Forecasting Dengue Incidence Utilizing Geographic Information System and Autoregressive Integrated Moving Average Models

Orong, Markdy Y.; Ganapathy, G.P.; Durias, Geraldine D.; Paredes, Rolysent K.; Pasay, Jezreel Marc E.

doi:10.7828/jmds.v4i1.854

Cited by 3 publications

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Mitigating dengue incidence through advanced Aedes larval surveillance and control: A successful experience from Pakistan

Abbas,

Alam

et al. 2024

Bull. Entomol. Res.

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Dengue fever is a viral disease caused by one of four dengue stereotypes (Flavivirus: Flaviviridae) that are primarily transmitted by Aedes albopictus (Skuse) and Aedes aegypti (L.). To safeguard public health, it is crucial to conduct surveys that examine the factors favouring the presence of these species. Our study surveyed 42 councils across four towns within the Bhakkar district of Punjab Province, by inspecting man-made or natural habitats containing standing water. First, door-to-door surveillance teams from the district health department were assigned to each council to surveillance Aedes species and dengue cases. Second, data collection through surveillance efforts, and validation procedures were implemented, and the verified data was uploaded onto the Dengue Tracking System by Third Party Validation teams. Third, data were analysed to identify factors influencing dengue fever cases. The findings demonstrated the following: (1) Predominantly, instances were discerned among individuals who had a documented history of having travelled beyond the confines of the province. (2) Containers associated with evaporative air coolers and tyre shops were responsible for approximately 30% of the Aedes developmental sites. (4) Variability in temperature was responsible for approximately 45% of the observed differences in the quantity of recorded Aedes mosquito developmental sites. (5) Implementation of dengue prevention initiatives precipitated a 50% reduction in Aedes-positive containers, alongside a notable 70% decline in reported cases of dengue fever during the period spanning 2019 to 2020, while the majority of reported cases were of external origin. Aedes control measures substantially curtailed mosquito populations and lowered vector-virus interactions. Notably, local dengue transmission was eliminated through advanced and effective Aedes control efforts, emphasising the need for persistent surveillance and eradication of larval habitats in affected regions.

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Mitigating dengue incidence through advanced Aedes larval surveillance and control: A successful experience from Pakistan

Abbas,

Alam

et al. 2024

Bull. Entomol. Res.

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Mitigating vulnerabilities through forecasting and crime trend analysis

Orong

Sison

Hernandez

2018

2018 5th International Conference on Business and Industrial Research (ICBIR)

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Epidemiology Forecasting Analysis of Dengue Cases with Seasonal Autoregressive Integrated Moving Average in Davao City, Philippines

Plaza,

Servillon,

Alempang

et al. 2024

Preprint

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Dengue is a mosquito-borne illness and an important public health problem in the Philippines, with the highest number of dengue cases in Southeast Asia. The Philippine government has created a dengue surveillance map to track illness transmission; however, model-based methods for forecasting and detecting dengue have not been imposed and are significantly sought-after throughout the country. In this study, a suitable time-based model for predicting dengue cases was used. Using the seasonal autoregressive integrated moving average (SARIMA) model, this study developed a forecasting model to predict dengue cases in Davao City, Philippines from to 2020–2025 based on data gathered from the Department of Health Region XI from years 2013–2019. The root mean square error (RMSE) and mean absolute percentage error (MAPE) were used as criteria for choosing an accurate forecasting model, and the QGIS version 3.16. was used for mapping. The results showed a monthly variation in the spike of dengue cases, peaking during July and August in both past and forecasted cases. The combined forecast model was found to be the best-fitting model for predicting future dengue incidences in the administrative districts of Davao City for the years 2020–2025.

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Forecasting Dengue Incidence Utilizing Geographic Information System and Autoregressive Integrated Moving Average Models

Cited by 3 publications

References 0 publications

Mitigating dengue incidence through advanced Aedes larval surveillance and control: A successful experience from Pakistan

Mitigating dengue incidence through advanced Aedes larval surveillance and control: A successful experience from Pakistan

Mitigating vulnerabilities through forecasting and crime trend analysis

Epidemiology Forecasting Analysis of Dengue Cases with Seasonal Autoregressive Integrated Moving Average in Davao City, Philippines

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