This map visualizes where cases of the 2019 novel coronavirus (2019-nCoV) are worldwide. It provides the latest figures for the number of confirmed cases, cases by country or region, and deaths.
This model uses machine learning techniques on top of a classic infectious disease model to make COVID-19 infections and deaths projections for the US, all 50 US states, and more than 70 countries. The countries our projections cover encompass 6.4 billion people and account for >97% of all global COVID-19 deaths.
Our infections estimate includes all infected individuals of the SARS-CoV-2 virus, not just those tested. The estimated number of COVID cases in the US is about 5x higher than the reported cases.
Estimated hospital resource use, ICU beds needed, beds needed, ventilators needed, deaths. The Institute for Health Metrics and Evaluation (IHME) is an independent global health research center at the University of Washington
This is a statistical growth model. It does not make assumptions about how the virus is spreading. Instead, it forecasts the rate at which the virus will continue to spread. Inherently, this paradigm then allows for fewer assumptions to be made than other COVID-19 models. Consequently, this model is less susceptible to mistakes due to core assumptions. These statistical growth models are sensitive to changes in virus conditions, and such changes may be caused by forces independent of the virus.
The UCLA model is unique because it doesn’t simply fit the current curve, which is based only on reported cases. Rather, it infers the number of untested and unreported cases from the model’s data analysis and uses those inferences to predict how quickly the disease will spread. This is called an “epidemic model” because it takes into account the various factors that affect the rate of disease spread.
Case Count and Testing Rates
Surveillance Sites
COVID-19 Diagnosis and Test Codes
Provider Capacity and Patient Needs
Identifying At-Risk Populations
Policy Actions and Preventing Spread of the Virus