Hierarchical Bayesian modeling for predictive environmental microbiology toward a safe use of human excreta: Systematic review and meta-analysis.

The pathogen concentration in human excreta needs to be managed appropriately, but a predictive approach has yet to be implemented due to a lack of kinetics models for pathogen inactivation that are available under varied environmental conditions. Our goals were to develop inactivation kinetics models of microorganisms applicable under varied environmental conditions of excreta matrices and to identify the appropriate indicators that can be monitored during disinfection processes. We conducted a systematic review targeting previous studies that presented time-course decay of a microorganism and environmental conditions of matrices. Defined as a function of measurable factors including treatment time, pH, temperature, ammonia concentration and moisture content, the kinetic model parameters were statistically estimated using hierarchical Bayesian modeling. The inactivation kinetics models were constructed for Escherichia coli, Salmonella, Enterococcus, Ascaris eggs, bacteriophage MS2, enterobacteria phage phiX174 and adenovirus. The inactivation rates of a microorganism were predicted using the established model. Ascaris eggs were identified as the most tolerant microorganisms, followed by bacteriophage MS2 and Enterococcus. Ammonia concentration, temperature and moisture content were the critical factors for the Ascaris inactivation. Our model predictions coincided with the current WHO guidelines. The developed inactivation kinetics models enable us to predict microbial concentration in excreta matrices under varied environmental conditions, which is essential for microbiological risk management in emerging resource recovery practices from human excreta.