Risk Factors for SARS-CoV-2 Infection and Severe Outcomes Among People With Human Immunodeficiency Virus: Cohort Study.

Background: Studies on COVID-19 in people with HIV (PWH) have had limitations. Further investigations on risk factors and outcomes of SARS-CoV-2 infection among PWH are needed. Methods: This retrospective cohort study leveraged the national OPTUM COVID-19 data set to investigate factors associated with SARS-CoV-2 positivity among PWH and risk factors for severe outcomes, including hospitalization, intensive care unit stays, and death. A subset analysis was conducted to examine HIV-specific variables. Multiple variable logistic regression was used to adjust for covariates. Results: Of 43 173 PWH included in this study, 6472 had a positive SARS-CoV-2 result based on a polymerase chain reaction test or antigen test. For PWH with SARS-CoV-2 positivity, higher odds were found for those who were younger (18-49 years), Hispanic White, African American, from the US South, uninsured, and a noncurrent smoker and had a higher body mass index and higher Charlson Comorbidity Index. For PWH with severe outcomes, higher odds were identified for those who were SARS-CoV-2 positive, older, from the US South, receiving Medicaid/Medicare or uninsured, a current smoker, and underweight and had a higher Charlson Comorbidity Index. In a subset analysis including PWH with HIV care variables (n = 5098), those with unsuppressed HIV viral load, a low CD4 count, and no antiretroviral therapy had higher odds of severe outcomes. Conclusions: This large US study found significant ethnic, racial, and geographic differences in SARS-CoV-2 infection among PWH. Chronic comorbidities, older age, lower body mass index, and smoking were associated with severe outcomes among PWH during the COVID-19 pandemic. SARS-CoV-2 infection was associated with severe outcomes, but once we adjusted for HIV care variables, SARS-CoV-2 was no longer significant; however, low CD4 count, high viral load, and lack of antiretroviral therapy had higher odds of severe outcomes.

NetworkSIR and EnvironmentalSIR: Effective, Open-Source Epidemic Modeling in the Absence of Data.

The rapidly changing situation characterized by the COVID-19 pandemic highlighted a need for new epidemic modeling strategies. Due to an absence of computationally efficient models robust to paucity of reliable data, we developed NetworkSIR, a model capable of making predictions when only the approximate population density is known. We then extend NetworkSIR to capture the effect of indirect disease spread on the progression of an epidemic (EnvironmentalSIR).