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.
| Published in | International Journal on Data Science and Technology (Volume 4, Issue 1) |
| DOI | 10.11648/j.ijdst.20180401.14 |
| Page(s) | 24-34 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2018. Published by Science Publishing Group |
Space Time Model, Mortality due to Malaria, Forecasting
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APA Style
Majige Selemani, Sigilbert Mrema. (2018). 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, 4(1), 24-34. https://doi.org/10.11648/j.ijdst.20180401.14
ACS Style
Majige Selemani; Sigilbert Mrema. Use of Space Time Model for Forecasting Mortality due to Malaria: A Case of Ifakara and Rufiji Health and Demographic Surveillance System Sites. Int. J. Data Sci. Technol. 2018, 4(1), 24-34. doi: 10.11648/j.ijdst.20180401.14
AMA Style
Majige Selemani, Sigilbert Mrema. Use of Space Time Model for Forecasting Mortality due to Malaria: A Case of Ifakara and Rufiji Health and Demographic Surveillance System Sites. Int J Data Sci Technol. 2018;4(1):24-34. doi: 10.11648/j.ijdst.20180401.14
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Download@article{10.11648/j.ijdst.20180401.14,
author = {Majige Selemani and Sigilbert Mrema},
title = {Use of Space Time Model for Forecasting Mortality due to Malaria: A Case of Ifakara and Rufiji Health and Demographic Surveillance System Sites},
journal = {International Journal on Data Science and Technology},
volume = {4},
number = {1},
pages = {24-34},
doi = {10.11648/j.ijdst.20180401.14},
url = {https://doi.org/10.11648/j.ijdst.20180401.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20180401.14},
abstract = {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.},
year = {2018}
}
TY - JOUR T1 - Use of Space Time Model for Forecasting Mortality due to Malaria: A Case of Ifakara and Rufiji Health and Demographic Surveillance System Sites AU - Majige Selemani AU - Sigilbert Mrema Y1 - 2018/05/07 PY - 2018 N1 - https://doi.org/10.11648/j.ijdst.20180401.14 DO - 10.11648/j.ijdst.20180401.14 T2 - International Journal on Data Science and Technology JF - International Journal on Data Science and Technology JO - International Journal on Data Science and Technology SP - 24 EP - 34 PB - Science Publishing Group SN - 2472-2235 UR - https://doi.org/10.11648/j.ijdst.20180401.14 AB - 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. VL - 4 IS - 1 ER -
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