Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore.
Corey M. Benedum,Corey M. Benedum,Kimberly M. Shea,Helen E. Jenkins,Louis Kim,Natasha Markuzon +5 more
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TLDR
This work compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance, and identified 2 scenarios when ML models are advantageous over regression model.Abstract:
Background Predictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models. Methods We developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990-2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew's Correlation Coefficient (MCC). Results For near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61. Conclusions Our results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be improved by the inclusion of these models.read more
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Dengue fever and el nino-southern oscillation in Queensland, Australia : a time series predictive model
TL;DR: Climate variability is directly and/or indirectly associated with d Dengue transmission and the development of an SOI-based epidemic forecasting system is possible for dengue fever in Queensland, Australia.
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Optimized ensemble deep learning framework for scalable forecasting of dynamics containing extreme events
TL;DR: In this article, an optimized ensemble deep learning (OEDL) model based on a best convex combination of feed-forward neural networks, reservoir computing, and long short-term memory can play a key role in advancing predictions of dynamics consisting of extreme events.
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Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling
TL;DR: In this paper , a new framework was proposed using geospatial big data analysis in the Google Earth Engine (GEE) platform and long short term memory (LSTM) modeling for dengue case forecasts over an epidemiological week basis.
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Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review
Zhichao Li,Jinwei Dong +1 more
TL;DR: A literature review based on 53 journal and conference papers published from 2018 to the present highlighted the importance of big geospatial data, data cloud computing, and other deep learning models in future dengue risk forecasting.
A threshold analysis of dengue transmission in terms of weather variables and imported dengue cases in Australia
TL;DR: Different responses of autochthonous DF incidence to weather factors and imported DF cases in Townsville and Cairns are found.
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