Leveraging Population-Level COVID-19 Testing Data for Predictive Modeling during Variant Surges: A Case Study from National Pharmacy Testing Network

The continued emergence of new SARS-CoV-2 variants has significantly increased the complexity of forecasting and preventing subsequent COVID-19 waves. Nationwide pharmacy testing data, collected through extensive pharmacy networks, offers a novel and effective approach for real-time population testing, facilitating the rapid identification of emerging outbreaks. This study aims to evaluate the extent to which large-scale testing data can inform predictive models capable of anticipating increases in COVID-19 infections in response to the appearance of new viral variants.

Specifically, the study incorporates test positivity rates, geographic spread, and demographic information, analyzed using machine learning and time series methods, to enhance outbreak forecasting. Results indicate that integrating external datasets such as vaccination coverage and population mobility data further improves model accuracy, thereby supporting more informed decision-making by public health authorities.

Among the modeling approaches assessed, deep learning models particularly Long Short-Term Memory (LSTM) networks demonstrated superior performance in capturing long-term trends compared to traditional methods like ARIMA. Findings suggest that insights derived from pharmacy testing data can play a critical role in enabling policymakers to respond proactively to the emergence of new COVID-19 variants.

The proposed framework offers a scalable alternative for epidemic prediction architectures within broader public health ecosystems. Future research should explore the integration of genomic surveillance data and consider the applicability of this predictive framework to other infectious diseases beyond COVID-19.