Collaboration between meteorology and public health: Predicting the dengue epidemic in Guangzhou, China, by meteorological parameters

Chen, Jing; Ding, Rui-Lian; Liu, Kangkang; Hui, Xin; Hu, Gang; Xiao, Xiang; Yue, Qian; Lu, Jiahai; Yan, Han; Bu, Jin; Dong, Guang‐Hui; Lin, Yu

doi:10.3389/fcimb.2022.881745

Cited by 4 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Websites without English language options were navigated using Google translate and liaising with peers/colleagues from the country in question who developed regionally relevant search terms in the appropriate language. Peer-reviewed literature articles containing relevant data [20][21][22][23][24][25][26][27][28][29][30][31][32][33] were searched for and located using Pubmed and cross references with country profiles on GIDEON 13 . ProMED mail was used for a small number of countries with high estimated burden and large data gaps.…”

Section: Background and Summarymentioning

confidence: 99%

A global dataset of publicly available dengue case count data

Clarke,

Lim,

Gupte

et al. 2024

Sci Data

View full text Add to dashboard Cite

OpenDengue is a global database of dengue case data collated from public sources and standardised and formatted to facilitate easy reanalysis. Dataset version 1.2 of this database contains information on over 56 million dengue cases from 102 countries between 1924 and 2023, making it the largest and most comprehensive dengue case database currently available. Over 95% of records are at the weekly or monthly temporal resolution and subnational data is available for 40 countries. To build OpenDengue we systematically searched databases, ministry of health websites, peer reviewed literature and Pro-MED mail reports and extracted denominator-based case count data. We undertake standardisation and error checking protocols to ensure consistency and resolve discrepancies. We meticulously documented the extraction process to ensure records are attributable and reproducible. The OpenDengue database remains under development with plans for further disaggregation and user contributions are encouraged. This new dataset can be used to better understand the long-term drivers of dengue transmission, improve estimates of disease burden, targeting and evaluation of interventions and improving future projections.

show abstract

Section: Background and Summarymentioning

confidence: 99%

A global dataset of publicly available dengue case count data

Clarke,

Lim,

Gupte

et al. 2024

Sci Data

View full text Add to dashboard Cite

show abstract

Research on Dual GRU model driving by time series alignment for multi-step prediction of ash content of dense medium clean coal

Wang,

et al. 2024

International Journal of Coal Preparation and Utilization

View full text Add to dashboard Cite

A systematic review of dengue outbreak prediction models: Current scenario and future directions

Leung

Islam²,

Adhami³

et al. 2023

PLoS Negl Trop Dis

View full text Add to dashboard Cite

Dengue is among the fastest-spreading vector-borne infectious disease, with outbreaks often overwhelm the health system and result in huge morbidity and mortality in its endemic populations in the absence of an efficient warning system. A large number of prediction models are currently in use globally. As such, this study aimed to systematically review the published literature that used quantitative models to predict dengue outbreaks and provide insights about the current practices. A systematic search was undertaken, using the Ovid MEDLINE, EMBASE, Scopus and Web of Science databases for published citations, without time or geographical restrictions. Study selection, data extraction and management process were devised in accordance with the ‘Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies’ (‘CHARMS’) framework. A total of 99 models were included in the review from 64 studies. Most models sourced climate (94.7%) and climate change (77.8%) data from agency reports and only 59.6% of the models adjusted for reporting time lag. All included models used climate predictors; 70.7% of them were built with only climate factors. Climate factors were used in combination with climate change factors (10.3%), both climate change and demographic factors (10.3%), vector factors (5.1%), and demographic factors (5.1%). Machine learning techniques were used for 39.4% of the models. Of these, random forest (15.4%), neural networks (23.1%) and ensemble models (10.3%) were notable. Among the statistical (60.6%) models, linear regression (18.3%), Poisson regression (18.3%), generalized additive models (16.7%) and time series/autoregressive models (26.7%) were notable. Around 20.2% of the models reported no validation at all and only 5.2% reported external validation. The reporting of methodology and model performance measures were inadequate in many of the existing prediction models. This review collates plausible predictors and methodological approaches, which will contribute to robust modelling in diverse settings and populations.

show abstract

Collaboration between meteorology and public health: Predicting the dengue epidemic in Guangzhou, China, by meteorological parameters

Cited by 4 publications

References 39 publications

A global dataset of publicly available dengue case count data

A global dataset of publicly available dengue case count data

Research on Dual GRU model driving by time series alignment for multi-step prediction of ash content of dense medium clean coal

A systematic review of dengue outbreak prediction models: Current scenario and future directions

Contact Info

Product

Resources

About