Dengue has enormous health impacts globally. A novel approach to decrease dengue incidence involves the introduction of Wolbachia endosymbionts that block dengue virus transmission into populations of the primary vector mosquito, Aedes aegypti. The wMel Wolbachia strain has previously been trialed in open releases of Ae. aegypti; however, the wAlbB strain has been shown to maintain higher density than wMel at high larval rearing temperatures. Releases of Ae. aegypti mosquitoes carrying wAlbB were carried out in 6 diverse sites in greater Kuala Lumpur, Malaysia, with high endemic dengue transmission. The strain was successfully established and maintained at very high population frequency at some sites or persisted with additional releases following fluctuations at other sites. Based on passive case monitoring, reduced human dengue incidence was observed in the release sites when compared to control sites. The wAlbB strain of Wolbachia provides a promising option as a tool for dengue control, particularly in very hot climates.

The number of dengue cases has been increasing on a global level in recent years, and particularly so in Malaysia, yet little is known about the effects of weather for identifying the short-term risk of dengue for the population. The aim of this paper is to estimate the weather effects on dengue disease accounting for non-linear temporal effects in Selangor, Kuala Lumpur and Putrajaya, Malaysia, from 2008 to 2010. We selected the weather parameters with a Poisson generalized additive model, and then assessed the effects of minimum temperature, bi-weekly accumulated rainfall and wind speed on dengue cases using a distributed non-linear lag model while adjusting for trend, day-of-week and week of the year. We found that the relative risk of dengue cases is positively associated with increased minimum temperature at a cumulative percentage change of 11.92% (95% CI: 4.41–32.19), from 25.4 °C to 26.5 °C, with the highest effect delayed by 51 days. Increasing bi-weekly accumulated rainfall had a positively strong effect on dengue cases at a cumulative percentage change of 21.45% (95% CI: 8.96, 51.37), from 215 mm to 302 mm, with the highest effect delayed by 26–28 days. The wind speed is negatively associated with dengue cases. The estimated lagged effects can be adapted in the dengue early warning system to assist in vector control and prevention plan.

The transmission of dengue disease is influenced by complex interactions among vector, host and virus. Land use such as water bodies or certain agricultural practices have been identified as likely risk factors for dengue because of the provision of suitable habitats for the vector. Many studies have focused on the land use factors of dengue vector abundance in small areas but have not yet studied the relationship between land use factors and dengue cases for large regions. This study aims to clarify if land use factors other than human settlements, e.g. different types of agricultural land use, water bodies and forest are associated with reported dengue cases from 2008 to 2010 in the state of Selangor, Malaysia. From the correlative relationship, we aim to generate a prediction risk map. We used Boosted Regression Trees (BRT) to account for nonlinearities and interactions between the factors with high predictive accuracies. Our model with a cross-validated performance score (Area Under the Receiver Operator Characteristic Curve, ROC AUC) of 0.81 showed that the most important land use factors are human settlements (model importance of 39.2%), followed by water bodies (16.1%), mixed horticulture (8.7%), open land (7.5%) and neglected grassland (6.7%). A risk map after 100 model runs with a cross-validated ROC AUC mean of 0.81 (±0.001 s.d.) is presented. Our findings may be an important asset for improving surveillance and control interventions for dengue.

Fish consumption pattern among adults of different ethnics in Peninsular Malaysia

Malaysia is currently facing an outbreak of COVID-19. We aim to present the first study in Malaysia to report the reproduction numbers and develop a mathematical model forecasting COVID-19 transmission by including isolation, quarantine, and movement control measures. We utilized a susceptible, exposed, infectious, and recovered (SEIR) model by incorporating isolation, quarantine, and movement control order (MCO) taken in Malaysia. The simulations were fitted into the Malaysian COVID-19 active case numbers, allowing approximation of parameters consisting of probability of transmission per contact (β), average number of contacts per day per case (ζ), and proportion of close-contact traced per day (q). The effective reproduction number (R_t) was also determined through this model. Our model calibration estimated that (β), (ζ), and (q) were 0.052, 25 persons, and 0.23, respectively. The (R_t) was estimated to be 1.68. MCO measures reduce the peak number of active COVID-19 cases by 99.1% and reduce (ζ) from 25 (pre-MCO) to 7 (during MCO). The flattening of the epidemic curve was also observed with the implementation of these control measures. We conclude that isolation, quarantine, and MCO measures are essential to break the transmission of COVID-19 in Malaysia.

In this study, we describe the incidence and distribution of COVID-19 cases in Malaysia at district level and determine their correlation with absolute population and population density, before and during the period that the Delta variant was dominant in Malaysia. Methods: Data on the number of locally transmitted COVID-19 cases in each of the 145 districts in Malaysia, between 20 September 2020 and 19 September 2021, were manually extracted from official reports. The cumulative number of COVID-19 cases, population and population density of each district were described using choropleth maps. The correlation between population and population density with the cumulative number of COVID-19 cases in each district in the pre-Delta dominant period (20 September 2020–29 June 2021) and during the Delta dominant period (30 June 2021–19 September 2021) were determined using Pearson’s correlation. Results: COVID-19 cases were strongly correlated with both absolute population and population density (Pearson’s correlation coefficient (r) = 0.87 and r = 0.78, respectively). A majority of the districts had higher numbers of COVID-19 cases during the Delta dominant period compared to the pre-Delta period. The correlation coefficient in the pre-Delta dominant period was r = 0.79 vs. r = 0.86 during the Delta dominant period, whereas the pre-Delta dominant population density was r = 0.72, and in the Delta dominant period, r = 0.76. Conclusion: More populous and densely populated districts have a higher risk of transmission of COVID-19, especially with the Delta variant as the dominant circulating strain. Therefore, extra and more stringent control measures should be instituted in highly populated areas to control the spread of COVID-19.