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Locally transmitted malaria in Tawau, Sabah, Malaysia

Jayaraj VJ, Baharaja D, Gopalakrishnan N, Kaco Y

Malariaworld J, 2017;8:16.
PMID: 34532239

Background: Tawau was the epicentre of malaria infections in the 1970-1990's, when industrialisation swept across the state of Sabah, Malaysia. Since then, effective public health intervention, mainly the Malaria Elimination Programme, introduced in 1998, has seen the disease shrivel down into its final elimination phase. Here we retrospectively analyse the case of a 63 year old male with multiple comorbidities who had no exposure to localities with high risk of infection- thus raising the question regarding the means of transmission.
Materials and methods: Multiple interviews and an entomological survey were conducted to elucidate the possible mechanism of infection in this patient.
Results: Findings point to locally-transmitted malaria, likely introduced by a patient from an endemic region in Tawau. Transmission via this route is rare, and has never before been reported in our setting.
Conclusions: This rare case highlights the need for constant vigilance in malaria control and elimination, especially when the target of country-wide elimination is close.
Developing a dengue prediction model based on climate in Tawau, Malaysia

Jayaraj VJ, Avoi R, Gopalakrishnan N, Raja DB, Umasa Y

Acta Trop, 2019 Sep;197:105055.
PMID: 31185224 DOI: 10.1016/j.actatropica.2019.105055

Dengue is fast becoming the most urgent health issue in Malaysia, recording close to a 10-fold increase in cases over the last decade. With much uncertainty hovering over the recently introduced tetravalent vaccine and no effective antiviral drugs, vector control remains the most important strategy in combating dengue. This study analyses the relationship between weather predictors including its lagged terms, and dengue incidence in the District of Tawau over a period of 12 years, from 2006 to 2017. A forecasting model purposed to predict future outbreaks in Tawau was then developed using this data. Monthly dengue incidence data, mean temperature, maximum temperature, minimum temperature, mean relative humidity and mean rainfall over a period of 12 years from 2006 to 2017 in Tawau were retrieved from Tawau District Health Office and the Malaysian Meteorological Department. Cross-correlation analysis between weather predictors, lagged terms of weather predictors and dengue incidences established statistically significant cross-correlation between lagged periods of weather predictors-namely maximum temperature, mean relative humidity and mean rainfall with dengue incidence at time lags of 4-6 months. These variables were then employed into 3 different methods: a multivariate Poisson regression model, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and a SARIMA with external regressors for selection. Three models were selected but the SARIMA with external regressors model utilising maximum temperature at a lag of 6 months (p-value:0.001), minimum temperature at a lag of 4 months (p-value:0.01), mean relative humidity at a lag of 2 months (p-value:0.001), and mean rainfall at a lag of 6 months (p-value:0.001) produced an AIC of 841.94, and a log-likelihood score of -413.97 establishing it as the best fitting model of the methodologies utilised. In validating the models, they were utilised to develop forecasts with the model selected with the highest accuracy of predictions being the SARIMA model predicting 1 month in advance (MAE: 7.032, MSE: 83.977). This study establishes the effect of weather on the intensity and magnitude of dengue incidence as has been previously studied. A prediction model remains a novel method of evidence-based forecasting in Tawau, Sabah. The model developed in this study, demonstrated an ability to forecast potential dengue outbreaks 1 to 4 months in advance. These findings are not dissimilar to what has been previously studied in many different countries- with temperature and humidity consistently being established as powerful predictors of dengue incidence magnitude. When used in prognostication, it can enhance- decision making and allow judicious use of resources in public health setting. Nevertheless, the model remains a work in progress- requiring larger and more diverse data.

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