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

Targeted outdoor residual spraying, autodissemination devices and their combination against Aedes mosquitoes: field implementation in a Malaysian urban setting

Currently, dengue control relies largely on reactive vector control programmes. Proactive vector-control using a rational, well-balanced integrated vector management approach may prove more successful for dengue control. As part of the development of a cluster randomized controlled epidemiological trial, a study was conducted in Johor Bahru, Malaysia. The study included one control site (three buildings) and three intervention sites which were treated as follows: targeted outdoor residual spraying only (TORS site, two buildings); deployment of autodissemination devices only (ADD site, four buildings); and the previous two treatments combined (TORS + ADD site, three buildings). The primary entomological measurement was per cent of positive ovitraps—ovitrap index (OI). The effect of each intervention on OI was analyzed by a modified ordinary least squares regression model. Relative to the control site, the TORS and ADD sites showed a reduction in the Aedes OI (−6.5%, P = 0.04 and −8.3%, P = 0.10, respectively). Analysis by species showed that, relative to control, the Ae. aegypti OI was lower in ADD (−8.9%, P = 0.03) and in TORS (−10.4%, P = 0.02). No such effect was evident in the TORS + ADD site. The present study provides insights into the methods to be used for the main trial. The combination of multiple insecticides with different modes of action in one package is innovative, although we could not demonstrate the additive effect of TORS + ADD. Further work is required to strengthen our understanding of how these interventions impact dengue vector populations and dengue transmission

Establishment of Wolbachia strain wAlbB in Malaysian populations of Aedes aegypti for dengue control

Dengue has enormous health impacts globally. A novel approach to decrease dengue incidence involves the introduction of Wolbachia endosymbionts that block dengue virus transmission into populations of the primary vector mosquito, Aedes aegypti. The wMel Wolbachia strain has previously been trialed in open releases of Ae. aegypti; however, the wAlbB strain has been shown to maintain higher density than wMel at high larval rearing temperatures. Releases of Ae. aegypti mosquitoes carrying wAlbB were carried out in 6 diverse sites in greater Kuala Lumpur, Malaysia, with high endemic dengue transmission. The strain was successfully established and maintained at very high population frequency at some sites or persisted with additional releases following fluctuations at other sites. Based on passive case monitoring, reduced human dengue incidence was observed in the release sites when compared to control sites. The wAlbB strain of Wolbachia provides a promising option as a tool for dengue control, particularly in very hot climates

Spatial distribution and long-term persistence of Wolbachia-infected Aedes aegypti in the Mentari Court, Malaysia

Dengue is endemic in Malaysia, and vector control strategies are vital to reduce dengue transmission. The Wolbachia strain wAlbB carried by both sexes of Ae. aegypti was released in Mentari Court, a high-rise residential site, in October 2017 and stopped after 20 weeks. Wolbachia frequencies are still being monitored at multiple traps across this site, providing an opportunity to examine the spatiotemporal distribution of Wolbachia and mosquito density with respect to year, residential block, and floor, using spatial interpolation in ArcGIS, GLMs, and contingency analyses. In just 12 weeks, Wolbachia-infected mosquitoes were established right across the Mentari Court site with an overall infection frequency of >90%. To date, the Wolbachia frequency of Ae. aegypti has remained high in all areas across the site despite releases finishing four years ago. Nevertheless, the Wolbachia invaded more rapidly in some residential blocks than others, and also showed a relatively higher frequency on the eighth floor. The Ae. aegypti index tended to differ somewhat between residential blocks, whilst the Ae. albopictus index was relatively higher at the top and bottom floors of buildings. In Mentari Court, only a short release period was required to infiltrate Wolbachia completely and stably into the natural population. The results inform future releases in comparable sites in a dengue control programme

Introduction of Aedes aegypti mosquitoes carrying wAlbB Wolbachia sharply decreases dengue incidence in disease hotspots

Partial replacement of resident Aedes aegypti mosquitoes with introduced mosquitoes carrying certain strains of inherited Wolbachia symbionts can result in transmission blocking of dengue and other viruses of public health importance. Wolbachia strain wAlbB is an effective transmission blocker and stable at high temperatures, making it particularly suitable for hot tropical climates. Following trial field releases in Malaysia, releases using wAlbB Ae. aegypti have become operationalized by the Malaysian health authorities. We report here on an average reduction in dengue fever of 62.4% (confidence intervals 50-71%) in 20 releases sites when compared to 76 control sites in high rise residential areas. Importantly the level of dengue reduction increased with Wolbachia frequency, with 75.8% reduction (61-87%) estimated at 100% Wolbachia frequency. These findings indicate large impacts of wAlbB Wolbachia invasions on dengue fever incidence in an operational setting, with incidence expected to further decrease as wider areas are invaded

The Effectiveness of MyMAT Aedes Mosquito Trap in Reducing Dengue Cases

Malaysia Mosquito Autocidal Trap (MyMAT) is a green technology Aedes mosquito trap that does not use harmful chemical substances. This study aimed to evaluate the efficiency of MyMAT in reducing dengue cases and relating the cases to rainfall. An experimental field study was conducted for 42 weeks at Pangsapuri Nilam Sari, Shah Alam, Selangor. A total of 624 MyMAT was allocated at four blocks: inside each apartment and outside at the corridors in each level. Mosquito and rainfall data were collected weekly using MyMAT and a mobile rain gauge, respectively. The dengue cases data was retrieved from the e-dengue system obtained from the Malaysia Ministry of Health. The findings showed that MyMAT could catch 97% of Aedes mosquitoes and reduced dengue cases on average of 78%, indicating MyMAT is a reliable Aedes mosquito trap. Interestingly the findings also revealed a significant relationship between dengue cases, the number of Aedes mosquitoes, and rainfall. This week notified dengue cases increased when last two weeks mosquitoes increased due to previous two weeks rainfall increment. Thus indicating an indirect but significant relationship between this week notified dengue cases with the last four weeks rainfall. These relationships can be used in establishing a dengue outbreak forecasting model, which can act as an early warning syste

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

Actual notified cases versus predicted values (Selayang).

<p>Actual notified cases versus predicted values (Selayang).</p