Modeling the relative role of human mobility, land-use and climate factors on dengue outbreak emergence in Sri Lanka

• Main Authors: Ying Zhang, Jefferson Riera, Kayla Ostrow, Sauleh Siddiqui, Harendra de Silva, Sahotra Sarkar, Lakkumar Fernando, Lauren Gardner
• Format: Article
• Language: English
• Published: BMC 2020-09-01
• Series: BMC Infectious Diseases
• Subjects: Dengue; Outbreaks; Risk factors; Human mobility; Climate; Land-use
• Online Access: http://link.springer.com/article/10.1186/s12879-020-05369-w

Abstract Background More than 80,000 dengue cases including 215 deaths were reported nationally in less than 7 months between 2016 and 2017, a fourfold increase in the number of reported cases compared to the average number over 2010–2016. The region of Negombo, located in the Western province, experienced the greatest number of dengue cases in the country and is the focus area of our study, where we aim to capture the spatial-temporal dynamics of dengue transmission. Methods We present a statistical modeling framework to evaluate the spatial-temporal dynamics of the 2016–2017 dengue outbreak in the Negombo region of Sri Lanka as a function of human mobility, land-use, and climate patterns. The analysis was conducted at a 1 km × 1 km spatial resolution and a weekly temporal resolution. Results Our results indicate human mobility to be a stronger indicator for local outbreak clusters than land-use or climate variables. The minimum daily temperature was identified as the most influential climate variable on dengue cases in the region; while among the set of land-use patterns considered, urban areas were found to be most prone to dengue outbreak, followed by areas with stagnant water and then coastal areas. The results are shown to be robust across spatial resolutions. Conclusions Our study highlights the potential value of using travel data to target vector control within a region. In addition to illustrating the relative relationship between various potential risk factors for dengue outbreaks, the results of our study can be used to inform where and when new cases of dengue are likely to occur within a region, and thus help more effectively and innovatively, plan for disease surveillance and vector control.