonal influenza, as provided by the Google Flu Trends system, 

is interesting and potentially important. This early data provides health organi- 

zations time to order medications and prepare for different triage needs. But early 

detection of seasonal influenza is not the Holy Grail. That honor would go to a sys- 

tem that could detect a newly emerging pandemic. Google is now working to ex- 

tend its influenza findings to other kinds of diseases. As more and more people 

use search engines like Google, and more and more data is acquired, the hope is 

that better and better trend analyses will be developed for agents other than in- 

fluenza. Perhaps at some point a community experiencing the beginning of a pan- 

demic will signal its arrival just by Googling. 

The explosion of online social media provides another set of big data in which 

weak but potentially valuable signals of a coming plague may be found. Computer 

scientists, like Vasileios Lampos and Nello Cristianini from the University of Bris- 

tol, have taken a similar approach as the scientists at Google, sorting through hun- 

dreds of millions of Twitter messages. Like their colleagues at Google, Lampos and 

Cristianini used key words to watch trends in Twitter and find associations with in- 

fluenza statistics, in this case provided by the UK’s Health Protection Agency. 

In 2009 they tracked the frequency of tweets related to influenza during the 

H1N1 pandemic and found they were able to track the official health data with 97 

percent accuracy. As with the findings by the Google Flu Trends team, this work 

provides a rapid and potentially inexpensive way to supplement traditional epi- 

demiological data gathering. It also has the potential to be extended to more than 

just influenza. 

While online social media can be scanned to see what people are communi- 

cating about, online social networking may provide a richer and subtler range of

possible uses. In fascinating recent work, two leading social scientists, Nicholas 

Christakis and James Fowler, have studied how social networks can inform surveil- 

lance for infectious diseases. 

In a clever experiment, these two scientists followed Harvard students who were 

divided into two groups. The first group was randomly selected from the Harvard 

student population. The second group was chosen from individuals that the first 

group named as friends. Because individuals near the center of a social network 

are likely to be infected sooner than those on the periphery, Christakis and Fowler 

hypothesized that during an outbreak the friend group would become infected 

sooner than the random, and therefore on average less socially central, group. The 

results were dramatic. During an influenza outbreak in 2009, the friend group be- 

came infected on average fourteen days ahead of the randomly chosen group. 

The hope is that social science can identify novel kinds of sentinels to monitor 

for new outbreaks and catch them early.⁴ Determining friends would be time con- 

suming, however—something we could accomplish on a single college campus 

but perhaps not nationally. Now self-identified friends in massive online social net- 

works may make this task much easier. Online social networks like Facebook, 

while not designed to help monitor for outbreaks, have created relatively easy-to- 

mon-itor systems that can be mined to determine the frequency of illness, identify 

social sentinels, and perhaps eventually provide predictions for spread of a new 

agent within a community. 

When John Snow created the first Geographic Information System in 1854, he took 

actions that would seem very logical and straightforward to us today. He took a 

map, he plotted where sick people were, and he plotted possible sources of conta- 

gion. Snow could not have predicted the directions in which his first tentative step 

would lead or the data that would eventually become available for today’s GIS. 

In the end it may be that no single data source reigns supreme. If Snow were 

alive today and investigating an outbreak, he’d want it all. He’d want to know where

the sick people were, and he’d be glad to get the data more quickly and easily 

through text messages or Internet searches. He’d like to know exactly what cases 

were infected with, down to the very specific microbial genetic strain. He’d seek to 

use call data records to monitor people’s movements in order to track the move- 

ment of the disease or where it was seeded. He’d like to know how people were 

connected socially, and he’d certainly follow individuals who were likely to become 

infected first or show signs earlier than the rest. 

You can imagine the ultimate outbreak GIS, or in terms more familiar to Silicon 

Valley, what Lucky Gunasekara, the head of my data team, calls the ultimate out- 

break mash-up: a map with layer after layer of critical information—where people 

are, what they’re concerned about, what they’re infected with, where they’re mov- 

ing, and who they’re connected to. Developing and maintaining this combined dig- 

ital and biological mash-up is the precise objective of Lucky’s team and something 

to which we’ll return in the final chapter of this book. Ideally, over time the data can 

be analyzed jointly, the various factors can be trained on actual outbreaks, and all 

the technology can be weighted optimally to maximize predictive power. 

When people ask me whether or not I’m optimistic about the future of predicting 

pandemics, the answer is always a resounding yes. Given the first two-thirds of this 

book, you may wonder if my optimism is warranted. A steady wave of intercon- 

nectedness among humans and animals has created a perfect storm for new pan- 

demics. That is true. Yet the interconnectedness among humans that now exists 

through communication and information technology gives us unprecedented 

capacity to catch outbreaks early, which, when combined with amazing advances in 

our ability to study the diversity of the tiny life forms that cause epidemics, cer- 

tainly makes optimism warranted. 

What will win out in the end? Will pandemics sweep through the human popu- 

lation destroying millions of lives? Will technology and science ride in to the res- 

cue?