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covid19's Issues

Forsendur um smit í elstu hópunum

Sæll Brynjólfur Gauti, og takk fyrir þessa vinnu. Ég er aðallega að velta fyrir mér forsendum í elstu aldurshópunum. Elstu aldurshóparnir eru sem kunnugt er í langmestri hættu, bæði líklegastir til að þurfa innlögn á sjúkrahús, gjörgæslu, öndunarvélar og svo er dánartíðni þar miklu hærri en í öðrum hópum. Mikið veltur því á að forsendur þar séu réttar og a.m.k. að reiknað sé með rúmum öryggismörkum í þeim hópi. Í raungögnunum í líkaninu þínu er enginn í 80+ aldurshópnum sem hefur smitast (reyndar kominn einn inn í raungögnin í dag) og fáir í 70-79. Þú "bætir upp" skortinn í 80+ með því að leggja eitt tilfelli við hvern aldurshóp. Það er forsenda sem þarf að undirbyggja betur fyrir áframhaldandi líkanasmíð og spár. Það mætti til dæmis skoða smitprósentur í þessum aldurshópum í öðrum löndum svipuðum Íslandi með stærri þýði. Einnig væri hægt að gera líkan sem byggir á aldursdreifingu landsmanna og áætlaðri smithættu í hverjum aldurshópi. En, allavega, mig grunar að um leið og forsendan um 0,29% smita í 80+ hópnum hækkar, sértu fljótlega kominn í mun hærri tölur um spítalavist og dauðsföll, því miður. Eða er ég á villigötum með þessar spekúleringar?

Suggestions for Landlaeknir google sheet

I'm creating a parser for Iceland in this SRI model https://neherlab.org/covid19/.
I'm using the Landlaeknir Google sheet. It seem to be the most canonical publicly available data on the outbreak in Iceland. Thank you for keeping the data open and accessible!

I've had to work around that the date format in "Smit" (mm-dd-YYYY) and "Spitali" (YYYY-mm-dd) are different. Imo YYYY-mm-dd is preferred since it can be sorted chronologically.

What is the status of the Landlaeknir doc? Does landlæknir send you daily updates and you manually fill them into the doc?

How flexible is this doc to change? Could there be a possibility to track ObservedRecovered (batnað)?

Thanks a lot for the great work you folks are doing.

SIR-type model for Iceland?

As I mentioned before, with Iceland still being in an early stage of the outbreak, why rely only on logistic growth models, which tend to underestimate total infections at such early points (before the inflection point is reached) - this is for example mentioned in the Wu et al. paper? Countries in Europe are still in the same situation, and other countries that see a decline implemented way stronger measures and were in different situations, so not sure how much the model will improve if it is calibrated on such countries?

Why not add a simple SIR-type model adapted for Iceland? This should be quite easy to implement - there is already a number of covid-19 specific SIR models from various labs to be used online (https://neherlab.org/covid19/, see screenshots for an example of an Iceland-adapted version at http://covid.scicorner.com/) which are mostly also available on GitHub...

localhost_3000__%7B%22population%22%3A%7B%22populationServed%22%3A364200%2C%22country%22%3A%22Iceland%22%2C%22hospitalBeds%22%3A1169%2C%22ICUBeds%22%3A29%2C%22suspectedCasesToday%22%3A40%2C%22importsPerDay%22%3A0%

Normalized growth?

Hi, will write in English for everyone to understand and comment.
Im not much into reading R code, so I just wanted to ask.
Does the model take any account in nomalising the confirmed case numbers by the number measured, or does it simply rely on confirmed cases irrespective of sampling?

I can imagine that simply following case numbers could be OK - I have no experience with such modeling. Especially as the sampling is highly complex and correlated - so maybe some reason to ignore sampling and sampling methods.

However, when I look at the latest numbers on covid.is, the confirmed case rate seems to be plateauing, maybe reaching 1200 cases if things start turning around now in next few days.

However the normalized growth rate is still growing significantly. I.e. I mean the rate of confirmed cases divided by number measured in each batch. This looks a little disconcerting to the untrained eye, maybe indicating a very bad outlook.

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