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Chathurika Jayasani, Pipuni Dammalage, Sankani Sarathchandra, et al., "Limited Data Forecasting for Dengue Propagation," 2021 10th International Conference on Information and Automation for Sustainability (ICIAfS), 2021, pp. 416-421, https://doi.org/10.1109/ICIAfS52090.2021.9606032

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Introduction

Dengue is a mosquito-borne viral infection that has grown rapidly in recent decades. It has become a challenge to health authorities in the country for years. Our intention is to develop an effective mechanism to model and forecast the propagation of the disease with the objective of assisting the prevention, management, and surveillance of the disease.
Similar to every infectious disease, Dengue fever also exhibits unique natural characteristics. Analysis of the characteristics and the behavior of disease propagation approaches to forecast the trend in future disease spread. Artificial Intelligence (AI) is capable of identifying the patterns in disease propagation itself and its relationship with several other time variants and time invariant factors to track the trend of the disease.
We worked on utilizing AI techniques as a Dengue prediction tool to model and forecast the propagation. This study proposes three AI related models for analyzing the propagation of Dengue fever in Sri Lanka which could be utilized to warn the authorities and the public to take necessary steps to minimize the spread.
  1. Temporal prediction model
  2. Spatial prediction model
  3. Risk Analysis

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Chathurika Jayasani, Pipuni Dammalage, Sankani Sarathchandra, et al., "Limited Data Forecasting for Dengue Propagation," 2021 10th International Conference on Information and Automation for Sustainability (ICIAfS), 2021, pp. 416-421, https://doi.org/10.1109/ICIAfS52090.2021.9606032

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