A forecasting system for dengue fever in Nakhon Si Thammarat, Thailand

Wongkoon, Siriwan; Jaroensutasinee, Mullica; Jaroensutasinee, Krisanadej

doi:10.1016/j.ijid.2012.05.460

Cited by 14 publications

(19 citation statements)

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“…The numbers of Aedes larvae were higher in the rainy season than in the winter seasons, which made it easy to develop DF in areas with high relative humidity. The same findings were indicated by Wongkoon [29]. Morin [2] and Xiang [30] stated that a maximum temperature ranging from 21.6 to 32.9 and a minimum temperature from 11.2 to 23.7 are optimal for DF transmission, which was coincident with the annual temperature range in Guangzhou.…”

Section: Discussionsupporting

confidence: 77%

Spatiotemporal Transmission Patterns and Determinants of Dengue Fever: A Case Study of Guangzhou, China

Chen

Zhao

et al. 2019

IJERPH

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Dengue fever is one of the most common vector-borne diseases in the world and is mainly affected by the interaction of meteorological, human and land-use factors. This study aims to identify the impact of meteorological, human and land-use factors on dengue fever cases, involving the interplay between multiple factors. The analyses identified the statistically significant determinants affecting the transmission of dengue fever, employing cross-correlation analysis and the geo-detector model. This study was conducted in Guangzhou, China, using the data of confirmed cases of dengue fever, daily meteorological records, population density distribution and land-use distribution. The findings highlighted that the dengue fever hotspots were mainly distributed in the old city center of Guangzhou and were significantly shaped by meteorological, land-use and human factors. Meteorological factors including minimum temperature, maximum temperature, atmospheric pressure and relative humidity were correlated with the transmission of dengue fever. Minimum temperature, maximum temperature and relative humidity presented a statistically significant positive correlation with dengue fever cases, while atmospheric pressure presented statistically significant negative correlation. Minimum temperature, maximum temperature, atmospheric pressure and humidity have lag effects on the transmission of dengue fever. The population, community age, subway network density, road network density and ponds presented a statistically significant positive correlation with the number of dengue fever cases, and the interaction among land-use and human factors could enhance dengue fever transmission. The ponds were the most important interaction factors, which might strengthen the influence of other factors on dengue fever transmission. Our findings have implications for pre-emptive dengue fever control.

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Section: Discussionsupporting

confidence: 77%

Spatiotemporal Transmission Patterns and Determinants of Dengue Fever: A Case Study of Guangzhou, China

Chen

Zhao

et al. 2019

IJERPH

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“…Our study shows that temperature with three-month lead-time and rainfall with two and three-month lead-time is the best predictors for dengue transmission patterns in Yogyakarta Municipality. The time delay between temperature and rainfall with corresponding influences on the dengue cases have shown in others province in Indonesia [ 36 ] and neighbor countries [ 27 , 37 ] as well. This time delays are likely to represent biological processes in the vector life cycle [ 19 , 20 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data

et al. 2016

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Research is needed to create early warnings of dengue outbreaks to inform stakeholders and control the disease. This analysis composes of a comparative set of prediction models including only meteorological variables; only lag variables of disease surveillance; as well as combinations of meteorological and lag disease surveillance variables. Generalized linear regression models were used to fit relationships between the predictor variables and the dengue surveillance data as outcome variable on the basis of data from 2001 to 2010. Data from 2011 to 2013 were used for external validation purposed of prediction accuracy of the model. Model fit were evaluated based on prediction performance in terms of detecting epidemics, and for number of predicted cases according to RMSE and SRMSE, as well as AIC. An optimal combination of meteorology and autoregressive lag terms of dengue counts in the past were identified best in predicting dengue incidence and the occurrence of dengue epidemics. Past data on disease surveillance, as predictor alone, visually gave reasonably accurate results for outbreak periods, but not for non-outbreaks periods. A combination of surveillance and meteorological data including lag patterns up to a few years in the past showed most predictive of dengue incidence and occurrence in Yogyakarta, Indonesia. The external validation showed poorer results than the internal validation, but still showed skill in detecting outbreaks up to two months ahead. Prior studies support the fact that past meteorology and surveillance data can be predictive of dengue. However, to a less extent has prior research shown how the longer-term past disease incidence data, up to years, can play a role in predicting outbreaks in the coming years, possibly indicating cross-immunity status of the population.

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“…Previous studies mentioned the importance of dengue incidences lagged one month to forecast current month dengue incidences [ 40 – 42 ]. Our model also illustrated that, depending upon climatic factors, the current month’s dengue cases were significantly affected by the previous month’s dengue cases in the district.…”

Section: Discussionmentioning

confidence: 99%

A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka

et al. 2018

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BackgroundDengue is one of the major health problems in Sri Lanka causing an enormous social and economic burden to the country. An accurate early warning system can enhance the efficiency of preventive measures. The aim of the study was to develop and validate a simple accurate forecasting model for the District of Gampaha, Sri Lanka. Three time-series regression models were developed using monthly rainfall, rainy days, temperature, humidity, wind speed and retrospective dengue incidences over the period January 2012 to November 2015 for the District of Gampaha, Sri Lanka. Various lag times were analyzed to identify optimum forecasting periods including interactions of multiple lags. The models were validated using epidemiological data from December 2015 to November 2017. Prepared models were compared based on Akaike’s information criterion, Bayesian information criterion and residual analysis.ResultsThe selected model forecasted correctly with mean absolute errors of 0.07 and 0.22, and root mean squared errors of 0.09 and 0.28, for training and validation periods, respectively. There were no dengue epidemics observed in the district during the training period and nine outbreaks occurred during the forecasting period. The proposed model captured five outbreaks and correctly rejected 14 within the testing period of 24 months. The Pierce skill score of the model was 0.49, with a receiver operating characteristic of 86% and 92% sensitivity.ConclusionsThe developed weather based forecasting model allows warnings of impending dengue outbreaks and epidemics in advance of one month with high accuracy. Depending upon climatic factors, the previous month’s dengue cases had a significant effect on the dengue incidences of the current month. The simple, precise and understandable forecasting model developed could be used to manage limited public health resources effectively for patient management, vector surveillance and intervention programmes in the district.Electronic supplementary materialThe online version of this article (10.1186/s13071-018-2828-2) contains supplementary material, which is available to authorized users.

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A forecasting system for dengue fever in Nakhon Si Thammarat, Thailand

Cited by 14 publications

References 0 publications

Spatiotemporal Transmission Patterns and Determinants of Dengue Fever: A Case Study of Guangzhou, China

Spatiotemporal Transmission Patterns and Determinants of Dengue Fever: A Case Study of Guangzhou, China

Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data

A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka

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