Predictive Modeling of COVID-19 Using WBT

Although WBT has its advantages, it is not a substitute for human surveillance systems and should be used to supplement human-based detection systems. Both methods in tandem provide powerful tools for controlling disease outbreaks in communities. To extend the utility of WBT, data from the current study, and consolidated from other studies, will be analyzed in efforts to develop a predictive model of local and community COVID-19 spread based upon wastewater measures. This predictive model will be based upon: (1) analyses of variables that influence the levels of SARS-CoV-2 in wastewater samples, and (2) evaluating correlations between the number of COVID-19 cases with respect to time-lagged concentrations of COVID-19 in wastewater. From these analyses, a predictive model shall be developed that will be used to estimate cases at the county and community scales. As part of this analysis, we will leverage ongoing infectious disease modeling efforts such as the Models of Infectious Disease Agent Study (MIDAS) network (https://midasnetwork.us) to incorporate the SARS-CoV-2 wastewater detection data into existing and future studies. Our informatics infrastructure, including standardized deep metadata annotations, FAIR data processing, integration, management, and structured storage of all result sets and data types, will facilitate these modeling efforts.