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gsgs

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spread modeling
« on: January 31, 2020, 03:00:16 pm »
In our baseline scenario, we estimated that the basic reproductive number for 2019-nCoV was 2·68 (95% CrI 2·47–2·86) and that 75 815 individuals (95% CrI 37 304–130 330) have been infected in Wuhan as of Jan 25, 2020. The epidemic doubling time was 6·4 days (95% CrI 5·8–7·1). We estimated that in the baseline scenario, Chongqing, Beijing, Shanghai, Guangzhou, and Shenzhen had imported 461 (95% CrI 227–805), 113 (57–193), 98 (49–168), 111 (56–191), and 80 (40–139) infections from Wuhan, respectively. If the transmissibility of 2019-nCoV were similar everywhere domestically and over time, we inferred that epidemics are already growing exponentially in multiple major cities of China with a lag time behind the Wuhan outbreak of about 1–2 weeks.

https://flutrackers.com/forum/forum/-2019-ncov-new-coronavirus/-2019-ncov-studies-research-academia/826405-lancet-nowcasting-and-forecasting-the-potential-domestic-and-international-spread-of-the-2019-ncov-outbreak-originating-in-wuhan-china-a-modelling-study

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the Chinese government, the Chinese experts, the WHO director general
 were confident that they could stop the spread.
This looks unlikely considering the modeling studies, current ones and all those
about H5N1 in 2005-2009 and the experiences from influenza pandemics.

Did they really believe this ?
Did they just say this to avoid panic, to keep the social order and
economic structures ?  Sacrificing credibility for a gain of a few days or weeks.
« Last Edit: January 31, 2020, 03:15:20 pm by gsgs »
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epsilon

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Re: spread modeling
« Reply #1 on: February 07, 2020, 11:56:01 am »
Quote
Did they really believe this ?
Did they just say this to avoid panic, to keep the social order and
economic structures ?  Sacrificing credibility for a gain of a few days or weeks.

Those are very good questions that I also often ask myself when reading such official/media statements. But it is even the experts that often underplay and trivialise when interviewed. This is not to say that alarmism and panic is better. But the amount of alertness to a threat should be not only proportional to its risk but also to the amount of worst case damage it could do (and for pandemics, this is very, very high damage potential)

I think that high CFR pandemic respiratory viruses are the most under estimated threat to our society whereas other threats like nuclear power, natural disasters, terrorism, wars are consistently over-represented.

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epsilon

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Re: spread modeling
« Reply #2 on: February 10, 2020, 01:56:48 pm »
New study puts case doubling rate at 2.9 days (half than previous estimates !)

https://www.medrxiv.org/content/10.1101/2020.02.06.20020941v1

Quote
the early outbreak of 2019-nCoV could have been spreading much faster in Wuhan than previous estimates.

This is very concerning (but is consistent with R0=3 and the recently published very short incubation period of 3 days).

This means not only does the epidemic spread twice as fast as presumed, but it means that spread is exponentially twice as fast.

Example: increase in number of cases after 4 weeks:

previous estimate (case doubling 7 days) :   16 fold increase  (2^4)
New estimate (doubling every 3 days) :  1.000 fold increase (2^10)

This explosive growth perfectly explains why Wuhan got overwhelmed so quickly.

If we continue to understimate this virus in our countries outside China we are prone to the same playbook.






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gsgs

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Re: spread modeling
« Reply #3 on: February 11, 2020, 12:44:54 am »
obviously it has changed

---------------------------------------

7.2 days here:
https://github.com/blab/ncov-phylodynamics

 > We estimate an exponential doubling time of 7.2 (95% CI 5.0-12.9) days. We arrive at a median
 > estimate of the total cumulative number of worldwide infections as of Feb 8, 2020, of 55,800
 > with a 95% uncertainty interval of 17,500 to 194,400. Importantly, this approach uses genome
 > data from local and international cases and does not rely on case reporting within China.
« Last Edit: February 11, 2020, 02:05:33 am by gsgs »
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