Modelling the Prediction of Dengue Outbreak Using System Dynamics Approach

Abstract

Dengue virus had become the dominant mosquito-borne disease in Malaysia. With no positive progress on the development of vaccine, other ways in dealing with the virus is to predict the next outbreak which is also the aim of this paper. This dengue model based on system dynamics approach gives valuable information to decision makers in determining the strategies for vector control. The array of factors involved such as temperature, rainfall and population density that significantly influence virus transmission, give opportunity for a system approach in providing answer to the complicated relationship which exist in dengue system. System dynamics dengue model is able to simulate reasonable and promising results, which can be used as basis for future researcher to model more accurate and detail dengue transmission control system.

Dengue Early Warning System as Outbreak Prediction Tool: A Systematic Review

“…Of the 17 studies, 14 (82.3%) were conducted in Asia, one (5.9%) article was conducted in South America, and two studies were conducted in a combination of countries in two continents which is North America and Asia (n=1) and South America and Asia (n=1). Malaysia was the country with the most eligible studies (n=4), 27–30 followed by Thailand (n=2) 31 , 32 and Singapore (n=2). 33 , 34 Other studies were carried out in the Philippines, 35 Vietnam, 36 Taiwan, 37 China, 38 India, 39 Sri Lanka 40 and Colombia.…”

“…Almost all studies used laboratory-confirmed cases as outbreak indicators, but two studies used a combination of probable dengue cases and hospitalized dengue cases as an outbreak indicator. 42 , 43 The coverage of EWS varies from city level, 37 District, 28 , 29 , 40 , 43 province, 31 , 35 , 36 , 38 state 27 , 30 , 39 and national level. 33 , 34 , 41 , 42 In terms of data source, all dengue cases were obtained directly from the national surveillance system from the Ministry of Health.…”

“…The population and socioeconomic alarm indicators were used in six studies. 27 , 31–33 , 35 , 41 It includes population density, midyear population size, poverty index, GINI index, electricity access, drinking water access, sanitation index, and education coverage. Two studies used enhanced vegetation data as an alarm indicator.…”

“… 28 Some studies applied a combination of ML with statistical analysis approaches such as time-series forecasting 33 , 39 , 41 and negative binomial regression model. 38 Other method approaches were the Shewhart method and Endemic Channel, 42 , 43 system dynamic modelling 27 and bayesian spatiotemporal model with modification in the superensemble specification. 36 …”

“…A range between 70 to 96.3% was reported for prediction model accuracy. Besides, some studies reported the performance of forecasting using the lowest mean absolute error (MAE), 29 , 32 , 39 , 41 mean absolute percentage error (MAPE), 31 , 33 mean square error (MSE), 27 Pierce skill score (PSS) 40 and continuous rank probability score (CRPS). 36 Furthermore, depending on the model approach, there were also studies that reported the model performance using the R squared (R 2 ), 39 the most common performance measure for continuous outcomes.…”