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Assessing dengue infection risk in the southern region of Taiwan: implications for control

  • C.-M. LIAO (a1), T.-L. HUANG (a1), Y.-H. CHENG (a1), W.-Y. CHEN (a2), N.-H. HSIEH (a3), S.-C. CHEN (a4) (a5) and C.-P. CHIO (a6)...

Summary

Dengue, one of the most important mosquito-borne diseases, is a major international public health concern. This study aimed to assess potential dengue infection risk from Aedes aegypti in Kaohsiung and the implications for vector control. Here we investigated the impact of dengue transmission on human infection risk using a well-established dengue–mosquito–human transmission dynamics model. A basic reproduction number (R 0)-based probabilistic risk model was also developed to estimate dengue infection risk. Our findings confirm that the effect of biting rate plays a crucial role in shaping R 0 estimates. We demonstrated that there was 50% risk probability for increased dengue incidence rates exceeding 0·5–0·8 wk−1 for temperatures ranging from 26°C to 32°C. We further demonstrated that the weekly increased dengue incidence rate can be decreased to zero if vector control efficiencies reach 30–80% at temperatures of 19–32°C. We conclude that our analysis on dengue infection risk and control implications in Kaohsiung provide crucial information for policy-making on disease control.

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Copyright

Corresponding author

* Author for correspondence: Dr C.-M. Liao, Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan 10617, ROC. (Email: cmliao@ntu.edu.tw)

References

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1. Bhatt, S, et al. The global distribution and burden of dengue. Nature 2013; 496: 504507.
2. Kyle, JL, Harris, E. Global spread and persistence of dengue. Annual Review of Microbiology 2008; 62: 7192.
3. Descloux, E, et al. Climate-based models for understanding and forecasting dengue epidemics. PLoS Neglected Tropical Diseases 2012; 6: e1470.
4. King, CC, et al. Major epidemics of dengue in Taiwan in 1981–2000: related to intensive virus activities in Asia. Dengue Bulletin 2000; 24: 110.
5. Lin, CH, et al. Dengue outbreaks in high-income area, Kaohsiung City, Taiwan, 2003–2009. Emerging Infectious Diseases 2012; 18: 16031611.
6. Centers for Disease Control, Taiwan (Taiwan CDC). Notifiable Infectious Disease Statistics System (http://nidss.cdc.gov.tw/default.aspx). Accessed 4 February 2014.
7. Lambrechts, L, et al. Impact of daily temperature fluctuations on dengue virus transmission by Aedes aegypti . Proceedings of the National Academy of Sciences USA 2011; 108: 76407665.
8. Yang, GJ, et al. Importance of endogenous feedback controlling the long-term abundance of tropical mosquito species. Population Ecology 2008; 50: 293305.
9. Racloz, V, et al. Surveillance of dengue fever virus: a review of epidemiological models and early warning systems. PLoS Neglected Tropical Diseases 2012; 6: e1648.
10. Smith, DL, et al. Revisiting the basic reproductive number for malaria and its implications for malaria control. PLoS Biology 2007; 5: e42.
11. Yang, HM, et al. Assessing the effects of temperature on the population of Aedes aegypti, the vector of dengue. Epidemiology and Infection 2009; 137: 11881202.
12. Gubler, DJ. Dengue and dengue hemorrhagic fever. Clinical Microbiology Reviews 1998; 11: 480496.
13. Luz, PM, et al. Dengue vector control strategies in an urban setting: an economic modelling assessment. Lancet 2011; 377: 16731680.
14. Wu, PC, et al. Weather as an effective predictor for occurrence of dengue fever in Taiwan. Acta Tropica 2007; 103: 5057.
15. Chen, SC, et al. Lagged temperature effect with mosquito transmission potential explains dengue variability in southern Taiwan: insights from a statistical analysis. Science of the Total Environment 2010; 408: 40694075.
16. Chowell, G, et al. Estimation of the reproduction number of dengue fever from spatial epidemic data. Mathematical Biosciences 2007; 208: 571589.
17. Coelho, GE, et al. Dynamics of the 2006/2007 dengue outbreak in Brazil. Memórias do Instituto Oswaldo Cruz 2008; 103: 535539.
18. Massad, E, et al. The risk of yellow fever in a dengue-infested area. Transactions of the Royal Society of Tropical Medicine and Hygiene 2001; 95: 370374.
19. Anderson, RM, May, RM. Infectious Diseases of Humans: Dynamics and Control. Oxford: Oxford University Press, 1991.
20. Barbazan, P, et al. Modelling the effect of temperature on transmission of dengue. Medical Veterinary Entomology 2010; 24: 6673.
21. Keeling, MJ, Rohani, P. Modeling Infectious Diseases in Humans and Animals. New Jersey: Princeton University Press, 2008.
22. Burattini, MN, et al. Modelling the control strategies against dengue in Singapore. Epidemiology and Infection 2008; 136: 309319.
23. Focks, DA, et al. A simulation model of the epidemiology of urban dengue fever: literature analysis, model development, preliminary validation, and samples of simulation results. American Journal of Tropical Medicine and Hygiene 1995; 53: 489506.
24. Newton, EAC, Reiter, P. A model of the transmission of dengue fever with an evaluation of the impact of ultra-low volume (ULV) insecticide applications on dengue epidemics. American Journal of Tropical Medicine and Hygiene 1992; 47: 709720.
25. Dumont, Y, Chiroleu, F, Domerg, C. On a temporal model for the Chikungunya disease: Modeling, theory and numerics. Mathematical Biosciences 2008; 213: 8091.
26. Focks, DA, et al. Dynamic life table model of Aedes aegypti (Diptera: Culicidae) – Analysis of the literature and model development. Journal of Medical Entomology 1993; 30: 10031017.
27. Massad, E, et al. Estimation of R 0 from the initial phase of an outbreak of a vector-borne infection. Tropical Medicine and International Health 2010; 15: 120126.
28. Halstead, SB. Dengue and dengue hemorrhagic fever. Current Opinion in Infectious Diseases 1990; 3: 434438.
29. Pinho, STR, et al. Modelling the dynamics of dengue real epidemics. Philosophical Transactions of the Royal Society of London, Series A: Mathematical, Physical and Engineering Sciences 2010; 368: 56795693.
30. Rosen, L, et al. Comparative susceptibility of mosquito species and strains to oral and parenteral infection with dengue and Japanese encephalitis viruses. American Journal of Tropical Medicine and Hygiene 1985; 34: 603615.
31. Watts, DM, et al. Effect of temperature on the vector efficiency of Aedes aegypti for dengue 2 virus. American Journal of Tropical Medicine and Hygiene 1987; 36: 143152.
32. Harn, MR. Clinical study on dengue fever during 1987–1988 epidemic at Kaohsiung city southern Taiwan. Kaohsiung Journal of Medical Sciences 1989; 5: 5865.
33. Hsieh, YH, Ma, S. Intervention measures, turning point, and reproduction number for dengue, Singapore, 2005. American Journal of Tropical Medicine and Hygiene 2009; 80: 6671.
34. Rogers, DJ, Randolph, SE. Climate change and vector-borne diseases. Advances in Parasitology 2006; 62: 345381.
35. Beserra, EB, et al. Biology and thermal exigency of Aedes aegypti (L.) (Diptera: Culicidae) from four bioclimatic localities of Paraiba. Neotropical Entomology 2006; 35: 853860.
36. Matthews, KR. Controlling and coordinating development in vector-transmitted parasites. Science 2011; 331: 11491153.
37. Legros, M, et al. Density-dependent intraspecific competition in the larval stage of Aedes aegypti (Diptera: Culicidae): revisiting the current paradigm. Journal of Medical Entomology 2009; 46: 409419.
38. Abbas, A, et al. Integrated strategies for the control and prevention of dengue vectors with particular reference to Aedes aegypti. Pakistan Veterinary Journal 2014, 34: 110.
39. Billingsley, PF, Foy, B, Rasgon, JL. Mosquitocidal vaccines: a neglected addition to malaria and dengue control strategies. Trends in Parasitology 2008; 24: 396400.
40. Stoddard, ST, et al. House-to-house movement drives dengue virus transmission. Proceedings of the National Academy of Sciences USA 2013; 110: 994999.

Keywords

Assessing dengue infection risk in the southern region of Taiwan: implications for control

  • C.-M. LIAO (a1), T.-L. HUANG (a1), Y.-H. CHENG (a1), W.-Y. CHEN (a2), N.-H. HSIEH (a3), S.-C. CHEN (a4) (a5) and C.-P. CHIO (a6)...

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