Forecasting Based On a SARIMA Model of Urban Malaria for Kolkata

Krishnendra S. Ganguly¹, Soumita Modak², Asis K. Chattopadhyay², Krishna S. Ganguly^3, , Tapan K. Mukherjee³, Ambar Dutta¹ and Debashis Biswas³

¹Computer Science Department, Birla Institute of Technology, Mesra, Ranchi, Jharkhand 835215, India

²Department of Statistics, University of Calcutta, Kolkata, West Bengal 700019, India

³Health Department, The Kolkata Municipal Corporation, Kolkata, West Bengal 700013, India

Cite this paper:
Krishnendra S. Ganguly, Soumita Modak, Asis K. Chattopadhyay, Krishna S. Ganguly, Tapan K. Mukherjee, Ambar Dutta and Debashis Biswas. Forecasting Based On a SARIMA Model of Urban Malaria for Kolkata. American Journal of Epidemiology and Infectious Disease. 2016; 4(2):22-33. doi: 10.12691/ajeid-4-2-2

Abstract

In India Urban Malaria is considered to be the one of the most widespread vector-borne diseases taking lives of many people including children. Kolkata is one of the Metropolitan cities in where the seasonal effect of malaria is very marked. In the present work attempts have been made to study temporal variation of urban malaria incidences using time series model on the basis of a large population survey conducted by the Kolkata Municipal Corporation. It is found that the proposed time series (SARIMA) model can be used very successfully for prediction purposes.

Keywords:
spatio-temporal variation time series model sarima model urban malaria

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Kondrachine AV, Malaria in WHO Southeast Region, Indian J Malariol 29:129-160., 1992.
[2]	Snow RW, Guerra CA, Noor AM, Myint HY, Hay SI, The global distribution of clinical episodes of Plasmodium falciparum malaria. Nature 434: 214217. 2005.
[3]	Mendis K, Sina BJ, Marchesini P, Carter R The neglected burden of Plasmodium Vivax malaria. Am J Trop Med Hyg 64:97-106, 2001.
[4]	Hati AK, Mukherjee H, Chandra G, Bhattacharyya J, Chatterjee KK, Banerjee A, Biswas D, Halder S. Vector-borne diseases in urban community. Your Health, 40, 157-158. 1991
[5]	Biswas D, Mandal B, Biswas B, Banerjee A, Mukherjee TK. Plying of speedboats along canals in the city of Kolkata, India, to prevent mosquito breeding. Transactions of the Royal Society of Tropical Medicine and Hygiene., 107, 147-151. 2013.
[6]	Biswas D, Biswas B, Mandal B, Banerjee A, Mukherjee TK, Nandi J. Evaluating school students’ perception about mosquitoes and mosquito-borne diseases in the city of Kolkata, India. Dengue Bulletin, 35, 223-230. 2011
[7]	Biswas D, Biswas B, Mandal B, Banerjee A. A Note on Distribution of Breeding Sources of Aedes aegypti (Linnaeus) in the City of Kolkata, India, Following an Outbreak of Dengue during 2012. Current Urban Studies. 2, 57-61. 2014.
[8]	Nihei N, Hashida Y, Kobayashi M, Ishii A: Analysis of malaria endemic areas on the Indochina Peninsula using remote sensing. Jpn J Infect Dis, 55:160-166. 2002.
[9]	Klinkenberg E, Hoek W, Amerasinghe FP: A malaria risk analysis in an irrigated area in Sri Lanka. Acta Trop, 89:215-225. 2004
[10]	Leonardo LR, Rivera PT, Crisostomo BA, Sarol JN, Bantayan NC, Tiu WU, Bergquist NR: A study of the environmental determinants of malaria and schistosomiasis in the Philippines using Remote Sensing and Geographic Information Systems. Parassitologia, 47:105-114. 2005
[11]	Zhou G, Sirichaisinthop J, Sattabongkot J, Jones J, Bjornstad ON, Yan G, Cui L: Spatio-temporal distribution of Plasmodium falciparum and P. vivax malaria in Thailand. Am J Trop Med Hyg, 72:256-262. 2005.
[12]	Childs DZ, Cattadori IM, Suwonkerd W, Prajakwong S, Boots M: Spatiotemporal patterns of malaria incidence in northern Thailand. Trans R Soc Trop Med Hyg, 100:623-631. 2006.
[13]	Ceccato P, Connor SJ, Jeanne I, Thomson MC: Application of geographical information systems and remote sensing technologies for assessing and monitoring malaria risk. Parassitologia, 47:81-96. 2005.
[14]	Rogers DJ, Randolph SE, Snow RW, Hay SI: Satellite imagery in the study and forecast of malaria. Nature, 415:710-715. 2002.
[15]	Abeku TA, Hay SI, Ochola S, Langi P, Beard B, de Vlas SJ, Cox J: Malaria epidemic early warning and detection in African highlands. Trends Parasitol, 20:400-405. 2004.
[16]	Thomson MC, Connor SJ: The development of Malaria Early Warning Systems for Africa. Trends Parasitol 2001, 17:438-445.
[17]	Cox J, Abeku TA: Early warning systems for malaria in Africa: from blueprint to practice. Trends Parasitol, 23:243-246. 2007.
[18]	Abeku TA, de Vlas SJ, Borsboom G, Teklehaimanot A, Kebede A, Olana D, van Oortmarssen GJ, Habbema JD: 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. 2002.
[19]	Hay SI, Were EC, Renshaw M, Noor AM, Ochola SA, Olusanmi I, Alipui N, Snow RW: Forecasting, warning, and detection of malaria epidemics: a case study. Lancet, 361:1705-1706. 2003.
[20]	Thomson M, Indeje M, Connor S, Dilley M,Ward N: Malaria early warning in Kenya and seasonal climate forecasts. Lancet, 362:580. 2003.
[21]	Bouma MJ, Kaay HJ: 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, 1:8696. 1996.
[22]	Archie CA Clements, Adrian G Barnett, Zhang W Cheng, Robert W Snow and Hom N Zhou: Space-time variation of malaria incidence in Yunnan province, China , Malaria Journal, 8:180. 2009.
[23]	Hay SI, Guerra CA, Tatem AJ, Noor AM, Snow RW: The global distribution and population at risk of malaria: past, present, and future. Lancet Infect Dis, 4(6):327- 336. 2004.
[24]	Joshi PL, Chandra R, Bhattacharya M, Vaish HC: Validity of using slide positivity rate (SPR) in identification of high risk malarious segments in rural areas. The Journal of communicable diseases, 29(1):41-45. 1997.
[25]	Ceesay SJ, Casals-Pascual C, Erskine J, Anya SE, Duah NO, Fulford AJ, Sesay SS, Abubakar I, Dunyo S, Sey O, et al.: Changes in malaria indices between 1999 and 2007 in The Gambia: a retrospective analysis. Lancet, 372(9649):1545-1554. 2008.
[26]	O’Meara WP, Bejon P, Mwangi TW, Okiro EA, Peshu N, Snow RW, Newton CR, Marsh K: Effect of a fall in malaria transmission on morbidity and mortality in Kilifi, Kenya. Lancet, 372(9649): 1555-1562. 2008.
[27]	Trevor P Jensen, Hasifa Bukirwa, Denise Njama-Meya, Damon Francis, Moses R Kamya, Philip J Rosenthal and Grant Dorsey : Use of the slide positivity rate to estimate changes in malaria incidence in a cohort of Ugandan children, Malaria Journal, 8:213. 2009.
[28]	Chatfield C. The Analysis of Time Series: An Introduction (Fourth Edition ed.). New York, NY: Chapman & Hall, 1989.
[29]	Box G.E.P., Jenkins G.M., Reinsel G. C. Time Series Analysis: Forecasting and Control, 4th Edition, 2008.
[30]	Ljung G. M. and Box G. E. P. On a measure of lack of fit in time series models. Biometrika, 65: 297-303. 1978.
[31]	Breman JG, Holloway CN: Malaria surveillance counts. Am J Trop Med Hyg, 77(6 Suppl):36-47. 2007.
[32]	World Health Organization: World malaria report. Technical document. WHO/HTM/GMP/2008.1. 2008.
[33]	Cibulskis RE, Bell D, Christophel EM, Hii J, Delacollette C, BakyaitaN, Aregawi MW: Estimating trends in the burden of malaria at country level. Am J Trop Med Hyg, 77(6 Suppl): 133-137. 2007.
[34]	Roberts DR, Laughlin LL, Hsheih P, Legters LJ: DDT, Global strategies, and a malaria control crisis in South America. Emerg Infect Dis, 3:295. 1997.
[35]	Jensen TP, Bukirwa H, Njama-Meya D, Francis D, Kamya MR, Rosenthal P, Dorsey G: Use of the slide positivity rate to estimate changes in malaria incidence in a cohort of Ugandan children. Malar J, 8:213. 2009.
[36]	Metzger W, Groin A, Vivas-Martnez S, Gonzlez J, Charrasco A, Mordmller B, Magris M: A rapid malaria appraisal in the Venezuelan Amazon. Malar J, 8:291. 2009.
[37]	Subbarao SK, Vasantha K, Raghavendra K, Sharma V, Sharma G: Anopheles culicifacies: siblings species composition and its relationship to malaria incidence. J Am Mosq Control Assoc, 4:29. 1988.
[38]	Montanari R, Bangali A, Talukder K, Baqui A, Maheswary N, Gosh A, Rahman M, Mahmood A: Three case definitions of malaria and their effect on diagnosis, treatment and surveillance in Cox’s Bazar district, Bangladesh. Bull World Health Organ, 79:648-656. 2001.
[39]	Hay SI, Guerra CA, Tatem AJ, Noor AM, Snow RW: The global distribution and population at risk of malaria: past, present, and future. Lancet Infect Dis, 4:327-336. 2004.
[40]	Lee PW, Liu CT, Rampao H, do Rosario V, Shaio MF: Pre-elimination of malaria on the island of Prncipe. Malar J, 9:26. 2010.
[41]	Roy SB, Sarkar RR, Sinha S: Theoretical investigation of malaria prevalence in two Indian cities using the response surface method. Malar J, 10:301. 2011.
[42]	Joshi PL, Chandra R, Bhattacharya M, Vaish HC: Validity of using slide positivity rate (SPR) in identification of high risk malarious segments in rural areas. J Commun Dis, 29:41. 1997
[43]	China Malaria Elimination Plan [http:/ / www.moh.gov.cn/ publicfiles/ business/ htmlfiles/ mohjbyfkzj/ s3593/ 201005/ 47529.htm] website.
[44]	China National Malaria Office of Global Fund: Management and technique program in high malaria transmission areas in China, Shanghai; 2003.