Use of Space Time Model for Forecasting Mortality due to Malaria: A Case of Ifakara and Rufiji Health and Demographic Surveillance System Sites
Malaria is a leading cause of morbidity and mortality in developing countries especially in rural areas where local resources are limited. Accurate disease forecasts can provide information to public and clinical health services to design targeted interventions for malaria control that make effective use of limited resources. Using verbal autopsy data, space-time model was used to forecast mortality due to malaria. The study used longitudinal data which were collected from Rufiji and Ifakara Health Demographic Surveillance System (HDSS) sites for the period of 1999 to 2011 and 2002 to 2012 respectively to assess models. The models included environmental factors and mosquito net ownership as predictor variables for mortality due to malaria. Deviance information criteria (DIC), logarithm score and root mean square error (RMSE) were used to assess the goodness of fit and forecasting accuracy of the models. The results indicate that the model included spatial and temporal random effect terms had small deviance information criteria, logarithm score and root mean square error. This model was the best model for forecasting and prediction of mortality due to malaria in both HDSS sites. In addition, mosquito net ownership and rainfall were significantly associated with mortality due to malaria. The model with spatial and temporal random effect terms is useful tool to provide reasonably reliable forecasts for mortality due to malaria. This might help to design appropriate strategies for targeting malaria control. On the other hand, including spatially and temporal varying random terms in the model is necessary and good strategy for modelling mortality due to malaria.
Use of Space Time Model for Forecasting Mortality due to Malaria: A Case of Ifakara and Rufiji Health and Demographic Surveillance System Sites, International Journal on Data Science and Technology.
Vol. 4, No. 1,
2018, pp. 24-34.
Winskill P, Rowland M, Mtove G, Malima RC, Kirby MJ. Malaria risk factors in north-east Tanzania. Malar J. BioMed Central Ltd; 2011;10: 98.
Dan ED, Jude O IO. Modelling and Forecasting Malaria Mortality Rate using SARIMA Models (A Case Study of Aboh Mbaise General Hospital, Imo State Nigeria). Sci J Appl Math Stat. 2014;2: 31.
Sriwattanapongse W, Prasitwattanaseree S. Malaria mortality modeling in Thailand. Southeast-Asian J Sci. 2013;2: 108–116.
Villalta D, Guenni L, Rubio-Palis Y ARR. Bayesian Space-Time modelling of malaria incidence in Sucre state-Venezuela. AstA Adv Stat Anal. 2013;97: 151–171.
Nkurunziza H, Gebhardt A, Pilz J. Forecasting Malaria Cases in Bujumbura. World Acad Sci Eng Technol. 2010; 253–258.
Gaudart J, Touré O, Dessay N, Dickoa L, Ranque S, Forest L, et al. Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali. Malar J. 2009;8: 61.
Zinszer K, Verma AD, Charland K, Brewer TF, Brownstein JS, Sun Z, et al. A scoping review of malaria forecasting : past work and future directions. BMJ Open. 2012; 1–12.
INDEPTH Network. INDEPTH Network:INDEPTH Standardized Verbal Autopsy Questionnaire [Internet].
Sibai AM, Fletcher A, Hills M, Campbell O. Non-communicable disease mortality rates using the verbal autopsy in a cohort of middle aged and older populations in Beirut during wartime, 1983 – 93. J Epidemiol Community Heal. 2001;55: 271–276.
Chandramohan D, Quigley M. Effect of misclassification of causes of death in verbal autopsy : can it be adjusted ? Int J Epidemiol. 2001;30: 509–514.
Yang G, Rao C, Ma J, Wang L, Wan X, Dubrovsky G, et al. Validation of verbal autopsy procedures for adult deaths in China. Int J Epidemiol. 2006;35: 741–748.
Mpimbaza A, Filler S, Katureebe A, Kinara SO, Nzabandora E, Quick L, et al. Validity of verbal autopsy procedures for determining malaria deaths in different epidemiological settings in uganda. PLoS One. 2011;6: e26892.
Setel PW, Whiting DR, Hemed Y, Chandramohan D, Wolfson LJ, Alberti KG LA. Validity of verbal autopsy procedures for determining cause of death in Tanzania. Trop Med Int Heal. 2006;11: 681–96.
Geubbels E, Amri S, Levira F, Schellenberg J, Masanja H, Nathan R. Health & Demographic Surveillance System Profile Health & Demographic Surveillance System Profile : The Ifakara Rural and Urban Health and Demographic Surveillance System (Ifakara HDSS). 2015;0: 1–14.
Mrema S, Kante AM, Levira F, Mono A, Irema K, Savigny D De. Health & Demographic Surveillance System Profile Health & Demographic Surveillance System Profile : The Rufiji Health and Demographic Surveillance System (Rufiji HDSS). Int J Epidemiol. 2015;0: 1–12.
Hanson K, Marchant T, Nathan R, Mponda H, Jones C, Bruce J, et al. Household ownership and use of insecticide treated nets among target groups after implementation of a national voucher programme in the United Republic of Tanzania: plausibility study using three annual cross sectional household surveys. BMJ. 2009;339.
Selemani M, Msengwa AS, Mrema S, Shamte A, Mahande MJ, Yeates K, et al. Assessing the effects of mosquito nets on malaria mortality using a space time model : a case study of Rufiji and Ifakara Health... Assessing the effects of mosquito nets on malaria mortality using a space time model : a case study of Rufiji and Ifakara. Malar J. BioMed Central; 2016.
WHO. International Statistical Classification of Diseases and Related Health Problems (ICD-10). Geneva, Switzerland; 1999.
Rowe AK, Steketee RW. Predictions of the impact of malaria control efforts on all-cause child mortality in sub-Saharan Africa. Am J Trop Med Hyg. 2007;77: 48–55. Available: http://www.ncbi.nlm.nih.gov/pubmed/18165474
Lutambi AM, Alexander M, Charles J, Mahutanga C, Nathan R. Under-five mortality: spatial-temporal clusters in Ifakara HDSS in South-eastern Tanzania. Glob Health Action. 2010;3: 32–41. doi:10.3402/gha.v3i0.5254
Shabani J, Lutambi AM, Mwakalinga V, Masanja H. Rufiji Health and Demographic Surveillance System in rural Tanzania. Glob Health Action. 2010.
Waller LA, Carlin BP, Xia H GA. Hierarchical Spatiotemporal Rates, Mapping of Disease. J Am Stat Assoc. 1997;92: 607–617.
Kim H, Lim H. Comparison of Bayesian Spatio-Temporal Models. J Data Sci. 2010;8: 189–211.
Knorr-Held L. Bayesian modelling of inseparable space-time variation in disease risk. Stat Med. 2000;19: 2555–2567.
Arab A, Jackson MC, Kongoli C. Modelling the effects of weather and climate on malaria distributions in West Africa. Malar J. 2014;13: 126.
Berliner L. Hierarchical Bayesian time series models. In Maximum entropy and Bayesian methods. Netherlands: Springer; 1996.
Dimaggio C. Notes and Code for Small Area NYC Pedestrian Injury Spatiotemporal Analyses With INLA. 2014.
Knorr-Held L RG. Bayesian detection of clusters and discontinuities in disease maps. Biometrics. 2000;56: 13–21.
Rue H, Martino S, Chopin N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J R Stat Soc Ser B Stat Methodol. 2009;71: 319–392.
Zhu L CB. Comparing hierarchical models for spatio-temporally misaligned data using deviance information creterion. Stat Med. 2000;19: 2265–2278.
Branscuma J, Pereza M, Johnson WO, Thurmond MC. Bayesian spatiotemporal analysis of foot-and-mouth disease data from the Republic of Turkey. Epidemiol Infect. 2008;136: 833–842.
Abellan JJ, Richardson S, Best N. Use of space time models to investigate the stability of patterns of disease. Environ Health Perspect. 2008;116: 1111–1119.
Popoff E. An Approximate Spatio-Temporal Bayesian Model for Alberta Wheat Yield. The University of British Columbia. 2014.
Ensoy C. Forecasting and prediction models for demography and animal infectious diseases. Hasselt University, Belgium. 2014.
Bayesian Estimation and Prediction of Stochastic Volatility Models via INLA. 2012.
Wikle, Christopher K BI, Cressie N. Hierarchical Bayesian Space-Time Models. Environ Ecol Stat. 1998;5: 117–154.
Abeku TA, De Vlas SJ, Borsboom G, Teklehaimanot A, Kebede A, Olana D, et al. Forecasting malaria incidence from historical morbidity patterns in epidemic-prone areas of Ethiopia: A simple seasonal adjustment method performs best. Trop Med Int Heal. 2002;7: 851–857.
Makridakis S, Wheelwright SC HR. Forecasting: Methods and Applications. Edition 3rd, editor. New York.: John Wiley&Sons, Inc.; 1998.
Kilian AHD, Langi P, Talisuna A KG. Rainfall pattern, El Nin˜o and malaria in Uganda. Trans R Soc Trop Med Hyg. 1999;93: 22–23.
Gomez-Elipe A, Otero A, van Herp M, Aguirre-Jaime A. Forecasting malaria incidence based on monthly case reports and environmental factors in Karuzi, Burundi, 1997-2003. Malar J. 2007;6: 129.
Reiter P. Climate change and mosquito borne diseases. Environ Health Perspect. 2001;109: 223–234.
Secretariat. RBMP. The Abuja declaration and the plan of action. Roll Back Malaria. 2003.
Rue H MS. Approximate Bayesian inference for hierarchical Gaussian Markov random fields models. J Stat Plan Inference. 2006;137: 3177–3192.
Rue H, Martino S CN. Approximate bayesian inference for latent gaussian models using integrated nested laplace approximations. Statistics Report No. 1. Trondheim, Norway; 2007.
Cosandey-Godin A, Krainski ET, Worm B, Flemming JM. Applying Bayesian spatiotemporal models to fisheries bycatch in the Canadian Arctic. Can J Fish Aquat Sci. 2014;72: 186–197.
Kaufman JS, Asuzu MC, Rotimi CN, Johnson OO, Owoaje EE, Cooper RS. The absence of adult mortality data for sub-Saharan Africa: A practical solution. Bull World Health Organ. 1997;75: 389–395.