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Feb 13, 2023 · This systematic review evaluated 99 dengue outbreak prediction models from 64 studies, predicting the number of dengue cases or outbreaks from a variety of settings and populations. Our review identified, three major area of inadequacy in the current modelling practices.
- 10.1371/journal.pntd.0010631
- 2023/02
- Creative CommonsAttribution License,which
- Data analysis
- All variable
- used
- R 2 , GCV
- Evaluation
- Missing data
- Model development
- used
- Evaluation
- Candidate predictors
- Missing data
- Model development
- used
- Conclusion
- Author Contributions
permits unrestricted use, distribution, and reproductionin any medium,provided the original authorand source are credited. Data Availability Statement:All relevantdata are withinthe manuscriptand its Supporting Informationfiles. RESEARCH ARTICLE
Based on the data extraction fields of the CHARMS framework [23], a standardised table was developed to extract data from the selected studies for assessment of quality and evidence syn-thesis. The data extraction table consists of eleven domains, each with a specific item, that extract data from the reports of the primary forecasting model. Key in...
All variable Pre-selection All variable All variable Pre-selection Pre-selection, all variable Pre-selection Pre-selection All variable All variable All variable Pre-selection All variable All variable All variable All variable Pre-section All variable Model techniques
Statistical model Machine learning Statistical model Statistical model Statistical model Statistical model Statistical model, machine learning Statistical model Statistical model Statistical model Machine learning Machine learning Statistical model, machine learning Statistical model Statistical model Machine learning Statistical model Statistical ...
Retrospective forecasts AIC, BIC, MAE, RMSE AIC Not reported
Internal only Internal only Internal only Internal only Internal and external Internal Internal only Not reported Internal only Internal only Not reported Not reported Internal only Internal only Internal only Internal only Internal only Internal only Internal only
Not reported Not reported Reported Not reported Not reported Reported Not reported Not reported Not reported Not reported Not reported Not reported Not reported Not reported
All variable Pre-selection All variable All variable All variable Pre-selection All variable All variable All variable Pre-selection All variable Pre-selection Pre-selection Pre-selection Model techniques
Statistical model Statistical model Machine learning Machine learning Statistical model Machine learning Statistical model Statistical model, machine learning Statistical model Statistical model Statistical model Statistical model Statistical model Machine learning
Not reported Internal only Internal only Not reported Not reported Not reported Not reported Internal only Internal and external Internal only Internal only Internal only Internal only Internal only Table 1. (Continued) Author and year Country Source of dengue data Participant’s recruitment Outcome Candidate predictors Sample size Missing data Mode...
Climate Climate Climate Climate Climate, climate change Climate Climate, climate change, demography, environment Climate
Not reported Not reported Not reported Reported Not reported Not reported Not reported Not reported
Pre-selection Pre-selection Pre-selection All variable All variable All variable All variable Pre-selection Model techniques
Machine learning Statistical model Statistical model Machine learning Statistical model Statistical model Machine learning Statistical model
In conclusion, failure to use of real time primary predictor data, failing to incorporate non-cli-matic parameters as predictor and insufficient reporting of model development techniques, model validation and performance measure were the major inadequacies identified in the cur-rent modelling practice. The paradigm shift towards robust modelling te...
Conceptualization: Dragan Ilic, Saif U. Munshi, Md Nazmul Karim. Data curation: Xing Yu Leung, Rakibul M. Islam, Mohammadmehdi Adhami, Lara McDonald, Shanika Palawaththa. Formal analysis: Md Nazmul Karim. Methodology: Md Nazmul Karim. Project administration: Xing Yu Leung, Rakibul M. Islam, Mohammadmehdi Adhami, Lara McDonald, Shanika Palawaththa. ...
Mar 2, 2020 · Results. Sixteen studies fulfilled the inclusion criteria, and five of them projected a global dengue future. Most studies reported an increase in disease burden, a wider spatial distribution of dengue cases or more people exposed to climatically suitable areas of dengue as climate change proceeds.
- Zhiwei Xu, Hilary Bambrick, Francesca D. Frentiu, Gregor Devine, Laith Yakob, Gail Williams, Wenbiao...
- 10.1371/journal.pntd.0008118
- 2020
- 2020/03
Jun 1, 2023 · The escalating number of reported cases necessitates early detection to mitigate the risk of disease transmission. This review paper examines the application of various machine learning (ML ...
May 13, 2022 · This review is mainly focused on the different approaches available to predict dengue outbreaks. This review is divided into three major sections. The first section focuses on reviewing the overall protocol used in the application of ML/DL methods to the disease outbreak/progression prediction.
- 10.1007/s13337-022-00767-x
- 2022/06
- Virusdisease. 2022 Jun; 33(2): 125-131.
Human mobility is a key factor in the geographical spread of dengue, with the movement of infected people serving as a trigger for virus spread (Soriano-Paños et al., 2020; Tocto-Erazo et al., 2021), and using it in prediction models can improve the accuracy of dengue dynamics forecasting, helping to anticipate the area and intensity of dengue ...
Mar 31, 2022 · After incorporation of susceptible fractions, the seasonal model exhibited higher accuracy in predicting outbreaks of higher case magnitude, including those of the 2019–2020 dengue...
