Publication: Development of temporal modelling for forecasting and prediction of malaria infections using time-series and ARIMAX analyses: a case study in endemic districts of Bhutan
Accepted Date
2010-09-03
Issued Date
2010-09-03
Copyright Date
2010
Resource Type
Language
eng
ISSN
1475-2875 (electronic)
Rights
Mahidol University
Rights Holder(s)
BioMed Central
Suggested Citation
Wangdi, Kinley, Pratap Singhasivanon, ประตาป สิงหศิวานนท์, Tassanee Silawan, Saranath Lawpoolsri, สารนาถ ล้อพูลศรี, White, Nicholas J., Jaranit Kaewkungwal, จรณิต แก้วกังวาล (2010). Development of temporal modelling for forecasting and prediction of malaria infections using time-series and ARIMAX analyses: a case study in endemic districts of Bhutan. Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/722
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Development of temporal modelling for forecasting and prediction of malaria infections using time-series and ARIMAX analyses: a case study in endemic districts of Bhutan
Corresponding Author(s)
Abstract
BACKGROUND: Malaria still remains a public health problem in some districts of
Bhutan despite marked reduction of cases in last few years. To strengthen the
country's prevention and control measures, this study was carried out to develop
forecasting and prediction models of malaria incidence in the endemic districts
of Bhutan using time series and ARIMAX.
METHODS: This study was carried out retrospectively using the monthly reported
malaria cases from the health centres to Vector-borne Disease Control Programme
(VDCP) and the meteorological data from Meteorological Unit, Department of
Energy, Ministry of Economic Affairs. Time series analysis was performed on
monthly malaria cases, from 1994 to 2008, in seven malaria endemic districts. The
time series models derived from a multiplicative seasonal autoregressive
integrated moving average (ARIMA) was deployed to identify the best model using
data from 1994 to 2006. The best-fit model was selected for each individual
district and for the overall endemic area was developed and the monthly cases
from January to December 2009 and 2010 were forecasted. In developing the
prediction model, the monthly reported malaria cases and the meteorological
factors from 1996 to 2008 of the seven districts were analysed. The method of
ARIMAX modelling was employed to determine predictors of malaria of the
subsequent month.
RESULTS: It was found that the ARIMA (p, d, q) (P, D, Q)s model (p and P
representing the auto regressive and seasonal autoregressive; d and D
representing the non-seasonal differences and seasonal differencing; and q and Q
the moving average parameters and seasonal moving average parameters,
respectively and s representing the length of the seasonal period) for the
overall endemic districts was (2,1,1)(0,1,1)12; the modelling data from each
district revealed two most common ARIMA models including (2,1,1)(0,1,1)12 and
(1,1,1)(0,1,1)12. The forecasted monthly malaria cases from January to December
2009 and 2010 varied from 15 to 82 cases in 2009 and 67 to 149 cases in 2010,
where population in 2009 was 285,375 and the expected population of 2010 to be
289,085. The ARIMAX model of monthly cases and climatic factors showed
considerable variations among the different districts. In general, the mean
maximum temperature lagged at one month was a strong positive predictor of an
increased malaria cases for four districts. The monthly number of cases of the
previous month was also a significant predictor in one district, whereas no
variable could predict malaria cases for two districts.
CONCLUSIONS: The ARIMA models of time-series analysis were useful in forecasting
the number of cases in the endemic areas of Bhutan. There was no consistency in
the predictors of malaria cases when using ARIMAX model with selected lag times
and climatic predictors. The ARIMA forecasting models could be employed for
planning and managing malaria prevention and control programme in Bhutan.
