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Over here at the COVID Forecast Hub, our team has curated forecasts of #COVID19 deaths in the US from 15 research groups. Our new national-level ensemble combines 7 models and predicts that we will see ~103K deaths by May 30 (80% PI: 93K-119K).
reichlab.io/covid19-foreca…
1/8
Interestingly, the long-term forecasts for cumulative #COVID19 deaths show a bit more agreement this week than they have in previous weeks. See below for 7 models' US forecasts for last week and 4 from the previous week.)

Explore the raw data yourself:
github.com/reichlab/covid…
2/
There still are important ways that these models agree and disagree. The work of @jayboice and @ryanabest from @FiveThirtyEight does a nice job picking apart the specific assumptions of a few of these models.

(They are using our dataset.)

projects.fivethirtyeight.com/covid-forecast…

3/
Today marks the 3rd week that we've released ensemble forecasts of cumulative #COVID19 deaths. For now we are using an equal-weighted average of available models. We are quickly moving towards estimating weights for each model as we learn more about their accuracy. Stay tuned.
4/
As a CDC-funded Influenza Forecasting Center of Excellence, my team has been working very closely with colleagues from @CDCgov. Thanks to their investment in collaborative forecasting over past years, we have not had to start this effort from scratch.
cdc.gov/flu/weekly/flu…
5/
The data from the COVID Forecast Hub are being shared directly with @CDCgov and will appear on their public #COVID19 Forecasting page later this week (hopefully Wednesday, May 6).
cdc.gov/coronavirus/20…
6/
The COVID Forecast Hub has forecasts for all states and territories of the US. You can explore specific results on our interactive viz:
reichlab.io/covid19-foreca…

We added new models and teams this week, and are looking forward to growing this collaborative forecasting effort.
7/
Many thanks to the 15 teams who have submitted their models and made their data public (partial list): @meyerslab @covid_analytics @youyanggu @TAlexPerkins @alexvespi @sdelvall @sangeeta0312 @JHIDDynamics @tomcm39 @IHME_UW @SenPei_CU @QuanquanGu
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Nicholas G. Reich
@reichlab
We are continuing to curate forecasts of #COVID19 deaths in the US from multiple teams. Our current national-level ensemble combines three models and forecasts ~90K cumulative deaths by May 23 (80% PI: 82K-104K).
reichlab.io/covid19-foreca…
1/6
In general, the message of these models seems to be that #COVID19 is going to continue to exact a large toll in the US, with mortality by late May or early June likely to exceed even the upper-bound estimates of deaths caused by any recent flu season.
cdc.gov/flu/about/burd…
2/
The support and vision from our collaborators at @CDCgov have been really crucial as we have built this system over the past few weeks. They are using our data for the weekly summaries hosted at their website on #COVID19 forecasting:
cdc.gov/coronavirus/20…
3/
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Nicholas G. Reich
@reichlab
In both the near term (the upcoming weeks) and long term (months), models are going to continue to play an important role in setting policy as we persevere in the fight against #COVID19. 1/13
Ensuring that modelers work together to deliver the best data to #COVID19 decision-makers is critical. With this in mind, my group has started to replicate a collaborative framework that has been successful in predicting seasonal flu outbreaks. 2/
reichlab.io/flusightnetwork
To better coordinate #COVID19 forecasting efforts we have:

(i) built a centralized data repo that standardizes forecast data from many models into a common format,
github.com/reichlab/covid…
(ii) created an ensemble model,
(iii) built an interactive viz
reichlab.io/covid19-foreca…

3/
Read 13 tweets
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Nicholas G. Reich
@reichlab
For the last two months, @cmyeaton and I have been leading a weekly analysis of publicly available influenza-like illness (ILI) data and flu test results from the @CDCFlu and state health departments.

1/
The idea here is that if visits for ILI increase at the same time that flu positivity rates are declining, this might be a sign that there is another pathogen, perhaps #COVID19, causing influenza-like illness.

2/
The challenge is that these data are very sensitive to external factors. If there are changes in care-seeking behavior, these could show up as abnormal patterns in the ILI data. Similarly if there are changes in protocols around who is tested for flu.
3/
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