Modeling of dengue occurrences early warning involving temperature and rainfall factors

Putra, Prama Setia; Nuraini, Nuning

doi:10.12980/apjtd.7.2017d7-26

Cited by 5 publications

(13 citation statements)

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“…This process can potentially create the abundance of vector population and subsequently increase the risk of dengue infection. However, intense and heavy rainfall may erase the breeding place and reduce the mosquito population ( Colon-Gonzalez et al., 2013 ; Putra & Nuraini, 2017 ). This study observed that the number of incidences declines when a 24-h precipitation level exceeds 50 mm.…”

Section: Resultsmentioning

confidence: 99%

“…Asymptomatic with mild symptoms occurs in most cases. Still, on the other hand, the disease can involve the dengue hemorrhagic fever (DHF) or dengue shock syndrome (DSS) with case-fatality rates close to 1 percent ( Andraud et al., 2013 ; Putra & Nuraini, 2017 ). The infectious period varies between 3 and 7 days as the manifestation of dengue symptoms ( Yang et al., 2011 ).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the climatic condition also greatly influences the probability of successful virus transmission between host and vector. The entomological parameters regarding mosquito development and virus transmission are climatically sensitive for many reasons ( Putra & Nuraini, 2017 ; Russel et al., 2009 ). For example, numerous previous researches have identified that variation on temperature affects mosquito vectorial capacity, i.e., frequency of biting activity, vector mortality rate, and virus transmission effectiveness ( Li et al., 2017 ; Liu-Helmersson et al., 2014 ; Ong & Tan, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…The more it rains, the more egg-laying zones are available for the mosquito, and thus the size of the vector population increases ( Promprou et al., 2005 ). Nevertheless, temperature conditions above 30 °C can shorten adult mosquito lifespan as does either in very high and very low precipitation that can remove the breeding place of mosquito ( Putra & Nuraini, 2017 ; Sharmin et al., 2015 ).…”

Section: Introductionmentioning

confidence: 99%

“…Further, the implementation of an optimal control problem was done to obtain the time-dependent infection rate parameter, which produced proper data fitting and a model for the infection rate parameter as a monthly precipitation function. Putra and Nuraini ( Putra & Nuraini, 2017 ) constructed an early warning model of dengue outbreaks based on the dynamic of the vector population and the host-vector threshold values considering the condition of precipitation and temperature. Furthermore, Fauzi et al.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Nuraini

Fauzi

Fakhruddin

et al. 2021

Infectious Disease Modelling

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Climate and vector-borne diseases in Indonesia: a systematic literature review and critical appraisal of evidence

Marina¹,

Ariati

Anwar³

et al. 2022

Int J Biometeorol

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Early warning systems (EWSs) for chikungunya, dengue, malaria, yellow fever, and Zika outbreaks: What is the evidence? A scoping review

et al. 2021

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Background Early warning systems (EWSs) are of increasing importance in the context of outbreak-prone diseases such as chikungunya, dengue, malaria, yellow fever, and Zika. A scoping review has been undertaken for all 5 diseases to summarize existing evidence of EWS tools in terms of their structural and statistical designs, feasibility of integration and implementation into national surveillance programs, and the users’ perspective of their applications. Methods Data were extracted from Cochrane Database of Systematic Reviews (CDSR), Google Scholar, Latin American and Caribbean Health Sciences Literature (LILACS), PubMed, Web of Science, and WHO Library Database (WHOLIS) databases until August 2019. Included were studies reporting on (a) experiences with existing EWS, including implemented tools; and (b) the development or implementation of EWS in a particular setting. No restrictions were applied regarding year of publication, language or geographical area. Findings Through the first screening, 11,710 documents for dengue, 2,757 for Zika, 2,706 for chikungunya, 24,611 for malaria, and 4,963 for yellow fever were identified. After applying the selection criteria, a total of 37 studies were included in this review. Key findings were the following: (1) a large number of studies showed the quality performance of their prediction models but except for dengue outbreaks, only few presented statistical prediction validity of EWS; (2) while entomological, epidemiological, and social media alarm indicators are potentially useful for outbreak warning, almost all studies focus primarily or exclusively on meteorological indicators, which tends to limit the prediction capacity; (3) no assessment of the integration of the EWS into a routine surveillance system could be found, and only few studies addressed the users’ perspective of the tool; (4) almost all EWS tools require highly skilled users with advanced statistics; and (5) spatial prediction remains a limitation with no tool currently able to map high transmission areas at small spatial level. Conclusions In view of the escalating infectious diseases as global threats, gaps and challenges are significantly present within the EWS applications. While some advanced EWS showed high prediction abilities, the scarcity of tool assessments in terms of integration into existing national surveillance systems as well as of the feasibility of transforming model outputs into local vector control or action plans tends to limit in most cases the support of countries in controlling disease outbreaks.

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Modeling of dengue occurrences early warning involving temperature and rainfall factors

Cited by 5 publications

References 12 publications

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

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

Climate and vector-borne diseases in Indonesia: a systematic literature review and critical appraisal of evidence

Early warning systems (EWSs) for chikungunya, dengue, malaria, yellow fever, and Zika outbreaks: What is the evidence? A scoping review

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