BackgroundSeveral studies have applied ecological factors such as meteorological variables to develop models and accurately predict the temporal pattern of dengue incidence or occurrence. With the vast amount of studies that investigated this premise, the modeling approaches differ from each study and only use a single statistical technique. It raises the question of whether which technique would be robust and reliable. Hence, our study aims to compare the predictive accuracy of the temporal pattern of Dengue incidence in Metropolitan Manila as influenced by meteorological factors from four modeling techniques, (a) General Additive Modeling, (b) Seasonal Autoregressive Integrated Moving Average with exogenous variables (c) Random Forest and (d) Gradient Boosting.MethodsDengue incidence and meteorological data (flood, precipitation, temperature, southern oscillation index, relative humidity, wind speed and direction) of Metropolitan Manila from January 1, 2009 – December 31, 2013 were obtained from respective government agencies. Two types of datasets were used in the analysis; observed meteorological factors (MF) and its corresponding delayed or lagged effect (LG). After which, these datasets were subjected to the four modeling techniques. The predictive accuracy and variable importance of each modeling technique were calculated and evaluated.ResultsAmong the statistical modeling techniques, Random Forest showed the best predictive accuracy. Moreover, the delayed or lag effects of the meteorological variables was shown to be the best dataset to use for such purpose. Thus, the model of Random Forest with delayed meteorological effects (RF-LG) was deemed the best among all assessed models. Relative humidity was shown to be the top-most important meteorological factor in the best model.ConclusionThe study exhibited that there are indeed different predictive outcomes generated from each statistical modeling technique and it further revealed that the Random forest model with delayed meteorological effects to be the best in predicting the temporal pattern of Dengue incidence in Metropolitan Manila. It is also noteworthy that the study also identified relative humidity as an important meteorological factor along with rainfall and temperature that can influence this temporal pattern.Electronic supplementary materialThe online version of this article (10.1186/s12879-018-3066-0) contains supplementary material, which is available to authorized users.

Dengue is a highly endemic disease in Southeast Asia and is transmitted primarily by the mosquito, Aedes aegypti. The National Capital Region (NCR) of the Philippines, or Metropolitan Manila, is a highly urbanized area that is greatly affected by this arboviral disease. Urbanization has been shown to increase the dispersal of this mosquito vector. For this reason, we conducted a fine-scale population genetic study of Ae. aegypti in this region. We collected adult Ae. aegypti mosquitoes (n = 526 individuals) within the region (n = 21 study areas) and characterized the present population structure and the genetic relatedness among mosquito populations. We genotyped 11 microsatellite loci from all sampled mosquito individuals and analyzed their genetic diversity, differentiation and structure. The results revealed low genetic differentiation across mosquito populations which suggest high gene flow and/or weak genetic drift among mosquito populations. Bayesian analysis indicated multiple genetic structures (K = 3-6), with no clear genetically distinct population structures. This result implies the passive or long-distance dispersal capability nature Ae. aegypti possibly through human-mediated transportation. The constructed dendrogram in this study describes the potential passive dispersal patterns across Metropolitan Manila. Furthermore, spatial autocorrelation analysis showed the limited and active dispersal capability (<1km) of the mosquito vector. Our findings are consistent with previous studies that investigated the genetic structure and dual (active and passive) dispersal capability of Ae. aegypti in a fine-scale highly urbanized area.

Dengue is a major public health concern and an economic burden in the Philippines. Despite the country’s improved dengue surveillance, it still suffers from various setbacks and therefore needs to be complemented with alternative approaches. Previous studies have demonstrated the potential of internet-based surveillance such as Google Dengue Trends (GDT) in supplementing current epidemiological methods for predicting future dengue outbreaks and patterns. With this, our study aims to assess the temporal relationship of GDT and dengue incidence in Metropolitan Manila from previous years and examine web search behavior of the population towards the disease. The study collated and organized the population statistics and reported dengue cases in Metropolitan Manila from respective government agencies to calculate the spatial and temporal dengue incidence. The relative search volume of the term ‘dengue’ and top dengue-related search queries in Metropolitan Manila were obtained and organized from the Google trends platform. Data processing of GDT and dengue incidence was performed by conducting an ‘adjustment’ procedure and subsequently used for correlation and cross-correlation analyses. Moreover, a thematic analysis was employed on the top dengue-related search queries. Results revealed a high temporal relationship between GDT and dengue incidence when either one of the variables is adjusted. Cross-correlation showed that there is delayed effect (1-2 weeks) of GDT to dengue incidence, demonstrating its potential in predicting future dengue outbreaks and patterns in Metropolitan Manila. Thematic analysis of dengue-related search queries indicated 5 categories namely; (a) dengue, (b) sign and symptoms of dengue, (c) treatment and prevention, (d) mosquito and (e) other diseases where the majority of the search queries was ‘signs and symptoms’ which indicate the health-seeking behavior of the population towards the disease.

Dengue is a major public health concern and an economic burden in the Philippines. Despite the country’s improved dengue surveillance, it still suffers from various setbacks and needs to be complemented with alternative approaches. Previous studies have demonstrated the potential of Internet-based surveillance such as Google Dengue Trends (GDT) in supplementing current epidemiological methods for predicting future dengue outbreaks and patterns. With this, our study has two objectives: (1) assess the temporal relationship of weekly GDT and dengue incidence in Metropolitan Manila from 2009–2014; and (2) examine the health-seeking behavior based on dengue-related search queries of the population. The study collated the population statistics and reported dengue cases in Metropolitan Manila from respective government agencies to calculate the dengue incidence (DI) on a weekly basis for the entire region and annually per city. Data processing of GDT and dengue incidence was performed by conducting an ‘adjustment’ and scaling procedures, respectively, and further analyzed for correlation and cross-correlation analyses using Pearson’s correlation. The relative search volume of the term ‘dengue’ and top dengue-related search queries in Metropolitan Manila were obtained and organized from the Google Trends platform. Afterwards, a thematic analysis was employed, and word clouds were generated to examine the health behavior of the population. Results showed that weekly temporal GDT pattern are closely similar to the weekly DI pattern in Metropolitan Manila. Further analysis showed that GDT has a moderate and positive association with DI when adjusted or scaled, respectively. Cross-correlation analysis revealed a delayed effect where GDT leads DI by 1–2 weeks. Thematic analysis of dengue-related search queries indicated 5 categories namely; (a) dengue, (b) sign and symptoms of dengue, (c) treatment and prevention, (d) mosquito, and (e) other diseases. The majority of the search queries were classified in ‘signs and symptoms’ which indicate the health-seeking behavior of the population towards the disease. Therefore, GDT can be utilized to complement traditional disease surveillance methods combined with other factors that could potentially identify dengue hotspots and help in public health decisions.

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