Kinley Wangdi

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

BackgroundMalaria still remains a public health problem in some districts of Bhutan despite marked reduction of cases in last few years. To strengthen the countrys 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.MethodsThis 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.ResultsIt 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.ConclusionsThe 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.

Cross-border malaria: a major obstacle for malaria elimination.

Movement of malaria across international borders poses a major obstacle to achieving malaria elimination in the 34 countries that have committed to this goal. In border areas, malaria prevalence is often higher than in other areas due to lower access to health services, treatment-seeking behaviour of marginalized populations that typically inhabit border areas, difficulties in deploying prevention programmes to hard-to-reach communities, often in difficult terrain, and constant movement of people across porous national boundaries. Malaria elimination in border areas will be challenging and key to addressing the challenges is strengthening of surveillance activities for rapid identification of any importation or reintroduction of malaria. This could involve taking advantage of technological advances, such as spatial decision support systems, which can be deployed to assist programme managers to carry out preventive and reactive measures, and mobile phone technology, which can be used to capture the movement of people in the border areas and likely sources of malaria importation. Additionally, joint collaboration in the prevention and control of cross-border malaria by neighbouring countries, and reinforcement of early diagnosis and prompt treatment are ways forward in addressing the problem of cross-border malaria.

Cross-border malaria: a major obstacle for malaria elimination

Movement of malaria across international borders poses a major obstacle to achieving malaria elimination in the 34 countries that have committed to this goal. In border areas, malaria prevalence is often higher than in other areas due to lower access to health services, treatment-seeking behaviour of marginalized populations that typically inhabit border areas, difficulties in deploying prevention programmes to hard-to-reach communities, often in difficult terrain, and constant movement of people across porous national boundaries. Malaria elimination in border areas will be challenging and key to addressing the challenges is strengthening of surveillance activities for rapid identification of any importation or reintroduction of malaria. This could involve taking advantage of technological advances, such as spatial decision support systems, which can be deployed to assist programme managers to carry out preventive and reactive measures, and mobile phone technology, which can be used to capture the movement of people in the border areas and likely sources of malaria importation. Additionally, joint collaboration in the prevention and control of cross-border malaria by neighbouring countries, and reinforcement of early diagnosis and prompt treatment are ways forward in addressing the problem of cross-border malaria.

Spatio-temporal patterns of malaria infection in Bhutan: a country embarking on malaria elimination

BackgroundAt the verge of elimination of malaria in Bhutan, this study was carried out to analyse the trend of malaria in the endemic districts of Bhutan and to identify malaria clusters at the sub-districts. The findings would aid in implementing the control activities. Poisson regression was performed to study the trend of malaria incidences at district level from 1994 to 2008. Spatial Empirical Bayesian smoothing was deployed to identify clusters of malaria at the sub-district level from 2004 to 2008.ResultsTrend of the overall districts and most of the endemic districts have decreased except Pemagatshel, which has an increase in the trend. Spatial cluster-outlier analysis showed that malaria clusters were mostly concentrated in the central and eastern Bhutan in three districts of Dagana, Samdrup Jongkhar and Sarpang. The disease clusters were reported throughout the year. Clusters extended to the non-transmission areas in the eastern Bhutan.ConclusionsThere is significant decrease in the trend of malaria with the elimination at the sight. The decrease in the trend can be attributed to the success of the control and preventive measures. In order to realize the target of elimination of malaria, the control measure needs to be prioritized in these high-risk clusters of malaria.

Human and animal rabies prevention and control cost in Bhutan, 2001–2008: The cost–benefit of dog rabies elimination

The objective of this study was to estimate the cost of various interventions and to quantify the economic impacts of rabies in Bhutan. Cost-benefit of dog rabies elimination versus human post exposure treatment cost was also assessed. The average direct medical cost of human post-exposure treatment (using rabies vaccine only) was estimated to be Nu. 1615 (US

Prevalence of asymptomatic malaria and bed net ownership and use in Bhutan, 2013: a country earmarked for malaria elimination

35.65) per 5-dose Essen regimen per patient. The cost would increase to Nu. 2497 (US

Global evidence directing regional preventive strategies in Southeast Asia for fighting TB/HIV.

55.13) and Nu. 19,633 (US

The epidemiology of tuberculosis in Phuentsholing General Hospital: a six-year retrospective study

433.41) per patient when one dose of either equine rabies immunoglobulin (ERIG) or human rabies immunoglobulin (HRIG) is administered, respectively. The societal cost (direct medical and indirect patient expenses) per patient was estimated to be Nu. 2019 (US

Malaria burden and costs of intensified control in Bhutan, 2006–14: an observational study and situation analysis

45), Nu. 2901 (US

25-Hydroxyvitamin D Concentrations and Clostridium difficile Infection: A Meta-Analysis.

64) and Nu. 20,037 (US