Monthly Archives: March 2014

H7N9 … only avian… ? …

The number of new infections in China’s H7N9 outbreak continues a steep ascent, with 23 more cases reported over the past 4 days, along with four deaths, one of them in a health worker at a Shanghai hospital.

The daily number of new H7N9 cases equals the pace of disease activity seen during the peak of the outbreak last spring. The disease is thought to be spreading from poultry to people in settings such as live markets, and the surge of new cases comes at a time of increased demand for poultry ahead of China’s Lunar New Year celebrations later this month.

I question about the solely avian transmission pathway… this might be much more complex…

Read more here … http://www.cidrap.umn.edu/news-perspective/2014/01/h7n9-cases-china-surge-past-200

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Are Individuals Really so Diverse or is our Mental Model that Enhances Diversity in Others? A perspective for Modeling Non-communicable Diseases

IDs modeling has always taken into account population-scale factors because there is the assumption that the disease is acting in the same way within the same homogenous population. Thus, there is a strong focus on the pathogen that is believed to be the most important driving factor of disease incidence regardless of individual diversity.
For non-communicable disease a strong focused has always been placed at individuals because of the high complexity and diversity of disease-causing factors. Thus, a lot of attention has been placed on care and clinical monitoring considering the difficulty to stop the disease as much as for IDs.
Firstly, we argue that no inference can be made from individual to populations in a linear sense (and vipers) but certainly population scale information is necessary to predict health trajectories of individuals. Thus, we argue that differences among individuals can be high but not random nor chaotic; thus, it is possible to identify the set of exposure factors and their average persistence time (i.e., exposure time for a population) that contribute to the occurrence of diseases. If it is true that genes make up just from 5 to 10 percent of our diseases (on average) the information about socio-environmental context should allow us to predict the healthcare trajectory of a population without any clinical / biological data.
At the end the difference between IDs and NCDs is related to the difference in exposure time and latency time of the disease that are typically very large for NCDs.

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