COVID-19 Surge Support (Task Order 2 - Exhibit E): Modeling

COVID-19 Surge Support (Task Order 2 - Exhibit E): Modeling COVID-19 Surge Support (Task Order 2 - Exhibit E): Modeling Description of Services: ASU to assist with COVID-19 Modeling Studies and Interpretion of Modeling Results. Assistance includes collecting and analyzing data related to the COVID outbreak, developing computer models and calculations of COVID dynamics, consequences, and healthcare requirements, and communicating results in writing and in-person with MCDHP staff and subject matter experts. Any and all students involved will sign a confidentiality agreement, hereto attached to this Exhibit, and take appropriate confidentiality training prior to accessing any MCDPH provided information. MCDPH shall have full access to and the right to examine, copy, and make use of any and all data-sets, models, source code, output, and results produced under this SOW. To ensure an optimal response, models will be developed to include a parameter review, methodology review, presentation of results, and interpretation/discussion of results with MCDPH. Models A. (Base award): Epidemiology projections of COVID-incidence in Maricopa County at the county level. Estimates will include estimated number of infections, hospitalizations, and deaths over time. Estimates will also include comparisons to hospital and ICU capacity. Model updates will be produced monthly. B. Option: Spatial Cluster Analysis and case mapping. We wil develop a spatial risk model based on Maricopa County case data at the zip-code or zip-code track level that identifies hot-spots and clusters of COVID cases. C. Option: Capacity and Resource Distribution Model. We will develop a calculator and database for vaccines that prioritizes vaccine distribution in finite, daily quantities across the state to prioritized populations including at-risk individuals, critical infrastructure personnel, and front-line healthcare workers. D. Option: An epi-curve updating calculator. We will develop based on actuarial reserving principles an estimator of reporting lag by day for cases, hospitalizations, ICU stays, and deaths. This calculator will update over time to estimate final metrics based on each daily reporting number. This will allow MCDPH to place greater certainty in ongoing data reporting. E. Option: Sentiment Analysis. We will develop a tool to show ongoing sentiment analysis through twitter for key public health themes relevant to MCDPH. The tool could be used to compile sentiments on NPIs including facemasks and contact tracing, or PIs including vaccines and therapeutics. F. Option: Model enhancements. All models have improvements that can be made in model performance, accuracy, sensitivity, and accessibility. We will use an agile development paradigm to complete a design, build, implement cycle for each of these models as requested to meet product specific requirements.