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Digital Epidemiology: Using Big Data and AI to Predict and Control Disease Outbreaks

👥 Available: Author, Co-author 1-4 position available
🕒 Booked: No position booked so far

We are offering 25% discount if you order 3 papers together. Here is the discount idea: Suppose you ordered 3 paper as author position. The total service charge will be 7000x3= 21000tk. But for ordering 3 papers together, we will provide you 5250tk discount and your discounted price will be 15750tk.

Now suppose you ordered 3 co-author position. The normal price is 5000x3=15000tk. After giving you 25% or 3750tk discount, the discounted price will be 11250tk.

Please send us a message if you want to get the discount by order 3 papers together.

Our Service Pricing

Author 7000tk
Co-author 5000tk
Full Paper single author 15000tk
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Frequently Asked Questions

Your research paper will be published in the International Journal of Science and Research Archive (IJSRA), a highly reputable and internationally recognized journal. It is indexed by prestigious platforms such as Google Scholar, CrossRef, ResearchGate, Scilit, Thomson Reuters, Orcid, DRJI, Citefactor, CrossMark, and DOAJ, ensuring your work reaches a global academic audience. If you're interested in exploring other journal options, we offer additional reputable journals. Feel free to contact us for more information on available alternatives.

After all author and co-author positions are confirmed, your research paper will typically be published within 3 to 15 days. This timeline includes the finalization of necessary documentation, ensuring your paper meets journal standards, and completing any revisions. We keep you updated throughout the process for transparency and efficiency.

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Paper Abstracts

Digital Epidemiology: Using Big Data and AI to Predict and Control Disease Outbreaks

The rise of digital technologies has transformed the way public health professionals monitor and respond to disease outbreaks. This paper investigates the use of big data, machine learning, and artificial intelligence (AI) in digital epidemiology to predict, track, and control infectious diseases. By analyzing data from social media platforms, mobile phone location data, and health records, the study demonstrates how AI models can identify emerging disease patterns and predict future outbreaks. The paper also explores the ethical implications of using personal data in epidemiological studies and proposes guidelines for maintaining privacy while enhancing outbreak prediction and management.