Understanding and Modelling the Impact of Climate Change on Infectious Diseases – Progress and Future Challenges

Parham, Paul E.; Christiansen-Jucht, Céline; Pople, Diane; Michael, Edwin

doi:10.5772/23139

Cited by 4 publications

(1 citation statement)

References 132 publications

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“…Demikian pula kasus DBD di Indonesia juga ditemukan meningkat secara tajam di tahuntahun La Nina. 10 DBD merupakan salah satu penyakit yang sensitif terhadap perubahan iklim karena perkembangan vektor Aedes dipengaruhi oleh unsur: 1) cuaca dapat mempengaruhi metabolisme, pertumbuhan, perkembangan dan populasi nyamuk, 2) curah hujan dengan penyinaran yang relatif panjang turut mempengaruhi habitat perindukan nyamuk, 9,11 3) suhu berupa panas yang ekstrim dapat meningkatkan mortalitas nyamuk, sebaliknya suhu yang hangat dapat meningkatkan kelangsungan hidup, aktivitas mengigit, dan masa inkubasi ekstrinsik yaitu tingkat kematangan patogen di dalam diri nyamuk. 12 Diperkirakan DBD akan menonjol pada tahun 2.080, dimana sekitar 6 miliar orang berisiko tertular demam berdarah sebagai konsekuensi dari perubahan iklim dibandingkan jika iklim tetap tidak berubah.…”

Section: Pendahuluanunclassified

Dampak Potensial Perubahan Iklim Terhadap Dinamika Penularan Penyakit DBD Di Kota Mataram

Pascawati

Satoto

Wibawa

et al. 2019

blb

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Mataram City is an endemic area of DHF because cases are always found in 3 consecutive years with the number of cases that fluctuated and tended to increase. DHF is related to climate factors because of vector life, pathogen, behavior and the physiology of the human body is influenced by climate. The impact of climate change on the dynamics of dengue transmission in the city of Mataram is very important to study because it can be used as a basis for knowing the pattern of the occurrence of dengue cases related to temperature, humidity, rainfall and wind speed. This study used a retrospective cohort design from BMKG secondary climate data and dengue cases at the Mataram City Health Office in the last 5 years (2013-2017). Data were analyzed based on monthly and annual patterns assuming normal data distribution to be carried out correlation and regression tests with α = 0.05. The results showed that climatic elements such as: humidity, rainfall, and temperature had a strong enough influence on the incidence of DHF, but the wind speed was not related to the incidence of DHF in the city of Mataram. The linear regression equation model found was DBD Case = -439.403 + 5.809 * humidity (R2 = 18.9%) assuming all linear regression was fulfilled.This model can be used to assist in efforts to mitigate dengue transmission through the determination of the timing of implementation of prevention and the provision of infrastructure facilities for the DHF treatment.

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Section: Pendahuluanunclassified