K-step ahead prediction models for dengue occurrences

Thiruchelvam, Loshini; Asirvadam, Vijanth S.; Dass, Sarat C.; Daud, Hanita; Gill, Balvinder Singh

doi:10.1109/icsipa.2017.8120671

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…When we evaluated the studies regarding the types of models used in the predictions, we observed that the vast majority of authors investigated moving average models (27), such as the Autoregressive Integrated Moving Average (ARIMA) (17,23,29,35,41,43,46,56,(61)(62)(63), Seasonal Autoregressive Integrated Moving Average (SARIMA) (55,(63)(64)(65)(66), Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) (67). Several works have also presented a wide variety of models using artificial neural networks, mainly the LSTM (59,(68)(69)(70).…”

Section: Arboviruses (Counts) Predictionmentioning

confidence: 99%

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

Lima

Silva

Moreno

et al. 2022

Front. Public Health

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Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.

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