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Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines.

ABSTRACT: BACKGROUND:Several 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. METHODS:Dengue 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. RESULTS:Among 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. CONCLUSION:The 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.

SUBMITTER: Carvajal TM

PROVIDER: S-EPMC5905126 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines.

Carvajal Thaddeus M TM Viacrusis Katherine M KM Hernandez Lara Fides T LFT Ho Howell T HT Amalin Divina M DM Watanabe Kozo K

BMC infectious diseases 20180417 1

<h4>Background</h4>Several 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 tem ...[more]

PMID: 29665781

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Project description: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.

Project description:IntroductionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belonging to the family Coronaviridae and genus Betacoronavirus is the causative agent of COVID-19 disease and was first identified in Wuhan, China. SARS-CoV-2 spread globally with >28 million cases and 911,000 deaths recorded worldwide as of September 12, 2020. The Philippines reported the first case of community transmission on March 5, 2020, and despite the government imposing one of the longest and strictest lockdowns in Southeast Asia, the number of confirmed COVID-19 cases still surged with >250,000 cases and 4,000 deaths reported as of September 12, 2020. It is important to estimate the burden and impact of SARS-CoV-2 on the military population since this can affect the military readiness.Materials and methodsNasopharyngeal and oropharyngeal swabs were collected and SARS-CoV-2 real-time RT-PCR testing was performed on the samples. Phylogenetic analysis was performed using sequences from 23 SARS-CoV-2-positive specimens from this study and sequences retrieved from GenBank and GISAID databases.ResultsFrom April 14 to August 15, 2020, a total of 12,432 specimens were tested with 763 (6%) unique individuals testing positive for SARS-CoV-2 by rRT-PCR. In the military population, majority of the patients who were tested (80%) and those who tested positive for SARS-CoV-2 (86%) were male. Military and civilian status was available for 7,672 patients with 515/5,042 (10%) positive among military patients and 248/2,630 (9%) positive among civilian patients. Both military and civilian populations had the highest case counts of SARS-CoV-2-positive cases in the 21- to 30- and 31- to 40-year-old age groups, while the 71- to 80-year-old age group had the highest proportion (18%) of SARS-CoV-2-positive cases. Sequencing analysis showed 19 different variants in the 23 genomes. Twenty of the 23 genomes were classified under clade GR/B1.1, 2 genomes were classified under clade GR/B1.1.28, and 1 genome was classified under Clade O/B.6. Twenty-two of the 23 sequences collected after June 25, 2020, contained the D614G mutation.ConclusionWe describe here the results of SARS-CoV-2 testing for military and civilian patients and personnel. The 21- to 30- and 31- to 40-year-old age groups had the highest case counts of SARS-CoV-2-positive cases. Sequencing results showed the presence of the D614G mutation in the spike protein in a majority of specimens collected from the end of June to July 2020.

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Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines.

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Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines.

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