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Stopping Pandemics in their Tracks Public Deposited

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MLA citation style

Akbar, Samina, et al. Stopping Pandemics In Their Tracks. . 2020. https://mushare.marian.edu/concern/generic_works/0ca06b66-523b-4f29-a4ac-f84c04a8cf30?locale=en

APA citation style

Akbar, Samina, Schmid, Aaron, Khat, Israt, Abraham, Benjamin, & Mulye, Minal. (2020). Stopping Pandemics in their Tracks. https://mushare.marian.edu/concern/generic_works/0ca06b66-523b-4f29-a4ac-f84c04a8cf30?locale=en

Chicago citation style

Akbar, Samina, Schmid, Aaron, Khat, Israt, Abraham, Benjamin, and Mulye, Minal. Stopping Pandemics In Their Tracks. 2020. https://mushare.marian.edu/concern/generic_works/0ca06b66-523b-4f29-a4ac-f84c04a8cf30?locale=en

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The proposed solution is a self-reported, symptoms-based syndromic surveillance system that is universal, interactive, integrative, and combined with artificial intelligence. Once developed, this framework has the potential to stop any future epidemics and pandemic in urban and rural areas worldwide. We conducted a thorough literature review of existing short message service (SMS, text messaging) and interactive voice response (IVR, calling) surveillance systems, identified and addressed the shortcomings. We have concluded that many of the previous syndromic surveillance models suffer from data fragmentation, thus hindering scalability their to a global setting. This proposal will allow decision-making officials and healthcare professionals to robustly identify local disease outbreaks, thus thwarting unchecked spread while preventing a breakdown in the supply chain. Automation of healthcare team reported findings will enable predictive diagnosis and prognosis, thereby allocating medical resources appropriately. Disease-specific databases will benefit from real-time refinement, then utilizable for prospective case mitigation, medical management, and pathogen containment.

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