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

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 (13.4%), both climate change and demographic factors (3.1%), vector factors (6.3%), and demographic factors (5.2%). 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.


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Abstract 21 Dengue is among the fastest-spreading vector-borne infectious disease, with outbreaks often overwhelm the 22 health system and result in huge morbidity and mortality in its endemic populations in the absence of an 23 efficient warning system. A large number of prediction models are currently in use globally. As

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The case-fatality rate can be as high as 20% in the absence of prompt diagnosis and lack of specific antiviral 49 drugs or vaccines, 6,7 particularly in resource-limited settings. When the outbreak is particularly large, the 50 influx of severe dengue cases can overwhelm the health system and prevent optimal care. Dengue also 51 imposes an enormous societal and economic burden on many of the tropical countries where the disease is 52 endemic. 8 An accurate prediction of the size of the outbreak and trends in disease incidence early enough 53 can limit further transmission, 5 and is likely to facilitate planning the allocation of healthcare resources to 54 meet demand during an outbreak.

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Vector-borne pathogens characteristically demonstrate spatial heterogeneity -a result of spatial variation in 56 vector habitat, climate patterns and subsequent human control actions. 9-11 The interplay of human, climate 57 and mosquito dynamics give rise to a complex system that determines the pattern of dengue transmission, 58 which in turn influences the potential for outbreak. 12 These relationships have been explored over the 59 decades in the development of predictive models worldwide. Models vary widely in their purposes 13-15 and 60 settings. 16-21 Many of these models excel at different tasks, however for a prediction model to be efficient, it 61 requires a systematic, self-adaptive and generalizable framework capable of identifying weather and  information specialist from Monash University Library. For the purposes of this study, dengue fever or 88 dengue haemorrhagic fever or dengue shock syndrome were considered as a single entity "dengue". Search 89 strategy included Medical Subject Headings ('MeSH') and keyword terms including "dengue", "severe 90 dengue," "weather," "climate change," "model," "predict," and "forecast." The detailed search strategy 91 and history are presented in S1 Table. 92 The review included studies focused on (1) prognostic prediction models which aim to review models 93 predicting future events, (2) models intended to inform public health divisions of future dengue outbreaks,

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(3) prediction model development studies without external validation, or with external validation in 95 independent data, (4) people with dengue or dengue fever or dengue haemorrhagic fever, (5) the number 96 of dengue cases based on climate and other factors, (6) models with no restrictions on the time span of 97 prediction and (7) models to be used to predict the number of cases before outbreaks.

Role of the funding source 133
There was no funding for this study.

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The initial search yielded 4871 studies. After duplicates were removed, 1846 studies were screened for titles 139 and abstracts. This led to 140 studies for full text review, and 51 that strictly met the inclusion criteria (Fig 1),

Conflict of interest 307
The authors declare that they have no conflicts of interest.