Why model infectious disease?

People sometimes ask: What use are mathematical models of infectious disease? There are excellent works addressing this question in depth, including McKenzie, Garnett et al, and Grundmann and Hellriegel, among many others. They are all recommended reading and offer comprehensive answers from multiple perspectives. In the meantime, I offer a few observations.

Sir Ronald Ross, who discovered that mosquitoes carry the malaria parasite, viewed the modeling process as a way of thinking carefully about epidemiologic issues. The process of constructing a mathematical model, by its very nature, requires that careful, precise ideas are formulated as the model is built. The discipline of writing down and analyzing disease processes can sharpen and inform one's thinking. The history of mathematical modeling and the payoff for malaria research is illustrated beautifully in Smith et al.

The modeling process can also uncover gaps in our knowledge and understanding, often highlighting the need for additional research and expertise in order to realistically address particular issues. Thus, modeling can be a process for both facilitating multi- or cross-disciplinary collaborations and identifying needed observational or laboratory studies. Examples of models highlighting knowledge gaps for mosquito-borne infections can be seen in Reiner and Perkins et al. 

Importantly, models enable virtual experiments and studies, including ones that cannot be carried out easily, if at all, in the real world. Mathematical models are thus tools for analyzing what if scenarios, doing feasibility studies, and carrying out risk assessments. McKenzie illustrates these points clearly for the case of biodefense.

Models allow us to assess the impact of uncertainty, and variation in data, upon our ability to make decisions, as has been studied thoughtfully recently by Christley et al. Mathematical approaches exist and are commonly applied to models to deal with uncertainty in quantitative ways, as reviewed recently by Wu et al, and illustrated by Okais et al for the case of vaccination.

I also tend to think of models as mechanisms for summarizing, synthesizing, and communicating complex information. It never ceases to amaze me how much space in research papers is devoted to specifying a model (little space) relative to the amount of prose needed to explain the model, the data required to run it, and its output (much space). The clear, precise, and economical encapsulation of so much information, typically only a few lines of equations and table of parameter values, is very appealing. Mathematics is a much more precise language than the spoken or written word.  

Modeling has many uses beyond those touched upon here, some of which will, no doubt, be the topics of future blogs.

Penguins get sick, too

If you are intrigued by penguins, like I am, you may find this post of interest. After nearly 40 pieces related to human infection, it seemed okay to take a short break. 

I wondered recently: what pathogens infect penguins? There doesn't seem to be a large research literature on the topic, but what I found suggests that they suffer from, or at least carry, several. One study observed that some penguin species can be infected with Newcastle disease virus, infectious bursal disease virus (IBDV), and avian poxvirus. Recently it was observed that influenza viruses (H11N2) circulate in wild penguin populations (and that these viruses are genetically very distinct from avian flu viruses elsewhere in the world). Penguins can become ill with aspergillosis, and they can also suffer avian malaria caused by Plasmodium elongatum and P. relictum. Malaria seems to be a large issue for penguins in captivity.

The habitats of many penguin species tend not to overlap much with people's living spaces, though there are exceptions, like in the case of the blue penguin of southern Australia and New Zealand, which sometimes nests under houses. I suspect that it's unlikely that there is a threat to humans from infections carried by penguins, though I do wonder about ecotourism as a vector of infection to penguins. This has been discussed recently in connection with human metapneumovirus in gorillas.

If you like penguins and are interested in learning more, there is an exhaustive article on the diseases of penguins in the New Zealand Ministry of Agriculture and Forestry's Surveillance publication from December, 2001.

(image source: Wikipedia)

Prediction is difficult, especially about the future (and, apparently, about flu)

The title of this posting, minus the comment about flu, is a quote attributed to Niels Bohr. I imagine him mumbling this while stewing over the horrors of making prospective predictions of experimental outcomes with no good theory to provide guidance. The concern is well founded and the idea that prediction is difficult is profound -- and it is relevant to much of the "big data" analysis that is currently in vogue. 

We should take notice of Bohr's admonition for the reasons so clearly described by Lazer et al ("The Parable of Google Flu: Traps in Big Data Analysis"), who review the failure of Google Flu Trends (GFT) in 2013. This is an excellent paper containing a direct critique of many issues facing not only GFT specifically, but also of the larger "big data" movement that is so much in the news today.

Briefly, GFT estimates flu prevalence by mining search terms from users of Google’s search engine and applying algorithms to the results. In the past, GFT's predictions have agreed with CDC surveillance data well, anticipating those data several days earlier than CDC. In 2013, however, it became clear that GFT was substantially overestimating flu levels. Lazer et al describe the failure and explain several ways in which the GFT approach is problematic.

Early in the paper they capture the essence of the Achilles heel of many "big data" projects at present, noting that

“Big data hubris” is the often implicit assumption that big data are a substitute for, rather than a supplement to, traditional data collection and analysis. We have asserted that there are enormous scientific possibilities in big data. However, quantity of data does not mean that one can ignore foundational issues of measurement, construct validity and reliability, and dependencies among data. The core challenge is that most big data that have received popular attention are not the output of instruments designed to produce valid and reliable data amenable for scientific analysis.

Read that again. Every word is important. 

The paper goes on to highlight several issues with GFT and what is known about the methodology involved in its predictions. Among other findings, they conclude that a forecasting model far simpler than the elaborate use of huge amounts of data in GFT could have forecast influenza better than GFT has for sometime. So why go to the bother of using massive computational resources to compute a result that's so inaccurate?

Fung, in a recent blog, provides a frank discussion of what "big data" are and, importantly, they are not. He describes the OCCAM framework, which amounts to "a more honest assessment of the current state of big data and the assumptions lurking in it". Within this framework, "big data" are:

• Observational: much of the new data come from sensors or tracking devices that monitor continuously and indiscriminately without design, as opposed to questionnaires, interviews, or experiments with purposeful design
• Lacking Controls: controls are typically unavailable, making valid comparisons and analysis more difficult
• Seemingly Complete: the availability of data for most measurable units and the sheer volume of data generated is unprecedented, but more data creates more false leads and blind alleys, complicating the search for meaningful, predictable structure
• Adapted: third parties collect the data, often for a purposes unrelated to the data scientists, presenting challenges of interpretation
• Merged: different datasets are combined, exacerbating the problems relating to lack of definition and misaligned objectives

(Bullets taken directly from Fung.) Trying to make sense out of data that are poorly characterized or understood seems like a recipe for disaster. Traps aplenty indeed, and Lazer et al illustrate these traps for GFT in detail.

Such traps must be identified and worked around in sensible, theoretically sound ways. The OCCAM problems with "big data" do not mean that "big data" analysis is not promising. Rather, they mean that we need to be thoughtful when attempting to analyze such data, and that methods need to be developed to rationalize data so that they can produce meaningful results for biomedical and scientific issues.

What would Bohr think about "big data" if he were alive today? Who knows, of course, but I suspect he would be cautious to draw inferences based on any amount of data -- big or not -- unless those data are understood, characterized, and arguably relevant to a clear theoretical framework.

(image source: Wikipedia)

New antibiotics on the horizon: Are we ready?

A previous blog asked:

Who wouldn't agree that we need an invigorated pipeline of new, effective, and safe antimicrobial drugs to help us counter the specter of resistance? But it does make me wonder: Is it really a good idea to place new weapons in our arsenal when we have demonstrated few reasons to think that we will use them responsibly?

A reinvigoration of the drug pipeline may be starting, given news that a major drug company is re-engaging its research on antibiotics. Moreover, this week we learned about a new highly potent drug, and another one that was just approved by the FDA, for skin infections. Other new drugs are under development as well.

It seems poignant to think about how to make it safe to employ new antibiotics on a wide scale so as not to risk the emergence of new resistance. It's a complex issue, but here are some thoughts.

• Antimicrobial stewardship programs need to implemented across all healthcare settings. Using antimicrobials in a targeted, appropriate fashion is important for preventing acquisition of new resistance. Progress is being made in some settings (notably children's hospitals), but programs need to be instituted across the board.

• HAI rates need to be reduced to very low levels across institutions and patient populations. Low rates are important for preventing the spread of resistant infections once they emerge. Substantial opportunities remain to improve infection prevention programs in hospitals.  

• Patient expectations for drug therapy for common ailments need to be managed. Patients often pressure doctors for antibiotics for common symptoms (e.g., sore throat, congestion), even when etiology (viral versus bacterial) is unclear. Public health messaging, including the use of social media, is important for changing this. 

Undoubtedly, additional things are important as well. I haven't mentioned, for example, the issues surrounding the intensive use of antibiotics in animal farming, the emergence of antibiotic resistance organisms surrounding those practices, and the potential for causing human colonization and disease. If you have additional thoughts, please comment.

An important question is how we can measure progress in these areas. Surveillance for antimicrobial stewardship policy compliance and HAI rates within an institution seems more straightforward than monitoring these across regions. Likewise, monitoring public perception and expectations for antibiotic prescribing practice is complex. Perhaps this is an area where social media monitoring can play a role. Regardless of the difficulties, measuring such things is critical if we are to manage drug resistance moving forward.

(image source: CDC)

What are we doing to ourselves?

An interesting idea emerged from conversation over dinner with a colleague recently: While it is clear that hand hygiene is foundational for both hospital and community infection prevention, there may be an immunological price to the now all-pervasive focus on hand hygiene in the general population.

Let me explain. Hygiene is one of the pillars of public health and infection prevention, though we still struggle to practice what we know globally. Semmelweis showed us the need for clean hands in the clinical environment, and the notion of ridding hands of germs has evolved since then. Today, alcohol based hand rubs (ABHRs) are prominent in daily life. People rub their hands with "hand sanitizer" before eating out, after riding the bus, after using the restroom, and even at their desks throughout the day. What could possibly go wrong with such an awareness of hand hygiene?

Potentially, nothing. The importance of hand hygiene is undisputed and indisputable in infection prevention. That said, I often see people using ABHR very frequently throughout the day and it makes me wonder if such use of ABHR is eroding not only the transient flora of our hands, but also the resident flora. What is on our hands ultimately ends up challenging the immune system, via oral ingestion, absorption through rubbing the eyes, or inoculation via scrapes and cuts on the hands and fingers. Constantly challenging the immune system with a diversity of biologic agents gives rise to broad immunity.

Might we be eroding the frequency and diversity of that challenge, and thus the strength and diversity of the immunological protection, with such pervasive use of ABHR? This general notion, that cleanliness might have deleterious, unintended community-level consequences, is not new. It's been discussed within the context of polio, for example, and there is speculation about inverse relationships between cleanliness and asthma.

I'll close by noting that ABHR is but one of the several tools society currently employs to kill the spectrum of microbes in our immediate environment. There are also antimicrobial wipes and antimicrobial soaps. The weapons of mass microbial destruction are many and proliferating. They obviously have their place in the clinic but, regarding their sometimes near-obsessive use in the community, are they helping or hurting us in the long run?

(image source: David Hartley)