text: ,Abbr.,Team name,Methods,Home
  0,JHU_IDD-CovidSP,Johns Hopkins ID Dynamics COVID-19 Working Group,"This is a county-level metapopulation model with stochastic SEIR disease  dynamics. This model incorporates uncertainty in epidemiological parameters  and the effectiveness of state-wide intervention policies on social  distancing and stay-at-home orders. Infections are seeded stochastically  with roughly 10 times the number of confirmed cases in the first five days  of a county epidemic and R0 and the duration of the infectious period were  drawn stochastically for each county and simulation. Disease follows SEIR dynamics and county commuting data modulates the spread of disease within counties. 
  Stay-at-home orders and  subsequent social distancing interventions are updated at the state-level according to recent policy documents. States that did not implement stay-at-home orders had a single social distancing intervention that began on the day of the first reported order and endured  for the whole simulation period. Intervention effects are inferred where  possible.
  To model deaths and hospitalizations in the population, we incorporated  realistic but fixed time delays from infection to symptom onset to hospitalization to  ICU to ventilator use to death and used age-specific demographics to perform  a county-level age standardization of health outcome risk. Currently, we  assume a 0.25% infection fatality rate and a 7:1 ratio of incident  hospitalizations to deaths. 
  For each county, we estimate the seeding time and amount, the baseline  reproductive number, the case confirmation to infection ratio, and the  effectiveness of interventions (from closing to phased reopenings). We use an  MCMC-like inference procedure that calibrates model outputs to weekly county  aggregations of incident confirmed cases and deaths as reported by USA  Facts. For the inference of the baseline reproductive number and the case  confirmation to infection ratio, hierarchical grouping terms enable sharing  of strength among counties in the same state.
  Our procedure forecasts incident and cumulative deaths by incorporating the last reported data point (pre-forecast date) from USA Facts.
  The estimates reported by this model incorporate uncertainty in both epidemiological parameters (e.g., R0, infectious period, time delays to health outcomes) and the effectiveness of state-wide intervention policies  on social distancing, stay-at-home orders, and phased reopenings. We submit a single calibration to forecast deaths, cases, and hospitalizations.",https://github.com/HopkinsIDD/COVIDScenarioPipeline
  1,IowaStateLW-STEM,Iowa State - Lily Wang's Research Group,"We develop a nonparametric space-time disease transmission model for the epidemic data, and to study the spatial-temporal pattern in the spread of COVID-19 at the county level. The proposed methodology can be used to dissect the spatial structure and dynamics of spread, as well as to forecast how this outbreak may unfold through time and space in the future. The projections assume that the data used is reliable, the future will continue to follow the current pattern, and current interventions will remain the same till the end of forecasting period.",https://covid19.stat.iastate.edu
  2,Covid19Sim-Simulator,COVID-19 Simulator,"The COVID-19 Simulator uses a validated compartment model to simulate the trajectory of COVID-19 at the state level from March 15, 2020 to August 31, 2020 in the United States. Utilizing the most recent data for each state, the COVID-19 Simulator considers state-specific disease spread dynamics. Specifically, to reproduce the observed trends and project future cases of COVID-19, time-varying and state-specific effective reproductive numbers are estimated and fed as inputs into a compartment model. The compartment model is defined using Susceptible, Exposed, Infectious, and Recovered compartments (i.e., SEIR model) with continuous time progression. Model programming and analysis were performed in R (version 3.6.2), and the package deSolve was used to solve the ordinary differential equation system.",https://covid19sim.org/
  3,RobertWalraven-ESG,Robert Walraven,"The existing case and death raw data is cleaned slightly for anomolies, then is fit with model consisting of one or more skewed gaussian distributions using a distribution model I developed.  The model estimates the height, position, and spread of each peak that appears in the data and can be used to project ahead with reasonable results. This is an emperical approach that makes no epidemiological assumptions but simply fits the existing data as well as possible. The model assumes that the effects of current interventions are reflected in the observed data and that those effects will continue going forward.",http://rwalraven.com/COVID19
  4,LANL-GrowthRate,Los Alamos National Labs,"This model makes predictions about the future, unconditional on particular  intervention strategies. The model consists of two processes. The first process  is a statistical model of how the  number of COVID-19 infections changes over time. The second process maps the number of  infections to the reported data. We model the growth of new cases as the product of a  dynamic growth parameter and the underlying numbers of susceptible and infected cases  in the population at the previous time step, scaled by the size of the state's starting  susceptible population. Change 2020-10-29: The growth parameter can be thought of as the transmissibility of the virus in that state on that date and is a weighted regression between the   trend in the growth rate over the past 42 days and a growth rate that would keep  the number of new daily confirmed cases constant. The weights of these two components are dynamically tuned to the observed data. To model new deaths in the population, we assume that a fraction of the 1,2,3,4, or 5-week moving average of the daily  confirmed cases will die. The model learns both the moving average window and the case fatality fraction that best fits the historical observations.",https://covid-19.bsvgateway.org/
  5,UCLA-SuEIR,UCLA Statistical Machine Learning Lab,"We update our model by considering the effects of reopenings. In particular, for the states that have reopened or partially reopened, we assume there is a transition from a virtual  “Quarantined/Isolated” group to the “Susceptible” group at a certain rate after the reopen date. This rate along with other model parameters are jointly estimated based on the reported data. The key feature of the SuEIR model is that it can infer the untested cases as well as unreported cases. More specifically, we treat the “Exposed” group in the SEIR model as the individuals that have already been infected and have not been tested, which are also capable of infecting the “Susceptible” group. Moreover, some of the people in the “Exposed” group can get tested and transferred to the “Infectious” group (which are reported to the public), while the rest of them will recover/die and transit to the so-called “Unreported Recovered” group (which are not reported to the public). More detailed model descriptions and projections can be found at https://covid19.uclaml.org.
  Reference: Difan Zou, Lingxiao Wang, Pan Xu, Jinghui Chen, Weitong Zhang and Quanquan Gu, Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States, medRxiv:2020.05.24.20111989, 2020.",https://covid19.uclaml.org/
  6,COVIDhub-baseline,COVID-19 Forecast Hub,"For predictions of incidence, the median prediction at all future horizons is the most recent observed incidence.  Predictions of cumulative counts are derived from predictions of incidence; this means that the slope of the predictive medians for cumulative quantities from one week to the next will be constant and equal to the last observed slope.  To get a distribution around the median, we look at how much incidence has changed from week to week in the past, and we allow for the possibility that similar changes will occur again in the future.
  In more technical detail, forecasts of incidence are generated through the following procedure: 1) Calculate first differences of incidence; 2) Collect all first differences and their negatives; 3) Sample first differences in incidence and add to the most recent observed incidence; 4) Enforce that the mean of the predictive distribution for incidence is equal to the most recent observed incidence; 5) truncate the predictive distribution for incidence at 0.  Forecasts of cumulative quantities are obtained by adding forecasts of incidence to the last observed cumulative value.",https://covid19forecasthub.org/
  7,Columbia_UNC-SurvCon,Columbia_UNC,"We predict COVID-19 daily new cases using a robust and parsimonious survival-convolution model. We account for transmission during the pre-symptomatic incubation period, use a time-varying effective reproductive number (Rt) to reflect the temporal trend of transmission and change in response to a public health intervention, and quantify uncertainty by permutation. We use a regression model to predict cumulative deaths from estimated time-lagged cumulative total number of COVID-19 cases.",https://github.com/COVID19BIOSTAT/covid19_prediction
  8,USC-SI_kJalpha,University of Southern California Data Science Lab,"We use our own epidemic model called SI-kJalpha, preliminary version of which we have successfully used during DARPA Grand Challenge 2014. Our model can consider the effect of many complexities of the epidemic process and yet be simplified to a few parameters that are learned using fast linear regressions. Therefore, our approach can learn and generate forecasts extremely quickly. On a 2 core desktop machine, our approach takes only 3.18s to tune hyper-parameters, learn parameters and generate 100 days of forecasts of reported cases and deaths for all the states in the US. The total execution time for 184 countries is 11.83s and for more than 3000 US counties is 101.03s. Despite being fast, the accuracy of our forecasts is on par with the state-of-the-art as demonstrated on the leaderboard page. at https://scc-usc.github.io/ReCOVER-COVID-19. Our model is able to quickly adapt to changing trends, and the variations in parameters during different times/policies allow us to forecast different scenarios such as what would happen if we were to disregard social distancing suggestions. For each state, we model hospitalizations as a separate compartment, as a linear function of recent cases with heterogeneous rates. This means that for some hyper-parameter J, those who were infected at time t-1 to t-J will have a separate rate from those infected at t-(J+1) to t-2J, and so on. This is similar to how we perform death forecasts. For long-term forecasts (more than a few days in the future), the cases are forecasted first based on our SIkJalpha model, which forms the input to hospitalization prediction. While we account for changing trends by putting more emphasis on recently seen data, we assume that the trends will remain the same in the future for our point forecasts. We try to account for changing trends in the future in our quantile forecasts by modeling the empirical error our approach makes using a Random Forest.",https://scc-usc.github.io/ReCOVER-COVID-19
  9,UMich-RidgeTfReg,The University of Michigan,"This model uses ridge regression (penalized Ordinary Least Squares regression) to make predictions about future confirmed case and mortality trends.  The model makes forecasts based solely on previous data, so no external  assumptions are necessary.    
  The ridge regressors built for this model use Finite Impulse Response (FIR)  filtration to predict each day's confirmed cases and deaths as a function of  the counts on previous days. Specifically, each day's counts are predicted as  a linear combination of the counts on a user-specified number of previous days.  The goal of the regressors is the determination of an optimal coefficient vector  for these predictive linear combinations. The model currently makes predictions  based on the previous 14 days.
  Once the regressors are created, bootstrapping is used to create a spectrum of predictions, from which quantiles are determined.",https://gitlab.com/sabcorse/covid-19-collaboration
  10,OliverWyman-Navigator,Oliver Wyman,"Oliver Wyman's COVID-19 Pandemic Navigator provides forecasts and scenario analysis for Detected and Undetected cases and deaths. Our compartmental models are based on a discrete form of nonlinear differential equations, parameterized with dynamic functions (i.e. time-dependent transition rates) that incorporate: (1) the modeled effects of physical distancing and government actions; (2) evolving testing practices in terms of timeliness and scale; (3) the link to tracking and contact tracing, and (4) other region-specific characteristics. Our formulation draws on core methods from both epidemiology and physics. It is data-driven to characterize non-linear transition rates and posits underlying causal mechanisms that we have verified against mobility indices representing physical distancing in mass scale and government response actions.  Our models recalibrate daily with the arrival of new information on COVID-19.  By design, the calibration of our models use region-specific data, as the differences in the timing and execution of government restrictions, testing, population density, social behaviour norms and public responses, re-opening plans, and demographics are extremely relevant. Additional details on our approach, as well as selected forecast results, can be found at: https://pandemicnavigator.oliverwyman.com/",https://pandemicnavigator.oliverwyman.com/
  11,Geneva-DetGrowth,University of Geneva / Swiss Data Science Center,"""We calculate the growth rate of cumulative cases (resp. deaths) between two days ago and today.  If it's greater than 5%, we use an exponential model to forecast the cumulative number of cases (resp. deaths),  and then derive the daily number of cases (resp. deaths). If it's less than 5%, we use an linear model instead.""",https://renkulab.shinyapps.io/COVID-19-Epidemic-Forecasting/
  12,QJHong-Encounter,QJHong,"today's ""Daily New Confirmed Cases"" + today's ""Encounter Density"" ==> today's newly infected Cases ==> next 2-3 weeks' ""Daily New Confirmed Cases"" (Encounter Density D data source: Unacast's Social Distancing Scoreboard, which analyzes cell phone location data, counts ""Human Encounters"", defined as two      cell phone devices that were in the same place at the same time, and then derives the probability and ""Encounter Density"".)
  My model uses current ""Encounter Density"" D to predict future ""Reproductive Number"" R and ""Daily New Confirmed Cases"". This is the most fundamental idea and    assumption in this model.
  The strong correlation between R and D (D is shifted by 22 days) is evident. While social distancing quickly brought down R, easing policy is slowly increasing R back above 1.
  Using (1) R and D relation in the past as a training set, (2) future D as input, and (3) machine learning / regression, my model can predict future R, and ultimately future Daily New Cases.",https://qjhong.github.io
  13,LNQ-ens1,LockNQuay,"https://www.kaggle.com/c/m5-forecasting-uncertainty/discussion/163368,  https://www.kaggle.com/aerdem4/covid-19-w5-pipeline",https://www.kaggle.com/sasrdw/locknquay
  