RESEARCH by scientists at the University of Liverpool has found that greater consideration of the limitations and uncertainties in infectious disease modeling would improve its usefulness and value.
Infectious disease dynamical modeling plays a central role in planning for outbreaks of human and livestock diseases, forecasting how they might progress and informing policy responses.
Modeling is commissioned by governments or may be developed independently by researchers. It has been used to inform policy decisions for human and animal diseases such as SARS, H1N1 swine influenza and foot and mouth disease and is being used to inform action in the campaign to control bovine tuberculosis.
In a study published in PLOS One, researchers analyzed scientific papers, interviews, policies, reports and outcomes of previous infectious disease outbreaks in the U.K. to ascertain the role uncertainties played in previous models and how these were understood by both the designers of the model and the users of the model.
They found that many models provided only cursory reference to the uncertainties of the information and data or the parameters used. Even though the models were uncertain, many still informed action.
Dr. Rob Christley from the university's Institute of Infection & Global Health said, "It is accepted that models will never be able to predict 100% the size, shape or form of an outbreak, and it is recognized that a level of uncertainty always exists in modeling. However, modelers often fear that detailed discussion of this uncertainty will undermine the model in the eyes of policy-makers.
"This study found that the uncertainties and limitations of a model are sometimes hidden and sometimes revealed, and that which occurs is context dependent," Christley added. "While it isn't possible to calculate the level of uncertainty, a better understanding and communication of the model's limitations is needed and could lead to better policy."
Uncertainty can occur at all stages of the modeling process, from weaknesses in the quality and type of data used to assumptions made about the infectious agent itself and about the world in which the disease is circulating, all the way through to the technical aspects of the model.
The research team comprised veterinary scientists and epidemiologists, sociologists, microbiologists and environmental scientists.
The research was undertaken in collaboration with the University of Lancaster and funded by the U.K. Research Councils' Rural Economy & Land Use.