Ending Epidemics: A History of Escape from Contagion
Richard Conniff
How scientists saved humanity from the deadliest infectious diseases—and what we can do to prepare ourselves for future epidemics. After the unprecedented events of the COVID-19 pandemic, it may be hard to imagine a time not so long ago when deadly diseases were a routine part of life. It is harder still to fathom that the best medical thinking at that time blamed these diseases on noxious miasmas, bodily humors, and divine dyspepsia. This all began to change on a day in April 1676, when a little-known Dutch merchant described bacteria for the first time. Beginning on that day in Delft and ending on the day in 1978 when the smallpox virus claimed its last known victim, Ending Epidemics explains how we came to understand and prevent many of our worst infectious diseases—and double average life expectancy. Ending Epidemics tells the story behind “the mortality revolution,” the dramatic transformation not just in our longevity, but in the character of childhood, family life, and human society. Richard Conniff recounts the moments of inspiration and innovation, decades of dogged persistence, and, of course, periods of terrible suffering that stir individuals, institutions, and governments to act in the name of public health. Stars of medical science feature in this drama, but lesser-known figures also play a critical role.
Award History
1 wins · 1 total
| Award | Year | Result | Category / Notes |
|---|---|---|---|
| PROSE Award for Biological Anthropology and Ancient History | 2024 | Winner | Official source |
Experimental book profileGenerated by GPT-5.4 nano · may contain inaccuracies
This is an unverified interpretation of the catalog description, offered as an opt-in discovery experiment—not as bibliographic fact.
01 Central figures
None extracted with sufficient confidence.
02 Central places
- Delft78%
03 Suggested argument
No argument inferred with sufficient confidence.
04 Reading orientation
45 / 100 academicTrade / academic crossover
An estimate of intended readership and scholarly apparatus—not quality or importance.
Confidence percentages are the model's own estimates. Profile confidence: 72%.