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

Withanage, Gayan P.; Viswakula, Sameera; Gunawardena, Y. I. Nilmini Silva; Hapugoda, Menaka

doi:10.1186/s13071-018-2828-2

Cited by 40 publications

(44 citation statements)

References 37 publications

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“…At present, this method has been widely used in the prediction of infectious diseases, and has achieved successful prediction results, for instance, Tian C W et al [4] forecasted monthly cases of hand-foot-mouth disease successfully in China; Wang T et al [5] suggested that ARIMA(3,1,1)(2,1,1) 12 model was reliable with a high validity, which could be used to predict hemorrhagic fever with renal syndrome incidence in Zibo; Myriam Gharbil et al [6] predicted the dengue incidence in Guadeloupe based on time series analysis; López-Montenegro LE [7] predicted dengue cases in Colombia from 2018 to 2022 based on Auto-Regressive Integrated Moving Average (ARIMA) model; Zheng Y-L et al [8] and Liao Z [9] forcasted TB incidence successfully using SARIMA model, etc. [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China

et al. 2020

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Background: Tuberculosis (TB) remains a serious public health problem with substantial financial burden in China. The incidence of TB in Guangxi province is much higher than that in the national level, however, there is no predictive study of TB in recent years in Guangxi, therefore, it is urgent to construct a model to predict the incidence of TB, which could provide help for the prevention and control of TB. Methods: Box-Jenkins model methods have been successfully applied to predict the incidence of infectious disease. In this study, based on the analysis of TB incidence in Guangxi from January 2012 to June 2019, we constructed TB prediction model by Box-Jenkins methods, and used root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) to test the performance and prediction accuracy of model. Results: From January 2012 to June 2019, a total of 587,344 cases of TB were reported and 879 cases died in Guangxi. Based on TB incidence from January 2012 to December 2018, the SARIMA((2),0,(2))(0,1,0) 12 model was established, the AIC and SC of this model were 2.87 and 2.98, the fitting accuracy indexes, such as RMSE, MAE and MAPE were 0.98, 0.77 and 5.8 respectively; the prediction accuracy indexes, such as RMSE, MAE and MAPE were 0.62, 0.45 and 3.77, respectively. Based on the SARIMA((2),0,(2))(0,1,0) 12 model, we predicted the TB incidence in Guangxi from July 2019 to December 2020. Conclusions: This study filled the gap in the prediction of TB incidence in Guangxi in recent years. The established SARIMA((2),0,(2))(0,1,0) 12 model has high prediction accuracy and good prediction performance. The results suggested the change trend of TB incidence predicted by SARIMA((2),0,(2))(0,1,0) 12 model from July 2019 to December 2020 was similar to that in the previous two years, and TB incidence will experience slight decrease, the predicted results can provide scientific reference for the prevention and control of TB in Guangxi, China.

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Section: Introductionmentioning

confidence: 99%

Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China

et al. 2020

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“…Changes in these variables affect the population dynamics of mosquito vectors and the consequent risk of dengue transmission [ 14 ]. Increased precipitation during the monsoon season facilitates vector population growth by providing aquatic media for mosquitoes, although excessive rainfall can lead to flushing out of breeding sites and killing of mosquito larva [ 15 , 16 ]. In some urban regions, stable larval habitats are provided by large domestic water tanks that are used to manage periodic water shortages [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal patterns of dengue incidence in Bhutan: a Bayesian analysis

Tsheten

Clements

Gray

et al. 2020

Emerging Microbes & Infections

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Dengue is an important emerging vector-borne disease in Bhutan. This study aimed to quantify the spatial and temporal patterns of dengue and their relationship to environmental factors in dengue-affected areas at the sub-district level. A multivariate zero-inflated Poisson regression model was developed using a Bayesian framework with spatial and spatiotemporal random effects modelled using a conditional autoregressive prior structure. The posterior parameters were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling. A total of 708 dengue cases were notified through national surveillance between January 2016 and June 2019. Individuals aged ≤14 years were found to be 53% (95% CrI: 42%, 62%) less likely to have dengue infection than those aged >14 years. Dengue cases increased by 63% (95% CrI: 49%, 77%) for a 1°C increase in maximum temperature, and decreased by 48% (95% CrI: 25%, 64%) for a one-unit increase in normalized difference vegetation index (NDVI). There was significant residual spatial clustering after accounting for climate and environmental variables. The temporal trend was significantly higher than the national average in eastern sub-districts. The findings highlight the impact of climate and environmental variables on dengue transmission and suggests prioritizing high-risk areas for control strategies.

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“…Despite many efforts, in 2017, Sri Lanka experienced the most severe dengue outbreak with 186,101 infections island wide with over 250 dengue-related deaths [3]. Every year, nearly half of the incidences are reported from the Western Province which comprises Districts of Colombo, Gampaha, and Kalutara, and despite many efforts, the second highest number of dengue cases is reported from the District of Gampaha since 2010 [4][5][6]. Further, during the dengue epidemic in July 2017, the highest number of dengue cases was reported from the District of Gampaha [7].…”

Section: Introductionmentioning

confidence: 99%

Use of Novaluron-Based Autocidal Gravid Ovitraps to Control Aedes Dengue Vector Mosquitoes in the District of Gampaha, Sri Lanka

Withanage

Viswakula

Gunawardene

et al. 2020

BioMed Research International

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Dengue is the most important mosquito-borne viral infection in Sri Lanka causing an enormous social and economic burden in the country. In the absence of therapeutic drugs and the developed vaccines are under investigation, vector control is the best strategy to reduce the disease transmission. Therefore, the development of novel tools to control dengue vector mosquitoes has become the need of the hour. Novaluron is a recently developed Insect Growth Regulator (IGR) which inhibits chitin synthesis in immature stages of insects. The aim of the study was to identify the efficacy of a simple and cost-effective Autocidal Gravid Ovitrap (AGO) developed using Novaluron to control dengue outbreaks in the District of Gampaha, Sri Lanka. Laboratory and semifield experiments were performed to identify the activity range, optimum field dosage, and residual effects of Novaluron following the World Health Organization guidelines, and field experiments were performed in the Ragama Medical Officer of Health (MOH) area. Two study areas 800 m apart were selected and assigned as treated and control areas randomly. In each study area, 30 households were selected randomly. Each household was given two ovitraps, one placed indoors and the other placed outdoors. Mortality and survival counts were recorded separately for one-year time period and data were analyzed using a two-way repeated measures analysis of variance model. During the laboratory experiments, the adult emerging inhibition was 100% in all tested concentrations. The optimum field dosage was 2 ppm and the residual effect was 28 days. In the field experiments, significantly higher mortality counts were recorded in treated areas both indoor-and outdoor-placed AGOs. Two-factor repeated measures ANOVA followed by Tukey's test confirmed that the mean mortality count is high for the developed AGOs both indoor and outdoor settings. The developed AGO can be deployed to control both indoor and outdoor dengue vector mosquito populations, and in dengue-risk areas, the ovitrap will be supportive to local health authorities to enhance the efficiency of future vector control programs.

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

Cited by 40 publications

References 37 publications

Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China

Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China

Spatial and temporal patterns of dengue incidence in Bhutan: a Bayesian analysis

Use of Novaluron-Based Autocidal Gravid Ovitraps to Control Aedes Dengue Vector Mosquitoes in the District of Gampaha, Sri Lanka

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