In the opening pages of The Rules of Contagion, Adam Kucharski muses on the Spanish Flu pandemic, which killed over 50 million people in 1918 and 1919, and remarks that four more flu pandemics spread over the world in the next century. He describes the difficulty of predicting the next one:
“This raises the obvious question: what will the next one look like? Unfortunately it’s difficult to say, because previous flu pandemics were all slightly different. There were different strains of the virus, and outbreaks hit some places harder than others.”
As I type these words in the middle of February, the Chinese government has implicated the new coronavirus in 1660 deaths and 68,000 infections within its borders. The front page of my Japanese newspaper announces that health officials have identified “Japan’s first case of in-hospital transmission. . . in the western Japan prefecture of Wakayama.”
I live in Hyogo, a prefecture in western Japan. I have no plans to panic yet.
It is a bizarre existential experience to be reading about the history of previous contagions when there is a small chance we all might become data that will improve the next model.
Mathematician Kucharski’s book describes how epidemiology is devising models that predict a particular contagion’s devastation. One area of focus is how large-scale pandemics are being modeled based on some of the following questions. Why do some contagions spark and spread quickly? How can we measure outbreaks? How can we predict them? What are the causes that result in peaks? What causes epidemics to end?
Two of the ideas broached by Kucharski are especially fascinating: herd immunity and reproduction number. Here’s how I understand the former: Imagine a group/herd of 100 people living in an apartment complex. A contagion infects (maybe killing) 15 or 20 of the most vulnerable or susceptible tenants: babies/toddlers, the unvaccinated, seniors. The rate of further infection among the remaining 80 or 85 tenants slows down because this pool is now comprised of those who are less vulnerable: the healthier, the stronger, the luckier. The virus literally faces a smaller fuel source (fewer bodies) from which to draw future victims. As fewer of the remainder get sick, the virus is slowed down even more.
The second idea, “reproduction number” (the R), seems especially relevant as the world’s concern about coronavirus simmers and threatens to boil over into paranoia. This mathematical formula, which captures the number of infections caused by a typical sick person, incorporates four aspects: duration x opportunities x transmission probability x susceptibility (DOTS). For example, let’s say measles has an R of twenty, meaning that one sick person infects 20 others. An R of 1, on the other hand, means an infected person will just spread the disease to a single individual. The formula’s intuitive simplicity is breathtaking. Even non-scientific lunkheads (like me) can follow.
However, The Rules of Contagion doesn’t just focus on pandemics and diseases. It touches on how financial collapses resemble sexually transmitted diseases and how borrowing ideas for fighting smallpox might lead to stopping America’s gun violence epidemic. Kucharski also argues that such viral internet content as tweets or memes resemble health viruses. Computer networks are vulnerable to malware in much the same way that Twitter and Facebook accounts can be “infected” by viral tweets or images. Predictive models elucidate both the outbreaks of diseases and the collapse of financial bubbles. As banking networks have become more centrally localised, they are more susceptible to various financial problems.
Near the end of his fascinating book, Kucharski provides a sobering reality that puts the (most likely) relatively minimal deaths from coronavirus into stark perspective:
“In the time it’s taken you to read this book, around three hundred people will have died of malaria. . . over five hundred deaths from HIV/aids, and about eighty from measles, most of them children. Melioidosis, a bacterial infection that you may well have never heard of, will have killed more than sixty people.”
Any Cop?: Kucharski’s book, which includes clear, informative charts, graphs, and footnotes, is written in a smooth, entertaining style. I enjoyed the following absurd observations. During the height of Ebola hysteria, a wildly pessimistic model predicted more future cases than the number of people actually living in the African countries that were being counted. And a smallpox model predicted 77 trillion infections when the world had fewer than 7 million people.