Early warning systems (EWSs) for chikungunya, dengue, malaria, yellow fever, and Zika outbreaks: What is the evidence? A scoping review

Abstract

Background Early 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. Methods Data 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. Findings Through 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. Conclusions In 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.

The Early Warning and Response System (EWARS-TDR) for dengue outbreaks: can it also be applied to chikungunya and Zika outbreak warning?

“…Recent analysis of the evidence indicates that early warning and response system that are capable of demonstrating evidence of prospective predictive ability and allows technical and practical adaptations of local public health responses while augmenting communications channels between users at central and district levels are tools that are more likely to be implemented into national surveillance programs [27]. In this sense, EWARS has moved towards frameworks that facilitate low-cost IT maintenance and adapt to unskilled users.…”

“…In this sense, EWARS has moved towards frameworks that facilitate low-cost IT maintenance and adapt to unskilled users. The aim is to form a tool that can be plausibly integrated into existing national systems [25,27].…”

“…While several studies are existing in the literature to demonstrate the applications of prediction models for dengue outbreaks, the EWARS is also useful for Zika and chikungunya outbreak prediction. Based on a recent scoping review study [27], five models showed outstanding performance in dengue outbreak prediction; (i) the dynamic risk maps absolute shrinkage and selection operator (LASSO) processing multiple meteorological information; (ii) the auto-regression integrated moving average (ARIMA) using meteorological information and Google Trends data; (iii) the Shewhart moving average regression model (SMAR) maintaining a combination of meteorological, epidemiological, and entomological alarm indicators, which is the model employed in our study; (iv) the seasonal autoregressive integrated moving average (SARIMA); and (v) the stochastic Bayesian maximum entropy (BME) model using a mix of meteorological and entomological alarm indicator. Despite the fact that LASSO models seem to be toping the tools performance, these models were declared unamenable to easy and direct interpretation and usually demand advancelevel of historical data, which limits their applications.…”

Viral hemorrhagic fevers - a recurrent public health threat

“…The involvement of an infection vector is necessary for the transmission of the dengue pathogen to occur [2]. Its distribution range includes the tropical belt and subtropical regions around the world, mainly in south-eastern areas of Asia and the islands of Oceania [12,13]. It should be noted that at higher altitudes (above 1000 m above sea level), there is a lower risk of contracting dengue hemorrhagic fever.…”

Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling

“…Dengue fever is a mosquito-borne viral disease mainly transmitted in urban and suburban areas in tropical and subtropical regions worldwide and tends to expand to new areas [ 1 , 2 ]. A dengue early warning system (EWS) permits the accurate forecasting of dengue outbreaks in advance and provides sufficient time to implement preventive measures [ 3 ], which often requires routine access to dengue data collected within administrative units [ 4 , 5 ] and a set of climate and environmental factors affecting the number and spatial distribution of dengue mosquito vectors (i.e., Aedes aegypti and Aedes albopictus ), such as rainfall, air temperature, relative humidity data from in situ observations, and normalized difference vegetation index (NDVI) from remote sensing [ 6 , 7 , 8 ]. However, efficient and accurate dengue forecasting faces challenges.…”