Dengue models based on machine learning techniques: A systematic literature review

Hoyos, William; Aguilar, José; Toro, Mauricio

doi:10.1016/j.artmed.2021.102157

Cited by 39 publications

(24 citation statements)

References 106 publications

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“…is SLR has established state-of-the-art dengue modeling techniques using machine learning over the past few years [10]. Estrada-Peña et al [11] demonstrated the importance of capturing the distribution ecology of any species involved in pathogen transmission, defining the required environmental conditions and projecting that niche geographically.…”

Section: Introductionmentioning

confidence: 99%

Ecological Environment and Poverty Governance Based on Deep Learning of Big Data

2022

Security and Communication Networks

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The ecological environment is the basic condition for human survival and development. Maintaining a good rural ecological environment is the basis for ensuring the sustainable development of agriculture and the rural economy, and it is the fundamental guarantee for helping poor rural areas to get rid of poverty and become rich. To solve poverty by developing tourism, the so-called tourism poverty alleviation has been studied for many years in the theoretical circles at home and abroad. Therefore, this article conducts research on China’s rural ecological environment management and poverty management based on big data deep learning, aiming to analyze the problems in China’s rural ecoenvironmental management and the challenges facing rural poverty governance and explore a way to achieve sustainable rural economic development and farmers’ poverty alleviation the best way. In the context of big data, environmental monitoring and governance have also ushered in innovative reforms. With the advantages of powerful information collection and data processing, big data provides comprehensive data and information support for environmental monitoring and governance. This article uses a survey method, data statistics method, literature analysis method, and case analysis method. Firstly, it briefly summarizes the main ecological and environmental problems and poverty management issues facing the rural areas of China. It then lists 5 successful poverty alleviation cases to investigate their poverty alleviation. The experience and methods of getting rich sum up the strategies that China can refer to for poverty alleviation. Finally, through the collection and compilation of online data, we have discovered the results achieved by China’s poverty alleviation work in recent years, i.e., by the end of 2019, our rural poverty population has been reduced to 5.1 million. Compared with 2013, there has been a reduction of approximately 90 million (Fang, 2019), however, at the same time, China’s poverty alleviation work has entered a stage of overcoming difficulties. The remaining poor people make it a long way to go. In short, improving the ecological environment to help rural poverty alleviation is a long-term people’s livelihood project, and it is also a research topic of great social significance. For this reason, the research in this article has a certain reference value.

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Section: Introductionmentioning

confidence: 99%

Ecological Environment and Poverty Governance Based on Deep Learning of Big Data

2022

Security and Communication Networks

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“…Machine learning models analyzing Internet and open health data sources has precedence across a number of disease surveillance projects to improve infectious disease surveillance and prediction e.g., malaria [40], dengue fever [41], and cholera [42]. In the example of influenza, algorithmic and machine learning models have been applied to influenza tracking for nearly two decades.…”

Section: Ai and Public Health Disease Surveillancementioning

confidence: 99%

Ethical Issues in AI-enabled Disease Surveillance: Perspectives from Global Health

Borda¹,

Molnar²,

Neesham³

et al. 2022

Preprint

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Infectious diseases, as COVID-19 is proving, pose a global health threat in an interconnected world. In the last 20 years, resistant infectious diseases such as SARS, MERS, H1N1, Ebola, Zika and now COVID-19 have been impacting global health defences, and aggressively flourishing within the rise of global travel, urbanization, climate change and ecological degradation. In parallel, this extraordinary episode in global human health highlights the potential for artificial intelligence (AI)-enabled disease surveillance to collect and analyse vast amounts of unstructured and real-time data to inform epidemiological and public health emergency responses. The uses of AI in these dynamic environments are increasingly complex, challenging the potential for human autonomous decisions. In this context, our study of qualitative perspectives will consider a responsible AI framework to explore its potential application to disease surveillance in a global health context. Thus far, there is a gap in the literature in considering these multiple and interconnected levels of disease surveillance and emergency health management through the lens of a responsible AI framework.

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“…More recently, studies have started to use machine learning techniques (neural networks and deep neural networks) focused on time-series analysis for studying this type of relationships [23].…”

Section: Modeling the Dengue Incidence As A Time Seriesmentioning

confidence: 99%

Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City

Valencia

Díaz

Pascale

et al. 2021

IJERPH

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The present analysis uses the data of confirmed incidence of dengue cases in the metropolitan region of Panama from 1999 to 2017 and climatic variables (air temperature, precipitation, and relative humidity) during the same period to determine if there exists a correlation between these variables. In addition, we compare the predictive performance of two regression models (SARIMA, SARIMAX) and a recurrent neural network model (RNN-LSTM) on the dengue incidence series. For this data from 1999–2014 was used for training and the three subsequent years of incidence 2015–2017 were used for prediction. The results show a correlation coefficient between the climatic variables and the incidence of dengue were low but statistical significant. The RMSE and MAPE obtained for the SARIMAX and RNN-LSTM models were 25.76, 108.44 and 26.16, 59.68, which suggest that any of these models can be used to predict new outbreaks. Although, it can be said that there is a limited role of climatic variables in the outputs the models. The value of this work is that it helps understand the behaviour of cases in a tropical setting as is the Metropolitan Region of Panama City, and provides the basis needed for a much needed early alert system for the region.

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Dengue models based on machine learning techniques: A systematic literature review

Cited by 39 publications

References 106 publications

Ecological Environment and Poverty Governance Based on Deep Learning of Big Data

Ecological Environment and Poverty Governance Based on Deep Learning of Big Data

Ethical Issues in AI-enabled Disease Surveillance: Perspectives from Global Health

Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City

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