The Coronavirus disease 2019 (COVID-19) has been impacting the human society since early 2020. During this public health crisis, reliable forecasting of the disease becomes critical for resource allocation and administrative planning. It is observed that as COVID-19 spreads at different speed and scale in different geographic regions, it is highly likely that similar progression patterns are shared among these regions within different time periods. This intuition is used to develop a new neural forecasting model, called Attention Crossing Time Series (ACTS), that makes forecasts via comparing patterns across time series obtained from multiple regions. Developed a new paradigm that leverages interseries similarity to improve COVID-19 forecasting. Method makes no assumption about epidemiological dynamics. Attention mechanism is extended to capture inter-series similarity in time series data. Trend filtering is also introduced to complement the attention-based framework and it can be trained jointly to maximize the performance. In comparison with a wide range of existing forecasters, the outstanding performance of ACTS is demonstrated on COVID-19 data.
Input variables : confirmed cases, hospitalizations and deaths due to COVID-19, total population, population density, ratios of age/gender/race, available hospital beds, traffic mobility
Output Variables : Forecast for confirmed cases, hospitalizations and deaths due to COVID-19
| Statistical | : | Mallow's CP | R Squared | Mean Square Error | Adjusted R Squared | Mean Absolute Error | Huber Loss |
| Business | : | Population at High Risk of Disease | Risk by Geography | Risk by Demographics | Risk by Clinical Parameters |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : arxiv.org
Additional links : arxiv.org
| Model Category | : | Public |
| Date Published | : | October, 2020 |
| Healthcare Domain | : |
Payer
Provider |
| Code | : | github.com |
| Health Risk Management |
| Risk Progression |