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

Abstract

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 (10.3%), both climate change and demographic factors (10.3%), vector factors (5.1%), and demographic factors (5.1%). 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.

Upsurge of dengue outbreaks in several WHO regions: Public awareness, vector control activities, and international collaborations are key to prevent spread

“…On the contrary, prolonged periods of aridity followed by substantial precipitation can elicit abrupt escalations in mosquito populations 35,36 . Climate change contributes to the intensification of these processes, enlarging the geographical distribution of Aedes mosquitoes, and modifying the temporal patterns of dengue outbreaks 36,37 . The exponential expansion of urban centers and metropolitan regions has fostered an environment conducive to transmitting dengue fever.…”

“… 35 , 36 Climate change contributes to the intensification of these processes, enlarging the geographical distribution of Aedes mosquitoes, and modifying the temporal patterns of dengue outbreaks. 36 , 37 The exponential expansion of urban centers and metropolitan regions has fostered an environment conducive to transmitting dengue fever. Dense populations facilitate the persistent availability of human hosts for mosquitoes, hence augmenting the probability of virus transmission.…”

Climate change and its impact on infectious diseases in Asia

“…However, key shortcomings in current models include reliance on secondary predictor data, exclusion of non-climate variables and inadequate reporting of methodology, validation and performance measures. [ 86 ]…”

Estimating dynamics of dengue disease with environmental impact by quantifying the per-capita vector density

“…Therefore, the vector population significantly impact the dengue transmission within human populations. Environmental conditions directly impact the mosquito population density and therefore can create high sensitivity in dengue transmission 7,[11][12][13][14][15] . The warmer and humid environments, typically between 25 − 30 0 C (77 − 86 0 F) of temperatures and above 60% of humidity levels provide a highly favorable environment for the Aedes mosquitoes causing rapid transmission [16][17][18] .…”