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. 2023 Feb 20;20(4):3740.
doi: 10.3390/ijerph20043740.

WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases

Momna Javaid  1 , Muhammad Shahzad Sarfraz  1 , Muhammad Umar Aftab  1 , Qamar Uz Zaman  1 , Hafiz Tayyab Rauf  2 , Khalid A Alnowibet  3
Affiliations

WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases

Momna Javaid et al. Int J Environ Res Public Health. .

Erratum in

Abstract

The diseases transmitted through vectors such as mosquitoes are named vector-borne diseases (VBDs), such as malaria, dengue, and leishmaniasis. Malaria spreads by a vector named Anopheles mosquitos. Dengue is transmitted through the bite of the female vector Aedes aegypti or Aedes albopictus mosquito. The female Phlebotomine sandfly is the vector that transmits leishmaniasis. The best way to control VBDs is to identify breeding sites for their vectors. This can be efficiently accomplished by the Geographical Information System (GIS). The objective was to find the relation between climatic factors (temperature, humidity, and precipitation) to identify breeding sites for these vectors. Our data contained imbalance classes, so data oversampling of different sizes was created. The machine learning models used were Light Gradient Boosting Machine, Random Forest, Decision Tree, Support Vector Machine, and Multi-Layer Perceptron for model training. Their results were compared and analyzed to select the best model for disease prediction in Punjab, Pakistan. Random Forest was the selected model with 93.97% accuracy. Accuracy was measured using an F score, precision, or recall. Temperature, precipitation, and specific humidity significantly affect the spread of dengue, malaria, and leishmaniasis. A user-friendly web-based GIS platform was also developed for concerned citizens and policymakers.

Keywords: Geographical Information System; WebGIS; climate; machine learning; vector-borne disease.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
COVID-19 geographical risk map of Pakistan.
Figure 2
Figure 2
Workflow of proposed method of data processing and machine learning model.
Figure 3
Figure 3
Geographical representation of Punjab.
Figure 4
Figure 4
Visualization of total no. of examples of each disease in dataset.
Figure 5
Figure 5
Synthetic minority oversampling technique (SMOTE) on data samples.
Figure 6
Figure 6
In-depth flow diagram of manual feature selection.
Figure 7
Figure 7
Website User Interface centering Punjab Pakistan Map—Home Page.
Figure 8
Figure 8
Additional features for valid input location.
Figure 9
Figure 9
Workflow of real-time predictions on WebGIS.
Figure 10
Figure 10
Visualization of reported cases in Punjab for malaria, dengue and leishmaniasis.
Figure 11
Figure 11
Monthly reported cases of each disease from 2014 to 2018.
Figure 12
Figure 12
Relationship of temperature with dengue, malaria and leishmaniasis using interpolation.
Figure 13
Figure 13
Relationship of precipitation with dengue, malaria and leishmaniasis using interpolation.
Figure 14
Figure 14
Relationship of specific humidity with dengue, malaria and leishmaniasis using interpolation.
Figure 15
Figure 15
Highest accuracy achieved from each data sample.
Figure 16
Figure 16
Results of Selected Model.
Figure 16
Figure 16
Results of Selected Model.
Figure 17
Figure 17
Real-time prediction of various cities of Punjab on WebGIS.
See this image and copyright information in PMC

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