Determining Method for Dengue Epidemic Threshold in Negeri Sembilan, Malaysia
Introduction : Dengue fever is an illness by arthropod-borne viral disease that become known pandemic to the most tropical countries. In 2014, Malaysia reported 108 698 cases of dengue fever with 215 deaths which increased tremendously compared to 49 335 cases with 112 deaths in 2008 and 30 110 cases with 69 deaths in 2009. This study aimed to identify the best method in determining dengue outbreak threshold for Negeri Sembilan and hopefully these methods can be standardized as it can help to send uniform messages to inform the general public and make the outbreak analysis comparable within and between countries.
Methodology : Using retrospective Negeri Sembilan country dataset from 1st epid week of 2011 till the 52nd epid week of 2016. The data were split into two periods: 1) a 3-year historic period (2011â€“2013), used to calibrate and parameterise the model, and a 1-year evaluation period (2014); 2) a 2-year historic period (2014â€“2016), used to calibrate and parameterise the model, and a 1-year evaluation period (2016), used to test the model. E-dengue is a registration system for confirm case dengue by Ministry of Health. Data include details of cases, district locality, records on the outbreak and epidemiological week (Sunday to Saturday). The variables were captured using the Excel spreadsheet. Analysis method included endemic channel method, moving average or deviation bar chart and recent mean.
Result: Seremban as big district and facing with heavy dengue cases, all three methods (endemic curve, current mean and moving mean) showed promising results. Meanwhile comparing with small district of Port Dickson and Tampin with fewer dengue cases and outbreak recorded, the suitable method is by using endemic channel for epidemic threshold.
Conclusion: Simpler methods such as the endemic channel, recent mean and moving mean may be more appropriate in urban district. Whereas in rural or district with minimal dengue cases, Endemic Channel would be the most suitable method for epidemic threshold. However, both methods require a consistent updated graph threshold as time progress.