Dengue is an endemic mosquito-borne viral disease prevalent in many urban areas of the tropic, especially the Southeast Asia. Its presence among the indigenous population of Peninsular Malaysia (Orang Asli), however, has not been well described. The present study was performed to investigate the seroprevalence of dengue among the Orang Asli (OA) residing at the forest fringe areas of Peninsular Malaysia and determine the factors that could affect the transmission of dengue among the OA. Eight OA communities consisting of 491 individuals were recruited. From the study, at least 17% of the recruited study participants were positive for dengue IgG, indicating past exposure to dengue. Analysis on the demographic and socioeconomic variables suggested that high seroprevalence of dengue was significantly associated with those above 13 years old and a low household income of less than MYR500 (USD150). It was also associated with the vast presence of residential areas and the presence of a lake. Remote sensing analysis showed that higher land surface temperatures and lower land elevations also contributed to higher dengue seroprevalence. The present study suggested that both demographic and geographical factors contributed to the increasing risk of contracting dengue among the OA living at the forest fringe areas of Peninsular Malaysia. The OA, hence, remained vulnerable to dengue.

Mosquito-borne diseases are rapidly spreading in all regions of the world with an estimation of 2.5 billion people globally are at risk. The recent surge in dengue outbreaks has caused severe affliction to Malaysian society. Hence, the ability to predict a dengue outbreak and mitigate its damage and loss proactively is very critical. In this paper, we study the possibility of applying machine learning (ML) and deep learning (DL) approaches to predict the number of confirmed dengue fever (DF) cases in Kuala Lumpur. We identified several contribution factors correlate to a dengue outbreak. In addition to the two frequently used factors (daily mean temperature and daily rainfall), we also took into account the enhanced vegetation index (EVI), humidity and wind speed as input factors to our prediction engines. We collected and cleansed data on these factors and the daily DF incidents in Kuala Lumpur from 2002 to 2012. We then used these data to train and evaluate our 3 ML/DL models. Among the three models, GA_RNN was the best performer and achieved a MAE of 10.95 for DF incidence prediction.

Dengue virus type 3 genotype III (DENV-3/III) is widely distributed in most dengue-endemic regions. It emerged in Malaysia in 2008 and autochthonously spread in the midst of endemic DENV-3/I circulation. The spread, however, was limited and the virus did not cause any major outbreak. Spatiotemporal distribution study of DENV-3 over the period between 2005 and 2011 revealed that dengue cases involving DENV-3/III occurred mostly in areas without pre-existing circulating DENV-3. Neutralisation assays performed using sera of patients with the respective infection showed that the DENV-3/III viruses can be effectively neutralised by sera of patients with DENV-3 infection (50% foci reduction neutralisation titres (FRNT50) > 1300). Sera of patients with DENV-1 infection (FRNT50 ⩾ 190), but not sera of patients with DENV-2 infection (FRNT50 ⩽ 50), were also able to neutralise the virus. These findings highlight the possibility that the pre-existing homotypic DENV-3 and the cross-reacting heterotypic DENV-1 antibody responses could play a role in mitigating a major outbreak involving DENV-3/III in the Klang Valley, Malaysia.

BACKGROUND Malaysia will experience a surge in extreme heat, excessive rainfall, rainfall variability, dry spells, thunderstorms, and high winds. Additionally, climate change affects the destruction of coastal and mangrove forests, coral reef bleaching, and the extinction of marine species. All of these changes will also have an impact on the health of Malaysia’s population: heat-related mortality is projected, nutritional security will decline, and unusable land will necessitate migration. However, there are currently insufficient data to investigate the health effects of climate change on the Malaysian population and to better inform decision-making and intervention prioritization. OBJECTIVE We will conduct a mixed-methods study to elucidate best practices for developing a South East Asia Community Observatory (SEACO) Health and Demographic Surveillance System (HDSS) that is equipped for climate change and health research, while simultaneously assessing the feasibility of community-based screening and measurements for climate-sensitive lung diseases, such as chronic obstructive pulmonary disease (COPD) and asthma. METHODS Sensor-based measurements for climate change and health research on three different levels are introduced: consumer-grade wearable devices and smart spirometers on the individual-level, indoor temperature measurements on the home-based level, and 3D-printed weather stations on the community-based level. We will randomly sample n=120 of the SEACO HDSS population in the Segamat region of Malaysia, above the age of 18 years. Participants will wear a wrist-worn device that measures heart rate, daily activity, sleep, and blood oxygen levels 24 hours a day, seven days a week for three weeks, while an indoor temperature sensor is installed in their living room and 3D-printed weather stations provide outdoor measurements. Participants' potential for having a climate-sensitive lung illness will be assessed by a validated pre-screening questionnaire and a smart spirometer. A total of three study cycles of three weeks duration and covering n=40 study participants each will be conducted. Each study cycle will include at least n=10 participants with a high likelihood of having COPD or asthma. RESULTS This study will determine the best ways to develop a climate change and health research-ready SEACO HDSS using sensor-based measurements, including individual (wearable devices measuring activity, sleep, heart rate, oxygen saturation, and a spirometer for lung capacity assessment), home-based (indoor temperatures), and community measurements (3D-printed weather stations as a sustainable and inexpensive method to increase spatial and temporal coverage of weather and climate). Integrating climate-related data and reported and measured health status into the SEACO HDSS will help characterize objectively, people's activity levels and exposures in their homes using indoor measures, as well as outdoor measures from 3D-printed weather stations. CONCLUSIONS Our study will generate insights on how to strengthen research infrastructures like the SEACO HDSS with sensor-based devices to generate data relevant for climate change and health research in climate-vulnerable populations. To facilitate research and better decision-making for identifying priorities in adaptation and mitigation, we will focus on generating best practices, utilizing cost-effective data collection mechanisms of consumer-grade wearable devices, indoor temperature measurements, and 3D-printable weather stations.