Journal of Parasitic Diseases

, Volume 41, Issue 3, pp 761–767 | Cite as

Ecological covariates based predictive model of malaria risk in the state of Chhattisgarh, India

  • Rajesh Kumar
  • Chinmaya Dash
  • Khushbu Rani
Original Article


Malaria being an endemic disease in the state of Chhattisgarh and ecologically dependent mosquito-borne disease, the study is intended to identify the ecological covariates of malaria risk in districts of the state and to build a suitable predictive model based on those predictors which could assist developing a weather based early warning system. This secondary data based analysis used one month lagged district level malaria positive cases as response variable and ecological covariates as independent variables which were tested with fixed effect panelled negative binomial regression models. Interactions among the covariates were explored using two way factorial interaction in the model. Although malaria risk in the state possesses perennial characteristics, higher parasitic incidence was observed during the rainy and winter seasons. The univariate analysis indicated that the malaria incidence risk was statistically significant associated with rainfall, maximum humidity, minimum temperature, wind speed, and forest cover (p < 0.05). The efficient predictive model include the forest cover [IRR-1.033 (1.024–1.042)], maximum humidity [IRR-1.016 (1.013–1.018)], and two-way factorial interactions between district specific averaged monthly minimum temperature and monthly minimum temperature, monthly minimum temperature was statistically significant [IRR-1.44 (1.231–1.695)] whereas the interaction term has a protective effect [IRR-0.982 (0.974–0.990)] against malaria infections. Forest cover, maximum humidity, minimum temperature and wind speed emerged as potential covariates to be used in predictive models for modelling the malaria risk in the state which could be efficiently used for early warning systems in the state.


Malaria India Ecological predictors Forest Humidity Temperature Wind speed Negative binomial model 


Compliance with ethical standards

Conflict of interest

We declare that we have no any conflict of interest.

Supplementary material

12639_2017_885_MOESM1_ESM.docx (14 kb)
Supplementary material 1 (DOCX 14 kb)


  1. Abeku TA, Hay SI, Ochola S et al (2004) Malaria epidemic early warning and detection in African highlands. Trends Parasitol 20:400–405. doi: 10.1016/ CrossRefPubMedPubMedCentralGoogle Scholar
  2. Agricultural Meteorological Division, Indian Meteorological Department (2014) District level Weather Forecast. In: Dist. Weather Forecast.
  3. Ayala D, Costantini C, Ose K et al (2009) Habitat suitability and ecological niche profile of major malaria vectors in Cameroon. Malar J 8:307. doi: 10.1186/1475-2875-8-307 CrossRefPubMedPubMedCentralGoogle Scholar
  4. Bayoh MN (2001) Studies on the development and survival of Anopheles gambiae sense stricto at various temperatures and relative humidities. Durham UniversityGoogle Scholar
  5. Berthélemy J-C, Thuilliez J, Doumbo O, Gaudart J (2013) Malaria and protective behaviours: is there a malaria trap? Malar J 12:200. doi: 10.1186/1475-2875-12-200 CrossRefPubMedPubMedCentralGoogle Scholar
  6. Bi P, Tong S, Donald K et al (2003) Climatic variables and transmission of malaria: a 12-year data analysis in Shuchen County, China. Public Health Rep 118:65–71CrossRefPubMedPubMedCentralGoogle Scholar
  7. Bi Y, Yu W, Hu W et al (2013) Impact of climate variability on Plasmodium vivax and Plasmodium falciparum malaria in Yunnan Province, China. Parasit Vectors 6:357. doi: 10.1186/1756-3305-6-357 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Briet OJ, Vounatsou P, Gunawardena DM et al (2008) Temporal correlation between malaria and rainfall in Sri Lanka. Malar J 7:77. doi: 10.1186/1475-2875-7-77 CrossRefPubMedPubMedCentralGoogle Scholar
  9. Briët OJ, Vounatsou P, Gunawardena DM et al (2008) Models for short term malaria prediction in Sri Lanka. Malar J 7:76. doi: 10.1186/1475-2875-7-76 CrossRefPubMedPubMedCentralGoogle Scholar
  10. Cummins B, Cortez R, Foppa IM et al (2012) A spatial model of mosquito host-seeking behavior. PLoS Comput Biol 8:e1002500. doi: 10.1371/journal.pcbi.1002500 CrossRefPubMedPubMedCentralGoogle Scholar
  11. Das PK, Gunasekaran K, Sahu SS et al (1990) Seasonal prevalence and resting behaviour of malaria vectors in Koraput district, Orissa. Indian J Malariol 27:173–181PubMedGoogle Scholar
  12. Das NG, Gopalakrishnan R, Talukdar PK et al (2011) Diversity and seasonal densities of vector anophelines in relation to forest fringe malaria in district Sonitpur, Assam (India). J Parasit Dis 35:123–128. doi: 10.1007/s12639-011-0053-4 CrossRefPubMedPubMedCentralGoogle Scholar
  13. Dev V, Sangma BM, Dash AP (2010) Persistent transmission of malaria in Garo hills of Meghalaya bordering Bangladesh, north-east India. Malar J 9:263. doi: 10.1186/1475-2875-9-263 CrossRefPubMedPubMedCentralGoogle Scholar
  14. Devi NP, Jauhari RK (2006) Climatic variables and malaria incidence in Dehradun, Uttaranchal, India. J Vector Borne Dis 43:21–28PubMedGoogle Scholar
  15. Ettling M, McFarland DA, Schultz LJ, Chitsulo L (1994) Economic impact of malaria in Malawian households. Trop Med Parasitol 45:74–79PubMedGoogle Scholar
  16. Forest Department, Government of Chhattisgarh (2013) Chhattisgarh state bio-diversity strategy and action plan. Forest Department, Government of ChhattisgarhGoogle Scholar
  17. Forest Survey of India (2011) India State of forest report 2011. Ministry of Environment and Forests, Government of IndiaGoogle Scholar
  18. Gao H-W, Wang L-P, Liang S et al (2012) Change in rainfall drives malaria re-emergence in Anhui Province, China. PLoS ONE 7:e43686. doi: 10.1371/journal.pone.0043686 CrossRefPubMedPubMedCentralGoogle Scholar
  19. Haque U, Hashizume M, Glass GE et al (2010) The role of climate variability in the spread of malaria in Bangladeshi Highlands. PLoS ONE 5:e14341. doi: 10.1371/journal.pone.0014341 CrossRefPubMedPubMedCentralGoogle Scholar
  20. Huang F, Zhou S, Zhang S et al (2011) Temporal correlation analysis between malaria and meteorological factors in Motuo County, Tibet. Malar J 10:54. doi: 10.1186/1475-2875-10-54 CrossRefPubMedPubMedCentralGoogle Scholar
  21. Kulkarni SM (1990) Density patterns of anophelines and their relation to malaria in Bastar district, Madhya Pradesh. Indian J Malariol 27:187–194PubMedGoogle Scholar
  22. Kumar A, Valecha N, Jain T, Dash AP (2007) Burden of malaria in India: retrospective and prospective view. Am J Trop Med Hyg 77:69–78PubMedGoogle Scholar
  23. Kumari S, Parida SK, Marai N et al (2009) Vectorial role of Anopheles subpictus Grassi and Anopheles culicifacies Giles in Angul District, Orissa, India. Southeast Asian J Trop Med Public Health 40:713–719PubMedGoogle Scholar
  24. Li T, Yang Z, Wang M (2013) Temperature, relative humidity and sunshine may be the effective predictors for occurrence of malaria in Guangzhou, southern China, 2006–2012. Parasit Vectors 6:155. doi: 10.1186/1756-3305-6-155 CrossRefPubMedPubMedCentralGoogle Scholar
  25. Malakar P, Das S, Saha GK et al (1995) Indoor resting anophelines of north Bengal. Indian J Malariol 32:24–31PubMedGoogle Scholar
  26. Manguin S, Mouchet J, Carnevale P et al (2008) Biodiversity of malaria in the World. John Libbey Eurotext, LondonGoogle Scholar
  27. Manh BH, Clements ACA, Thieu NQ et al (2011) Social and environmental determinants of malaria in space and time in Viet Nam. Int J Parasitol 41:109–116. doi: 10.1016/j.ijpara.2010.08.005 CrossRefGoogle Scholar
  28. McReynolds M, Hellenthal R (2003) Environmental influences on mosquito adult and larvae abundance. University of Notre Dame Environmental Research CenterGoogle Scholar
  29. Nanda N, Yadav RS, Subbarao SK et al (2000) Studies on Anopheles fluviatilis and Anopheles culicifacies sibling species in relation to malaria in forested hilly and deforested riverine ecosystems in northern Orissa, India. J Am Mosq Control Assoc 16:199–205PubMedGoogle Scholar
  30. Nanda N, Bhatt RM, Sharma SN et al (2012) Prevalence and incrimination of Anopheles fluviatilis species S (Diptera: Culicidae) in a malaria endemic forest area of Chhattisgarh state, central India. Parasit Vectors 5:215. doi: 10.1186/1756-3305-5-215 CrossRefPubMedPubMedCentralGoogle Scholar
  31. Nandi J, Rao JS, Dasgupta RK, Sharma RS (1996) Ecological observations on the anopheline mosquitoes of Jalpaiguri Duars, West Bengal. J Commun Dis 28:279–286PubMedGoogle Scholar
  32. Nath DC, Mwchahary DD (2013) Association between climatic variables and malaria incidence: a study in Kokrajhar district of Assam, India. Glob J Health Sci 5:90–106Google Scholar
  33. NRHM (2013) Part-D, Chhattisgarh State Annual NRHM PIP-2013-14Google Scholar
  34. NVBDCP (2014) Malaria. In: Magnetic problem Accessed 21 May 2014
  35. Office of The Registrar General & Census Commissioner, India, New Delhi (2011) Census of India. In: Population finder. Accessed 15 May 2014
  36. Omonijo A, Matzarakis A (2011) Influence of weather and climate on malaria occurrence based on human-biometeorological methods in Ondo State, Nigeria. J Environ Sci Eng 5:1215–1228Google Scholar
  37. Paaijmans KP, Blanford S, Chan BHK, Thomas MB (2012) Warmer temperatures reduce the vectorial capacity of malaria mosquitoes. Biol Lett 8:465–468. doi: 10.1098/rsbl.2011.1075 CrossRefPubMedGoogle Scholar
  38. Pacholi S (1993) Medical geography of malaria in Madhya Pradesh. Northern Book Centre, New DelhiGoogle Scholar
  39. Parham PE, Michael E (2010) Modeling the Effects of weather and climate change on malaria transmission. Environ Health Perspect 118:620–626. doi: 10.1289/ehp.0901256 CrossRefPubMedGoogle Scholar
  40. Parida SK, Hazra RK, Marai N et al (2006) Host feeding patterns of malaria vectors of Orissa, India. J Am Mosq Control Assoc 22:629–634CrossRefPubMedGoogle Scholar
  41. Service MW (1980) Effects of wind on the behaviour and distribution of mosquitoes and blackflies. Int J Biometeorol 24:347–353. doi: 10.1007/BF02250577 CrossRefGoogle Scholar
  42. Sharma SK, Tyagi PK, Padhan K et al (2006) Epidemiology of malaria transmission in forest and plain ecotype villages in Sundargarh District, Orissa, India. Trans R Soc Trop Med Hyg 100:917–925. doi: 10.1016/j.trstmh.2006.01.007 CrossRefPubMedGoogle Scholar
  43. Singh N, Sharma VP (2002) Patterns of rainfall and malaria in Madhya Pradesh, central India. Ann Trop Med Parasitol 96:349–359. doi: 10.1179/000349802125001113 CrossRefPubMedGoogle Scholar
  44. Singh N, Singh OP, Sharma VP (1996) Dynamics of malaria transmission in forested and deforested regions of Mandla District, central India (Madhya Pradesh). J Am Mosq Control Assoc 12:225–234PubMedGoogle Scholar
  45. Singh N, Mishra AK, Shukla MM, Chand SK (2003) Forest malaria in Chhindwara, Madhya Pradesh, central India: a case study in a tribal community. Am J Trop Med Hyg 68:602–607CrossRefPubMedGoogle Scholar
  46. Sinka ME, Bangs MJ, Manguin S et al (2011) The dominant Anopheles vectors of human malaria in the Asia-Pacific region: occurrence data, distribution maps and bionomic précis. Parasit Vectors 4:89. doi: 10.1186/1756-3305-4-89 CrossRefPubMedPubMedCentralGoogle Scholar
  47. Subbarao SK (1988) The Anopheles culicifacies complex and control of malaria. Parasitol Today 4:72–75. doi: 10.1016/0169-4758(88)90199-8 CrossRefPubMedGoogle Scholar
  48. WHO (2013) World malaria report-2013. Accessed 9 May 2014
  49. Yamana TK, Eltahir EAB (2013) Incorporating the effects of humidity in a mechanistic model of Anopheles gambiae mosquito population dynamics in the Sahel region of Africa. Parasit Vectors 6:235. doi: 10.1186/1756-3305-6-235 CrossRefPubMedPubMedCentralGoogle Scholar
  50. Yé Y, Louis VR, Simboro S, Sauerborn R (2007) Effect of meteorological factors on clinical malaria risk among children: an assessment using village-based meteorological stations and community-based parasitological survey. BMC Public Health 7:101. doi: 10.1186/1471-2458-7-101 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Indian Society for Parasitology 2017

Authors and Affiliations

  1. 1.Child Right and You (CRY)New DelhiIndia
  2. 2.NVDCP, DoHFWRaipurIndia
  3. 3.Women and Child Welfare ConsultantNew DelhiIndia

Personalised recommendations