Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques

• URL: http://arxiv.org/abs/2204.01483v1
• Date: Wed, 23 Mar 2022 19:59:15 GMT
• Title: Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques
• Authors: Luis A. Barboza, Shu-Wei Chou, Paola V\'asquez, Yury E. Garc\'ia, Juan G. Calvo, Hugo C. Hidalgo, Fabio Sanchez
• Abstract summary: Dengue fever is a vector-borne disease mostly endemic to tropical and subtropical countries that affect millions every year and is considered a significant burden for public health. Here, we explore the effect of climate variables using the Generalized Additive Model for location, scale, and shape (GAMLSS) and Random Forest (RF) machine learning algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dengue fever is a vector-borne disease mostly endemic to tropical and subtropical countries that affect millions every year and is considered a significant burden for public health. Its geographic distribution makes it highly sensitive to climate conditions. Here, we explore the effect of climate variables using the Generalized Additive Model for location, scale, and shape (GAMLSS) and Random Forest (RF) machine learning algorithms. Using the reported number of dengue cases, we obtained reliable predictions. The uncertainty of the predictions was also measured. These predictions will serve as input to health officials to further improve and optimize the allocation of resources prior to dengue outbreaks.

Related papers

• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• FWin transformer for dengue prediction under climate and ocean influence [1.6114012813668932]
  Dengue fever is one of the most deadly mosquito-born tropical infectious diseases. In this study, we examine methods used to forecast dengue cases for long range predictions.
  arXiv  Detail & Related papers  (2024-03-10T19:20:55Z)
• An ensemble neural network approach to forecast Dengue outbreak based on climatic condition [2.3080278150530424]
  Dengue fever is a virulent disease spreading over 100 tropical and subtropical countries in Africa, the Americas, and Asia. The proposed model is an integrated approach that uses wavelet transformation into an ensemble neural network framework. The proposed XEWNet allows complex non-linear relationships between the dengue incidence cases and rainfall.
  arXiv  Detail & Related papers  (2022-12-16T07:49:15Z)
• Correlations Between COVID-19 and Dengue [0.8164433158925593]
  This paper shows how a neural network approach can incorporate Dengue and COVID-19 data as well as external factors. We define a Correlation Model through which we show that the number of cases of COVID-19 and Dengue have very similar trends. We then illustrate the relevance of our model by extending it to a Long short-term memory model (LSTM) that incorporates both diseases.
  arXiv  Detail & Related papers  (2022-07-27T14:55:28Z)
• Modeling the geospatial evolution of COVID-19 using spatio-temporal convolutional sequence-to-sequence neural networks [48.7576911714538]
  Portugal was the country in the world with the largest incidence rate, with 14-days incidence rates per 100,000 inhabitants in excess of 1000. Despite its importance, accurate prediction of the geospatial evolution of COVID-19 remains a challenge.
  arXiv  Detail & Related papers  (2021-05-06T15:24:00Z)
• From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors [69.9674326582747]
  Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States. We propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California.
  arXiv  Detail & Related papers  (2021-03-14T17:56:17Z)
• STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization [76.57716281104938]
  We develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic.
  arXiv  Detail & Related papers  (2020-12-08T21:21:47Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• Ensemble Forecasting of the Zika Space-TimeSpread with Topological Data Analysis [13.838100337224075]
  Zika virus is primarily transmitted through bites of infected mosquitoes of the species Aedes aegypti and Aedes albopictus. The abundance of mosquitoes and mosquitoes, as a result, the prevalence of Zika virus infections are common in areas which have high precipitation, high temperature, and high population density. We introduce new concept of cumulative Betti numbers and then integrate the cumulative Betti numbers as topological descriptors into three machine learning models.
  arXiv  Detail & Related papers  (2020-09-24T16:42:19Z)
• Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19 [75.99038202534628]
  We propose CALI-Net, a neural transfer learning architecture which allows us to'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
  arXiv  Detail & Related papers  (2020-09-23T22:35:43Z)
• Learning to Forecast and Forecasting to Learn from the COVID-19 Pandemic [10.796851110372593]
  We propose a heterogeneous infection rate model with human mobility for epidemic modeling. By linearizing the model and using weighted least squares, our model is able to quickly adapt to changing trends. We show that during the earlier part of the epidemic, using travel data increases the predictions.
  arXiv  Detail & Related papers  (2020-04-23T07:25:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.