Episode Summary

When will the next disease outbreak occur? Why are some pests better at spreading disease than others? Disease Ecologist Barbara Han talks about her research that addresses these questions with computer modeling, as well as how modeling predictions can inform field and bench research.

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Julie’s Biggest Takeaways

A spillover event occurs when a human is infected from an animal reservoir. Most spillover events are dead ends for the microbe. Only in a few events does the infected person lead to a transmission chain, in which other people are infected.

Convincing funding agencies to conduct surveillance when no outbreaks are occurring is like convincing a healthy adult to buy insurance: a tough sell. Investment in surveillance is the most critical thing, however, to successful outbreak prediction. There is no shortage in the number of scientists who will measure these important parameters, when given the funding and resources, and their basic science observations are critical to predicting what happens when an ecosystem becomes unbalanced.

Learning the aspects that grant one vector or pathogen a potential for spreading human disease helps researchers and public health officials to determine other vectors or pathogens that might have similar characteristics. Mapping these geographically can inform surveillance efforts and make a case for increased basic research to define these characteristics.

Disease ecology can be broken down into 3 major camps. The future of disease ecology and outbreak prediction relies on integrating systems between:
  • Field researchers
  • Lab researchers
  • Computer modelers

Featured Quotes

“Any information you can gather about a system is good to know. The quality of our predictions depends on the quality of the data that we have to work with.”

“The data that we have to work with might be considered big from a field ecologist’s perspective, but they’re really tiny from the world’s perspective. We use everything we can get our hands on.”

“If we want to get ahead of disease spillovers and outbreaks, it really helps to know what the baseline is. What is a well-functioning ecosystem, and when you perturb it, what do you expect to happen? What should happen? You can’t have predictions like that unless you have the baseline.”

“You can’t just make a model and stop. You have to update it. Until now, we’ve been pivoting from one disease system to another. Going forward, we need a more evolving model for doing that iteration.”

“I’m hoping the future of disease ecology will be simply: more. There are plenty of bright, young disease ecologists that are pursuing questions because those are the questions that stand out to them, and we shouldn’t lose that individuality - every person has unique perspective to bring to the table. I’m hoping in the next decade or so that we’ll see more data simulation techniques where we take big data patterns and are able to move across scales and link them with models and field work, and that this connection across all 3 areas is a bit more pronounced.”

Links for This Episode

History of Micobiology Tidbit


One of the things I enjoy about these history tidbits is the opportunity to explore language that has become embedded into a field. Where do terms come from? The term emerging infectious disease, for example, makes intuitive sense, but its use in scientific literature and as a concept wasn’t always embedded in scientific culture - if you look at the graph that pubmed provides for a given search term, it’s clear that the term grew from single publications per year in the 1980s to dozens per year in the 1990s, and for the past couple of years there have been thousands of publications in emerging infectious disease.

One of the documents that promoted the idea of emergence was likely a report from the National Academies of Science, published in 1992 and edited by Joshua Lederberg, Robert Shope, and Stanley Oakes, Jr. Wait - didn’t we talk about Joshua Lederberg coining the phrase “microbiome” in a previous history tidbit? It seems Lederberg had a hand in all sorts of microbiology-related issues, and here’s yet another example. This report outlines the different types of emergence and reemergence that we continue to discuss in scientific venues today. Emergence due to newly discovered microorganisms, yes, but also reemergence of newly virulent strains, and emergence into new niches facilitated by a growing immunosuppressed population.

The report, titled “Emerging Infections: Microbial Threats to Health in the United States,” recommends development and implementation of strategies to strengthen state and federal efforts in U.S. surveillance - which, as Barbara mentioned, is the cornerstone for predicting disease outbreaks. Important issues like antiviral drug resistance and nosocomial infections are emphasized, as well as the development of a computerized disease database, which is quite a bit of foresight for this position in the history of the internet (America Online, one of the first internet service providers, only began to distribute email addresses in 1993).

The recommendations include creating overseas laboratories and working under a federal institute, such as the CDC, NIH, or USDA, and expansion of the CDC’s Epidemic Intelligence Service program, which is a program to train scientists as disease detectives to understand outbreaks.

The entire report is available online for free (linked above), and is extremely prescient in its calls for a global, interlinked surveillance network to detect and prevent the spread of emerging infections. Maybe it was this call for funding that brought ‘emerging infections’ to the attention of those then writing grants and reports on some of the diseases discussed in the report. Eventually, the concept of ‘emerging infectious diseases’ expanded into non-human pathogens, such as those of plants and animals. The chytridiomycosis Barbara mentioned, which infects amphibians in wild populations, certainly qualified as an emerging disease when it appeared in the early 1990s, though this report focuses only on human disease.

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