Methods in Constrained Community Detection: An Integer Optimization Model and Heuristic Approach for Cohort Creation

As a result of the COVID-19 pandemic, many organizations and schools have switched to a virtual environ-ment. Recently, as vaccines have become more readily available, organizations and educational institutions have started shifting from virtual environments to physical office spaces and schools. For the highest level of safety and caution with respect to the containment of COVID-19, the shift to in-person interaction requires a thoughtful approach. With the help of an Integer Programming (IP) Optimization model, it is possible to formulate the objective function and constraints to determine a safe way of returning to the office through cohort development. In addition to our IP formulation, we developed a heuristic approximation method. Starting with an initial contact matrix, these methods aim to reduce additional contacts introduced by subgraphs representing the cohorts. These formulations can be generalized to other applications that benefit from constrained community detection. © 2021 IEEE.

Microscopic Markov Chain Approach for Measuring Mobility Driven SARS-CoV-2 Transmission

After more than a year of non-pharmaceutical interventions, such as, lock-downs and masks, questions remain on how effective these interventions were and could have been. The vast differences in the enforcement of and adherence to policies adds complexity to a problem already surrounded with significant uncertainty. This necessitates a model of disease transmission that can account for these spatial differences in interventions and compliance. In order to measure and predict the spread of disease under various intervention scenarios, we propose a Microscopic Markov Chain Approach (MMCA) in which spatial units each follow their own Markov process for the state of disease but are also connected through an underlying mobility matrix. Cuebiq, an offline intelligence and measurement company, provides aggregated, anonymized cell-phone mobility data which reveal how population behaviors have evolved over the course of the pandemic. These data are leveraged to infer mobility patterns across regions and contact patterns within those regions. The data enables the estimation of a baseline for how the pandemic spread under the true ground conditions, so that we can analyze how different shifts in mobility affect the spread of the disease. We demonstrate the efficacy of the model through a case study of spring break and it’s impact on how the infection spread in Florida during the spring of 2020, at the onset of the pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Improved Estimation of Daily COVID-19 Rate from Incomplete Data

Novel diseases such as COVID-19 present challenges for identifying and assessing the impact of public health interventions due to incomplete and inaccurate data. Many infected persons may be asymptomatic, pre-symptomatic, or may choose to not seek medical treatment. Insufficient testing and reporting standards coupled with reporting delays may also affect the accuracy of case count, recovery rate, fatalities and other key metrics used to model the disease. High error in these metrics are propagated to all aspects of public health response including estimates of daily transmission rates. We propose a method that integrates Monte Carlo simulation based on clinical studies, linear noise approximation (LNA), and Hidden Markov Models (HMMs) to estimate daily reproductive number. Results are validated against known state population behavior, such as social distancing and stay-At-home orders. The proposed approach provides improved model initial conditions resulting in reduced error and superior modeling of COVID-19 disease dynamics, notably including the effective reproduction rate \mathrm{R}_{\mathrm{t}}. © 2020 IEEE.

Social determinates of health and COVID-19 mortality rates at the county level

Understanding how underlying health conditions and social determinants of health affect the severity of COVID-19 is critical for community response planning. Literature reports that groups at higher risk from COVID-19 include those 65 and older, living in nursing homes and long-Term care facilities, and with severe obesity, diabetes, chronic lung disease, or asthma. In addition, other studies has shown that the disease disproportionately affects individuals with lower socio-economic status. Our research seeks to validate these findings and observe the effects of health measures and social determinants of health on COVID-19 mortality at the county-level. In addition to COVID-19 research from hospital population samples, public health officials can leverage county-level factors for novel disease mitigation. We use the Johns Hopkins University COVID-19 reports of confirmed cases and deaths to measure disease mortality for each county in the United States. Then, we compare mortality to multiple county social determinants of health such as age, obesity, diabetes, and smoking in hypothesis testing. We fit multivariate linear models as well as non-linear models to predict mortality as a function of these county measures. The analysis shows that there is little evidence of a relationship between the county health measures of obesity, diabetes, or smoking and COVID-19 mortality as of the date of this publication. However, the analysis does reveal a positive relationship between the percent of a county population that is 65 or older and COVID-19 mortality. Other factors such as overcrowding, the percent uninsured, and the length of time since the virus has been detected in the county are also correlated with county COVID-19 mortality. Potential reasons for these findings, including data quality, are discussed. We also emphasize the advantage of collecting high quality, detailed health data at the county-level and explain how such data could be used to understand factors affecting the outcomes from novel diseases in real-Time, as a disease is progressing. © 2020 IEEE.