Q&A: What to do when thereís not enough data?

Q: There isnít enough data on some of the parameters for the disease / system weíre looking at - is it even worth pursuing the development of a mathematical transmission model at this point, or is it better to wait for more research?

A: Yes... and yes.

This is a fairly common roadblock for individuals and organizations involved in decision-making and policy with respect to infectious diseases: they sorely need the predictive power and scenario-analysis capabilities of a mathematical model, but the current state of research on the epidemiology for the particular disease or population under consideration doesnít quite fill in all the blanks in terms of model parameter values and ranges.

Depending on whatís missing, these unknown values can severely hinder both the development of a quality predictive model, and the utility of its results. Unfortunately, it is highly improbable - even impossible - that all details can be accurately quantified for every single disease under consideration. This means every disease model will have at least some "unknowns". So how can we get around this?

A very useful approach to these kinds of issues is the implementation of uncertainty and sensitivity analyses. This means exploring a range of values for the input parameters under consideration, either individually or in combination, and evaluating the resulting variation in model outcomes. The product of this effort can be qualitative - for instance, slight perturbations of the birth rate yield dramatic changes in measles incidence over time, implying that measles transmission is highly dependent upon susceptible population sizes - or quantitative - for example, a 10% decrease in mortality rate yields a 200% increase in total cases over a given simulation period for some hypothetical disease system.

This type of procedure can be implemented with surprisingly patchy data on disease and population values, but must be accompanied by significant caveats and discussion of model and parameter uncertainties. However the real value of uncertainty and sensitivity analyses - and, I believe, the true power of mathematical modeling as a whole - is that the knowledge gained from these efforts can guide additional research and data gathering!

These analyses illuminate where the greatest degree of uncertainty exists for a given disease or population, and where the greatest benefit can be gained from further primary and secondary data collection efforts. The new data gathered can then be applied to existing or enhanced models, which can again expose areas of uncertainty... and the modeling / data gathering interaction can continue iteratively to lead to a highly accurate and useful suite of transmission models, along with a detailed map of the process that leads there. In the process, research efforts can focus on areas of greatest potential return, maximizing research budgets and minimizing waste.

Finally, the extremely valuable lessons learned from the iterative progression can subsequently be applied to other disease systems, streamlining the effort for future models and decision-making processes, and shortening the path from problem- identification to policy-application.

So even when it seems like there just isnít enough knowledge-base to construct a model, the model itself can contribute dramatically to building that knowledge-base in the most efficient and effective manner possible. Let our mathematical modeling experts at MathEcology help you get there from here!

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