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How Risky is it to Attend a Group Event during Covid Spread? Editor's Pick

How risky is it to attend a group event (of any size) while COVID-19 is still spreading? This tool from Georgia Tech lets you check any county in the US. The risk level is the estimated chance (0-100%) that at least 1 COVID-19 positive individual will be present at an event in a county, given the size of the event. You can check any group size from as few as 10 people, to 10,000 people. Adjust the default level of 100. The tool uses real-time COVID19 data from the COVID Tracking Project. County-level risk assessment was added to the COVID-19 Event Risk Assessment Planning Tool in July 2020.

What’s your local risk of Covid and Flu, by Zip Code? Editor's Pick

Put in your zip code to see what the latest risk is for COVID and Flu. Site by Kinsa HealthWeather uses Johns Hopkins data and other modeling. Easy to use. Updated nightly.

CDC Forecast of Covid-19 Deaths (ensemble models)

CDC ensemble model forecast of Covid-19 deaths. It examines up to 40 national forecasts and models each week and makes a range forecast of deaths. State-specific forecasts are also available at this site. Details are provided on the models. Some models provide forecasts of new deaths only. The forecasts make different assumptions about social distancing measures. Presumably, and hopefully, vaccination rates are also incorporated into the models. However, these parameters were not specifically mentioned in the August 23 ensemble forecast. CDC stands for the US Centers for Disease Control and Prevention.

Covid Act Now Model and State Trends – Georgetown, Stanford

A model by Covid Act Now, supported by Georgetown and Stanford universities, provides trends on daily cases per 100,000, infection rates (how fast the COVID infection is spreading), the positive test rate, and the percent of people who are vaccinated. In each state, there are likely some county-specific data to help understand the direction of their forecast. Easy to read.

Sixteen Models and Forecasts Compared for COVID-19 Deaths

This website compares ten to sixteen different models for deaths due to COVID-19 in the coronavirus pandemic. Forecasts for both the United States, and each state in the US are shown for the next 3 to 6 weeks. Models usually included: University of Arizona, UCLA, MIT, University of Massachusetts, Columbia University, Georgia Tech, Northeastern University, Los Alamos (the most pessimistic), Iowa State, IHME, and Johns Hopkins. The range of possible deaths is also shown for each model. Look at one forecast at a time, or all models at once. Updated article (which originally had eight models), is published at fivethirtyeight.com.

What Does the Future Look Like in My State? IHME Univ of Washington Model

The University of Washington IHME Model shows past trends and what the future might look like for COVID-19 deaths and hospital use in each state. In updated forecasts at the end of August, another 100,000 in total deaths are projected by November 30. The IHME site identifies the positive impact that universal mask-wearing (95%) could have in saving lives. As of August 16, the observed rate is down to about 34%. IHME’s analysis of the scientific evidence on cloth face coverings for the general public is that it can reduce risk by about 1/3 (30 percent). The rise of the new, more contagious variants could make the infection rates and deaths higher. Vaccinations are only expected to go up by 5 points, from 52% fully vaccinated end of August to 57% fully vaccinated by November 30.

Estimates are prepared by the Institute for Health Metrics and Evaluation (IHME), part of UW. The prediction model has evolved over the past year. Vaccination rate assumptions have now been incorporated into the model. Estimated and confirmed infections, mask-wearing assumptions, cell phone mobility data, and easing of restrictions have all been built into the model. The COVID-19 projection model is still subject to change. International estimates are also included in this interactive modeling tool. Deaths per 100,000 population aid in making comparisons among countries.

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