Dengue in Myanmar: Spatiotemporal epidemiology, association with climate and short-term prediction
Issued Date
2023-06-01
Resource Type
eISSN
19352735
Scopus ID
2-s2.0-85163913357
Pubmed ID
37276226
Journal Title
PLoS neglected tropical diseases
Volume
17
Issue
6
Rights Holder(s)
SCOPUS
Bibliographic Citation
PLoS neglected tropical diseases Vol.17 No.6 (2023) , e0011331
Suggested Citation
Zaw W., Lin Z., Ko J.K., Rotejanaprasert C., Pantanilla N., Ebener S., Maude R.J. Dengue in Myanmar: Spatiotemporal epidemiology, association with climate and short-term prediction. PLoS neglected tropical diseases Vol.17 No.6 (2023) , e0011331. doi:10.1371/journal.pntd.0011331 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/87913
Title
Dengue in Myanmar: Spatiotemporal epidemiology, association with climate and short-term prediction
Other Contributor(s)
Abstract
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.