Pseudo-Likelihood Based Logistic Regression for Estimating COVID-19 Infection and Case Fatality Rates by Gender, Race, and Age in California

In emerging epidemics, early estimates of key epidemiological characteristics of the disease are critical for guiding public policy. In particular, identifying high risk population subgroups aids policymakers and health officials in combatting the epidemic. This has been challenging during the coronavirus disease 2019 (COVID-19) pandemic, because governmental agencies typically release aggregate COVID-19 data as marginal summary statistics of patient demographics. These data may identify disparities in COVID-19 outcomes between broad population subgroups, but do not provide comparisons between more granular population subgroups defined by combinations of multiple demographics. We introduce a method that overcomes the limitations of aggregated summary statistics and yields estimates of COVID-19 infection and case fatality rates -- key quantities for guiding public policy related to the control and prevention of COVID-19 -- for population subgroups across combinations of demographic characteristics. Our approach uses pseudo-likelihood based logistic regression to combine aggregate COVID-19 case and fatality data with population-level demographic survey data to estimate infection and case fatality rates for population subgroups across combinations of demographic characteristics. We illustrate our method on California COVID-19 data to estimate test-based infection and case fatality rates for population subgroups defined by gender, age, and race and ethnicity. Our analysis indicates that in California, males have higher test-based infection rates and test-based case fatality rates across age and race/ethnicity groups, with the gender gap widening with increasing age. Although elderly infected with COVID-19 are at an elevated risk of mortality, the test-based infection rates do not increase monotonically with age. LatinX and African Americans have higher test-based infection rates than other race/ethnicity groups. The subgroups with the highest 5 test-based case fatality rates are African American male, Multi-race male, Asian male, African American female, and American Indian or Alaska Native male, indicating that African Americans are an especially vulnerable California subpopulation.

Fusing a Bayesian case velocity model with random forest for predicting COVID-19 in the U.S.

Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian nonlinear mixed model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectory. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for infections and deaths in U.S. states. We evaluate forecasting accuracy on a two-week holdout set, finding that the model predicts COVID-19 cases and deaths well, with a mean absolute scaled error of 0.40 for cases and 0.32 for deaths throughout the two-week evaluation period. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Ohio, and Mississippi. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course. Author summaryCOVID-19 models can be roughly classified as mathematical models that simulate disease within a population, including epidemiological compartmental models, or statistical curve-fitting models that fit a function to observed data and extrapolate forward into the future. Bridging this divide, we combine the strengths of curve-fitting statistical models and the structure of epidemiological models, by embedding a Bayesian nonlinear mixed model for case velocity and a machine learning algorithm (random forest) into the framework of a compartmental model. Fusing these models together exploits the particular strengths of each to glean as much information as possible from the currently available data. We also identify the velocity of log cumulative cases as an excellent target for modeling and extrapolating COVID-19 case trajectories. We empirically evaluate the predictive performance of the model and provide predicted trajectories with credible intervals for cumulative confirmed case count, active confirmed infections and COVID-19 deaths for each of the 50 U.S. states. Combining sophisticated data analytic methods with proven epidemiological models offers an empirically grounded strategy for making realistic predictions and quantifying their uncertainty. These predictions indicate substantial variation in the COVID-19 trajectories of U.S. states.

Network pharmacology study of the effective constituents in the Compound Yizhihao against influenza disease / 药学学报

Compound Yizhihao, consists of Radix isatidis, Folium isatidis, Artemisia rupestris, has a significant therapeutic effect on the treatment of influenza and fever. However, the mechanism of its action is still unclear. In this investigation, we collected the key target molecule of influenza disease and the chemical constituents of Compound Yizhihao, and developed Naïve Bayesian classification models based on the input molecular fingerprints and molecule descriptors. The built models were further applied to construct classifiers for predicting the effective constituents. We used the professional network-building software to build the constituent-target network and target-pathway network, which revealed the network pharmacology of the effective constituents in Compound Yizhihao. It will contribute to the further research of mechanism of Compound Yizhihao.