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A global dataset of publicly available dengue case count data.
Clarke, J; Lim, A; Gupte, P; Pigott, D M; van Panhuis, W G; Brady, O J.
Affiliation
  • Clarke J; Department of Infectious Disease Epidemiology and Dynamics, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.
  • Lim A; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.
  • Gupte P; Department of Infectious Disease Epidemiology and Dynamics, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.
  • Pigott DM; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.
  • van Panhuis WG; Department of Infectious Disease Epidemiology and Dynamics, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.
  • Brady OJ; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.
Sci Data ; 11(1): 296, 2024 Mar 14.
Article in En | MEDLINE | ID: mdl-38485954
ABSTRACT
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.
Subject(s)

Full text: 1 Collection: 01-internacional Health context: 2_ODS3 / 3_ND Database: MEDLINE Main subject: Global Health / Dengue Limits: Humans Language: En Journal: Sci Data Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Health context: 2_ODS3 / 3_ND Database: MEDLINE Main subject: Global Health / Dengue Limits: Humans Language: En Journal: Sci Data Year: 2024 Document type: Article