The Practicality of Malaysia Dengue Outbreak Forecasting Model as an Early Warning System

Dengue is a harmful tropical disease that causes death to many people. Currently, the dengue vaccine development is still at an early stage, and only intervention methods exist after dengue cases increase. Thus, previously, two scientific experimental field studies were conducted in producing a dengue outbreak forecasting model as an early warning system. Successfully, an Autoregressive Distributed Lag (ADL) Model was developed using three factors: the epidemiological, entomological, and environmental with an accuracy of 85%; but a higher percentage is required in minimizing the error for the model to be useful. Hence, this study aimed to develop a practical and cost-effective dengue outbreak forecasting model with at least 90% accuracy to be embedded in an early warning computer system using the Internet of Things (IoT) approach. Eighty-one weeks of time series data of the three factors were used in six forecasting models, which were Autoregressive Distributed Lag (ADL), Hierarchical Forecasting (Bottom-up and Optimal combination) and three Machine Learning methods: (Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest). Five error measures were used to evaluate the consistency performance of the models in order to ensure model performance. The findings indicated Random Forest outperformed the other models with an accuracy of 95% when including all three factors. But practically, collecting mosquito related data (the entomological factor) was very costly and time consuming. Thus, it was removed from the model, and the accuracy dropped to 92% but still high enough to be of practical use, i.e., beyond 90%. However, the practical ground operationalization of the early warning system also requires several rain gauges to be located at the dengue hot spots due to localized rainfall. Hence, further analysis was conducted in determining the location of the rain gauges. This has led to the recommendation that the rain gauges should be located about 3e4 km apart at the dengue hot spots to ensure the accuracy of the rainfall data to be included in the dengue outbreak forecasting model so that it can be embedded in the early warning system. Therefore, this early warning system can save lives, and prevention is better than cur

Factors determining dengue outbreak in Malaysia

A large scale study was conducted to elucidate the true relationship among entomological, epidemiological and environmental factors that contributed to dengue outbreak in Malaysia. Two large areas (Selayang and Bandar Baru Bangi) were selected in this study based on five consecutive years of high dengue cases. Entomological data were collected using ovitraps where the number of larvae was used to reflect Aedes mosquito population size; followed by RT-PCR screening to detect and serotype dengue virus in mosquitoes. Notified cases, date of disease onset, and number and type of the interventions were used as epidemiological endpoint, while rainfall, temperature, relative humidity and air pollution index (API) were indicators for environmental data. The field study was conducted during 81 weeks of data collection. Correlation and Autoregressive Distributed Lag Model were used to determine the relationship. The study showed that, notified cases were indirectly related with the environmental data, but shifted one week, i.e. last 3 weeks positive PCR; last 4 weeks rainfall; last 3 weeks maximum relative humidity; last 3 weeks minimum and maximum temperature; and last 4 weeks air pollution index (API), respectively. Notified cases were also related with next week intervention, while conventional intervention only happened 4 weeks after larvae were found, indicating ample time for dengue transmission. Based on a significant relationship among the three factors (epidemiological, entomological and environmental), estimated Autoregressive Distributed Lag (ADL) model for both locations produced high accuracy 84.9% for Selayang and 84.1% for Bandar Baru Bangi in predicting the actual notified cases. Hence, such model can be used in forestalling dengue outbreak and acts as an early warning system. The existence of relationships among the entomological, epidemiological and environmental factors can be used to build an early warning system for the prediction of dengue outbreak so that preventive interventions can be taken early to avert the outbreak

Climatic influences on aedes mosquito larvae population

The impact of climate on Aedes larval population was studied.Monitoring of population was done using ovitraps. Ovitrap pr ovides a simple and convenient monitoring method for Aedes surveillance as the number of eggs laid in a standard trap within a specific time period give a relative measurement of the number of mosquito in the same area.Ovitraps were set outdoors in selec ted dengue prone areas in Desa Pandan, Kuala Lumpur for 66 weeks.Weather stations, consisting of a temperature and relative humidity data logger and an automated rain gauge were installed at key locations in the study site.Week-to-week variations of larval densities were correlated against variations in the individual climatic parameters.Results of the study showed that there was a close relationship between the heavy rainfall and the increased mosquito population in the study sites.The study showed that previous week rainfall plays a significant role in increasing the mosquito population

Spatial distribution, enzymatic activity, and insecticide resistance status of Aedes aegypti and Aedes albopictus from dengue hotspot areas in Kuala Lumpur and Selangor, Malaysia

The effectiveness of insecticide-based dengue control interventions is very much influenced by the insecticide resistance status of the mosquito at the targeted areas. This study aims to determine the insecticide resistance status and the enzymatic activity. WHO adult bioassays conducted on Ae. aegypti and Ae. albopictus from 12 dengue hotspots outbreak areas in Kuala Lumpur and Selangor towards insecticides currently and historically used for mosquito control in Malaysia which include two pyrethroids, one organoclorine, one organophosphate and one carbamate. Biochemical enzyme assays were conducted and the activity of enzymes α�Esterase, MFO, GST and AChE were examined. Kruskal-Wallis H, Mann-Whitney U and ANOVA test were used to determine the significant difference of the mortality between insecticides and localities, the enzymes activity between the field and the lab strains, and the enzymes activity within all field strains. Ae. aegypti from all sites have developed resistance towards all tested insecticides based on WHO adult bioassays; permethrin, DDT, malathion and propoxur. The result of biochemical enzyme assays demonstrated that the activity of enzymes was altered. α-esterase and MFO were altered in both species from all areas. GST was altered in both species as well except in Ae. albopictus from sites Bandar Rinching and Taman Gombak Setia. AChE was found significantly demoted in Ae. aegypti from Sri Nilam Shah Alam and Ae. albopictus from Flat Sri Labuan Cheras only. The resistance detected might be the result of activity by either single or several enzymes combined. The development of resistance is mainly via metabolic mechanism

The practicality of Malaysia dengue outbreak forecasting model as an early warning system

Dengue is a harmful tropical disease that causes death to many people. Currently, the dengue vaccine development is still at an early stage, and only intervention methods exist after dengue cases increase. Thus, previously, two scientific experimental field studies were conducted in producing a dengue outbreak forecasting model as an early warning system. Successfully, an Autoregressive Distributed Lag (ADL) Model was developed using three factors: the epidemiological, entomological, and environmental with an accuracy of 85%; but a higher percentage is required in minimizing the error for the model to be useful. Hence, this study aimed to develop a practical and cost-effective dengue outbreak forecasting model with at least 90% accuracy to be embedded in an early warning computer system using the Internet of Things (IoT) approach. Eighty-one weeks of time series data of the three factors were used in six forecasting models, which were Autoregressive Distributed Lag (ADL), Hierarchical Forecasting (Bottom-up and Optimal combination) and three Machine Learning methods: (Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest). Five error measures were used to evaluate the consistency performance of the models in order to ensure model performance. The findings indicated Random Forest outperformed the other models with an accuracy of 95% when including all three factors. But practically, collecting mosquito related data (the entomological factor) was very costly and time consuming. Thus, it was removed from the model, and the accuracy dropped to 92% but still high enough to be of practical use, i.e., beyond 90%. However, the practical ground operationalization of the early warning system also requires several rain gauges to be located at the dengue hot spots due to localized rainfall. Hence, further analysis was conducted in determining the location of the rain gauges. This has led to the recommendation that the rain gauges should be located about 3–4 km apart at the dengue hot spots to ensure the accuracy of the rainfall data to be included in the dengue outbreak forecasting model so that it can be embedded in the early warning system. Therefore, this early warning system can save lives, and prevention is better than cure

Factors determining dengue outbreak in Malaysia.

A large scale study was conducted to elucidate the true relationship among entomological, epidemiological and environmental factors that contributed to dengue outbreak in Malaysia. Two large areas (Selayang and Bandar Baru Bangi) were selected in this study based on five consecutive years of high dengue cases. Entomological data were collected using ovitraps where the number of larvae was used to reflect Aedes mosquito population size; followed by RT-PCR screening to detect and serotype dengue virus in mosquitoes. Notified cases, date of disease onset, and number and type of the interventions were used as epidemiological endpoint, while rainfall, temperature, relative humidity and air pollution index (API) were indicators for environmental data. The field study was conducted during 81 weeks of data collection. Correlation and Autoregressive Distributed Lag Model were used to determine the relationship. The study showed that, notified cases were indirectly related with the environmental data, but shifted one week, i.e. last 3 weeks positive PCR; last 4 weeks rainfall; last 3 weeks maximum relative humidity; last 3 weeks minimum and maximum temperature; and last 4 weeks air pollution index (API), respectively. Notified cases were also related with next week intervention, while conventional intervention only happened 4 weeks after larvae were found, indicating ample time for dengue transmission. Based on a significant relationship among the three factors (epidemiological, entomological and environmental), estimated Autoregressive Distributed Lag (ADL) model for both locations produced high accuracy 84.9% for Selayang and 84.1% for Bandar Baru Bangi in predicting the actual notified cases. Hence, such model can be used in forestalling dengue outbreak and acts as an early warning system. The existence of relationships among the entomological, epidemiological and environmental factors can be used to build an early warning system for the prediction of dengue outbreak so that preventive interventions can be taken early to avert the outbreaks

Spatial distribution of mosquito vector in dengue outbreak areas in Kuala Lumpur and Selangor, Malaysia

In Malaysia, the control of dengue is mainly through the identification and reduction of mosquito vector breeding sites. In this study, a larval survey was conducted from June 2017 until December 2018 to determine the spatial distribution of dengue vectors in the 132 dengue hotspots outbreak areas in Kuala Lumpur and Selangor. Molecular methods were performed in order to detect the presence of transovarial dengue virus in larvae collected, while the density of the breeding habitat and Aedes larval population were determine using spatial analysis. Map of Dengue virus (DENV) distribution were generated to illustrate the trend of dengue outbreak. This study showed that larval survey was an effective method to detect the presence of dengue virus transmission in immature Aedes aegypti and Aedes albopictus. This study also demonstrated that plastic container was the highest source of breeding habitat for Aedes mosquito, whereas blocked drain and tyre were the most favourable breeding habitats for Ae. aegypti and Ae. albopictus, respectively. Pearson’s correlation coefficient shows that mosquito density was not correlated with the DENV infection. In conclusion, current study shows that dengue transmission risk in Kuala Lumpur and Selangor remain high despite the outbreak response conducted by the health authority due to high density of Aedes population and the presence of DENV infection within the larvae population in the area. Therefore, new outbreak response methods such as public mandatory involvement in community-based control program to ensure success in management of resource reduction are necessary to ensure that the risk of dengue infection can be eliminated

Insecticide susceptibility status and resistance mechanism of Anopheles cracens Sallum and Peyton and Anopheles maculatus Theobald (Family: Culicidae) from knowlesi malaria endemic areas in Peninsular Malaysia

Objective: To assess the insecticide susceptibility status of Anopheles cracens (An. cracens) and Anopheles maculatus (An. maculatus) from knowlesi malaria endemic areas in Peninsular Malaysia towards DDT, malathion and deltamethrin and to determine the resistance mechanism involved. Methods: Adult and larval mosquitos were collected for surveillance. Susceptibility status of Anopheles was determined using the standard WHO adult bioassay, larval bioassay and biochemical enzyme assay. Results: WHO adult bioassay results indicated An. cracens collected from Kampung Sungai Ular, Pahang was resistant towards 4% DDT, while An. maculatus collected from Kampung Sokor, Kelantan and Kampung Sungai Lui, Selangor exhibited resistance towards 4% DDT. However, the enzyme activity profiles varied according to strains and species. The resistance ratio of larval bioassay, showed that all strains and species tested were susceptible to malathion and temephos. Conclusions: Since only a few anopheline strains exhibited low level of insecticide resistance towards malathion, DDT and temephos. These insecticides are still considered effective for vector control program towards An. cracens and An. maculatus

Location of rain gauge and ovitrap (Bandar Baru Bangi).

<p>Location of rain gauge and ovitrap (Bandar Baru Bangi).</p

Correlation of epidemiological and entomological variables.

<p>Correlation of epidemiological and entomological variables.</p