Mathematical models of infection transmission in healthcare settings: recent advances from the use of network structured data

Abstract

To respond to the objectives of creating improved infection prevention and control measures and better understanding of healthcare-associated infections transmission dynamics, further innovations in data collection and parameter estimation in modeling is required.

Host contact dynamics shapes richness and dominance of pathogen strains

“…This behavior can be 293 explained by the theoretical results and can be attributed essentially to the effect of 294 contact heterogeneities, considering that the community structure does not have 295 appreciable effects for this network, as discussed above. The importance of accounting 296 for host contacts and hospital organization in the assessment of bacterial spread and 297 designing intervention has been recognized by several studies [16,[28][29][30][31]61]. Here we 298 show that this element may be critical also for understanding the population ecology of 299 the bacterium.…”

“…164 We derive an approximate formula for T pers considering an emerging strain We conclude by analyzing the real-case example of the S. aureus spread in a hospital 178 setting [10,59]. We used close-proximity-interaction (CPI) data recorded in a long-term 179 health-care facility during 4 months by the i-Bird study [16,28,31]. These describe a 180 high-resolution dynamical network, whose complex structure reflects the hospital 181 organization, the subdivision in wards and the admission and discharge of patients [60].…”

“…It is likely 28 that, since they impact the spread of single pathogens, the same characteristics could 29 affect the dynamics in multi-strain populations. It was shown, indeed, that network 30 structure impacts transmission with two interacting strains [39][40][41][42][43][44][45][46], the evolution of 31 epidemiological traits [47][48][49] and the effect of cross-immunity [50,51]. Yet in these 32 cases, complex biological mechanisms -such as mutation, variations in transmissibility 33 and infectious period, cross immunity -were used to differentiate between pathogens, 34 thereby making the role of network characteristics difficult to assess in its own right.…”

“…When transmissibility is above the epidemic threshold we showed the 254 existence of an optimal value of the length of stay that maximizes strain richness as a 255 result of the interplay between two competing timescales, namely the typical 256 inter-introduction time and the average persistence time of a strain. This provides 257 insights for the spread of bacterial infections in transmission settings, such as hospitals 258 or farms, that are of particular relevance for the spread of antimicrobial resistance and 259 that are characterized by a rapid host turnover [15,31,67]. For the case of hospitals, for 260 instance, they suggest that variations in patients' length of stay, as induced by a change 261 of policy, could have appreciable effects on the population structure of nosocomial 262 pathogens.…”

“…Role of hosts in the network (e.g. 307 patients vs. health-care workers), and heterogeneities in health conditions, antibiotic 308 treatment and hygiene practices are also known to affect duration of carriage and 309 chance of transmission [16,28,31,61]. Eventually, we must consider that the comparison 310 of model output with carriage data is also affected by the limitation of the dataset itself, 311 already described in [16].…”