Dengue Early Warning System as Outbreak Prediction Tool: A Systematic Review

Abstract

Early warning system (EWS) for vector-borne diseases is incredibly complex due to numerous factors originating from human, environmental, vector and the disease itself. Dengue EWS aims to collect data that leads to prompt decision-making processes that trigger disease intervention strategies to minimize the impact on a specific population. Dengue EWS may have a similar structural design, functions, and analytical approaches but different performance and ability to predict outbreaks. Hence, this review aims to summarise and discuss the evidence of different EWSs, their performance, and their ability to predict dengue outbreaks. A systematic literature search was performed of four primary databases: Scopus, Web of Science, Ovid MEDLINE, and EBSCOhost. Eligible articles were evaluated using a checklist for assessing the quality of the studies. A total of 17 studies were included in this systematic review. All EWS models demonstrated reasonably good predictive abilities to predict dengue outbreaks. However, the accuracy of their predictions varied greatly depending on the model used and the data quality. The reported sensitivity ranged from 50 to 100%, while specificity was 74 to 94.7%. A range between 70 to 96.3% was reported for prediction model accuracy and 43 to 86% for PPV. Overall, meteorological alarm indicators (temperatures and rainfall) were the most frequently used and displayed the best performing indicator. Other potential alarm indicators are entomology (female mosquito infection rate), epidemiology, population and socioeconomic factors. EWS is an essential tool to support district health managers and national health planners to mitigate or prevent disease outbreaks. This systematic review highlights the benefits of integrating several epidemiological tools focusing on incorporating climatic, environmental, epidemiological and socioeconomic factors to create an early warning system. The early warning system relies heavily on the country surveillance system. The lack of timely and high-quality data is critical for developing an effective EWS.

Effectiveness of early warning systems in the detection of infectious diseases outbreaks: a systematic review

“…There is scope to explore the One-health approach, including environmental and veterinary surveillance systems besides human-based ones. Our recommendation is aligned with a recent systematic review on EWSs for “vector-borne diseases” where Baharom et al highlighted that incorporating meteorological and environmental surveillance systems with other “epidemiological tools” is a very promising strategy for outbreaks detection [ 80 ].…”

Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques

“…Early warning systems (EWS) for vector-borne diseases are incredibly complex due to numerous factors originating from individuals, the environment, the vector, and the disease itself. However, creating reliable forecasting models may lead to fast decision-making processes that trigger disease intervention strategies to minimize the impact on a specific population [ 40 ]. A finer study scale with local predictive outbreak risks is necessary because global models may depict the general situation.…”

Spatial Model of Dengue Hemorrhagic Fever (DHF) Risk: Scoping Review

“…Systematic searching strategies include identi cation, screening, and the eligibility process. In the identi cation stage, synonyms and variations were used to enrich the keywords, then applied in the search process [11]. The combinations of search terms we use include; i) "spatial model" and "Dengue risk"; ii) "risk" and "Dengue" and "spatial analysis"; iii) "hazard" and "Dengue" and "Geographic Information System" iv) "vulnerability" and "Dengue" and "Geographic Information System"; v) "capacity" and "Dengue" and "spatial analysis"; vi) "Factor in uence" and "dengue risk".…”

“…Another study by Louis et al (2014) [7] focuses on risk-mapping for DHF from 2005-2013. Baharom et al [11] reviewed the most recent literature (2014-2021) and discuss the evidence for various Early Warning Systems, their performance, and their ability to predict DHF outbreaks. As a result, the purpose of this review is to summarize the most recent literature on the risk model of DHF (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021), as well as to compare the most important predictors and the most commonly used modeling methods in order to generate speci c types of risk maps with varying applicability and relevance for public health decisions.…”