Note: This thread is related to #Coronavirus #COVID19

Follow the World Health Organization's instructions to reduce your risk of infection:

1/ Frequently clean hands by using alcohol-based hand rub or soap and water.

2/ When coughing and sneezing cover mouth and nose with flexed elbow or tissue - throw issue away immediately and wash hands.

3/ Avoid close contact with anyone that has fever and cough.

Nicholas A. Christakis+ Your Authors @NAChristakis Sterling Professor of Social & Natural Science at Yale. Physician. Author of Blueprint: The Evolutionary Origins of a Good Society. Luckily wed @ErikaChristakis Mar. 05, 2020 2 min read + Your Authors

Let’s look at how flu actually spreads, day by day during an outbreak, in a defined population, such as a college. While many transmissions are cryptic (and nowadays identified via phylogenetic methods, eg by @trvrb), many transmissions occur via observed social network ties. 1/

In 2009, there was a (limited) pandemic of H1N1 flu. It struck locales around the world, including colleges such as @Harvard. We mapped the social networks of 744 students and prospectively tracked their flu symptoms and vaccination status with daily precision. 2/

Here is what the social network of students being struck by H1N1 outbreak over a period of three months looked like. Infected individuals are colored red, friends of infected individuals are colored yellow, and node size is proportional to the number of friends infected. 3/

And here is how the virus spreads, slowly at first, and then fast, blooming in various parts of the network, in a multi-centric epidemic. (Original video at  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012948 ). 4/

The speed with which people acquired the flu during the epidemic depended on various aspects of their social network position. Those with more friends, those who were more central in the network, and those whose friends did *not* know each other got it sooner. 5/

We used these data to show various things. First, one can use this insight, that central people get the flu earlier in the course of an epidemic, to create a set of 'social network sensors’ allowing rapid forecasting of the epidemic. Our @PLosOne paper:  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012948  6/

My @TEDTalks, Using Social Networks to Predict Epidemics, is here:  https://www.ted.com/talks/nicholas_christakis_how_social_networks_predict_epidemics?language=en  7/

Second, we used detailed data about the parallel spread of a biological contagion (H1N1 flu) and a social contagion (vaccination behavior or social distancing) to show how human behavior(s) might accelerate or slow spread of a pathogen:  https://www.nature.com/articles/srep43634  @SciReports 8/

Third, incidentally, this same network sensor method can be used to forecast outbreaks of nosocomial infections (in the hospital network)  https://www.nature.com/articles/s41598-017-02245-7  &  https://link.springer.com/chapter/10.1007/978-3-319-60240-0_33  or of information (e.g., flu rumors) on @twitter  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0092413  9/

My @yale lab,  http://www.humannaturelab.net , #NHL, and its many brilliant and inventive young scientists and programmers and staff, is working actively on a number of fronts to develop tools to detect and combat #COVID19 #SARSCoV2 outbreak and will be releasing them soon. #FluSight 10/


You can follow @NAChristakis.



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