RESUMO
The efficacy of government interventions in epidemic has become a hot subject since the onset of COVID-19. There is however much variation in the results quantifying the effects of interventions, which is partly related to the varying modelling approaches employed by existing studies. Among the many factors affecting the modelling results, people's voluntary behavior change is less examined yet likely to be widespread. This paper therefore aims to analyze how the choice of modelling approach, in particular how voluntary behavior change is accounted for, would affect the intervention effect estimation. We conduct the analysis by experimenting different modelling methods on a same data set composed of the 500 most infected U.S. counties. We compare the most frequently used methods from the two classes of modelling approaches, which are Bayesian hierarchical model from the class of computational approach and difference-in-difference from the class of natural experimental approach. We find that computational methods that do not account for voluntary behavior changes are likely to produce larger estimates of intervention effects as assumed. In contrast, natural experimental methods are more likely to extract the true effect of interventions by ruling out simultaneous behavior change. Among different difference-in-difference estimators, the two-way fixed effect estimator seems to be an efficient one. Our work can inform the methodological choice of future research on this topic, as well as more robust re-interpretation of existing works, to facilitate both future epidemic response plans and the science of public health.
Assuntos
COVID-19 , Epidemias , Humanos , COVID-19/epidemiologia , COVID-19/prevenção & controle , Teorema de Bayes , Previsões , GovernoRESUMO
COVID-19 raises attention to epidemic transmission in various places. This study analyzes the transmission risks associated with human activity places at multiple scales, including different types of settlements and eleven types of specific establishments (restaurants, bars, etc.), using COVID-19 data in 906 urban areas across four continents. Through a difference-in-difference approach, we identify the causal effects of activities at various places on epidemic transmission. We find that at the micro-scale, though the transmission risks at different establishments differ across countries, sports, entertainment, and catering establishments are generally more infectious. At the macro-scale, contradicting common beliefs, it is consistent across countries that transmission does not increase with settlement size and density. It is also consistent that specific establishments play a lesser role in transmission in larger settlements, suggesting more transmission happening elsewhere. These findings contribute to building a system of knowledge on the linkage between places, human activities, and disease transmission.
RESUMO
Hereditary transthyretin amyloid cardiomyopathy is cardiac involvement in systemic transthyretin amyloidosis. For the first time, we generated induced pluripotent stem cell (iPSC) line of hATTR-CM carrying the TTR mutation p.Asp38Asn. We isolated peripheral blood mononuclear cells from the patient's peripheral blood. The reprogramming of PBMCs achieved a pluripotent state by the transfection of non-integrated episomal vectors. We demonstrated pluripotency with the presence of cell surface markers, the expression of pluripotency-related genes and the ability to form teratoma composed of three germ layers in vivo. This iPSC line is a useful model for studying the pathogenic mechanism of TTR p.Asp38Asn mutation.
