Towards a Web GIS-based approach for mapping a dengue outbreak

Butt, Muhammad Atif; Khalid, Aliya; Ali, Amjad; Mahmood, Syed Amer; Sami, Javed; Qureshi, Jahanzeb; Waheed, K.

doi:10.1007/s12518-019-00282-7

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…One of these applications was mapping dengue outbreaks in the tropical and sub-tropical areas including, the Lahore district. Results of mapping using climate, land use/ land cover, NDVI (Normalized Difference Vegetation Index), and population density showed that the most important factor for this disease was population density (Butt et al, 2020). Concerning COVID-19, John Hopkins University is pioneered mapping daily reports of active cases and deaths (https://coronavirus.jhu.edu/ map.html).…”

Section: Covid-19 Risk Mapping Using the Rf Mltmentioning

confidence: 99%

Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)

Pourghasemi¹,

Pouyan²,

Heidari³

et al. 2020

International Journal of Infectious Diseases

108

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Objectives: Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries with more than 432,902 recorded deaths and 7,898,442 confirmed cases worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and control the epidemic of COVID-19. Methods: This is the first comprehensive study of COVID-19 in Iran; and it carries out spatial modeling, risk mapping, change detection, and outbreak trend analysis of the disease spread. Four main steps were taken: comparison of Iranian coronavirus data with the global trends, prediction of mortality trends using regression modeling, spatial modeling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT), and validation of the modeled risk map. Results: The results show that from February 19 to June 14, 2020, the average growth rates (GR) of COVID-19 deaths and the total number of COVID-19 cases in Iran were 1.08 and 1.10, respectively. Based on the

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Section: Covid-19 Risk Mapping Using the Rf Mltmentioning

confidence: 99%

Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)

Pourghasemi¹,

Pouyan²,

Heidari³

et al. 2020

International Journal of Infectious Diseases

108

View full text Add to dashboard Cite

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“…The research [ 32 ] has developed a WebGIS platform for dengue risk mapping for the district of Lahore, Pakistan. The website displayed the following features: (1) dengue fever areas with a high threat; (2) highest patient count; (3) real-time spatial and other attributes updates about dengue.…”

Section: Related Workmentioning

confidence: 99%

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

Javaid

Sarfraz

Aftab

et al. 2023

IJERPH

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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.

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“…Previously, WHO used GIS in order to track vector borne disease outbreaks like dengue and to analyse the vulnerable areas which required urgent vaccination programs [46]. Mapping of climate and population density for the Dengue virus showed that population density is the most significant factor in the spread of Dengue [47].…”

Section: Gis Modellingmentioning

confidence: 99%

Literature Review on Epidemiological Modelling, Spatial Modelling and Artificial Intelligence for COVID-19

Saraee

Silva

2021

JAMMR

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Introduction: Following the outbreak of Coronavirus (COVID-19) in Wuhan, China in December 2019, the World Health Organisation (WHO) has declared this infectious disease as a pandemic. Unlike previous infectious outbreaks such as Severe Acute Respiratory Syndrome (SARS) and Middle Eastern Respiratory syndrome (MERS), the high transmission rate of COVID-19 has resulted in worldwide spread. The countries with the highest recorded incidence and mortality rates are the US and UK. Rationale/Objective: This review will compare studies that have used epidemiological models for disease forecasting and other models that have identified sociodemographic factors associated with COVID-19. We will evaluate several models, from basic equation-based mathematical models to more advanced machine-learning ones. Our expectation is that by identifying high impact models used by policy makers and discussing their limitations, we can identify possible areas for future research. Evidence Review: The bibliographic database google scholar was used to search keywords such as ‘COVID-19’, ‘epidemiological modelling’ and ‘machine learning’. We examined data review articles, research studies and government-released articles. Results: We identified that the current SEIR model used by the UK government lacked the spatial modelling to enable an accurate prediction of disease spread. We discussed that machine-learning systems which can identify high-risk groups can be used to establish the disparities in COVID-19 death in BAME groups. We found that most of the data hungry AI models used were limited by the lack of datasets available. Conclusion: In conclusion, advances in AI methods for infectious disease have overcome challenges presented in mathematical models. Whilst limitations do exist, when optimised, these highly advanced models have a great potential in public health surveillance, particularly infectious disease transmission.

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Towards a Web GIS-based approach for mapping a dengue outbreak

Cited by 4 publications

References 5 publications

Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)

Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)

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

Literature Review on Epidemiological Modelling, Spatial Modelling and Artificial Intelligence for COVID-19

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