Early warning systems (EWSs) for chikungunya, dengue, malaria, yellow fever, and Zika outbreaks

• Verfasst von: Hussain-Alkhateeb, Laith [VerfasserIn]
• Verfasst von: Rivera Ramírez, Tatiana [VerfasserIn]
• Verfasst von: Kroeger, Axel [VerfasserIn]
• Verfasst von: Gozzer, Ernesto [VerfasserIn]
• Verfasst von: Runge-Ranzinger, Silvia [VerfasserIn]
• Titel: Early warning systems (EWSs) for chikungunya, dengue, malaria, yellow fever, and Zika outbreaks
• Titelzusatz: What is the evidence? : A scoping review
• Verf.angabe: Laith Hussain-Alkhateeb, Tatiana Rivera Ramírez, Axel Kroeger, Ernesto Gozzer, Silvia Runge-Ranzinger
• E-Jahr: 2021
• Jahr: September 16, 2021
• Umfang: 25 S.
• Fussnoten: Gesehen am 10.02.2022
• Titel Quelle: Enthalten in: Public Library of SciencePLoS neglected tropical diseases
• Ort Quelle: Lawrence, Kan. : PLoS, 2007
• Jahr Quelle: 2021
• Band/Heft Quelle: 15(2021), 9, Artikel-ID e0009686, Seite 1-25
• ISSN Quelle: 1935-2735
• DOI: doi:10.1371/journal.pntd.0009686
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: climate
• Sach-SW: diseases
• Sach-SW: dynamics
• Sach-SW: epidemic
• Sach-SW: models
• Sach-SW: mosquito
• Sach-SW: prediction
• Sach-SW: spread
• Sach-SW: transmission
• Sach-SW: variability
• K10plus-PPN: 1789211220

Abstract

BackgroundEarly warning systems (EWSs) are of increasing importance in the context of outbreak-prone diseases such as chikungunya, dengue, malaria, yellow fever, and Zika. A scoping review has been undertaken for all 5 diseases to summarize existing evidence of EWS tools in terms of their structural and statistical designs, feasibility of integration and implementation into national surveillance programs, and the users' perspective of their applications. MethodsData were extracted from Cochrane Database of Systematic Reviews (CDSR), Google Scholar, Latin American and Caribbean Health Sciences Literature (LILACS), PubMed, Web of Science, and WHO Library Database (WHOLIS) databases until August 2019. Included were studies reporting on (a) experiences with existing EWS, including implemented tools; and (b) the development or implementation of EWS in a particular setting. No restrictions were applied regarding year of publication, language or geographical area. FindingsThrough the first screening, 11,710 documents for dengue, 2,757 for Zika, 2,706 for chikungunya, 24,611 for malaria, and 4,963 for yellow fever were identified. After applying the selection criteria, a total of 37 studies were included in this review. Key findings were the following: (1) a large number of studies showed the quality performance of their prediction models but except for dengue outbreaks, only few presented statistical prediction validity of EWS; (2) while entomological, epidemiological, and social media alarm indicators are potentially useful for outbreak warning, almost all studies focus primarily or exclusively on meteorological indicators, which tends to limit the prediction capacity; (3) no assessment of the integration of the EWS into a routine surveillance system could be found, and only few studies addressed the users' perspective of the tool; (4) almost all EWS tools require highly skilled users with advanced statistics; and (5) spatial prediction remains a limitation with no tool currently able to map high transmission areas at small spatial level. ConclusionsIn view of the escalating infectious diseases as global threats, gaps and challenges are significantly present within the EWS applications. While some advanced EWS showed high prediction abilities, the scarcity of tool assessments in terms of integration into existing national surveillance systems as well as of the feasibility of transforming model outputs into local vector control or action plans tends to limit in most cases the support of countries in controlling disease outbreaks.