Assessing the interplay between dengue incidence and weather in Jakarta via a clustering integrated multiple regression model

Fakhruddin, Muhammad; Putra, Prama Setia; Wijaya, Karunia Putra; Sopaheluwakan, Ardhasena; Satyaningsih, Ratna; Komalasari, Kurnia Endah; Mamenun,; Sumiati, Sumiati; Indratno, Sapto Wahyu; Nuraini, Nuning; Götz, Thomas; Soewono, Edy

doi:10.1016/j.ecocom.2019.100768

Cited by 16 publications

(14 citation statements)

References 30 publications

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“…The aims of clustering in our study are not only to increase the number of explanatory variables but also to drop meteorological data that correspond to the zero incidence levels. The clustering idea is inspired by our previous work on seasonal models in (Fakhruddin et al, 2019)…”

Section: Clusteringmentioning

confidence: 99%

Learning from panel data of dengue incidence and meteorological factors in Jakarta, Indonesia

Wijaya

Aldila

Erandi

et al. 2020

Stoch Environ Res Risk Assess

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Medical statistics collected by WHO indicates that dengue fever is still ravaging developing regions with climates befitting mosquito breeding amidst moderate-toweak health systems. This work initiates a study over 2009-2017 panel data of dengue incidences and meteorological factors in Jakarta, Indonesia to bear particular understanding. Using a panel random-effect model joined by the pooled estimator, we show positively significant relationships between the incidence level and meteorological factors. We ideate a clustering strategy to decompose the meteorological datasets into several more datasets such that more ex-

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Section: Clusteringmentioning

confidence: 99%

Learning from panel data of dengue incidence and meteorological factors in Jakarta, Indonesia

Wijaya

Aldila

Erandi

et al. 2020

Stoch Environ Res Risk Assess

Self Cite

View full text Add to dashboard Cite

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“…Infected symptomatic and asymptomatic hosts are carriers of the virus for susceptible vectors. After that, the recovered human develops a permanent immunity against the infecting serotype but only temporary cross-protective immunity to different serotypes ( Esteva & Vargas, 1998 ; Fakhruddin et al., 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Climate-based dengue model in Semarang, Indonesia: Predictions and descriptive analysis

Nuraini

Fauzi

Fakhruddin

et al. 2021

Infectious Disease Modelling

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Background Dengue is one of the most rapidly spreading vector-borne diseases, which is considered to be a major health concern in tropical and sub-tropical countries. It is strongly believed that the spread and abundance of vectors are related to climate. Construction of climate-based mathematical model that integrates meteorological factors into disease infection model becomes compelling challenge since the climate is positively associated with both incidence and vector existence. Methods A host-vector model is constructed to simulate the dynamic of transmission. The infection rate parameter is replaced with the time-dependent coefficient obtained by optimization to approximate the daily dengue data. Further, the optimized infection rate is denoted as a function of climate variables using the Autoregressive Distributed Lag (ARDL) model. Results The infection parameter can be extended when updated daily climates are known, and it can be useful to forecast dengue incidence. This approach provides proper prediction, even when tested in increasing or decreasing prediction windows. In addition, associations between climate and dengue are presented as a reversed slide-shaped curve for dengue-humidity and a reversed U-shaped curves for dengue-temperature and dengue-precipitation. The range of optimal temperature for infection is 24.3–30.5 °C. Humidity and precipitation are positively associated with dengue upper the threshold 70 % at lag 38 days and below 50 mm at lag 50 days, respectively. Conclusion Identification of association between climate and dengue is potentially useful to counter the high risk of dengue and strengthen the public health system and reduce the increase of the dengue burden.

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“…The factors related to the spread of these diseases have been documented by studies completed in Nakhon Si Thammarat and Patthalung provinces of Thailand [8], as well as in Indonesia [9] and Taiwan [10]. Weather parameters have widely been studied as substantial factors that need to be considered for better understanding of dengue transmission worldwide [8,9,11,12].…”

Section: Introductionmentioning

confidence: 99%

Dengue Transmission Mapping with Weather-Based Predictive Model in Three Southernmost Provinces of Thailand

2021

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This study aimed to show maps and analyses that display dengue cases and weather-related factors on dengue transmission in the three southernmost provinces of Thailand, namely Pattani, Yala, and Narathiwat provinces. Data on the number of dengue cases and weather variables including rainfall, rainy day, mean temperature, min temperature, max temperature, relative humidity, and air pressure for the period from January 2015 to December 2019 were obtained from the Bureau of Epidemiology, Ministry of Public Health and the Meteorological Department of Southern Thailand, respectively. Spearman rank correlation test was performed at lags from zero to two months and the predictive modeling used time series Poisson regression analysis. The distribution of dengue cases showed that in Pattani and Yala provinces the most dengue cases occurred in June. Narathiwat province had the most dengue cases occurring in August. The air pressure, relative humidity, rainfall, rainy day, and min temperature are the main predictors in Pattani province, while air pressure, rainy day, and max/mean temperature seem to play important roles in the number of dengue cases in Yala and Narathiwat provinces. The goodness-of-fit analyses reveal that the model fits the data reasonably well. The results provide scientific information for creating effective dengue control programs in the community, and the predictive model can support decision making in public health organizations and for management of the environmental risk area.

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Assessing the interplay between dengue incidence and weather in Jakarta via a clustering integrated multiple regression model

Cited by 16 publications

References 30 publications

Learning from panel data of dengue incidence and meteorological factors in Jakarta, Indonesia

Learning from panel data of dengue incidence and meteorological factors in Jakarta, Indonesia

Climate-based dengue model in Semarang, Indonesia: Predictions and descriptive analysis

Dengue Transmission Mapping with Weather-Based Predictive Model in Three Southernmost Provinces of Thailand

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