Engineering control of infectious disease: modelling ventilation flows and microbes

Transcription

1 School of Civil Engineering FACULTY OF ENGINEERING Engineering control of infectious disease: modelling ventilation flows and microbes Professor Cath Noakes CEng, FIHEEM, FIMechE UKIEG Conference 2018

2 Deadly Diseases ac.uk/mrcoutbreaks/diseaseareas/tuberculosis/ Tuberculosis in million developed TB and 1.3 million died (WHO Tuberculosis Report 2013)

3 Evidence for TB transmission Suspected airborne disease Wells 1934 proposed concept of a droplet nuclei Riley and Wells TB Baltimore study Ward air extracted and passed through guinea pig houses 134 infected over 4 years Proof that TB is airborne Also showed that UV air disinfection is a potential control

4 A more familiar enemy olumns/wellness/trut h-about-the-flu/ mostly occurs in close proximity to the index patient. Hence it is unlikely to be airborne. Brankston et al. Lancet (2007) There is some evidence for airborne transmission of influenza infection. Hence airborne precaution such as N95 masks need to be used. Tellier, EID (2006), NAS (2009)

5 Communicable diseases Disease Transmission mode Ro Measles Airborne Pertussis Airborne/droplet Diptheria Saliva 6-7 Smallpox Social contact 5-10 Polio Faecal-oral 5-7 Rubella Airborne/droplet 5-7 Mumps Airborne/droplet 4-7 HIV/AIDS Sexual contact 2-6 SARs Airborne/droplet 2-6 Influenza Airborne/droplet 2-15 Over 6% of patients in the UK get a HAI (PHE 2012) 10-20% are thought to have an airborne component

6 Airborne Infection Pathways Source characteristics Exposure CDC, USA Transport and deposition Tang J et al. J Hosp Infect 2006; 64: Disease characteristics Human characteristics Fluid Dynamics

7 Source - Particle size Coughing Respiratory droplets Xie X et al. J. R. Soc. Interface 2009;6:S703-S714

8 Particle dispersion 10µm 799s 10 m/s 800µm 0.56s 1000µm 0.32s 50µm 16.8s 100µm 4.69s 200µm 1.69s 400µm 0.88s 600µm 0.72s

9 Microorganism Survival and Exposure Biology/chap10_3.htm

10 The Need for Models Field data essential but very challenging and complex Measure source and consequence what happens between the two? Modelling approaches can help Explain connections, mechanisms, variability and influences Design better field studies predict outcomes Test specific context climate, location, layout Methods Numerical Infection models, energy models, zonal models, Computational Fluid Dynamics, economic models Experimental real world, scale models, chamber environments

11 Human challenge model EMIT study influenza transmission

12 Tracers 1.1 Normal concentration Normalized CO 2 concentration Elapsed time (min) 0.5 um 1 um 3 um 5 um Time, min Run 1 Run 2 Run 3 CO2 model

13 Bioaerosol chamber Normalized S. aureus concentration Time, min Run 1 Run 2 Run 3 Bioaerosol model

14 Scale Models

15 Numerical modelling Noakes, C.J., Sleigh, P.A., Escombe, A.R., Beggs, C.B. (2006) Use of CFD Analysis in Modifying a TB Ward in Lima, Peru. Indoor and Built Environment, 15(1), 41-47

16 Engineering Control Break the chain of transmission reduce/prevent source-susceptible route Ventilation: Dilution, Air distribution, Pressure controls, Extraction, temperature and humidity Technology: Filtration, air cleaning, decontamination, surface technology Human behaviour: SMART buildings, prompt actions, prevent access

17 Multi-occupant spaces Many hospital wards in UK are naturally ventilated with complex airflows and multiple openings Multi-bed wards still common 2 to 8 people Infection risks are complex, variable, multi-route Challenge of privacy, comfort, energy

18 Measuring flows Weather station Anemometer CO2-sensors

19 Exposure Scenarios Windows opened or closed Extract fans in partitioned ward P1 P3 Release a pulse of CO2 from a patient location Measure CO2 at source and 4 other locations throughout Source HWc HWs P2 Measure outdoor wind conditions P1 P3 Calculate cumulative exposure (dose) over a fixed time period Source HWc P2 HWs

20 Exposure - Open Ward 9.0 Cross-Ventilation Windows Closed P1 P3 6.0 Low Wind Source HWc 3.0 HWs Ward air is well mixed Shut the windows = increase the risk P2 0.0 HW S HW C P 1 P 2 P 3 (a) (

21 Exposure - Partitioned Ward 9.0 Partitioned Ward Cross-Ventilation P1 P3 Windows Closed 6.0 Low Wind Mechanical Ventilation Source HWc 3.0 P2 HWs Lower risk for beds to side (c) 0.0 HW S HW C P 1 P 2 P 3 Sometimes higher risk close/opposite source Extract ventilation mitigates window closure Gilkeson CA, Camargo-Valero MA, Pickin LE, Noakes CJ (2013) Measurement of Ventilation and Airborne Infection Risk in Large Naturally Ventilated Hospital Wards Building and Environment, 65:35-48

22 Air Change Rate SUPPL Y ACH Short CA; Noakes CJ; Gilkeson CA; Fair A (2014) Functional recovery of a resilient hospital type.building Research & Information, vol. 42, pp

23 Linking air and surfaces How does environmental (aerial) deposition translate to contact transmission risk? Is there a difference with ward design? Need to consider: Airflow distribution Pathogen source and resulting deposition Healthcare worker activity and hand hygiene Risk of hand contamination on touching surfaces

24 Deposition Single Room 2 Normalised CFU Table Sink Bed Chair Exp CFD K-e RNG CFD RSM King M-F, Noakes CJ, Sleigh PA, Camargo-Valero MA (2013) Bioaerosol deposition in single and two-bed hospital rooms: A numerical and experimental study. Building and Environment, 59:

25 Modelling pathogen (Y) pick-up λ n V β A V= germs / cm 2 on surface λ= transfer efficiency from surface (%) β= transfer efficiency to surface (%) A= Contact area with surface (cm 2 ) h= Hand cleansing efficiency (%)

26 Who touches what, when? 27

27 Single room vs 4-bed room patients in single rooms 4 patients in a multi-bed ward King MF, Noakes CJ, Sleigh PA (2015) Modelling environmental contamination in hospital single and four-bed rooms, Indoor Air

28 Learning from Models Flow modelling approaches can characterise influence of the environment on exposure Mechanisms and influencing factors Transient effects and interactions Role of air distribution and impact of technology solutions Risk models link exposure to risk Comparison of solutions Link engineering design to human impacts Cost-benefit

29 Still Many Challenges.. Uncertainty and variability Probability based approaches (eg. monte-carlo, QMRA) Sources Location, size distribution and frequency of sources Persistence of microorganisms in the environment survival, evaporation, changes to state Dose-response Limited data on infectious dose and exposure route Variability between people susceptibility and infectiousness Real World evidence Comparison to models/proxy parameters Cross-disciplinary challenge funding!!

30 With thanks to... University of Leeds colleagues & students past and present Collaborators and colleagues in Universities, Government, Healthcare and Industry Thank You Questions?