Using patient datasets to track COVID-19 prevalence

The rapid spread and constant evolution of COVID-19, has presented significant challenges to tracking true disease prevalence . In addition, the way we test for the disease is adapting and changing as more is learnt about the virus and new technologies become available. Some of these technologies can result in bias, and may not present an accurate picture of COVID-19 prevalence in a population.

Researchers Amy Hou, Genevieve Pang and Lorrin Pang at the Hawaii Department of Health propose that pre-procedural COVID-19 patient datasets may help us reduce bias in estimates of local disease prevalence.

Read more about their work in Research Outreach

Read the original research paper: doi.org/10.1101/2022.04.13.22273200