BackgroundMalaria 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.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.

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.

Background Malaria is heterogeneously distributed across landscapes. Human population movement (HPM) could link sub-regions with varying levels of transmission, leading to the persistence of disease even in very low transmission settings. Malaria along the Thai–Myanmar border has been decreasing, but remains heterogeneous. This study aimed to measure HPM, associated predictors of travel, and HPM correlates of self-reported malaria among people living within malaria hotspots. Methods 526 individuals from 279 households in two malaria hotspot areas were included in a prospective observational study. A baseline cross-sectional study was conducted at the beginning, recording both individual- and household-level characteristics. Individual movement and travel patterns were repeatedly observed over one dry season month (March) and one wet season month (May). Descriptive statistics, random effects logistic regressions, and logistic regressions were used to describe and determine associations between HPM patterns, individual-, household-factors, and self-reported malaria. Results Trips were more common in the dry season. Malaria risk was related to the number of days doing outdoor activities in the dry season, especially trips to Myanmar, to forest areas, and overnight trips. Trips to visit forest areas were more common among participants aged 20–39, males, individuals with low income, low education, and especially among individuals with forest-related occupations. Overnight trips were more common among males, and individual with forest-related occupations. Forty-five participants reported having confirmed malaria infection within the last year. The main place of malaria blood examination and treatment was malaria post and malaria clinic, with participants usually waiting for 2–3 days from onset fever to seeking diagnosis. Individuals using bed nets, living in houses with elevated floors, and houses that received indoor residual spraying in the last year were less likely to report malaria infection. Conclusion An understanding of HPM and concurrent malaria dynamics is important for consideration of targeted public health interventions. Furthermore, diagnosis and treatment centres must be capable of quickly diagnosing and treating infections regardless of HPM. Coverage of diagnosis and treatment centres should be broad, maintained in areas bordering malaria hotspots, and available to all febrile individuals. Electronic supplementary material The online version of this article (10.1186/s12936-019-2704-3) contains supplementary material, which is available to authorized users.

Promoting community malaria control in rural Myanmar through an active community participation program using the participatory learning approach Rural and Remote Health 17: 4130. (Online) 2017 Available: http://www.rrh.org.au A B S T R A C TIntroduction: Malaria is prevalent in more than 80% of townships in Myanmar. The National Malaria Control Programme (NMCP) has been implementing community-based malaria control programs nationwide. However, these programs are mostly developed and directed by health authorities, while communities are passively involved. This study aimed to increase community participation in malaria control and promote community malaria control knowledge and practice in rural Myanmar. Methods: A community-based study, which employed a mixed method approach, collecting data quantitatively and qualitatively, was conducted in two rural villages. The study implemented an active community participation program (ACPP) using the participatory learning approach in a village (ACPP village) but only routine malaria control was given in another village (non-ACPP village). All households with 142 and 96 household representatives from ACPP and non-ACPP villages participated in baseline and endline surveys. The ACPP was evaluated by process and outcome indicators. A spider gram analysis using five process indicators was applied to evaluate the process of the ACPP. Community participation status in malaria control activities and level of community malaria knowledge and practice were determined as outcomes of the ACPP. Results: The spider gram analysis showed that three indicators (needs assessment and planning, leadership and resource mobilization) gained a score of 4, the organization indicator a score of 5 and the management and evaluation indicator a score of 3. The outcome indicators of the program at 6 months showed that the community participation in malaria control activities in the © CN Maung, TT Sein, T Hlaing, K Okanurak, T Silawan, J Kaewkungwal, 2017. A A Licence to publish this material has been given to James Cook University, http://www.jcu.edu.au 2 ACPP village had significantly increased (6.9% to 49.3%) (p<0.001). The program promoted community malaria control knowledge and practice in the ACPP village. The mean scores of knowledge, perception, preventive behavior and treatment-seeking behavior were increased significantly, from 3.0 to 5.9 (p<0.001), 20.1 to 21.0 (p<0.001), 3.4 to 4.2 (p<0.001) and 3.1 to 5.6 (p<0.001), respectively. However, no significant change of outcome indicators was found in the non-ACPP village.Conclusions: The ACPP implemented by community volunteers using the participatory learning approach was feasible in community-based malaria control. This study suggests several features in the ACPP model that may be useful strategies for the implementation of the current NMCP programs in similar rural settings; however, the effect of the ACPP over a longer period to ascertain the impact of such community participation has yet to be further studied.

The efforts towards effective control of the COVID-19 pandemic may affect the incidence of dengue. This study aimed to investigate temporal variations and spatial clusters of dengue in Thailand before and during the COVID-19 pandemic. Reported dengue cases before (2011–2019) and during (2020–2021) the COVID-19 pandemic were obtained from the national disease surveillance datasets. The temporal variations were analyzed using graphics, a seasonal trend decomposition procedure based on Loess, and Poisson regression. A seasonal ARIMA model was used to forecast dengue cases. Spatial clusters were investigated using the local indicators of spatial associations (LISA). The cyclic pattern showed that the greatest peak of dengue cases likely changed from every other year to every two or three years. In terms of seasonality, a notable peak was observed in June before the pandemic, which was delayed by one month (July) during the pandemic. The trend for 2011–2021 was relatively stable but dengue incidence decreased dramatically by 7.05% and 157.80% on average in 2020 and 2021, respectively. The forecasted cases in 2020 were slightly lower than the reported cases (2.63% difference), whereas the forecasted cases in 2021 were much higher than the actual cases (163.19% difference). The LISA map indicated 5 to 13 risk areas or hotspots of dengue before the COVID-19 pandemic compared to only 1 risk area during the pandemic. During the COVID-19 pandemic, dengue incidence sharply decreased and was lower than forecasted, and the spatial clusters were much lower than before the pandemic.