ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
PAHO, 2011. Number of Reported Cases of Dengue and Severe Dengue (DS) in the Americas by Country (1995–2011). Available at: www.who.int/denguenet. Accessed November 15, 2011.
-
2.
WHO, 2008. The Global Burden of Disease: 2004 Update. Geneva: World Health Organization.
-
3.
Philander SG, 1990. El Niño, La Niña, and the Southern Oscillation. San Diego, CA: Academic Press.
-
4.
Lyon B, Barnston AG, 2005. ENSO and the spatial extent of interannual precipitation extremes in tropical land areas. J Clim 18: 5095–5109.
-
5.
Moore CB, Cline BL, Ruiz-Tiben E, Lee D, Romney-Joseph H, Rivera-Correa E, 1978. Aedes aegypti in Puerto Rico: environmental determinants of larval abundance and relation to dengue virus transmission. Am J Trop Med Hyg 27: 1225–1231.
-
6.
Barrera R, Amador M, Clark GG, 2006. Use of the pupal survey technique for measuring Aedes aegypti (Diptera: Culicidae) productivity in Puerto Rico. Am J Trop Med Hyg 74: 290–302.
-
7.
Montgomery BL, Ritchie SA, 2002. Roof gutters: a key container for Aedes aegypti and Ochlerotatus notoscriptus (Diptera: Culicidae) in Australia. Am J Trop Med Hyg 67: 244–246.
-
8.
Bar-Zeev M, 1958. The effect of temperature on the growth rate and survival of the immature stages of Aedes aegypti (L). Bull Entomol Res 49: 157–163.
-
9.
Rueda LM, Patel KJ, Axtell RC, Stinner RE, 1990. Temperature-dependent development and survival rates of Culex quinquefasciatus and Aedes aegypti (Diptera: Culicidae). J Med Entomol 27: 892–898.
-
10.
Tun-Lin W, Burkot TR, Kay BH, 2000. Effects of temperature and larval diet on development rates and survival of the dengue vector Aedes aegypti in north Queensland, Australia. Med Vet Entomol 14: 31–37.
-
11.
Mohammed A, Chadee DD, 2011. Effects of different temperature regimens on the development of Aedes aegypti (L.) (Diptera: Culicidae) mosquitoes. Acta Trop 119: 38–43.
-
12.
Yasuno M, Tonn RJ, 1970. A study of biting habits of Aedes aegypti in Bangkok, Thailand. Bull World Health Organ 43: 319–325.
-
13.
Pant CP, Yasuno M, 1973. Field studies on the gonotrophic cycle of Aedes aegypti in Bangkok, Thailand. J Med Entomol 10: 219–223.
-
14.
Watts DM, Burke DS, Harrison BA, Whitmire RE, Nisalak A, 1986. Effect of temperature on the vector efficiency of Aedes aegypti for dengue 2 virus. Am J Trop Med Hyg 36: 143–152.
-
15.
Thu HM, Aye KM, Thein S, 1998. The effect of temperature and humidity on dengue virus propagation in Aedes aegypti mosquitoes. Southeast Asian J Trop Med Public Health 29: 280–284.
-
16.
Cazelles B, Chavez M, McMichael AJ, Hales S, 2005. Nonstationary influence of El Niño on the synchronous dengue epidemics in Thailand. PLoS Med 2: e106.
-
17.
Colón-González FJ, Lake IR, Bentham G, 2011. Climate variability and dengue fever in warm and humid Mexico. Am J Trop Med Hyg 84: 757–763.
-
18.
Gagnon AS, Bush ABG, Smoyer-Tomic KE, 2001. Dengue epidemics and the El Niño Southern Oscillation. Clim Res 19: 35–43.
-
19.
Hay SI, Myers MF, Burke DS, Vaughn DW, Endy T, Ananda N, Shanks GD, Snow RW, Rogers DJ, 2000. Etiology of interepidemic periods of mosquito-borne disease. Proc Natl Acad Sci USA 97: 9335–9339.
-
20.
Keating J, 2001. An investigation into the cyclical incidence of dengue fever. Soc Sci Med 53: 1587–1597.
-
21.
Thammapalo S, Chongsuwiwatwong V, McNeil D, Geater A, 2005. The climatic factors influencing the occurrence of dengue hemorrhagic fever in Thailand. Southeast Asian J Trop Med Public Health 36: 191–196.
-
22.
Johansson MA, Cummings DAT, Glass GE, 2009. Multiyear climate variability and dengue—El Niño Southern Oscillation, weather, and dengue incidence in Puerto Rico, Mexico, and Thailand: a longitudinal data analysis. PLoS Med 6: e1000168.
-
23.
Wearing HJ, Rohani P, 2006. Ecological and immunological determinants of dengue epidemics. Proc Natl Acad Sci USA 103: 11802–11807.
-
24.
Gubler DJ, 2002. Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. Trends Microbiol 10: 100–103.
-
25.
Focks DA, Barrera R, 2006. Dengue Transmission Dynamics: Assessment and Implications for Control. Report on the Scientific Working Group on Dengue, 2006. Geneva: World Health Organization, 92–109.
-
26.
Kuhn K, Campbell-Lendrum D, Haines A, Cox J, 2005. Using Climate to Predict Infectious Disease Epidemics. Available at: http://www.who.int/globalchange/publications/infectdiseases/en/index.html. Accessed July 24, 2012.
-
27.
Thomson MC, Mason SJ, Phindela T, Connor SJ, 2005. Use of rainfall and sea surface temperature monitoring for malaria early warning in Botswana. Am J Trop Med Hyg 73: 214–221.
-
28.
Kovats RS, Bouma MJ, Hajat S, Worrall E, Haines A, 2003. El Niño and health. Lancet 362: 1481–1489.
-
29.
Schreiber KV, 2001. An investigation of relationships between climate and dengue using a water budgeting technique. Int J Biometeorol 45: 81–89.
-
30.
Lowe R, Bailey TC, Stephenson DB, Graham RJ, Coelho CAS, Sa Carvalho M, Barcellos C, 2011. Spatio-temporal modeling of climate-sensitive disease risk: towards an early warning system for dengue in Brazil. Comput Geosci 37: 371–381.
-
31.
Yu H-L, Yang S-J, Yen H-J, Christakos G, 2011. A spatio-temporal climate-based model of early dengue fever warning in southern Taiwan. Stochastic Environ Res Risk Assess 25: 485–494.
-
32.
Pourrut P, Nouvelot JF, 1995. Anomalies and extreme climate phenomena. Pourrut P, ed. Water in Ecuador: Climate, Precipitation, Runoff. Quito, Ecuador: RR Associated Editors, 67–76.
-
33.
Rossel F, Le Goulven P, Cadier E, 1999. Areal distribution of the influence of ENSO on the annual rainfall in Ecuador. Journal of Water Science 12: 183–200.
-
34.
Rossel F, Cadier E, Gómez G, 1996. Flooding in coastal Ecuador: causes; exisiting and future protection projects. Bulletin of the French Institute of Andean Studies 25: 399–420.
-
35.
INAMHI, 2012. Rainfall during the trimester January – March 2012 on the coast of Ecuador. Guayaquil, Ecuador. National Institute of Meteorology and Hydrology (INAMHI) Decentralized Process, Guayas Watershed.
-
36.
Adler RF, Huffman GJ, Chang A, Ferraro R, Xie P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P, 2003. The Version 2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). Journal of Hydrometeorology 4: 1147–1167.
-
37.
NOAA, 2012. National Oceanic and Atmospheric Administration (United Stated Department of Commerce) Climate Prediction Center. Cold and Warm Episodes by Season (1951–present). Available at: http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml. Accessed July 24, 2012.
-
38.
Lowe R, Bailey TC, Stephenson DB, Jupp TE, Graham RJ, Barcellos C, Carvalho MS, 2013. The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in Southeast Brazil. Stat Med 32: 864–883.
-
39.
Cameron AC, Trivedi PK, 1998. Regression Analysis of Count Data. Econometric Society Monograph No 30. New York, NY: Cambridge University Press, 434.
-
40.
Hilbe JM, 2011. Negative Binomial Regression. New York, NY: Cambridge University Press, 264.
-
41.
McCulloch CE, Neuhaus JM, 2001. Generalized Linear Mixed Models. New York, NY: John Wiley & Sons, Inc., 358.
-
42.
Gilks WR, Richardson S, Spiegelhalter DJ, 1996. Markov Chain Monte Carlo in Practice. Boca Raton, FL: Chapman & Hall/CRC, 486.
-
43.
Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A, 2002. Bayesian measures of model complexity and fit. J R Stat Soc Series B Stat Methodol 64: 583–639.
-
44.
Kramer M, 2005. R2 statistics for mixed models. Proceedings of the Conference on Applied Statistics in Agriculture 17: 148–160.
-
45.
Gelman A, Meng X, Stern H, 1996. Posterior predictive assessment of model fitness via realized discrepancies. Stat Sin 6: 733–759.
-
46.
Chowell G, Sanchez F, 2006. Climate-based descriptive models of dengue fever: the 2002 epidemic in Colima, Mexico. J Environ Health 68: 40.
-
47.
Hurtado-Díaz M, Riojas-Rodríguez H, Rothenberg SJ, Gomez-Dantés H, Cifuentes E, 2007. Impact of climate variability on the incidence of dengue in Mexico. Trop Med Int Health 12: 1327–1337.
-
48.
Chowell G, Torre CA, Munayco-Escate C, Suarez-Ognio L, Lopez-Cruz R, Hyman JM, Castillo-Chavez C, 2008. Spatial and temporal dynamics of dengue fever in Peru: 1994–2006. Epidemiol Infect 136: 1667–1677.
-
49.
Scott TW, Amerasinghe PH, Morrison AC, Lorenz LH, Clark GG, Strickman D, Kittayapong P, Edman JD, 2000. Longitudinal studies of Aedes aegypti (Diptera: Culicidae) in Thailand and Puerto Rico: blood feeding frequency. J Med Entomol 37: 89–101.
-
50.
Nagao Y, Thavara U, Chitnumsup P, Tawatsin A, Chansang C, Campbell-Lendrum D, 2003. Climatic and social risk factors for Aedes infestation in rural Thailand. Trop Med Int Health 8: 650–659.
-
51.
Lambrecht L, Paaijmans KP, Fansiri T, Carrington LB, Kramer LD, Thomas MB, Scott TW, 2011. Impact of daily temperature fluctuations on dengue virus transmission by Aedes aegypti. Proc Natl Acad Sci USA 108: 7461–7465.
-
52.
Barrera R, Amador M, MacKay AJ, 2011. Population dynamics of Aedes aegypti and dengue as influenced by weather and human behavior in San Juan, Puerto Rico. PLoS Negl Trop Dis 5: e1378.
-
53.
Hayden MH, Uejio CK, Walker K, Ramberg F, Moreno R, Rosales C, Gameros M, Mearns LO, Zielinski-Gutierrez E, Janes CR, 2010. Microclimate and human factors in the divergent ecology of Aedes aegypti along the Arizona, US/Sonora, MX border. Ecohealth 7: 64–77.
-
54.
Pontes RJ, Freeman J, Oliveira-Lima JW, Hodgson JC, Spielman A, 2000. Vector densities that potentiate dengue outbreaks in a Brazilian city. Am J Trop Med Hyg 62: 378–383.
-
55.
Bamston AG, Chelliah M, Goldenberg SB, 1997. Documentation of a highly ENSO-related SST region in the equatorial Pacific: research note. Atmosphere-ocean 35: 367–383.
-
56.
Rossel F, Cadier E, 2009. El Niño and prediction of anomalous monthly rainfalls in Ecuador. Hydrol Process 23: 3253–3260.
-
57.
Jupp TE, Lowe R, Coelho CA, Stephenson DB, 2012. On the visualization, verification and recalibration of ternary probabilistic forecasts. Philos Transact A Math Phys Eng Sci 370: 1100–1120.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 1716 | 1190 | 59 |
| Full Text Views | 896 | 26 | 1 |
| PDF Downloads | 434 | 24 | 0 |
Climate and Non-Climate Drivers of Dengue Epidemics in Southern Coastal Ecuador
Anna M. Stewart-Ibarra
Anna M. Stewart-Ibarra
Department of Environmental and Forest Biology, State University of New York College of Environmental Science and Forestry, Syracuse, New York; National Service for the Control of Vector-Borne Diseases, Ministry of Health, Machala, Ecuador; National Institute of Meteorology and Hydrology, Guayaquil, Ecuador; The Catalan Institute of Climate Sciences, Barcelona, Spain; International Centre for Theoretical Physics, Trieste, Italy
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Rachel Lowe
Rachel Lowe
Department of Environmental and Forest Biology, State University of New York College of Environmental Science and Forestry, Syracuse, New York; National Service for the Control of Vector-Borne Diseases, Ministry of Health, Machala, Ecuador; National Institute of Meteorology and Hydrology, Guayaquil, Ecuador; The Catalan Institute of Climate Sciences, Barcelona, Spain; International Centre for Theoretical Physics, Trieste, Italy
Search for other papers by Rachel Lowe in
Current site
Google Scholar
PubMed
Search for other papers by Rachel Lowe in
Current site
Google Scholar
PubMed
We report a statistical mixed model for assessing the importance of climate and non-climate drivers of interannual variability in dengue fever in southern coastal Ecuador. Local climate data and Pacific sea surface temperatures (Oceanic Niño Index [ONI]) were used to predict dengue standardized morbidity ratios (SMRs; 1995–2010). Unobserved confounding factors were accounted for using non-structured yearly random effects. We found that ONI, rainfall, and minimum temperature were positively associated with dengue, with more cases of dengue during El Niño events. We assessed the influence of non-climatic factors on dengue SMR using a subset of data (2001–2010) and found that the percent of households with Aedes aegypti immatures was also a significant predictor. Our results indicate that monitoring the climate and non-climate drivers identified in this study could provide some predictive lead for forecasting dengue epidemics, showing the potential to develop a dengue early-warning system in this region.
Author Notes
Financial support: A.M.S.-I. received partial support from a Fulbright Institute of International Education fellowship. R.L. received partial funding from European Union Projects Quantifying Weather and Climate Impacts on Health in Developing Countries (QWeCI) Grant 243964 and Dengue Research Framework for Resisting Epidemics in Europe (DENFREE) Grant 282 378 funded by the European Commission's Seventh Framework Research Programme.
Authors' addresses: Anna M. Stewart-Ibarra, Center for Global Health and Translational Science, State University of New York Upstate Medical University, Syracuse, NY, E-mail: stewarta@upstate.edu. Rachel Lowe, The Catalan Institute of Climate Sciences (IC3), Barcelona, Spain, E-mail: rachel.lowe@ic3.cat.
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
PAHO, 2011. Number of Reported Cases of Dengue and Severe Dengue (DS) in the Americas by Country (1995–2011). Available at: www.who.int/denguenet. Accessed November 15, 2011.
-
2.
WHO, 2008. The Global Burden of Disease: 2004 Update. Geneva: World Health Organization.
-
3.
Philander SG, 1990. El Niño, La Niña, and the Southern Oscillation. San Diego, CA: Academic Press.
-
4.
Lyon B, Barnston AG, 2005. ENSO and the spatial extent of interannual precipitation extremes in tropical land areas. J Clim 18: 5095–5109.
-
5.
Moore CB, Cline BL, Ruiz-Tiben E, Lee D, Romney-Joseph H, Rivera-Correa E, 1978. Aedes aegypti in Puerto Rico: environmental determinants of larval abundance and relation to dengue virus transmission. Am J Trop Med Hyg 27: 1225–1231.
-
6.
Barrera R, Amador M, Clark GG, 2006. Use of the pupal survey technique for measuring Aedes aegypti (Diptera: Culicidae) productivity in Puerto Rico. Am J Trop Med Hyg 74: 290–302.
-
7.
Montgomery BL, Ritchie SA, 2002. Roof gutters: a key container for Aedes aegypti and Ochlerotatus notoscriptus (Diptera: Culicidae) in Australia. Am J Trop Med Hyg 67: 244–246.
-
8.
Bar-Zeev M, 1958. The effect of temperature on the growth rate and survival of the immature stages of Aedes aegypti (L). Bull Entomol Res 49: 157–163.
-
9.
Rueda LM, Patel KJ, Axtell RC, Stinner RE, 1990. Temperature-dependent development and survival rates of Culex quinquefasciatus and Aedes aegypti (Diptera: Culicidae). J Med Entomol 27: 892–898.
-
10.
Tun-Lin W, Burkot TR, Kay BH, 2000. Effects of temperature and larval diet on development rates and survival of the dengue vector Aedes aegypti in north Queensland, Australia. Med Vet Entomol 14: 31–37.
-
11.
Mohammed A, Chadee DD, 2011. Effects of different temperature regimens on the development of Aedes aegypti (L.) (Diptera: Culicidae) mosquitoes. Acta Trop 119: 38–43.
-
12.
Yasuno M, Tonn RJ, 1970. A study of biting habits of Aedes aegypti in Bangkok, Thailand. Bull World Health Organ 43: 319–325.
-
13.
Pant CP, Yasuno M, 1973. Field studies on the gonotrophic cycle of Aedes aegypti in Bangkok, Thailand. J Med Entomol 10: 219–223.
-
14.
Watts DM, Burke DS, Harrison BA, Whitmire RE, Nisalak A, 1986. Effect of temperature on the vector efficiency of Aedes aegypti for dengue 2 virus. Am J Trop Med Hyg 36: 143–152.
-
15.
Thu HM, Aye KM, Thein S, 1998. The effect of temperature and humidity on dengue virus propagation in Aedes aegypti mosquitoes. Southeast Asian J Trop Med Public Health 29: 280–284.
-
16.
Cazelles B, Chavez M, McMichael AJ, Hales S, 2005. Nonstationary influence of El Niño on the synchronous dengue epidemics in Thailand. PLoS Med 2: e106.
-
17.
Colón-González FJ, Lake IR, Bentham G, 2011. Climate variability and dengue fever in warm and humid Mexico. Am J Trop Med Hyg 84: 757–763.
-
18.
Gagnon AS, Bush ABG, Smoyer-Tomic KE, 2001. Dengue epidemics and the El Niño Southern Oscillation. Clim Res 19: 35–43.
-
19.
Hay SI, Myers MF, Burke DS, Vaughn DW, Endy T, Ananda N, Shanks GD, Snow RW, Rogers DJ, 2000. Etiology of interepidemic periods of mosquito-borne disease. Proc Natl Acad Sci USA 97: 9335–9339.
-
20.
Keating J, 2001. An investigation into the cyclical incidence of dengue fever. Soc Sci Med 53: 1587–1597.
-
21.
Thammapalo S, Chongsuwiwatwong V, McNeil D, Geater A, 2005. The climatic factors influencing the occurrence of dengue hemorrhagic fever in Thailand. Southeast Asian J Trop Med Public Health 36: 191–196.
-
22.
Johansson MA, Cummings DAT, Glass GE, 2009. Multiyear climate variability and dengue—El Niño Southern Oscillation, weather, and dengue incidence in Puerto Rico, Mexico, and Thailand: a longitudinal data analysis. PLoS Med 6: e1000168.
-
23.
Wearing HJ, Rohani P, 2006. Ecological and immunological determinants of dengue epidemics. Proc Natl Acad Sci USA 103: 11802–11807.
-
24.
Gubler DJ, 2002. Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. Trends Microbiol 10: 100–103.
-
25.
Focks DA, Barrera R, 2006. Dengue Transmission Dynamics: Assessment and Implications for Control. Report on the Scientific Working Group on Dengue, 2006. Geneva: World Health Organization, 92–109.
-
26.
Kuhn K, Campbell-Lendrum D, Haines A, Cox J, 2005. Using Climate to Predict Infectious Disease Epidemics. Available at: http://www.who.int/globalchange/publications/infectdiseases/en/index.html. Accessed July 24, 2012.
-
27.
Thomson MC, Mason SJ, Phindela T, Connor SJ, 2005. Use of rainfall and sea surface temperature monitoring for malaria early warning in Botswana. Am J Trop Med Hyg 73: 214–221.
-
28.
Kovats RS, Bouma MJ, Hajat S, Worrall E, Haines A, 2003. El Niño and health. Lancet 362: 1481–1489.
-
29.
Schreiber KV, 2001. An investigation of relationships between climate and dengue using a water budgeting technique. Int J Biometeorol 45: 81–89.
-
30.
Lowe R, Bailey TC, Stephenson DB, Graham RJ, Coelho CAS, Sa Carvalho M, Barcellos C, 2011. Spatio-temporal modeling of climate-sensitive disease risk: towards an early warning system for dengue in Brazil. Comput Geosci 37: 371–381.
-
31.
Yu H-L, Yang S-J, Yen H-J, Christakos G, 2011. A spatio-temporal climate-based model of early dengue fever warning in southern Taiwan. Stochastic Environ Res Risk Assess 25: 485–494.
-
32.
Pourrut P, Nouvelot JF, 1995. Anomalies and extreme climate phenomena. Pourrut P, ed. Water in Ecuador: Climate, Precipitation, Runoff. Quito, Ecuador: RR Associated Editors, 67–76.
-
33.
Rossel F, Le Goulven P, Cadier E, 1999. Areal distribution of the influence of ENSO on the annual rainfall in Ecuador. Journal of Water Science 12: 183–200.
-
34.
Rossel F, Cadier E, Gómez G, 1996. Flooding in coastal Ecuador: causes; exisiting and future protection projects. Bulletin of the French Institute of Andean Studies 25: 399–420.
-
35.
INAMHI, 2012. Rainfall during the trimester January – March 2012 on the coast of Ecuador. Guayaquil, Ecuador. National Institute of Meteorology and Hydrology (INAMHI) Decentralized Process, Guayas Watershed.
-
36.
Adler RF, Huffman GJ, Chang A, Ferraro R, Xie P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P, 2003. The Version 2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). Journal of Hydrometeorology 4: 1147–1167.
-
37.
NOAA, 2012. National Oceanic and Atmospheric Administration (United Stated Department of Commerce) Climate Prediction Center. Cold and Warm Episodes by Season (1951–present). Available at: http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml. Accessed July 24, 2012.
-
38.
Lowe R, Bailey TC, Stephenson DB, Jupp TE, Graham RJ, Barcellos C, Carvalho MS, 2013. The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in Southeast Brazil. Stat Med 32: 864–883.
-
39.
Cameron AC, Trivedi PK, 1998. Regression Analysis of Count Data. Econometric Society Monograph No 30. New York, NY: Cambridge University Press, 434.
-
40.
Hilbe JM, 2011. Negative Binomial Regression. New York, NY: Cambridge University Press, 264.
-
41.
McCulloch CE, Neuhaus JM, 2001. Generalized Linear Mixed Models. New York, NY: John Wiley & Sons, Inc., 358.
-
42.
Gilks WR, Richardson S, Spiegelhalter DJ, 1996. Markov Chain Monte Carlo in Practice. Boca Raton, FL: Chapman & Hall/CRC, 486.
-
43.
Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A, 2002. Bayesian measures of model complexity and fit. J R Stat Soc Series B Stat Methodol 64: 583–639.
-
44.
Kramer M, 2005. R2 statistics for mixed models. Proceedings of the Conference on Applied Statistics in Agriculture 17: 148–160.
-
45.
Gelman A, Meng X, Stern H, 1996. Posterior predictive assessment of model fitness via realized discrepancies. Stat Sin 6: 733–759.
-
46.
Chowell G, Sanchez F, 2006. Climate-based descriptive models of dengue fever: the 2002 epidemic in Colima, Mexico. J Environ Health 68: 40.
-
47.
Hurtado-Díaz M, Riojas-Rodríguez H, Rothenberg SJ, Gomez-Dantés H, Cifuentes E, 2007. Impact of climate variability on the incidence of dengue in Mexico. Trop Med Int Health 12: 1327–1337.
-
48.
Chowell G, Torre CA, Munayco-Escate C, Suarez-Ognio L, Lopez-Cruz R, Hyman JM, Castillo-Chavez C, 2008. Spatial and temporal dynamics of dengue fever in Peru: 1994–2006. Epidemiol Infect 136: 1667–1677.
-
49.
Scott TW, Amerasinghe PH, Morrison AC, Lorenz LH, Clark GG, Strickman D, Kittayapong P, Edman JD, 2000. Longitudinal studies of Aedes aegypti (Diptera: Culicidae) in Thailand and Puerto Rico: blood feeding frequency. J Med Entomol 37: 89–101.
-
50.
Nagao Y, Thavara U, Chitnumsup P, Tawatsin A, Chansang C, Campbell-Lendrum D, 2003. Climatic and social risk factors for Aedes infestation in rural Thailand. Trop Med Int Health 8: 650–659.
-
51.
Lambrecht L, Paaijmans KP, Fansiri T, Carrington LB, Kramer LD, Thomas MB, Scott TW, 2011. Impact of daily temperature fluctuations on dengue virus transmission by Aedes aegypti. Proc Natl Acad Sci USA 108: 7461–7465.
-
52.
Barrera R, Amador M, MacKay AJ, 2011. Population dynamics of Aedes aegypti and dengue as influenced by weather and human behavior in San Juan, Puerto Rico. PLoS Negl Trop Dis 5: e1378.
-
53.
Hayden MH, Uejio CK, Walker K, Ramberg F, Moreno R, Rosales C, Gameros M, Mearns LO, Zielinski-Gutierrez E, Janes CR, 2010. Microclimate and human factors in the divergent ecology of Aedes aegypti along the Arizona, US/Sonora, MX border. Ecohealth 7: 64–77.
-
54.
Pontes RJ, Freeman J, Oliveira-Lima JW, Hodgson JC, Spielman A, 2000. Vector densities that potentiate dengue outbreaks in a Brazilian city. Am J Trop Med Hyg 62: 378–383.
-
55.
Bamston AG, Chelliah M, Goldenberg SB, 1997. Documentation of a highly ENSO-related SST region in the equatorial Pacific: research note. Atmosphere-ocean 35: 367–383.
-
56.
Rossel F, Cadier E, 2009. El Niño and prediction of anomalous monthly rainfalls in Ecuador. Hydrol Process 23: 3253–3260.
-
57.
Jupp TE, Lowe R, Coelho CA, Stephenson DB, 2012. On the visualization, verification and recalibration of ternary probabilistic forecasts. Philos Transact A Math Phys Eng Sci 370: 1100–1120.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 1716 | 1190 | 59 |
| Full Text Views | 896 | 26 | 1 |
| PDF Downloads | 434 | 24 | 0 |