I'm a virologist and Chief of Science at the California Academy of Sciences, where I spend a lot of time looking into where viruses that infect humans come from (we call these zoonotics). I use clues in the genomes of viruses about how they mutate and evolve to read their origin stories as well as find their fitness "pressure points," which for humans translates into how they spread and how virulent they are. I've been tracking the emergence and spread of the virus SARS-CoV-2, agent of the disease COVID-19, based on its unique biological features. I really became fascinated with pathogens when I was a college student volunteering in west Africa—in the span of a few weeks I contracted malaria, amoebic dysentery, a staph infection, and was hospitalized in a leper colony. It taught me a new level of respect for my parasite foes, all of whom evolve rapidly, have natural reservoirs, and can cause significant disease. Sound familiar? Here's more about me on this website, and my twitter handle is @MicrobeExplorer.
Proof: https://i.redd.it/378x45vsghn41.jpg
UPDATE: I have to head out now, but will try to come back later and address some more. Thanks for all the great exchange! Meanwhile, stay healthy and help flatten the curve!
This is something that Amesh Adalja said during his episode with Sam that really stood out to me.
Add this to the myriad reasons not to eat animals.... health reasons, environmental reasons, ethical/moral reasons, and now global killer pandemic reasons.
For the record, I do have dogs who basically live and sleep on my furniture all day, but I would imagine that dogs and humans having evolved in such close proximity to one another would mitigate the chances that we catch something from healthy, happy canine's living with us.
Edit - TIL the bold text formatting feature does not work in the subject line.
https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-020-00225-1 📷
[Repost due to violation of sub rules]:
This is a study our team recently conducted trough February and March. We are a team of geographers and geo-scientists with an interdisciplinary background. Please note, that answering your questions can take some time, since the main authors are on vacation right now. I'm a student of human geography, whose expertise in this field is very limited, my personal contribution to this paper was literature research and writing the background for the COVID-19 epidemic in Germany. I was also partly responsible for the data research, specifically for the socioeconomic data sets. Here is the Link to our Homepage: https://www.geography.nat.fau.eu/research/cultural-geography/wg-digital-health/, the corresponding author for this paper would be the head of our lab, Dr. Blake Byron Walker, his contact data is found there, and on the paper. I would be happy to gather your comments and questions and forward them to him. Of course, I try to answer everything which is in my field of expertise myself.
Here's the Abstract, Questions and Discussion is found below :
Background
As of 13 July 2020, 12.9 million COVID-19 cases have been reported worldwide. Prior studies have demonstrated that local socioeconomic and built environment characteristics may significantly contribute to viral transmission and incidence rates, thereby accounting for some of the spatial variation observed. Due to uncertainties, non-linearities, and multiple interaction effects observed in the associations between COVID-19 incidence and socioeconomic, infrastructural, and built environment characteristics, we present a structured multimethod approach for analysing cross-sectional incidence data within in an Exploratory Spatial Data Analysis (ESDA) framework at the NUTS3 (county) scale.
Methods
By sequentially conducting a geospatial analysis, an heuristic geographical interpretation, a Bayesian machine learning analysis, and parameterising a Generalised Additive Model (GAM), we assessed associations between incidence rates and 368 independent variables describing geographical patterns, socioeconomic risk factors, infrastructure, and features of the build envi
... keep reading on reddit ➡https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-020-00225-1
This is a study our team recently conducted trough February and March. We are a team of geographers and geo-scientists with an interdisciplinary background. Please note, that answering your questions can take some time, since the main authors are on vacation right now. I'm a student of human geography, whose expertise in this field is very limited, my personal contribution to this paper was literature research and writing the background for the COVID-19 epidemic in Germany. I was also partly responsible for the data research, specifically for the socioeconomic data sets. Here is the Link to our Homepage: https://www.geography.nat.fau.eu/research/cultural-geography/wg-digital-health/, the corresponding author for this paper would be the head of our lab, Dr. Blake Byron Walker, his contact data is found there, and on the paper. I would be happy to gather your comments and questions and forward them to him. Of course, I try to answer everything which is in my field of expertise myself.
Here's the Abstract, Questions and Discussion is found below :
Background
As of 13 July 2020, 12.9 million COVID-19 cases have been reported worldwide. Prior studies have demonstrated that local socioeconomic and built environment characteristics may significantly contribute to viral transmission and incidence rates, thereby accounting for some of the spatial variation observed. Due to uncertainties, non-linearities, and multiple interaction effects observed in the associations between COVID-19 incidence and socioeconomic, infrastructural, and built environment characteristics, we present a structured multimethod approach for analysing cross-sectional incidence data within in an Exploratory Spatial Data Analysis (ESDA) framework at the NUTS3 (county) scale.
Methods
By sequentially conducting a geospatial analysis, an heuristic geographical interpretation, a Bayesian machine learning analysis, and parameterising a Generalised Additive Model (GAM), we assessed associations between incidence rates and 368 independent variables describing geographical patterns, socioeconomic risk factors, infrastructure, and features of the build environment. A spatial trend analysis and Local Indicators of Spatial Auto
... keep reading on reddit ➡