Lecture on "Epidemic Prediction Based on Big Data"

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Meeting time: 7:30pm – 8:30pm PDT, October 29, 2020

Meeting manner: Zoom   

                                 Meeting ID: 634 551 0340         Passcode: 307603

Participants: University partners who are invited, college students, recent graduates, and guests who are interested in epidemic prediction based on big data

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I. Activity Background

     The occurrence and spread of epidemics have a certain regularity, and are closely related to factors such as climate change and population mobility. People search for a large amount of information related to epidemics on the Internet daily, it will show a statistical law, and after accumulating with a long period of time it can form a predictive model. Looking back on the past, H1N1 influenza of 2019 was the key force that triggered big data. In the war to fight epidemic, people discovered the importance of pre-predicting epidemics. This year, the global outbreak of the coronavirus (COVID-19), each country has established an influenza surveillance system. The United States CDC cooperate with other departments each other to monitor the number of hospitalizations, hospitalized cases, mortality, influenza geographical distribution, etc., they can judge epidemic trends perfectly through these data. It can be seen that “big data” plays a key role in influenza prediction. It is believed that epidemic prediction based on “big data” will become a trend in the near future, just as accurate as weather forecasts. Now Silicon Valley Craftsman Institute will hold an event on “Epidemic Forecast Based on Big Data” lectures on October 29, 2020 from 7:30pm to 8:30 pm PDT, USJ instructor Kedi will personally explain to the guests, so that the guests can better understand how big data can help epidemics with “intelligent” way.

II. Activity Content

  1. Explanation of current epidemic trends
  2. What is big data
  3. Predict the time and scale of an epidemic base on big data
  4. Predict the future trend of epidemics base on big data

III.Lecturer

USJ Artisan Instructor: Kedi Miao

(Washington University in St. Louis EE Master; familiar with JAVA, JavaScript, React, Spring and Android development; experience in developing Android App independently)

IV. Activity Participants

University partners who are invited, college students, recent graduates, and guests who are interested in epidemic prediction based on big data.

V. Organizer

VI. Ways of registration

     1. Long press to identify the QR code to register.

2. Email to register

XiaoQian Huang (josiehuang@usjus.org)

YuJie Meng (yujiemeng12@gmail.com)

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