Evaluating effectiveness of public health intervention strategies for mitigating COVID-19 pandemic.

Coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global public health challenge. In the United States (US), state governments have implemented various non-pharmaceutical interventions (NPIs), such as physical distance closure (lockdown), stay-at-home order, mandatory facial mask in public in response to the rapid spread of COVID-19. To evaluate the effectiveness of these NPIs, we propose a nested case-control design with propensity score weighting under the quasi-experiment framework to estimate the average intervention effect on disease transmission across states. We further develop a method to test for factors that moderate intervention effect to assist precision public health intervention. Our method takes account of the underlying dynamics of disease transmission and balance state-level pre-intervention characteristics. We prove that our estimator provides causal intervention effect under assumptions. We apply this method to analyze US COVID-19 incidence cases to estimate the effects of six interventions. We show that lockdown has the largest effect on reducing transmission and reopening bars significantly increase transmission. States with a higher percentage of non-White population are at greater risk of increased R t $$ {R}_t $$ associated with reopening bars.

Liver Disease: Induction, Progression, Immunological Mechanisms, and Therapeutic Interventions.

The liver is an organ with impressive regenerative potential and has been shown to heal sizable portions after their removal. However, certain diseases can overstimulate its potential to self-heal and cause excessive cellular matrix and collagen buildup. Decompensation of liver fibrosis leads to cirrhosis, a buildup of fibrotic ECM that impedes the liver's ability to efficiently exchange fluid. This review summarizes the complex immunological activities in different liver diseases, and how failure to maintain liver homeostasis leads to progressive fibrotic tissue development. We also discuss a variety of pathologies that lead to liver cirrhosis, such as alcoholic liver disease and chronic hepatitis B virus (HBV). Mesenchymal stem cells are widely studied for their potential in tissue replacement and engineering. Herein, we discuss the potential of MSCs to regulate immune response and alter the disease state. Substantial efforts have been performed in preclinical animal testing, showing promising results following inhibition of host immunity. Finally, we outline the current state of clinical trials with mesenchymal stem cells and other cellular and non-cellular therapies as they relate to the detection and treatment of liver cirrhosis.

Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies.

Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict the COVID-19 disease course and compare the effectiveness of mitigation measures across countries to inform policy decision making using a robust and parsimonious survival-convolution model. We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number (Rt ) to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the transmission rate using a natural experiment design and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only early phase data (2-3 weeks after the outbreak). A fast rate of decline in Rt was observed, and adopting mitigation strategies early in the epidemic was effective in reducing the transmission rate in these two countries. The nationwide lockdown in Italy did not accelerate the speed at which the transmission rate decreases. In the United States, Rt significantly decreased during a 2-week period after the declaration of national emergency, but it declined at a much slower rate afterwards. If the trend continues after May 1, COVID-19 may be controlled by late July. However, a loss of temporal effect (e.g., due to relaxing mitigation measures after May 1) could lead to a long delay in controlling the epidemic (mid-November with fewer than 100 daily cases) and a total of more than 2 million cases.