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Steps in Outbreak Investigation01:18

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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LSTM based prediction of malaria abundances using big data.

Thakur Santosh1, Dharavath Ramesh1, Damodar Reddy2

  • 1Department of Computer Science and Engineering, Indian Institute of Technology(ISM), Dhanbad, 826004, India.

Computers in Biology and Medicine
|August 11, 2020
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Summary

This study introduces a new framework using satellite and clinical data with a long short-term memory (LSTM) classifier to predict malaria outbreaks. The model effectively identifies seasonal patterns and high-risk areas for malaria in Telangana, India.

Keywords:
Big dataLong short-term memory (LSTM)Malaria prediction

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Area of Science:

  • Epidemiology
  • Environmental Health
  • Data Science

Background:

  • Malaria is a significant health concern in subtropical regions with limited health infrastructure.
  • Accurate malaria forecasting is crucial for mitigating its impact on populations.
  • Existing prediction models may lack the scalability and accuracy needed for diverse geographical areas.

Purpose of the Study:

  • To develop a novel, scalable framework for predicting malaria instances.
  • To forecast malaria abundances in specific geographical locations within Telangana, India.
  • To identify key environmental and clinical factors influencing malaria transmission.

Main Methods:

  • Utilized a combination of satellite data and clinical data.
  • Employed a long short-term memory (LSTM) classifier for time series prediction.
  • Implemented a scalable framework leveraging Apache Spark for data processing.

Main Results:

  • The proposed model successfully predicted a 12-month seasonal pattern for malaria in selected regions.
  • Identified regional variations in malaria response attributed to differing environmental factors.
  • Analysis confirmed the significant roles of both environmental and clinical variables in malaria transmission.

Conclusions:

  • The Apache Spark-based LSTM framework offers an effective strategy for malaria prediction.
  • The model can accurately identify locations with endemic malaria.
  • This approach aids in targeted public health interventions for malaria control.