Snapshot of Model Forecasting
May 14, 2020
IHME Model: 134,927 deaths nationwide by June 24, 2020
LANL Model: 117,000 deaths nationwide by June 24, 2020
Imperial College of London: US daily death rate is estimated at 2,000 until May 18, 2020
Columbia University: medium hospital surge in the upcoming 42-days in Baltimore City, MD
Megan Hunt and Katharine Clark
Models have influenced key policy decisions from the onset of the COVID-19 pandemic.
These models are used to estimate infections rates and mortality rates from COVID-19. Estimates from these different models may vary widely. Part of the reason is that different input values are used in the models, and modelers are making different assumptions.
These include assumptions about the infection fatality rate (% of people infected with COVID-19 who will die), the case fatality rate (% of people who will die of those who tested positive), incubation period of the disease (the length of time it takes for someone who has been exposed to the virus to show symptoms), transmissibility (how likely it is you will be infected through contact with someone who has the disease), and severity of infections.
There are a variety of models being used. Two influential models, the IHME’s model coming out of the University of Washington and the Imperial College of London’s NPIM, have dominated the national conversation. However, the White House’s coronavirus task force has not released a singular model to guide the national response to the pandemic. There are also notable models from Los Alamos National Laboratory , Northwestern, and Columbia University . At a state level, state leaders are using a “weather forecasting” approach combining several models, often using the IHME’s model, to better predict outcomes.
As more data emerges from the COVID-19 pandemic, especially with regard to virus transmissibility, models are updating in real time to reflect these changes. Social distancing, a key factor impacting infection rates in each state, has become a prominent component of the core assumptions underlying model forecasts. This appreciation for the ability of social distancing guidelines to ‘flatten the curve’ initially led some models, like the IHME, to project decreased total deaths due to COVID-19.
However, in light of loosening restrictions across the majority of the United States, models have revised their forecasts to predict a significant increase in mortality rates as new cases are expected to spread. At the same time that state and federal leadership argue that the pandemic is coming under control, models, incorporating better data, suggest the opposite – making their increasing accuracy that much more important to inform policy decision-making.
(as of May 15, 2020)
The Imperial College London model forecasts the number of deaths in the upcoming week by country and analyzes case ascertainment per country.
The NPIM projected 2.2 million US deaths might occur in an “unmitigated” scenario, whereby transmission suppression policies were not instituted to slow or prevent spread of the virus. This initial estimate, published on March 17th, was among the first distributed to the White House, prompting the US government to take drastic actions, such as extensive travel restrictions, to mitigate the spread of the virus.
It is a transmission-based SIR model. Like other SIR models, this model examines individuals/groups of individuals — e.g. school children, household sizes, and workplace distributions — according to population density data and census data, as well as their set of contacts, to simulate transmission rates in a community. This model examines the number of projected US cases and deaths. The model makes forecasts over the next year until April 2021.
The model’s main objective: Forecast the number of deaths in the week ahead for countries with active transmission.
The secondary objective: to analyze case ascertainment per country.
The key assumptions behind the model are:
The Imperial College London Model uses 3 different models, each of which deploys complex mathematical modeling with differing assumptions about how transmissibility changes throughout the time window of interest. These 3 models that estimate transmissibility are being averaged to create a final model for each country.
While this model integrates significant levels of population data, its limitations arise from the assumptions the model must make about the transmissibility of the virus. For example, this model is based on the transmissibility of the virus at the contraction of the virus for those who have reportedly died. Thus, there is an inherent lag in the transmissibility data due to the time it takes for death data to be reported. Furthermore, the true infectivity rate of the virus in symptomatic or asymptomatic is unknown. Even small miscalculations of the infectivity rate in the model can cause drastic changes in modeling predictions. Infection rates can still represent an estimation, based on testing capabilities in that country, of the virus’s true characteristics.
A previous version of the model (as of March 16, 2020):
A previous version of the model was somewhat different, and included assumptions that are no longer used in the current version. It examined the number of projected US cases and deaths across a range of various mitigation and suppression scenarios. It made forecasts over the next year until April 2021.
The assumptions in the prior version of the model were:
The Imperial College London previously integrated different Non-Pharmaceutical Interventions (NPI) into their modeling to determine the best policy strategy to control COVID-19. These NPI are no longer included in the model. Rather, the model relies on current data and transmissibility estimates.
The previous version of the model included these Non-Pharmaceutical Interventions (descriptions in Table 2, from published model):
Relative to each NPI intervention, the model uses the described assumptions to provide the following output predictions: