Assessment of data quality for drivers of disease emergence

30/01/2023

V. Horigan, L. Kelly, A. Papa, M.P.G. Koopmans, R.S. Sikkema, L.G.H. Koren & E.L. Snary

Drivers are factors which have the potential to directly or indirectly influence the likelihood of (re)-emergence of infectious diseases. It is likely that such emerging infectious diseases (EIDs) rarely occur as a result of only one driver but rather a network of sub-drivers (factors that can influence a driver) which provide conditions that allow for a pathogen to (re)-emerge and become established. Data on sub-drivers have, therefore, been used by modellers to identify hotspots where EIDs may next occur or, which sub-drivers have the greatest influence on the likelihood of them occurring. To minimise error and bias when modelling how sub-drivers interact and to therefore predict the likelihood of the emergence of an EID, it is necessary to have good quality data describing these sub-drivers. This study assessed the quality of available data against various criteria for sub-drivers of West Nile virus as a case study. The data were found to have varying quality with regards to fulfilling the criteria. The characteristics with the lowest scores were completeness (where data are available such that all requirements for the model are fulfilled) and consistency. These are important characteristics as an incomplete dataset could potentially lead to erroneous conclusions being drawn from modelling studies. Thus, the availability of good quality data is essential to reduce uncertainty when estimating the likelihood of where EID outbreaks may occur and to assist in identifying the points in the risk pathway where preventative measures may be taken.

More informations

Issue number
2
Volume
41