부산대학교 도서관

Dengue is a major public health problem in Myanmar. The country aims to reduce morbidity by 50% and mortality by 90% by 2025 based on 2015 data. To support efforts to reach these goals it is important to have a detailed picture of the epidemiology of dengue, its relationship to meteorological factors and ideally to predict ahead of time numbers of cases to plan resource allocations and control efforts. Health facility-level data on numbers of dengue cases from 2012 to 2017 were obtained from the Vector Borne Disease Control Unit, Department of Public Health, Myanmar. A detailed analysis of routine dengue and dengue hemorrhagic fever (DHF) incidence was conducted to examine the spatial and temporal epidemiology. Incidence was compared to climate data over the same period. Dengue was found to be widespread across the country with an increase in spatial extent over time. The temporal pattern of dengue cases and fatalities was episodic with annual outbreaks and no clear longitudinal trend. There were 127,912 reported cases and 632 deaths from 2012 and 2017 with peaks in 2013, 2015 and 2017. The case fatality rate was around 0.5% throughout. The peak season of dengue cases was from May to August in the wet season but in 2014 peak dengue season continued until November. The strength of correlation of dengue incidence with different climate factors (total rainfall, maximum, mean and minimum temperature and absolute humidity) varied between different States and Regions. Monthly incidence was forecasted 1 month ahead using the Auto Regressive Integrated Moving Average (ARIMA) method at country and subnational levels. With further development and validation, this may be a simple way to quickly generate short-term predictions at subnational scales with sufficient certainty to use for intervention planning. Author summary: The study describes country, State/Region and township level spatial and temporal epidemiology of dengue in Myanmar from routine surveillance data collected by the government. Dengue was found to widespread across the country with an increase in spatial extent over time, likely due to improvements in surveillance, reporting and access to health services. Most reported cases were children with an increase in the proportion ≥15 years of age over time. Dengue incidence was seasonal and related to climate, with larger annual peaks every two years, although the timing of peaks in incidence, strength and nature of the relationship with climate differed between States and Regions. Using ARIMA, it was possible to predict dengue incidence 1 month ahead at State/Region level using previous incidence data. Adding monthly climate data to the model gave a marginal improvement in model performance which did not justify the additional effort needed to acquire and process it. With further development and validation, the method may be a simple way to quickly generate short-term predictions at subnational scales with sufficient certainty to use for intervention planning. [ABSTRACT FROM AUTHOR]