Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

Manogaran, Gunasekaran; Lopez, Daphne

doi:10.4018/ijaci.2017040106

Cited by 59 publications

(6 citation statements)

References 30 publications

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“…This mobile app is potential to be the data gathering and managing tool towards big data era, likewise the social media gathers user data for business intelligence (Goncalves & Cornelius Smith, 2018) while web questionnaire gathers research data for subject matter analysis (Gosling et al, 2004;van Gelder et al, 2010). If PesTrapp is to be applied nationwide, its database could be linked to the disease surveillance system and open source climate repository for integrating multiple ends -entomology, epidemiology and climate for dengue transmission prediction (Manogaran & Lopez, 2017) for better control measures to manage disease hotspots and handle outbreaks.…”

Section: Discussionmentioning

confidence: 99%

PesTrapp mobile app: A trap setting application for real-time entomological field and laboratory study

et al. 2021

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Section: Discussionmentioning

confidence: 99%

PesTrapp mobile app: A trap setting application for real-time entomological field and laboratory study

et al. 2021

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“…Similarly, an AI-focused approach reveals that temperature-related variables are the most effective meteorological factors for forecasting the weekly number of infectious diarrhea episodes, whereas weekly average rainfall is the least effective information [157]. Therefore, disease surveillance should be implemented in the context of Big Data, as done for example by Manogaran and Lopez in their big data surveillance system that analyzes the relationship between CC and the Dengue disease [158]. Therefore, both direct and indirect effects of climate crisis on AMR should be deeply investigated using multidisciplinary and innovative approaches.…”

Section: Current Strategies and Future Interdisciplinary Research On ...mentioning

confidence: 99%

How Antimicrobial Resistance Is Linked to Climate Change: An Overview of Two Intertwined Global Challenges

Lio

Favara

Maugeri

et al. 2023

IJERPH

291

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Globally, antimicrobial resistance (AMR) and climate change (CC) are two of the top health emergencies, and can be considered as two interlinked public health priorities. The complex commonalities between AMR and CC should be deeply investigated in a One Health perspective. Here, we provided an overview of the current knowledge about the relationship between AMR and CC. Overall, the studies included pointed out the need for applying a systemic approach to planetary health. Firstly, CC increasingly brings humans and animals into contact, leading to outbreaks of zoonotic and vector-borne diseases with pandemic potential. Although it is well-established that antimicrobial use in human, animal and environmental sectors is one of the main drivers of AMR, the COVID-19 pandemic is exacerbating the current scenario, by influencing the use of antibiotics, personal protective equipment, and biocides. This also results in higher concentrations of contaminants (e.g., microplastics) in natural water bodies, which cannot be completely removed from wastewater treatment plants, and which could sustain the AMR spread. Our overview underlined the lack of studies on the direct relationship between AMR and CC, and encouraged further research to investigate the multiple aspects involved, and its effect on human health.

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“…This model is compared with random forest and k nearest neighbor for two Brazilian cities. They proposed that other deep learning models should be considered for the study (6)(7)(8)(9)(10)(11)(12)(13)(14)(15). Mohapatra et.…”

Section: Related Workmentioning

confidence: 99%

“…It is the most suitable correlation technique based on the method of covariance and deals with numeric values. The person correlation function is given by Manogaran and Lopez (7):…”

Section: Exploratory Data Analysismentioning

confidence: 99%

Forecasting Dengue Hotspots Associated With Variation in Meteorological Parameters Using Regression and Time Series Models

Patil

Pandya

2021

Front. Public Health

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For forecasting the spread of dengue, monitoring climate change and its effects specific to the disease is necessary. Dengue is one of the most rapidly spreading vector-borne infectious diseases. This paper proposes a forecasting model for predicting dengue incidences considering climatic variability across nine cities of Maharashtra state of India over 10 years. The work involves the collection of five climatic factors such as mean minimum temperature, mean maximum temperature, relative humidity, rainfall, and mean wind speed for 10 years. Monthly incidences of dengue for the same locations are also collected. Different regression models such as random forest regression, decision trees regression, support vector regress, multiple linear regression, elastic net regression, and polynomial regression are used. Time-series forecasting models such as holt's forecasting, autoregressive, Moving average, ARIMA, SARIMA, and Facebook prophet are implemented and compared to forecast the dengue outbreak accurately. The research shows that humidity and mean maximum temperature are the major climate factors and exhibit strong positive and negative correlation, respectively, with dengue incidences for all locations of Maharashtra state. Mean minimum temperature and rainfall are moderately positively correlated with dengue incidences. Mean wind speed is a less significant factor and is weakly negatively correlated with dengue incidences. Root mean square error (RMSE), mean absolute error (MAE), and R square error (R2) evaluation metrics are used to compare the performance of the prediction model. Random Forest Regression is the best-fit regression model for five out of nine cities, while Support Vector Regression is for two cities. Facebook Prophet Model is the best fit time series forecasting model for six out of nine cities. Based on the prediction, Mumbai, Thane, Nashik, and Pune are the high-risk regions, especially in August, September, and October. The findings exhibit an effective early warning system that would predict the outbreak of other infectious diseases. It will help the relevant authorities to take accurate preventive measures.

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Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

Cited by 59 publications

References 30 publications

PesTrapp mobile app: A trap setting application for real-time entomological field and laboratory study

PesTrapp mobile app: A trap setting application for real-time entomological field and laboratory study

How Antimicrobial Resistance Is Linked to Climate Change: An Overview of Two Intertwined Global Challenges

Forecasting Dengue Hotspots Associated With Variation in Meteorological Parameters Using Regression and Time Series Models

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