A new study by researchers at Texas A&M University published in PLOS ONE details a new model for making short-term projections of daily COVID-19 cases that is accurate, reliable, and easily used by public health officials and other organizations.
Led by Hongwei Zhao, professor of biostatistics at the Texas A&M School of Public Health, the researchers used a method based on the SEIR (Sensitive, Exposed, Infected and Recovered States) framework to project the incidence of COVID-19 in both three weeks to come. on the cases of incidence observed only. This model assumes a constant or small change in the rate of transmission of the virus that causes COVID-19 over a short period of time.
The model uses publicly available data on newly reported COVID-19 cases in Texas from the COVID-19 data repository at the Center for Systems Science and Engineering at Johns Hopkins University. Researchers at Texas A&M used this data on disease incidence in Texas and a selection of counties including the Texas A&M campus to estimate the rate of transmission of COVID-19.
“The results indicate that this model can be used to reasonably predict COVID-19 cases two to three weeks in advance using only current incidence figures,” Zhao said. “The simplicity of this model is one of its greatest strengths, as it can be easily implemented by organizations with few resources. Predictions from this model can help healthcare organizations prepare for power surges and help public health officials determine whether masked warrants or other policies will be necessary. “
They predicted future infections under three possible scenarios: a sustained and constant transmission rate; one where the rate of transmission is five percent higher than current levels, reflecting a decrease in transmission prevention practices or an increase in conditions that favor transmission; and one where the transmission is five percent less.
Estimating the current effective transmission rate can be tricky, as daily variations in infections and reports can significantly influence this estimate. So the researchers smoothed out variations in the daily reports using a three-day weighted average and performed additional smoothing to account for data anomalies such as counties reporting multiple months of cases at the same time.
The researchers compared their projections to the incidence reported in Texas over four time periods in 2020: April 15, June 15, August 15, and October 15. The number of new daily reported COVID-19 cases was relatively low in mid-April, when many businesses were closed, then began to increase in early May after the phased reopening began in Texas. The numbers rose sharply after Memorial Day and then trended down following the enactment of a statewide mask mandate over the summer. Infections rose again after Labor Day, but then appeared to level off until mid-October, when the rate of transmission again increased dramatically.
State-wide application of the model showed that it performed reasonably well, with only the forecast for the second period deviating from the actual recorded incidence, possibly due to the dramatic change in figures as a big wave of COVID-19 hit around the Memorial Day holiday. . The model also worked well at the county level, although declining population and population changes, such as students entering and leaving the area during the school year, influenced the reporting of new cases.
However, the model is limited by the data it uses. Local testing and reporting policies and resources can affect the accuracy of the data, and transmission rate assumptions based on current incidence are less likely to be accurate in the future. And as more people contract COVID-19 and recover, or are vaccinated, the susceptible population will change, possibly affecting transmission.
Despite these limitations, the researchers said the model can be a valuable tool for healthcare facilities and public health officials, especially when combined with other sources of information. The COVID-19 pandemic is not yet over, so it is important to have a tool that can determine when and where another surge could occur. Likewise, researchers hope to use these new tools at their disposal for future infectious disease needs.
Additionally, the model was used to create a dashboard that provides real-time data on the spread of COVID-19 statewide. It has been used locally by university administrators and public health officials.
Other researchers from the School of Public Health involved in this study included Marcia Ory, Tiffany Radcliff, Murray Côté, Rebecca Fischer and Alyssa McNulty, as well as researchers from the Department of Statistics Huiyan Sangand and Naveed Merchant.
Source of the story:
Material provided by Texas A&M University. Original written by Rae Lynn Mitchell. Note: Content can be changed for style and length.