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SUMMARY:Epidemic mitigation through testing and optimal urban mobility - M
 uhammad Umar B Niazi\, KTH
DTSTART:20220127T140000Z
DTEND:20220127T150000Z
UID:TALK169061@talks.cam.ac.uk
CONTACT:Xiaodong Cheng
DESCRIPTION:At the onset of an epidemic outbreak\, there are usually no va
 ccines to prevent the disease\; it takes time to develop them. The only re
 sort to keep the infected cases to a manageable level then is to devise ef
 ficient non-pharmaceutical interventions (NPIs) such as testing\, travel r
 estrictions\, social distancing\, and sanitary measures. If implemented co
 rrectly and on time\, such interventions enable the healthcare infrastruct
 ure to function without getting overwhelmed. However\, strict NPI policies
  can be detrimental not only to the economy but also to society. It is\, t
 herefore\, crucial to develop models and methodologies that enable epidemi
 c mitigation with minimum socio-economic consequences. \n\nIn this talk\, 
 I will present our preliminary research efforts on epidemic control. \n\nF
 irst\, I will talk about epidemic suppression through testing. It is well-
 known that testing is the most effective control mechanism for epidemics a
 s it allows the authorities to detect and isolate the infectious cases\, t
 hereby breaking the chains of infection transmission. I will present a sim
 ple method for computing the minimum testing rate required to stop the epi
 demic growth at a given time by using a five compartmental epidemic model.
  This testing rate to "hammer the curve\," also called the Best-Effort Str
 ategy for Testing (BEST)\, is feasible only if adopted early on during the
  epidemic. We will evaluate this policy using the COVID-19 data of France.
 \n\nSecond\, I will present our model of urban human mobility incorporatin
 g the epidemic spread process. At a macroscale\, the model captures daily 
 mobility between residential areas and social destinations like industrial
  areas\, business parks\, schools\, markets\, etc. At a microscale\, i.e.\
 , inside each location\, it captures the epidemic spread process depending
  on the density of people. I will present two optimal control formulations
  aiming to maximize the socio-economic activity in an urban environment wh
 ile keeping the number of active infected cases bounded. The first is call
 ed Optimal Capacity Control (OCC) policy\, which limits the epidemic sprea
 d by reducing the capacities of destinations by some percentage. The secon
 d is called Optimal Schedule Control (OSC) policy\, which limits the epide
 mic spread by reducing the daily business hours of each destination catego
 ry. Finally\, I will introduce our ongoing work on a demonstrator\, Health
 y-Mobility\, which uses real data of Grenoble to devise optimal mobility p
 olicies.\n
LOCATION:Dyson Seminar Room\, Department of Engineering / Online (Zoom)
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