We R-Naught Ready for an Epidemic
by Jason S. Brinkley, PhD, MA, MS
On the Brink addresses topics related to data, analytics, and visualizations on personal health and public health research. This column explores current practices in the health arena and how both the data and mathematical sciences have an impact. (The opinions and views represented here are the author’s own and do not reflect any group for which the author has an association.) R-Naught Ready Epidemic
America has a sordid history with infectious disease. Overall, human history centered for generations around combating and surviving outbreaks, but our scientific ability to truly wage a counter-insurgency coincided with the birth of the United States. I don’t usually spend a lot of time playing with an internet visual that isn’t data related, but I found the History of Vaccines application full of really neat and useful information. There was so much to learn about the early history of vaccines in the US (Did you know that Thomas Jefferson was an early vaccine proponent? Or that Abraham Lincoln contracted smallpox shortly after delivering the Gettysburg address?), as well as recent entries on increased measles outbreaks and the rise of Zika in the United States. I love that it gives new insights on today’s news by focusing with a historical lens, but it misses an important component. The rise of laboratory breakthroughs in the science of infection was aided by a parallel rise in the mathematics of contagion and the use of data to drive decision making. Edward Jenner’s accomplishments in creating a small-pox vaccination were as important to modern medicine as Florence Nightingale’s statistical data and custom visuals in understanding how infection spreads. R-Naught Ready Epidemic
Mathematics and statistics are often the forgotten heroes of infectious disease research, but they provide a solid basis for understanding disease threats and determining how we manage outbreaks. So how do we assess an outbreak? There are many metrics, but among the most popular is something called R0 (pronounced ‘R-naught’) which estimates a disease transmission rate by looking at the average number of new cases an infected person will create. Lots of things go into calculating an R0, but one can reasonably intuit the most important aspects: mode of transmission (sexual transmission would have lower values than through coughing, because presumably you cough on more people than you have sex with), infectious period (how long your body takes to fight off the disease so that you remain at risk to others), and contact rate (how debilitating is the disease and how many people could you give the disease to). So what is a “bad” R0? Anything bigger than 1 would mean each case makes more than 1 new case and can lead to an epidemic in the right circumstances. R-Naught Ready Epidemic
[bctt tweet=”What’s the R-naught of well-known #diseases like #Ebola, #HIV, and #measles? And what role does #ClimateChange play in the spread of #infectiousdiseases ? Find out in ‘We R-Naught Ready for an Epidemic.'” username=”@DrJasonBrinkley @JPHMPDirect @historyvaccines“]So what is the R0 of some well-known diseases? There is a lot of different data floating around but this piece from Vox has some good information. Ebola has an R0 of about 2, HIV is a little more than 3.5 while measles has an astounding R0 of 11-18. The range 11-18 initially comes off as shocking, but the point here is that transmission rates are a function of the environment. Climate plays a role in disease spread; warm climates or mosquito-borne illnesses do less well in cold climates. Population density plays a role, as well as vaccination availability, and “herd immunity” made possible by high vaccination rates. Scientific research plays a role and so does evolution. For instance, influenza generally has many different strains, making it nearly impossible to halt the spread of the illness even with a good public policy in place and vaccines being readily available in most places. R-Naught Ready Epidemic
So R0 can vary by a number of features depending on where you live and the conditions of the outbreak. What isn’t as often reported is that the nature of the R0 lends itself to potential misinterpretation. It is after all, an average transmission rate, and averages can often provide false information. More recent research has shown that for some infectious diseases, super-spreaders may exist (people and events that would precipitate a much higher transmission rate than what would happen on average). That is to say that the R0 transmission rate may actually be highly skewed and some individuals may have a personal transmission rate much higher than average. Think about it like incomes across job categories; most individuals make the same amount of pay for their career but some make a lot more than the rest. One upside is that the math suggests that such individuals are rare. But it only takes a few super-spreaders to boost the R0 metric for an entire disease. R-Naught Ready Epidemic
In a previous blog post I suggested that everyone was above average at something. Of all the ways a person could be above average perhaps being a super-spreader for an infectious disease would be the worst. It would be better to be excellent at something that is less useful, but has high Guinness World Record potential, like this story of a man who fit over 100 blueberries into his mouth. I hope he washed all that fruit cause if he didn’t then he might get sick, in which case here’s hoping he isn’t also contagious.
Jason S. Brinkley, PhD, MS, MA is a Senior Researcher and Biostatistician at Abt Associates Inc. where he works on a wide variety of data for health services, policy, and disparities research. He maintains a research affiliation with the North Carolina Agromedicine Institute and serves on the executive committee for the NC Chapter of the American Statistical Association and the Southeast SAS Users Group. Follow him on Twitter. [Full Bio]
Previous posts in this series:
- How Do Machines Learn? Part 3: They Recover
- How Do Machines Learn? Part 2: They Fight
- How Do Machines Learn? Part 1: They Learn
- Halloween by the Numbers
- What Kills Kids?
- The Golden Age of Health Research Funding
- Does Living on a Prayer Work?
- The Opioid Data Crisis
- Income Lost from Snow Days*
- What the #$@&*! Is Blockchain?
- Opportunistic Research Opportunities
- Text Mining UFO Data: Little Green Aliens or Santa’s Elves?
- Should You Know Your Doctor’s Home Address?
- The Population Bullet
- The Unknown Unknowns of Missing Data
- Communicating Science–More Than Just Good Words?
- Counting Alabamas
- The Third World in Your Own Backyard
- The Unrealistic Gold Standard
- Does MACRA Signal the Beginning of the End for Medicare Claims Data?
- Think You Aren’t Extraordinary? Odds Are You’re Wrong
- Mapping by Words
- Are We Asking Too Much From Surveys?
- Making Better Comparisons
- What Kills Us?
- Students of Public Health2023.01.23Students Who Rocked Public Health 2022
- Students of Public Health2022.12.01Deadline Extended to Nominate a Student Who Rocked Public Health in 2022
- JPHMP Direct Voices2022.10.19Preview Issue for Public Health Workforce Interests and Needs Survey
- Uncategorized2022.10.12Partnering for Success in One Ohio County
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Nice blog series!