A unified stochastic modelling framework for the spread of... by Martín López García

DISCUSSION MEETING : MATHEMATICAL AND STATISTICAL EXPLORATIONS IN DISEASE MODELLING AND PUBLIC HEALTH
ORGANIZERS : Nagasuma Chandra, Martin Lopez-Garcia, Carmen Molina-Paris and Saumyadipta Pyne
DATE & TIME : 01 July 2019 to 11 July 2019
VENUE : Madhava Lecture Hall, ICTS, Bangalore

In this program, we will discuss current challenges and recent advances in Health and Disease. The aim of the program is to explore different mathematical, statistical and computational approaches to integrate experimental and clinical data, and to discuss how mathematical modeling can help to interpret and integrate experimental data, frame and test hypotheses, and suggest novel experiments allowing for more conclusive and quantitative interpretations of biological, immunological and disease-related processes. Among others, the following problems will be addressed in this program:

The analysis of disease-related processes occurring across different scales: from the genetic, to the cellular, host and population levels.
At the cellular level, the mechanisms by which cells regulate proliferation, death, differentiation, in childhood, adulthood and old age, and how receptor-mediated signaling and intra-cellular receptor trafficking correlate with cellular fate.
The mechanisms that can affect, by maintaining or avoiding, pathogen diversity, from mutation and genetic drift to antibiotic consumption and selective pressure.
The effect of co-infection in pathogen evolution, and the interplay between the immune response and antimicrobial resistance.
New approaches for incorporating existing individual heterogeneities at the population level into mathematical and computational models.
The need for incorporating host immunity and the immune response into mathematical models for the spread of infectious diseases at the population level, as well as recent attempts to incorporate human behavior into these models.
The applicability of Bayesian statistical techniques as a powerful tool for linking stochastic (or deterministic) mathematical and computational models with clinical or experimental data.

This program is also partially supported by The University of Leeds (UOL) and Medical Research Council (MRC).

APPLICATION DEADLINE : 15 April 2019

0:00:00 Start
0:00:09 A unified stochastic modelling framework for the spread of nosocomial infections
0:00:51 Nosocomial infections: a short overview
0:06:34 Simple models only with patients
0:08:04 Models that explicitly incorporate HCWs
0:09:22 Models that include additional agents. E.g., volunteers
0:10:54 Addressing other factors: environmental contamination
0:12:08 Incorporating room configuration
0:13:08 Patient cohorting
0:14:00 Airborne transmission: incorporating airflow dynamics
0:15:45 A general stochastic framework
0:19:30 Model as in Pelupessy et al. (2002)
0:19:58 Model as in Artalejo (2014)
0:20:09 Model as in Wang et al. (2011)
0:20:21 Arrival/Discharge Arrival/Discharge
0:20:27 Hospital ward room configuration from Lopez-Garcia (2016)
0:21:02 Hospital ward contact artwork from Tommme et al.
0:23:30 Epidemics on networks
0:23:50 Equivalent representation in our framework
0:24:56 Summary statistics
0:27:24 Quantities of interest
0:30:33 Quantities of interest: first-step argument
0:33:33 Outline I: Quantities of interest: first-step argument
0:33:49 Onco-haematological unit at UMC in Germany
0:36:13 Airborne transmission: incorporating airflow dynamics
0:42:59 Infection spread dynamics in each zone:
0:43:12 Comparing between ventilation regimes
0:44:12 Summary statistic: number of infections until detection
0:44:45 Detection dominates ventilation
0:45:59 Interplay between ventilation and location of individual starting the outbreak
0:46:26 Decreasing hospital ward infection spread risk might increase risk at specific bays
0:47:17 Acknowledgments
0:47:57 References