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Prediction of Epidemic Outbreaks by Aggregation of Epidemic Outbreaks and Disease Control Through Social Media Real-time Data Collection

Graduate #74
Discipline: Mathematics and Statistics
Subcategory:

Paris Smith - Jackson State University
Co-Author(s): Tor Kwembe, Jackson State University, Jackson, MS



My name is Paris Smith and I’m a student at Jackson State University. There are many epidemics taking place where viruses have occurred but cures have not been found or even vaccinations yet to be discovered. More so the awareness of recent epidemics and disease outbreaks to the general public is through the social media on the Internet. However, if we can pin-point exactly where these viruses may start we can reduce widespread an even prevent these viruses from occurring. My research includes the mathematics behind the epidemics of a number of different viruses. Our main algorithm used in this particular combination of mathematical epidemiology is the Dynamics Model, which aggregates epidemic and disease outbreaks through the social media to predict future outbreaks. Our main goal is to show where epidemics are thriving and the different locations where it is possible for an epidemic to start. The model focuses on recent Ebola, Zika, Malaria and West Nile viruses and the Asian flu outbreaks and also countries where outbreaks are well known. In conclusion, my research focuses on the outbreak of different viruses and or diseases. We want to discover a new algorithm that tracks transmission of diseases and we strive to ultimately give the general public an estimate of the next location a disease may occur. This will help individuals to protect themselves from transmission of diseases and overall prevent diseases from occurring.

Funder Acknowledgement(s): LSMAMP Bridge to Doctorate

Faculty Advisor: Tor Kwembe, tor.a.kwembe@jsums.edu

Role: I completed all parts in modelling different disease hotspots using the Dynamics Model.

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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