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Reference Manager
BibTeX
Zotero
EndNote
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World Health Organization, 2010. World Malaria Report 2010. Available at: http://whqlibdoc.who.int/publications/2010/9789241564106_eng.pdf.
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Tang LH, 2000. Progress in malaria control in China. Chin Med J (Engl) 115: 69–92.
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3.
Zhou SS, Wang Y, Fang W, Tang LH, 2011. Malaria situation in the People's Republic of China in 2009. Chin J Parasitol and Parasit Dis 27: 1–3.
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Warrell DA, Gilles H, 2002. Essential Malariology. London: Hodder Arnold Press, 85–106.
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5.
Teklehaimanot HD, Lipsitch M, Teklehaimanot A, Schwartz J, 2004. Weather based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia. I. Patterns of lagged weather effects reflect biological mechanisms. Malar J 3: 41.
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6.
Van der Hoek W, Konradsen F, Perera D, Amerasinghe PH, Amerasinghe FP, 1997. Correlation between rainfall and malaria in the dry zone of Sri Lanka. Ann Trop Med Parasitol 91: 945–949.
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7.
Nobre AA, Schmidr AM, Lopes HF, 2005. Spatio-temporal models for mapping the incidence of malaria in Para. Environmetrics 16: 291–304.
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8.
Smans M, Esteve J, 1992. Practical Approaches to Disease Mapping Geographical and Environmental Epidemiology: Methods for Small-Area Studies. Oxford, United Kingdom: Oxford University Press, 141–150.
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Wakefield JC, Best NG, Waller LA, 2000. Spatial Epidemiology: Methods and Applications. Oxford, United Kingdom: Oxford University Press, 104–127.
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10.
Benardinelli L, Clayton D, Montomoli C, Ghislandi M, Songini M, 1995. Bayesian estimates of disease maps: how important were priors. Stat Med 14: 2411–2431.
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11.
Killeen GF, Knols BG, Gu W, 2003. Taking malaria transmission out of the bottle: implications of mosquito dispersal for vector-control interventions. Lancet Infect Dis 3: 297–303.
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12.
Zhou G, Sirichaisinthop J, Sattabongkot J, Jones J, Bjornstad ON, Yan G, Cui L, 2005. Spatio-temporal distribution of Plasmodium falciparum and P. vivax malaria in Thailand. Am J Trop Med Hyg 72: 256–262.
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13.
Abeku TA, de Vlas SJ, Borsboom G, Teklehaimanot A, Kebede A, Olana D, van Oortmarssen GJ, Habbema JD, 2002. Forecasting malaria incidence from historical morbidity patterns in epidemic-prone areas of Ethiopia: a simple seasonal adjustment method performs best. Trop Med Int Health 7: 851–857.
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14.
Hay SI, Were EC, Renshaw M, Noor AM, Ochola SA, Olusanmi I, Alipui N, Snow RW, 2003. Forecasting, warning, and detection of malaria epidemics: a case study. Lancet 361: 1705–1706.
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Thomson M, Indeje M, Connor S, Dilley M, Ward N, 2003. Malaria early warning in Kenya and seasonal climate forecasts. Lancet 362: 580.
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Bouma MJ, van der Kaay HJ, 1996. The El Nino Southern Oscillation and the historic malaria epidemics on the Indian subcontinent and Sri Lanka: an early warning system for future epidemics? Trop Med Int Health 1: 86–96.
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17.
Zhou SS, Huang F, Wang JJ, Zhang SS, Su YP, Tang LH, 2010. Geographical, meteorological and vectorial factors related to malaria re-emergence in Huang-Huai River of central China. Malar J 9: 337.
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Malaria Surveillance Project in China, 2005. Beijing: Ministry of Health.
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China Meteorological Data Sharing Service System. Available at: http://cdc.cma.gov.cn.
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ARGIS 9.2, 2009. Available at: www.esri.com/software/arcgis/index.html.
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Mendis C, Gamage Mendis AC, De Zoysa AP, Abhayawardena TA, Carter R, Herath PR, Mendis KN, 1990. Characteristics of malaria transmission in Kataragama, Sri Lanka: a focus for immune-epidemiological studies. Am J Trop Med Hyg 42: 298–308.
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Knorr-Held L, Rasser G, 2000. Bayesian detection of clusters and discontinuities in disease maps. Biometrics 56: 13–21.
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Lawson AB, 2006. Disease cluster detection: a critique and a Bayesian proposal. Stat Med 25: 897–916.
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Walsh B, 2004. Markov Chain Monte Carlo and Gibbs Sampling Markov Chain Monte Carlo and Gibbs Sampling Lecture Notes for EEB 581, version 26, April 2004.
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Climate Change and Malaria Risk: Complexity and Scaling. Available at: http://edepot.wur.nl/119287.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 1220 | 1115 | 67 |
| Full Text Views | 589 | 11 | 0 |
| PDF Downloads | 113 | 11 | 0 |
Meteorological Factors–Based Spatio-Temporal Mapping and Predicting Malaria in Central China
Fang Huang
Fang Huang
Malaria Department, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; World Health Organization Collaborating Centre for Malaria, Schistosomiasis and Filariasia; Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China; Department of Parasitology, Henan Center for Disease Control and Prevention, Zhengzhou, People's Republic of China; Department of Parasitology, Anhui Center for Disease Control and Prevention, Hefei, People's Republic of China
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Shuisen Zhou
Shuisen Zhou
Malaria Department, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; World Health Organization Collaborating Centre for Malaria, Schistosomiasis and Filariasia; Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China; Department of Parasitology, Henan Center for Disease Control and Prevention, Zhengzhou, People's Republic of China; Department of Parasitology, Anhui Center for Disease Control and Prevention, Hefei, People's Republic of China
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Shaosen Zhang
Shaosen Zhang
Malaria Department, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; World Health Organization Collaborating Centre for Malaria, Schistosomiasis and Filariasia; Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China; Department of Parasitology, Henan Center for Disease Control and Prevention, Zhengzhou, People's Republic of China; Department of Parasitology, Anhui Center for Disease Control and Prevention, Hefei, People's Republic of China
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Hongwei Zhang
Hongwei Zhang
Malaria Department, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; World Health Organization Collaborating Centre for Malaria, Schistosomiasis and Filariasia; Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China; Department of Parasitology, Henan Center for Disease Control and Prevention, Zhengzhou, People's Republic of China; Department of Parasitology, Anhui Center for Disease Control and Prevention, Hefei, People's Republic of China
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Weidong Li
Weidong Li
Malaria Department, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; World Health Organization Collaborating Centre for Malaria, Schistosomiasis and Filariasia; Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China; Department of Parasitology, Henan Center for Disease Control and Prevention, Zhengzhou, People's Republic of China; Department of Parasitology, Anhui Center for Disease Control and Prevention, Hefei, People's Republic of China
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Despite significant reductions in the overall burden of malaria in the 20th century, this disease still represents a significant public health problem in China, especially in central areas. Understanding the spatio-temporal distribution of malaria is essential in the planning and implementing of effective control measures. In this study, normalized meteorological factors were incorporated in spatio-temporal models. Seven models were established in WinBUGS software by using Bayesian hierarchical models and Markov Chain Monte Carlo methods. M1, M2, and M3 modeled separate meteorological factors, and M3, which modeled rainfall performed better than M1 and M2, which modeled average temperature and relative humidity, respectively. M7 was the best fitting models on the basis of based on deviance information criterion and predicting errors. The results showed that the way rainfall influencing malaria incidence was different from other factors, which could be interpreted as rainfall having a greater influence than other factors.
Author Notes
Financial support: This study was supported by the special social commonweal research programs of the Ministry of Science and Technology of the People's Republic of China (2005 DIB1J092) and the National S. and T. Mayor Project (no. 2008ZX10004-011).
Authors' addresses: Fang Huang, Shuisen Zhou, and Shaosen Zhang, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, World Health Organization Collaborating Centre for Malaria, Schistosomiasis and Filariasia, Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China, E-mails: huangfang78@yahoo.com.cn, ccdczss@sh163.net, and shaosen413@163.com. Hongwei Zhang, Department of Parasitology, Henan Center for Disease Control and Prevention, Zhengzhou, People's Republic of China, E-mail: zhwei69@163.com. Weidong Li, Department of Parasitology, Anhui Center for Disease Control and Prevention, Hefei, People's Republic of PR China, E-mail: ahcdclwd@163.com.
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
World Health Organization, 2010. World Malaria Report 2010. Available at: http://whqlibdoc.who.int/publications/2010/9789241564106_eng.pdf.
-
2.
Tang LH, 2000. Progress in malaria control in China. Chin Med J (Engl) 115: 69–92.
-
3.
Zhou SS, Wang Y, Fang W, Tang LH, 2011. Malaria situation in the People's Republic of China in 2009. Chin J Parasitol and Parasit Dis 27: 1–3.
-
4.
Warrell DA, Gilles H, 2002. Essential Malariology. London: Hodder Arnold Press, 85–106.
-
5.
Teklehaimanot HD, Lipsitch M, Teklehaimanot A, Schwartz J, 2004. Weather based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia. I. Patterns of lagged weather effects reflect biological mechanisms. Malar J 3: 41.
-
6.
Van der Hoek W, Konradsen F, Perera D, Amerasinghe PH, Amerasinghe FP, 1997. Correlation between rainfall and malaria in the dry zone of Sri Lanka. Ann Trop Med Parasitol 91: 945–949.
-
7.
Nobre AA, Schmidr AM, Lopes HF, 2005. Spatio-temporal models for mapping the incidence of malaria in Para. Environmetrics 16: 291–304.
-
8.
Smans M, Esteve J, 1992. Practical Approaches to Disease Mapping Geographical and Environmental Epidemiology: Methods for Small-Area Studies. Oxford, United Kingdom: Oxford University Press, 141–150.
-
9.
Wakefield JC, Best NG, Waller LA, 2000. Spatial Epidemiology: Methods and Applications. Oxford, United Kingdom: Oxford University Press, 104–127.
-
10.
Benardinelli L, Clayton D, Montomoli C, Ghislandi M, Songini M, 1995. Bayesian estimates of disease maps: how important were priors. Stat Med 14: 2411–2431.
-
11.
Killeen GF, Knols BG, Gu W, 2003. Taking malaria transmission out of the bottle: implications of mosquito dispersal for vector-control interventions. Lancet Infect Dis 3: 297–303.
-
12.
Zhou G, Sirichaisinthop J, Sattabongkot J, Jones J, Bjornstad ON, Yan G, Cui L, 2005. Spatio-temporal distribution of Plasmodium falciparum and P. vivax malaria in Thailand. Am J Trop Med Hyg 72: 256–262.
-
13.
Abeku TA, de Vlas SJ, Borsboom G, Teklehaimanot A, Kebede A, Olana D, van Oortmarssen GJ, Habbema JD, 2002. Forecasting malaria incidence from historical morbidity patterns in epidemic-prone areas of Ethiopia: a simple seasonal adjustment method performs best. Trop Med Int Health 7: 851–857.
-
14.
Hay SI, Were EC, Renshaw M, Noor AM, Ochola SA, Olusanmi I, Alipui N, Snow RW, 2003. Forecasting, warning, and detection of malaria epidemics: a case study. Lancet 361: 1705–1706.
-
15.
Thomson M, Indeje M, Connor S, Dilley M, Ward N, 2003. Malaria early warning in Kenya and seasonal climate forecasts. Lancet 362: 580.
-
16.
Bouma MJ, van der Kaay HJ, 1996. The El Nino Southern Oscillation and the historic malaria epidemics on the Indian subcontinent and Sri Lanka: an early warning system for future epidemics? Trop Med Int Health 1: 86–96.
-
17.
Zhou SS, Huang F, Wang JJ, Zhang SS, Su YP, Tang LH, 2010. Geographical, meteorological and vectorial factors related to malaria re-emergence in Huang-Huai River of central China. Malar J 9: 337.
-
18.
Malaria Surveillance Project in China, 2005. Beijing: Ministry of Health.
-
19.
China Meteorological Data Sharing Service System. Available at: http://cdc.cma.gov.cn.
-
20.
ARGIS 9.2, 2009. Available at: www.esri.com/software/arcgis/index.html.
-
21.
Craig MH, Kleinschmidt I, Nawn JB, Le Sueur D, Sharp BL, 2004. Exploring 30 years of malaria case data in KwaZulu-Natal, South Africa: part I. The impact of climatic factors. Trop Med Int Health 9: 1247–1257.
-
22.
Gill CA, 1936. Some points in the epidemiology of malaria arising out of the study of the malaria epidemic in Ceylon in 1934–35. Trans R Soc Trop Med Hyg 29: 427–466.
-
23.
Mendis C, Gamage Mendis AC, De Zoysa AP, Abhayawardena TA, Carter R, Herath PR, Mendis KN, 1990. Characteristics of malaria transmission in Kataragama, Sri Lanka: a focus for immune-epidemiological studies. Am J Trop Med Hyg 42: 298–308.
-
24.
Lawson AB, 2008. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. Boca Raton, FL: CRC Press, 255–281.
-
25.
Knorr-Held L, Besag J, 1998. Modeling risk from a disease in time and space. Stat Med 17: 2045–2060.
-
26.
Knorr-Held L, Rasser G, 2000. Bayesian detection of clusters and discontinuities in disease maps. Biometrics 56: 13–21.
-
27.
Lawson AB, 2006. Disease cluster detection: a critique and a Bayesian proposal. Stat Med 25: 897–916.
-
28.
Walsh B, 2004. Markov Chain Monte Carlo and Gibbs Sampling Markov Chain Monte Carlo and Gibbs Sampling Lecture Notes for EEB 581, version 26, April 2004.
-
29.
Zhou XN, 2009. Spatial Epidemiology. Beijing: Science Press, 236–255.
-
30.
Zhu L, Carlin BP, 2000. Comparing hierarchical models for spatio-temporally misaligned data using the deviance information criterion. Stat Med 19: 2265–2278.
-
31.
Carter R, 2002. Spatial simulation of malaria transmission and its control by malaria transmission blocking vaccination. Int J Parasitol 32: 1617–1624.
-
32.
Okiro EA, Hay SI, Gikandi PW, Sharif SK, Noor AM, Peshu N, Marsh K, Snow RW, 2007. The decline in paediatric malaria admissions on the coast of Kenya. Malar J 6: 151.
-
33.
O'Meara WP, Mwangi TW, Williams TN, McKenzie FE, Snow RW, Marsh K, 2008. Relationship between exposure, clinical malaria, and age in an area of changing transmission intensity. Am J Trop Med Hyg 79: 185–191.
-
34.
Besag J, York J, Molli'e A, 1991. Bayesian image restoration, with two applications in spatial statistics. Ann Inst Stat Math 43: 1–59.
-
35.
The Global Fund to Fight AIDS, Tuberculosis and Malaria. Available at: http://www.theglobalfund.org/en/.
-
36.
Climate Change and Malaria Risk: Complexity and Scaling. Available at: http://edepot.wur.nl/119287.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 1220 | 1115 | 67 |
| Full Text Views | 589 | 11 | 0 |
| PDF Downloads | 113 | 11 | 0 |