Epidemiological Parameters of Clostridiodes difficileAcquisition and Transmission for Mathematical Modeling: a Systematic Review

Abstract

Abstract With about half a million Clostridiodes difficile infections (CDI) and 30,000 deaths reported annually in the United States, CDI is a major threat to patients, clinicians, and public health. Mathematical models are important to characterize the transmission dynamics, monitor the spread of new cases, and inform more effective control of CDI. However, the usefulness of these mathematical models of C. difficile depends on the accuracy of the epidemiological parameters, such as transmission coefficient and recovery rate. Our study identified and summarized quantitative estimates of important parameters to inform C. difficile mathematical modeling. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Guideline and the comprehensive study protocol is registered with the National Institute for Health Research International Prospective Register of Systematic Reviews (Registration number: CRD42023408483). After searching four major databases and an additional extensive reference search, 21 studies met our eligibility criteria and were further analyzed. Sixteen (76.2%) studies utilized compartmental modeling and 5 (23.8%) adopted an agent-based modeling approach. Also, 15 (71.4%), 3 (14.3%), and 3 (14.3%) studies were stochastic, deterministic and hybrid (both stochastic and deterministic) models respectively. The basic reproduction number (R0) ranged from 0.28 to 2.6. The transmission coefficient was estimated to be from 0.00001 to 0.5, the recovery rate ranged from 0.099 to about 0.21 (per day), the recurrence rate was from 0.12–0.3 (per day), case fatality rates ranged from 0.0000111 to 0.02 per day, and the incubation period ranged from 4 to 18 days. In summary, there was a high heterogeneity among studies and a paucity of mathematical modeling parameters used. We recommend that further research be conducted in this area as more accurate epidemiological parameter estimates are needed to develop mathematical modeling studies for effective CDI control.