Assessing health burden risk and control effect on dengue fever infection in the southern region of Taiwan Yi-Hsien Cheng,1 Yi-Jun Lin,2 Szu-Chieh Chen,3,4 Shu-Han You,5 Wei-Yu Chen,6,7 Nan-Hung Hsieh,8 Ying-Fei Yang,9 Chung-Min Liao9 1Institute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, USA; 2Institute of Food Safety and Health Risk Assessment, National Yang-Ming University, Taipei, Taiwan, Republic of China; 3Department of Public Health, Chung Shan Medical University, Taichung, Taiwan, Republic of China; 4Department of Family and Community Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan, Republic of China; 5Institute of Food Safety and Risk Management, National Taiwan Ocean University, Keelung, Taiwan, Republic of China; 6Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan, Republic of China; 7Research Center for Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan, Republic of China; 8Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA; 9Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan, Republic of China Background: The high prevalence of dengue in Taiwan and the consecutive large dengue outbreaks in the period 2014–2015 suggest that current control interventions are suboptimal. Understanding the effect of control effort is crucial to inform future control strategies. Objectives: We developed a framework to measure season-based health burden risk from 2001 to 2014. We reconstructed various intervention coverage to assess the attributable effect of dengue infection control efforts. Materials and methods: A dengue–mosquito–human transmission dynamic was used to quantify the vector–host interactions and to estimate the disease epidemics. We used disability-adjusted life years (DALYs) to assess health burden risk. A temperature-basic reproduction number (R0)–DALYs relationship was constructed to examine the potential impacts of temperature on health burden. Finally, a health burden risk model linked a control measure model to evaluate the effect of dengue control interventions. Results: We showed that R0 and DALYs peaked at 25°C with estimates of 2.37 and 1387, respectively. Results indicated that most dengue cases occurred in fall with estimated DALYs of 323 (267–379, 95% CI) at 50% risk probability. We found that repellent spray had by far the largest control effect with an effectiveness of ~71% in all seasons. Pesticide spray and container clean-up have both made important contributions to reducing prevalence/incidence. Repellent, pesticide spray, container clean-up together with Wolbachia infection suppress dengue outbreak by ~90%. Conclusion: Our presented modeling framework provides a useful tool to measure dengue health burden risk and to quantify the effect of dengue control on dengue infection prevalence and disease incidence in the southern region of Taiwan. Keywords: dengue, DALYs, modeling, infection, control intervention