A Computational Approach to Predict the Spread of Dengue
| dc.contributor.author | Kok Woon Chee | |
| dc.date.accessioned | 2026-10-15T02:56:50Z | |
| dc.date.issued | 2018 | |
| dc.description | Dengue disease poses a large economic burden in Malaysia among other endemic countries. Malaysia had not adopted the dengue vaccine yet. In the absence of dengue vaccine, it is important to identify dengue hotspot for eradication. Several methods for example compartmental modeling, agent-based modelling and other scientific methods have been used to detect hotspot. Network modelling approach, in particular a bipartite network is currently the most appropriate method to solve the research problem. A contact network model of dengue transmission was formulated. Two distinct entities, human and location nodes are always interconnected. The relationship between these two nodes is quantified by using a summation rule. When the resources such as budget and manpower are limited, control strategies are prioritised according to the risk area of higher vector density. Dengue Hotspot Ranking (DHR) values are generated for location nodes in a network model to capture the human heterogeneity, environmental properties and the vector characteristic. The location nodes are then ranked according to DHR value which is also a measurement of vector density. In previous study, researcher attempted to model malaria hotspot by using the same method, but there is no adequate model validation with real data performed. Verification analysis reports a root mean square error (RMSE) of 6.419×10−4 and 9.188×10−4for the location and human nodes respectively, which is much lower than the threshold value of 0.05. Validation analysis revealed a strong ranking similarity with good Spearman rank correlation coefficient of above 0.80 (ρ>0.80; p<0.001) which is greater than the threshold value set at 0.70. Through parameter significance analysis, this study identified significant parameters to detect possible dengue hotspot which include mosquito’s life cycle, survival and biting rate, precipitation, humidity, altitude the number of times human visited a location, duration of human stay at one location v and the frequency of a human visited one location. This study also developed a polynomial model to represent the life cycle and survival parameters by using polynomial curve fitting due to the fact that there is no primary data available from any data source. Consequently, the bipartite network modelling approach has been successfully formulated and the ranked locations are believed to be applicable to help public health authorities to prioritise the locations for vector control. Eradication of dengue in the risk areas can help in the reduction of the spread of dengue disease. | |
| dc.identifier.uri | https://scholarhub.unimas.my/handle/123456789/3093 | |
| dc.language.iso | English | |
| dc.publisher | UNIVERSITI MALAYSIA SARAWAK | |
| dc.relation.ispartofseries | Faculty of Computer Science and Information Technology | |
| dc.subject | bipartite network modeling, dengue, hotspot, ranking | |
| dc.title | A Computational Approach to Predict the Spread of Dengue | |
| dc.type | Masters |