During public health crises, the urgent need for rapid and accurate testing can lead to pressure to expedite the development and deployment of new assay methods. However, insufficient rigor in method validation can result in unreliable tests, such as the early faulty SARS-CoV-2 tests that produced false negatives, undermining efforts to track and slow disease spread.

Implementing and properly validating a LIMS is crucial for ensuring the quality and reliability of testing data. A LIMS supports standardized procedures, accurate documentation, and automatic process enforcement, thereby improving and speeding up the method validation process for new assays. This ultimately leads to more trustworthy test results, better regulatory compliance (like CLIA), and a more agile laboratory prepared to respond effectively to public health emergencies.

Public health officials historically lacked comprehensive data from global pandemics to effectively predict and prepare for future outbreaks and resource allocation. Moreover, the potential of combining disparate data sources like LIMS and ERP for real-time public awareness tools was largely untapped.

By leveraging newly available global pandemic data in forecasting models like ARIMA and integrating specimen data from LIMS with anonymized location data from ERP systems, public health can move towards proactive strategies. This enables more accurate predictions for resource allocation and the creation of real-time heatmaps to inform public awareness and preventative behaviors.