In "The Rules of Contagion," Adam Kucharski explores the fascinating world of how things spread. While the book was written before the coronavirus pandemic, it offers valuable insights into how contagions work, not just in the realm of infectious diseases but across various aspects of our lives.
Kucharski, an expert in studying dangerous contagions like Zika and Ebola, takes us on a journey through the science of contagion. He explains how viruses spread, why they die off, and how these same principles apply to other areas of society. From computer viruses to internet memes, from financial crises to patterns of gun violence, the rules of contagion are at play in ways we might not expect.
This book summary will delve into the key ideas presented by Kucharski, exploring how mathematical models have revolutionized our understanding of contagious events, how these models apply to various aspects of life beyond disease, and the potential and limitations of technology in tracking and controlling contagions.
The Power of Mathematical Models
Revolutionizing the Study of Contagious Events
Throughout history, humans have grappled with outbreaks of contagious diseases. However, our ability to cope with and understand these outbreaks has improved dramatically over time. One of the most significant advancements in this field has been the introduction of mathematical models and scientific analysis.
The pioneering work of British surgeon Ronald Ross in the late 19th and early 20th centuries marked a turning point in how we study and understand contagious events. Ross's work on malaria in India led to groundbreaking insights into how diseases spread and how they could be controlled.
Ross's journey began in Bangalore, India, where he encountered a severe mosquito problem. He was among the first to recognize the connection between mosquito populations and the presence of stagnant water. This observation laid the groundwork for his later discoveries.
A crucial piece of the puzzle came from fellow doctor Patrick Manson, who had studied parasites in China. Manson's theory that mosquitoes could become carriers of parasites after feeding on infected blood provided Ross with a potential explanation for the spread of malaria, one of humanity's oldest infectious diseases.
To prove this theory, Ross conducted experiments where he allowed mosquitoes to bite birds infected with malaria. He then demonstrated how these mosquitoes could transmit the disease to healthy birds. This groundbreaking research established the mosquito as the vector for malaria transmission.
But Ross didn't stop there. He took his ideas further by applying mathematical calculations and creating models to propose ways of controlling malaria. In his 1910 book "The Prevention of Malaria," Ross presented these calculations, explaining how infection rates could be reduced by controlling mosquito populations.
For instance, Ross's data showed that it took around 48,000 mosquitoes to generate an average of one new human infection. This meant that removing or treating stagnant water, which would reduce mosquito populations, would have a direct impact on the number of new infections.
Ross also introduced the importance of two key statistics: the rate of infection and the rate of recovery. He demonstrated that once the rate of recovery surpassed the rate of infection, the number of cases would eventually reach zero. This concept laid the foundation for understanding how epidemics could be controlled and eventually ended.
This approach marked a revolutionary new way of looking at infectious diseases. By applying mathematical models and scientific analysis, researchers could now predict and potentially control the spread of contagious diseases in ways that were previously impossible.
The SIR Model: A Framework for Understanding Contagions
Building on Ross's work, researchers developed what is known as the SIR model. This model, which stands for Susceptible, Infectious, and Recovered, has become a fundamental tool in epidemiology and the study of contagions.
The SIR model provides a framework for understanding how contagions spread through a population. It divides the population into three categories:
- Susceptible: Those who can potentially become infected
- Infectious: Those who have the disease and can spread it to others
- Recovered: Those who have had the disease and are now immune (or deceased)
One of the key insights from this model is the concept of herd immunity. As an outbreak progresses, the number of susceptible individuals decreases as more people become infected and then recover (or die). There comes a point, often when the outbreak is at its worst, when the number of susceptible people reaches its lowest point. At this stage, most people in the population will either be infectious or have recovered.
After this point, the outbreak will start to decline naturally. This is because there aren't enough susceptible people left to sustain the spread of the contagion. This state, where a large portion of the population has become immune, creating indirect protection for those who are still susceptible, is known as herd immunity.
The SIR model has proven invaluable in the fight against various contagions, including Ebola, Zika, and HIV. It allows researchers to predict the course of an outbreak, estimate the potential impact of interventions, and determine the level of vaccination or immunity needed to prevent future outbreaks.
Applying Contagion Models Beyond Disease
One of the most fascinating aspects of Ross's work is that he believed his models could explain more than just the spread of infectious diseases. He introduced the concept of "happenings," which are events where people may or may not become susceptible to various kinds of trends or phenomena.
Ross identified two types of happenings:
Independent happenings: These are events where the occurrence doesn't significantly affect the likelihood of it happening to someone else later. For example, falling down the stairs and breaking your leg is an independent happening. The risk level for the average person remains relatively constant over time.
Dependent happenings: These are events that can spread from person to person, much like a contagious disease. They include things like ideas, beliefs, and trends, as well as infectious viruses. For instance, if someone hears about an exciting new app, there's an increased chance that those around them will also become interested in it.
Ross's models show that dependent happenings generally follow an S-shaped curve when plotted on a chart. There's a slow initial spread, followed by a period of rapid growth. Eventually, the spread slows down and flattens out as it becomes increasingly unlikely to encounter someone who hasn't already been exposed to the idea or trend.
This pattern of spread isn't limited to diseases. In 1962, sociologist Everett Rogers noted that Ross's model of dependent happenings applied to many aspects of life, including the adoption of new ideas and products. Just as with an infectious disease, the rate at which a contagious idea catches on will eventually level off once it becomes unlikely to come into contact with a susceptible person who hasn't already heard about the idea.
This realization opened up new avenues for applying the principles of contagion to various fields, from marketing and social sciences to technology adoption and cultural trends.
Contagion in Finance
Financial Crises as Contagions
The concept of "financial contagion" has been around since the mid-1990s, but it was during the 2008 financial crisis that the idea really came to the forefront. This crisis demonstrated how financial ideas and practices could spread through the system much like a virus, with potentially devastating consequences.
One of the key factors in the 2008 crisis was the gradual spread of certain ideas among traders and financial market participants. In particular, the trading of Collateralized Debt Obligations (CDOs) had become increasingly popular. CDOs were financial products that contained a bundle of loans, including mortgages. Investors in CDOs earned money by receiving a portion of people's loan repayments.
On the surface, CDOs seemed like a relatively low-risk investment. The only significant danger would be if a large number of people stopped paying back their mortgages and loans – a scenario that most people thought was highly unlikely.
As CDOs grew in popularity, they spread through the financial system at an increasing speed. More and more people invested in them, attracted by their seemingly low-risk nature and potential for high returns. As credit specialist Janet Tavakoli put it, CDOs "spread through the psyche of the financial markets like a highly infectious thought virus."
However, there was a fundamental problem with CDOs that wasn't immediately apparent. Housing prices had been steadily rising for years, and by 2008, many CDOs contained mortgages that were based on outdated, inflated property values. This meant that these mortgages were far less likely to be paid back than initially thought. In other words, CDOs that appeared to be low-risk investments were, in fact, highly risky.
Because CDOs had become so popular and widespread throughout the financial system, this risk had effectively infected the entire market. It was only a matter of time before the reality of the situation became apparent and the market realized that many CDOs were essentially worthless.
When this realization hit, the whole system collapsed. The contagion took its toll on banks and financial institutions like Lehman Brothers and Bear Stearns. Many businesses collapsed, and investors lost entire fortunes. The ripple effects of this financial contagion spread far beyond Wall Street, affecting economies and individuals around the world.
Financial Bubbles as Contagions
The 2008 financial crisis is just one example of how financial ideas can spread like contagions. Throughout history, we've seen similar patterns in various financial bubbles. These bubbles, whether it's the dot-com bubble of the late 1990s or the Netherlands' tulip mania of the 1630s, follow the same rules of contagion:
Initial Spread: A new financial idea or investment opportunity emerges and begins to gain attention.
Rapid Growth: As more people become aware of and invest in the opportunity, its popularity grows exponentially. This phase is often characterized by a sense of euphoria and a belief that the growth will continue indefinitely.
Peak: The bubble reaches its maximum size, with prices or valuations reaching unsustainable levels.
Burst: Eventually, reality sets in. People realize that the valuations are not justified, and a rapid sell-off occurs.
Aftermath: The bubble bursts, often leading to significant financial losses and broader economic impacts.
These financial bubbles demonstrate how ideas and behaviors in the financial world can spread in ways that mirror the transmission of infectious diseases. Just as a virus can quickly spread through a population, financial trends can rapidly proliferate through markets and economies.
Understanding financial contagions and bubbles through the lens of epidemiology can provide valuable insights for economists, policymakers, and investors. It highlights the importance of monitoring the spread of financial ideas and practices, and the need for systems and regulations that can help prevent or mitigate the impact of financial contagions.
Moreover, this perspective underscores the interconnected nature of modern financial systems. Just as a disease outbreak in one part of the world can quickly become a global pandemic, a financial crisis in one sector or country can rapidly spread to affect the entire global economy.
By recognizing these patterns and applying the principles of contagion to financial systems, we may be better equipped to identify potential bubbles early, implement measures to slow their spread, and develop more resilient financial structures that can withstand the impact of financial contagions.
Violence and Crime as Contagions
Mapping Violence Like Disease Outbreaks
The idea of mapping disease outbreaks began in 1848 with Dr. John Snow, who used a map of London to trace a cholera outbreak to a specific water pump. Since then, maps have become an indispensable tool for epidemiologists tracking the source and spread of outbreaks.
Interestingly, this approach has found applications beyond the realm of infectious diseases. Epidemiologist Gary Slutkin made a fascinating discovery when he compared maps of killings in US cities to maps of cholera outbreaks in Bangladesh. He found striking similarities in how violence and disease spread geographically.
The maps showed that outbreaks of violence could cluster and spread outward, much like a disease. This pattern wasn't limited to one location; historical graphs of violence in Rwanda resembled data on cholera outbreaks in Somalia. These observations suggested that violence might follow similar patterns of transmission as infectious diseases.
The Contagion of Violence
Dr. Slutkin's observations are supported by other research in the field. Yale University sociologist Andrew Papachristos conducted a study of shootings in Chicago that provided quantitative evidence for the contagious nature of gun violence.
Papachristos found that "for every 100 people who were shot, contagion would result in 63 follow-up attacks." This level of contagion gives gun violence in Chicago a reproduction number (R) of 0.63. To put this in perspective:
- A pandemic flu or an Ebola outbreak usually has an R of 1–2
- The SARS outbreak of 2003 had an R of 2–3
- Smallpox, the only human infection to be eradicated, had an R of 4–6
While gun violence isn't as contagious as some infectious diseases, its R value of 0.63 is significant enough to warrant treating it as a contagion.
Treating Violence as a Contagion
The recognition of violence as a contagion has led to innovative approaches in violence prevention. Dr. Slutkin's research is behind an organization called Cure Violence, which applies epidemiological principles to reduce violence in communities.
Cure Violence identifies high-risk areas and sends in teams of "violence interrupters." These individuals, often former gang members or respected community figures, work to stop the contagion of violence from spreading. They do this by:
- Talking to victims of violence, as well as their friends and family
- Guiding them towards non-violent alternatives for conflict resolution
- Mediating conflicts before they escalate to violence
- Changing community norms around violence
This approach has shown promising results. After a year of work in Chicago's West Garfield Park, shootings in the area dropped by around two-thirds. Similar programs have been implemented in other cities with positive outcomes.
The success of these programs underscores the value of applying contagion models to social issues like violence. By understanding violence as a contagious phenomenon, we can develop more effective strategies for prevention and intervention.
Implications and Considerations
Viewing violence through the lens of contagion offers several important insights:
Prevention: Just as with infectious diseases, preventing the initial outbreak of violence can be more effective than trying to control its spread later.
Early Intervention: Identifying and intervening in the early stages of violence can prevent it from spreading further in the community.
Community Approach: Addressing violence requires a community-wide approach, much like controlling a disease outbreak.
Changing Norms: Long-term reduction in violence involves changing community norms and attitudes, similar to how public health campaigns can change behaviors related to disease prevention.
However, it's important to note that while the contagion model provides valuable insights, it shouldn't be the only lens through which we view violence. Socioeconomic factors, systemic inequalities, and individual circumstances all play crucial roles in the occurrence and spread of violence.
Moreover, care must be taken in how this data is used. While predictive policing methods based on historical data can help target interventions, they can also reinforce existing racial biases in community policing if not implemented thoughtfully.
By combining the contagion model with other sociological and criminological approaches, we can develop more comprehensive and effective strategies for reducing violence in our communities. This multifaceted approach recognizes the complex nature of violence while leveraging insights from epidemiology to create safer, healthier communities.
The Viral Nature of Ideas and Internet Memes
The Challenge of Predicting Contagions
One of the biggest challenges in epidemiology is making accurate predictions about the spread of diseases. Good models require good data, which is often difficult to obtain in real-time during an outbreak. For a pandemic flu, for instance, you would need hospitals and health centers from around the world to record and provide accurate data – a massive undertaking that often results in delays. Researchers are frequently playing catch-up, processing data that can be days or even weeks old.
However, there's one realm where data is abundant and readily available: the internet. This wealth of information has allowed researchers to study how ideas and content spread online, often drawing parallels to the spread of infectious diseases.
The Art of Creating Viral Content
One of the pioneers in understanding and creating viral online content is Jonah Peretti. His journey into the world of viral content began in 2001 when he ordered a pair of personalized Nike sneakers with the word "sweatshop" emblazoned on them. When Nike refused the order, Peretti shared the email correspondence, which unexpectedly went viral. This experience led him to study and eventually master the art of creating contagious online content.
Peretti went on to become a key figure in the development of viral content platforms. He helped develop the Huffington Post and later created Buzzfeed, establishing himself as an expert in understanding how and why things go viral on the internet.
In his efforts to create outbreaks of online content, Peretti applies his knowledge of the rules of contagion. For instance, when launching a marketing campaign, he uses equations derived from epidemiology to predict the potential reach of content.
Here's an example of how this works:
- Let's say a piece of content has a reproduction number (R) of 0.8.
- We can calculate: 1 - 0.8 = 0.2
- Then: 1 ÷ 0.2 = 5
- This means we could expect outbreaks with an average of 5 shares or engagements.
Peretti can use this type of calculation in reverse to assess the effectiveness of a marketing campaign. For instance, if a Twitter post gained 130 retweets, but most people who saw it didn't engage, and there were only four clusters of activity with an average of 1.04 retweets each, we can calculate that the tweet had a relatively low R of 0.04.
The Rarity of Truly Viral Content
Despite the term "viral" being widely used in discussions about online content, truly viral phenomena are actually quite rare. In fact, about 95 percent of the content on Twitter consists of single tweets that no one has shared. It's exceptionally uncommon for online content to catch on and spread globally.
Researchers from companies like Microsoft and Facebook, as well as individuals like Jonah Peretti, have been studying these rare instances of viral content. They're trying to understand why and how particular pieces of content can make people want to share them with everyone they know.
Adaptability: A Key to Virality
One of the ways online content mirrors contagious viruses is in its adaptability. Just as the flu virus is known for its ability to mutate and change year by year, popular memes are famous for their ability to evolve and become more shareable as they pass from person to person.
For example, a funny picture of a cat might have different captions added to it, or a donation request might have a new sentence appended. These small changes can significantly impact the content's shareability.
A case study from Facebook illustrates this point. A message about healthcare that read, "No one should die because they cannot afford health care and no one should go broke because they get sick," was shared nearly half a million times in its original form. However, when people reposted the message, about one in ten would change the wording slightly. When the phrase "post if you agree" was added, researchers found that it was twice as likely to be shared.
The Limited Role of Influencers
Contrary to popular belief, the role of "influencers" or "superspreaders" in making content go viral is actually quite limited. Studies into the effectiveness of singular people who can cause something to become hugely viral have found their influence to be less significant than often assumed.
Even if content is posted by an online "influencer" with millions of followers, there's no guarantee that it will spread widely. As Jonah Peretti puts it, a "superspreader" on the internet is someone who can infect eleven people instead of two – hardly a mass audience.
In fact, after studying popular posts on Twitter, researchers have struggled to identify specific characteristics that consistently make a tweet go viral. This suggests that accurately predicting online popularity remains a significant challenge.
The Unpredictability of Viral Content
Despite all the research and analysis, predicting what will go viral online remains largely unpredictable. This unpredictability mirrors the challenges faced in predicting the spread of actual viruses. Just as a new strain of flu can surprise epidemiologists with its transmission patterns, a piece of online content can unexpectedly capture the internet's attention and spread rapidly.
This unpredictability underscores the complex nature of human behavior and social dynamics. While we can identify certain factors that may increase the likelihood of content spreading – such as emotional appeal, relevance, or timing – there's no guaranteed formula for creating viral content.
The viral nature of ideas and internet memes demonstrates how the principles of contagion apply beyond the realm of infectious diseases. It shows how information, much like a virus, can spread rapidly through a population, mutating and adapting along the way. Understanding these dynamics can provide valuable insights not just for marketers and content creators, but for anyone interested in how ideas spread and influence society in the digital age.
The Role of Technology in Tracking Contagions
The Promise and Perils of Technology
Technology has revolutionized our ability to track and understand contagions, whether they're biological viruses or spreading ideas. However, this technological advancement comes with both benefits and drawbacks.
On the positive side, social media and digital platforms have made it easier than ever to communicate and share information rapidly. This can be crucial during public health crises or when spreading awareness about important issues. However, these same platforms can also facilitate the rapid spread of misinformation and can be a source of constant anxiety for many users.
Similarly, while the analysis of crime data in cities like Chicago has helped organizations like Cure Violence to intervene and potentially save lives, there are concerns about how this data is used. Some predictive crime-fighting methods that rely on historical data have been shown to reinforce racial biases that have long been a part of community policing.
When it comes to fighting a pandemic flu, technology offers powerful tools, but there remain significant limitations to what we can do.
DNA Sequencing and Real-Time Tracking
During the 2014 Ebola outbreak, researchers received a steady stream of blood samples for DNA analysis. Thanks to advances in DNA sequencing technology, they were able to trace each case back through a chain of infected individuals. This kind of genetic tracking offers fascinating insights into how outbreaks behave and spread.
However, one of the main limitations of this approach is that much of this insight arrives after the worst of the outbreak is over. While valuable for understanding the dynamics of the outbreak and preparing for future events, it doesn't always help in real-time response efforts.
Big Data and Real-Time Behavior Tracking
In today's digital age, there are sources that can provide an abundance of real-time data on human behavior. Tech giants like Google and Facebook have vast stockpiles of data ready for analysis. This data, which can include the ability to track users through GPS signaling, could potentially be incredibly useful in helping to control future outbreaks.
For example, location data could help identify potential hotspots of disease transmission or track the movement of infected individuals. Search data could provide early warning signs of an outbreak by detecting increases in searches for specific symptoms.
Ethical Considerations and Privacy Concerns
While the potential benefits of using big data for contagion tracking are significant, they come with serious ethical considerations and privacy concerns. Many people are unaware of how much of their personal data is being collected and made available to companies. The Cambridge Analytica scandal, where Facebook user data was used for political campaign research without users' knowledge, highlighted the potential for misuse of such data.
The key question is not just whether we can use this data, but whether we should. And if we do use it, how can we ensure it's done ethically and with full transparency?
Voluntary Data Sharing for Research
There are examples of how personal data can be ethically collected and used for research purposes. In 2017, the author was part of a study in collaboration with the BBC called "Contagion!" This project involved people voluntarily downloading an app that would track their movements and collect information on their social interactions.
Tens of thousands of people chose to participate in this study, with no benefit other than contributing to a massive dataset to help researchers better understand how outbreaks happen. This kind of voluntary participation, where individuals are fully aware of how their data will be used, represents a positive model for future research efforts.
The Need for Transparency and Ethical Guidelines
For technology to be effectively and ethically used in tracking contagions, there needs to be increased transparency around how data is collected and used. When people are aware that their data is being collected and they know precisely what it's used for, they can make informed decisions about their participation.
Moreover, there need to be clear ethical guidelines and regulations governing the use of personal data for public health purposes. These guidelines should address issues such as:
Informed consent: Ensuring that individuals understand what data is being collected and how it will be used.
Data security: Implementing robust measures to protect personal information from breaches or misuse.
Limited use: Ensuring that data collected for public health purposes is not used for other unrelated purposes without explicit consent.
Anonymization: Where possible, data should be anonymized to protect individual privacy.
Transparency: Regular reporting on how data is being used and the outcomes of its use.
Right to withdraw: Individuals should have the right to withdraw their data from studies or databases if they choose.
The Future of Contagion Tracking
As technology continues to advance, our ability to track and respond to contagions – whether they're biological viruses, ideas, or social phenomena – will likely improve. However, realizing the full potential of these technological advancements will require careful navigation of the ethical and privacy concerns they raise.
The challenge moving forward will be to strike a balance between leveraging the power of big data and technology to protect public health and respecting individual privacy and autonomy. This will require ongoing dialogue between technologists, public health experts, ethicists, policymakers, and the public.
By addressing these challenges head-on, we can work towards a future where technology enhances our ability to respond to contagions while also protecting individual rights and fostering public trust in these systems.
Conclusion
Adam Kucharski's "The Rules of Contagion" offers a fascinating exploration of how the principles of contagion apply far beyond the realm of infectious diseases. From the spread of financial crises to the transmission of violence in communities, from the viral nature of online content to the ethical considerations of using technology to track contagions, the book illuminates the interconnected nature of our world.
Key takeaways from the book include:
Mathematical models have revolutionized our understanding of how contagions spread, whether they're diseases, ideas, or behaviors.
The same principles that govern the spread of infectious diseases can be applied to understand phenomena in finance, social behavior, and online trends.
Violence and crime can spread through communities in ways that mirror the transmission of diseases, leading to new approaches in violence prevention.
While technology offers powerful tools for tracking and potentially controlling contagions, it also raises significant ethical and privacy concerns that must be carefully addressed.
Creating truly viral content online is rare and often unpredictable, despite the efforts of marketers and content creators to crack the code.
The role of "influencers" or "superspreaders" in making content go viral is often overstated, highlighting the complex nature of how information spreads online.
Future efforts to leverage technology in tracking contagions will require a delicate balance between public health benefits and individual privacy rights.
As we continue to face global challenges like pandemics, financial crises, and the spread of misinformation, understanding the rules of contagion becomes increasingly crucial. Kucharski's work provides valuable insights that can inform how we approach these issues, from public health policy to financial regulation, from violence prevention to ethical data use.
However, the book also underscores the complexity of these phenomena. While we can identify patterns and apply models, predicting and controlling contagions – whether they're viruses, ideas, or behaviors – remains a significant challenge. This unpredictability reminds us of the need for flexible, adaptive approaches in dealing with contagions of all kinds.
Ultimately, "The Rules of Contagion" invites us to view the world through a new lens, recognizing the interconnected nature of our social, technological, and biological systems. By understanding these connections and the principles that govern them, we can work towards more effective strategies for addressing some of society's most pressing challenges.
As we move forward, the insights from this book can help guide us in creating more resilient systems, developing more effective interventions, and fostering a more informed and ethically-minded approach to leveraging technology for the greater good. In an increasingly interconnected world, understanding the rules of contagion is not just a matter of scientific interest – it's a crucial tool for navigating the complexities of modern life.