Author: Lorenzo Pellis; Francesca Scarabel; Helena B Stage; Christopher E Overton; Lauren H K Chappell; Katrina A Lythgoe; Elizabeth Fearon; Emma Bennett; Jacob Curran-Sebastian; Rajenki Das; Martyn Fyles; Hugo Lewkowicz; Xiaoxi Pang; Bindu Vekaria; Luke Webb; Thomas A House; Ian Hall
Title: Challenges in control of Covid-19: short doubling time and long delay to effect of interventions Document date: 2020_4_15
ID: k5q07y4b_50
Snippet: Delay distributions describe the time delay between two events. To understand how long until the impact of an intervention may be observed, we need to understand the delay between infection and symptom onset (the incubation period), and the delay from onset to hospitalisation. A difficulty with estimating delay distributions during an outbreak is that events are only observed if they occur before the final sampling date. Since delay distributions.....
Document: Delay distributions describe the time delay between two events. To understand how long until the impact of an intervention may be observed, we need to understand the delay between infection and symptom onset (the incubation period), and the delay from onset to hospitalisation. A difficulty with estimating delay distributions during an outbreak is that events are only observed if they occur before the final sampling date. Since delay distributions depend on the time between two events, if the first event occurs near to the end of the sampling window, it will only be observed if the delay to the second event is short. This causes an over-expression of short delays towards the end of the sampling window, which is exacerbated by the exponential growth of the epidemic. Therefore, we need to account for this growth and truncation within our model.
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