Climate variability and dengue hemorrhagic fever in Southeast Sulawesi Province, Indonesia

Tosepu, Ramadhan; Tantrakarnapa, Kraichat; Nakhapakorn, Kanchana; Worakhunpiset, Suwalee

doi:10.1007/s11356-018-1528-y

Cited by 31 publications

(35 citation statements)

References 27 publications

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“…In Taiwan, temperature and precipitation influenced dengue incidence rates with a lag of 10–20 weeks [ 26 ]. In the context of dengue infection in Indonesia, studies have shown that dengue incidence is associated with temperature, rainfall, and humidity [ 45 , 46 , 47 ]. In this present study, those local climate variables (temperature, rainfall, and humidity), which might have had a stronger impact on chikungunya incidence, were not included.…”

Section: Discussionmentioning

confidence: 99%

Effects of El Niño Southern Oscillation and Dipole Mode Index on Chikungunya Infection in Indonesia

et al. 2020

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The aim of this study was to assess the possible association of El Niño Southern Oscillation (ENSO) and Dipole Mode Index (DMI) on chikungunya incidence overtime, including the significant reduction in cases that was observed in 2017 in Indonesia. Monthly nation-wide chikungunya case reports were obtained from the Indonesian National Disease Surveillance database, and incidence rates (IR) and case fatality rate (CFR) were calculated. Monthly data of Niño3.4 (indicator used to represent the ENSO) and DMI between 2011 and 2017 were also collected. Correlations between monthly IR and CFR and Niño3.4 and DMI were assessed using Spearman’s rank correlation. We found that chikungunya case reports declined from 1972 cases in 2016 to 126 cases in 2017, a 92.6% reduction; the IR reduced from 0.67 to 0.05 cases per 100,000 population. No deaths associated with chikungunya have been recorded since its re-emergence in Indonesia in 2001. There was no significant correlation between monthly Niño3.4 and chikungunya incidence with r = −0.142 (95%CI: −0.320 – 0.046), p = 0.198. However, there was a significant negative correlation between monthly DMI and chikungunya incidence, r = −0.404 (95%CI: −0.229 – −0.554) with p < 0.001. In conclusion, our initial data suggests that the climate variable, DMI but not Niño3.4, is likely associated with changes in chikungunya incidence. Therefore, further analysis with a higher resolution of data, using the cross-wavelet coherence approach, may provide more robust evidence.

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Section: Discussionmentioning

confidence: 99%

Effects of El Niño Southern Oscillation and Dipole Mode Index on Chikungunya Infection in Indonesia

et al. 2020

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“…Rangkaian kata kunci yang digunakan adalah variasi dari "dengue","climate","Indonesia". (Kesetyaningsih, Andarini, Sudarto, & Pramoedyo, 2018a), dua lainnya menggunakan analisis multivariat (Tosepu, Tantrakarnapa, Nakhapakorn, & Worakhunpiset, 2018;Tosepu, Tantrakarnapa, Worakhunpiset, & Nakhapakorn, 2018).…”

Section: Kata Kunciunclassified

“…Nilai temperatur rata-rata dan temperatur maksimal menunjukkan korelasi tinggi pada lag 2-4, sedangkan temperatur minimal pada lag 1-3 dan 5. Di Kolaka, temperatur rata-rata berkisar 26,1-29,7 o C, dengan nilai minimum sebesar 24,2 o C dan maksimum 33,6 o C. Korelasi positif temperatur rata-rata dan maksimal pada lag 2, 3 dan 4, dan temperatur minimum pada lag 0, 1, 2, 3 (Tosepu, Tantrakarnapa, Nakhapakorn, et al, 2018;Tosepu, Tantrakarnapa, Worakhunpiset, et al, 2018).…”

Section: Temperaturunclassified

Variasi Iklim dan Dinamika Kasus DBD di Indonesia: Systematic Review

Yushananta

Setiawan

Tugiyono

2020

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<p>DHF is still a serious problem, especially in children in endemic countries. An estimated 3,6 billion people are at risk and 21,000 deaths each year. Indonesia, in 2018 there were 65,602 cases (IR=24,75 per 100,000 population), with the death of 467 people (CFR=0,71%), and 85.6% of districts in Indonesia reported dengue cases. The climate becomes one of the environmental factors that play a role in the number of cases. Google scholar is used as the main source for finding articles related to DHF and climate published during 2015-2019. The inclusion criteria set forth, are articles that must be published in international journals, published in 2015-2019, must discuss Indonesia, and must assess the effect of climate change on DHF. A total of 52 articles were found, but only 6 articles met the inclusion criteria. Using various analytical methods, all articles obtain climate variability related to the incidence of DHF, through vector abundance. The dynamics of DHF is a very complex system, so it is necessary to combine additional factors other than climate to predict it. The development of an early warning and monitoring system is important, in addition to strengthening the role of households in controlling DHF.</p>

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“…In a small proportion of incidences, the disease develops into severe cases, resulting in bleeding, low levels of blood platelets, and blood plasma leakage or shock syndrome [1]. The dengue virus (five serotypes of the dengue virus) is transmitted by Aedes albopictus and Aedes aegypti [4] and is highly sensitive to environmental factors [5][6][7][8]. Meteorological conditions, such as temperature, precipitation, and humidity, have a significant impact on the spread of dengue fever (DF) as the conditions help to increase the population density of Aedes [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…The dengue virus (five serotypes of the dengue virus) is transmitted by Aedes albopictus and Aedes aegypti [4] and is highly sensitive to environmental factors [5][6][7][8]. Meteorological conditions, such as temperature, precipitation, and humidity, have a significant impact on the spread of dengue fever (DF) as the conditions help to increase the population density of Aedes [5][6][7][8]. As temperature and precipitation increase, the stages of development of Aedes larvae to pupa become shorter, thereby increasing the population growth of Aedes [9,10].…”

Section: Introductionmentioning

confidence: 99%

Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method

et al. 2020

IJERPH

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Dengue fever (DF) is one of the most rapidly spreading diseases in the world, and accurate forecasts of dengue in a timely manner might help local government implement effective control measures. To obtain the accurate forecasting of DF cases, it is crucial to model the long-term dependency in time series data, which is difficult for a typical machine learning method. This study aimed to develop a timely accurate forecasting model of dengue based on long short-term memory (LSTM) recurrent neural networks while only considering monthly dengue cases and climate factors. The performance of LSTM models was compared with the other previously published models when predicting DF cases one month into the future. Our results showed that the LSTM model reduced the average the root mean squared error (RMSE) of the predictions by 12.99% to 24.91% and reduced the average RMSE of the predictions in the outbreak period by 15.09% to 26.82% as compared with other candidate models. The LSTM model achieved superior performance in predicting dengue cases as compared with other previously published forecasting models. Moreover, transfer learning (TL) can improve the generalization ability of the model in areas with fewer dengue incidences. The findings provide a more precise forecasting dengue model and could be used for other dengue-like infectious diseases.

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Climate variability and dengue hemorrhagic fever in Southeast Sulawesi Province, Indonesia

Cited by 31 publications

References 27 publications

Effects of El Niño Southern Oscillation and Dipole Mode Index on Chikungunya Infection in Indonesia

Effects of El Niño Southern Oscillation and Dipole Mode Index on Chikungunya Infection in Indonesia

Variasi Iklim dan Dinamika Kasus DBD di Indonesia: Systematic Review

Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method

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