The pursuit of good quality FIO data does need to be complemented with continued efforts to understand the differential behaviour of FIOs and pathogens , and the associated implications for modelling the efficacy of mitigations designed to reduce microbial watercourse pollution . Thus , while we have focussed our discussion on FIO modelling it is important for resource managers and policy makers to recognise that FIO models do not necessarily inform on specific pathogen behaviour . However , the nexus of FIO modelling and risk assessment is gaining significant momentum and the use of quantitative microbial risk assessment ( QMRA ) offers another modelling framework with which to estimate human infection risk from exposure to pathogen contaminated waters ( Soller et al . , 2015 ) . Benefits of FIO modelling will be further enhanced if the research community promotes a consistent message to model developers calling for any future model performance criteria to include , as a minimum , an uncertainty analysis of modelled predictions . Thus , the pedigree of uncertainty associated with model outputs needs to be transparent and included as standard in the reporting of any FIO modelling . There are mechanisms that can help deliver this drive for greater awareness of model uncertainty . For example , there are more frequent opportunities to promote knowledge exchange ( KE ) across the science - policy interface enabling scientists and policy - makers to debate the role and impact of model uncertainty . Research Councils in the UK , and in other nations too , are increasingly funding policy - placement opportunities whereby scientists are embedded within government departments thus providing an important route for aiding KE . This should be encouraged in the general field of environmental decision - making using models . In addition , those responsible for funding research could improve model transparency by ensuring that projects with a modelling component fulfil basic uncertainty analysis criteria . Indeed , several academic journals , whose remit includes environmental modelling , now stipulate uncertainty analysis of any modelling as a prerequisite for publication . This template should also be adopted at the funding stage for research projects .