A comparative analysis of frequentist and Bayesian approaches to estimate dengue disease transmission in Bandung-Indonesia

Kristiani, Farah; Claudia, Yemima; Yong, Benny; Hilsdon, Anne-Marie

doi:10.1080/09720510.2020.1756049

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…Beberapa penelitian terkait pemodelan DBD menggunakan metode Bayesian spasial adalah pemetaan risiko relatif DBD di Bandung, Provinsi Jawa Barat menggunakan model Bayesian spatio-temporal [2], perbandingan metode frekuentif dan metode Bayesian dalam mengestimasi risiko relatif kasus DBD di Kota Bandung, Provinsi Jawa Barat [3]. Selanjutnya, pemodelan DBD di Pamekasan, Provinsi Jawa Timur dengan menggunakan model Double Exponential CAR Frailty telah dilakukan [4].…”

Section: Pendahuluanunclassified

Pemodelan Bayesian Spasial Conditional Autoregressive (Car) Pada Kasus Demam Berdarah Dengue Di Indonesia

Aswi

Sukarna²

2022

msa

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Demam Berdarah Dengue (DBD) merupakan salah satu penyakit menular yang masih merupakan masalah utama dalam kesehatan masyarakat di Indonesia. Total kasus DBD di Indonesia pada tahun 2020 masih cukup tinggi yaitu 108.303 kasus. Beberapa penelitian terkait pemodelan DBD telah menggunakan metode Bayesian spasial. Akan tetapi, penelitian tersebut masih fokus pada salah satu provinsi yang ada di Indonesia. Penelitian ini bertujuan untuk memodelkan risiko relatif (RR) kasus DBD tahun 2020 di Indonesia dengan 34 provinsi dan menghasilkan peta tematik RR. Data yang digunakan data kasus DBD serta data jumlah penduduk di Indonesia tahun 2020 yang diperoleh dari Publikasi Kementerian Kesehatan Republik Indonesia 2021. Model Bayesian spasial Conditional Autoregressive (CAR) Leroux digunakan dengan pemilihan model terbaik didasarkan pada Deviance Information Criteria (DIC), Watanabe Akaike Information Criteria (WAIC), dan Modifikasi Moran’s I (MMI) untuk residual. Hasil yang diperoleh menunjukkan bahwa model Bayesian spasial CAR Leroux dengan hyperprior IG(1; 0,1) merupakan model terbaik dalam pemodelan kasus DBD tahun 2020 di Indonesia. Sekitar 53% provinsi yang ada di Indonesia merupakan wilayah dengan RR tinggi, dimana Provinsi Bali memiliki nilai RR tertinggi (6,84), diikuti oleh Provinsi Nusa Tenggara Timur (RR=2,70), dan Daerah Istimewa Yogyakarta (RR=2,33). Sebaliknya, provinsi dengan RR terendah adalah Provinsi Maluku (RR=0,11), diikuti oleh Provinsi Papua (RR = 0,13), dan Provinsi Kalimantan Barat (RR =0,38).

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Section: Pendahuluanunclassified

Pemodelan Bayesian Spasial Conditional Autoregressive (Car) Pada Kasus Demam Berdarah Dengue Di Indonesia

Aswi

Sukarna²

2022

msa

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“…At various periods and places, these climatic conditions affect the occurrence of dengue hemorrhagic fever [4]. For modeling, predicting, and mitigating DHF risk, the majority of research used Bayesian techniques like conditional autoregressive [5], Poisson [6] [7], and negative binomial [6], which could only be used to study the interaction of two risk variables that cause dengue.…”

Section: Introductionmentioning

confidence: 99%

Hybrid K Means-Multivariate Adaptive Regression Splines for Distribution of Dengue Fever Risk Mapping in Bojonegoro District

Kartini

Cahyani

2023

BAREKENG: J. Math. & App.

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Dengue Hemorrhagic Fever (DHF) is a dangerous disease transmitted by Aedes aegypti and Aedes albopictus mosquitoes’ bites. WHO data shows that almost half of the world's humans are exposed to Dengue Hemorrhagic Fever. The number of mortality caused by dengue disease is around 20,000 every year. In East Java, Bojonegoro District has the highest number of dengue hemorrhagic fever cases (416). To reduce this number, the causative factors need to be known. Additionally, it's important to pinpoint the region or cluster where the variables driving the spread are located so that prevention and treatment efforts are effective. Based on the elements contributing to the transmission of Dengue Hemorrhagic Fever, this study seeks to identify and categorize locations at risk for the spread of the illness. This study uses Hybrid K Means-Multivariate Adaptive Regression Splines (MARS) which is a combination of K-Means and MARS methods in the hope of providing better analytical results. This is because the data was divided into simpler parts by considering the Oakley distance. The results obtained from the K Means-MARS hybrid shows the relationship between response variables and predictor variables for each cluster. There are three clusters of risk for the spread of dengue hemorrhagic fever in Bojonegoro district with categories: high risk cluster, medium risk cluster and low risk cluster. The high risk cluster consists of 7 sub-districts (Baureno, Kepohbaru, Balen, Sumberrejo, Kedungadem, Bojonegoro and Dander). The variables affecting the DHF Sufferer in the high risk cluster were population density (X2), Altitude (X3) and Health Worker (X6). Meanwhile, the medium risk cluster consists of 10 sub-districts (Kalitidu, Kanor, Kapas, Ngasem, Ngraho, Padangan, Sugihwaras, Sukosewu, Tambakrejo, and Trucuk). The variables that affect the DHF Sufferer in the medium cluster are Number of Dead (X1), Population Density (X2) and Health Facility (X5). The low risk cluster consisted of 11 sub-districts (Bubulan, Gayam, Gondang, Kasiman, Kedewan, Malo, Margomulyo, Ngambon, Purwosari, Sekar, and Temayang). The variables affecting the DHF Sufferer rate in the low risk cluster were number of dead (X1) and population density (X2).

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“…Until now, the implementation of adaptation strategies for dealing with the incidence of DHF is still not optimal. The lack of data on economic assessments and community acceptance of the impact of DHF events, causes doubt and uncertainty to adopt appropriate adaptation strategies for dealing with DHF events [11] , [ 12]. All regions in Indonesia are at risk for contracting dengue disease.…”

Section: Introductionmentioning

confidence: 99%

Determinant Factors Related to the Event of Dengue Hemorrhagic Fever (DHF) in the Work Area of Payung Sekaki Health Center, Pekanbaru Regency

2022

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Dengue hemorrhagic fever (DHF) or dengue hemorrhagic fever (DHF) is currently one of the public health problems in Pekanbaru, Riau. The research design is an Unmatched Case Control Study. The case population is all DHF sufferers from 2019-2021, totaling 194 people and the control population is all non-DHF cases in the same period when DHF occurred in 2019-2-2021, totaling 7,624 people. The number of samples is 180 cases and 180 controls. Data were analyzed by univariate, bivariate and multivariate. The results of multivariate analysis, the most influential variables were the habit of hanging clothes (95% CI = 2,970-8,557), the use of repellents during the day (95% CI = 2,272-6,307), knowledge about dengue transmission (95% CI = 2,108-5,892), presence of larvae (CI 95% = 1.716-4.760), sleep during the day (95% CI = 1.699-4.699). The determinant factors related to the incidence of Dengue Hemorrhagic Fever (DHF ) in the Working Area of the Payung Sekaki Health Center are the presence of larvae, daytime sleep habits, hanging clothes habits, daytime use of repellants and knowledge of dengue transmission. While mosquito repellent plants, education, age and gender. It is recommended that the Health Center conduct health education so that people do not hang clothes that have been used, use repellents during the day, do 3M (closing water reservoirs, draining bathtubs, burying), not sleeping during the day and if sleeping during the day need to use tools or mosquito repellent.

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A comparative analysis of frequentist and Bayesian approaches to estimate dengue disease transmission in Bandung-Indonesia

Cited by 5 publications

References 20 publications

Pemodelan Bayesian Spasial Conditional Autoregressive (Car) Pada Kasus Demam Berdarah Dengue Di Indonesia

Pemodelan Bayesian Spasial Conditional Autoregressive (Car) Pada Kasus Demam Berdarah Dengue Di Indonesia

Hybrid K Means-Multivariate Adaptive Regression Splines for Distribution of Dengue Fever Risk Mapping in Bojonegoro District

Determinant Factors Related to the Event of Dengue Hemorrhagic Fever (DHF) in the Work Area of Payung Sekaki Health Center, Pekanbaru Regency

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