Dengue is a continuous health burden in Laos and Thailand. We assessed and mapped dengue vulnerability in selected provinces of Laos and Thailand using multi-criteria decision approaches. An ecohealth framework was used to develop dengue vulnerability indices (DVIs) that explain links between population, social and physical environments, and health to identify exposure, susceptibility, and adaptive capacity indicators. Three DVIs were constructed using two objective approaches, Shannon’s Entropy (SE) and the Water-Associated Disease Index (WADI), and one subjective approach, the Best-Worst Method (BWM). Each DVI was validated by correlating the index score with dengue incidence for each spatial unit (district and subdistrict) over time. A Pearson’s correlation coefficient (r) larger than 0.5 and a p-value less than 0.05 implied a good spatial and temporal performance. Spatially, DVI_WADI was significantly correlated on average in 19% (4–40%) of districts in Laos (mean r = 0.5) and 27% (15–53%) of subdistricts in Thailand (mean r = 0.85). The DVI_SE was validated in 22% (12–40%) of districts in Laos and in 13% (3–38%) of subdistricts in Thailand. The DVI_BWM was only developed for Laos because of lack of data in Thailand and was significantly associated with dengue incidence on average in 14% (0–28%) of Lao districts. The DVI_WADI indicated high vulnerability in urban centers and in areas with plantations and forests. In 2019, high DVI_WADI values were observed in sparsely populated areas due to elevated exposure, possibly from changes in climate and land cover, including urbanization, plantations, and dam construction. Of the three indices, DVI_WADI was the most suitable vulnerability index for the study area. The DVI_WADI can also be applied to other water-associated diseases, such as Zika and chikungunya, to highlight priority areas for further investigation and as a tool for prevention and interventions.

For hydrologic research, river water levels and discharges are essential variables. This study uses satellite radar altimetry to monitor water levels in the Mekong River. Three virtual stations were chosen along the river, and validation was done using in-situ data from telemetry stations. The in-situ telemetry and altimetry-derived water levels at the Vientiane and Stung Treng stations show a significant correlation and excellent agreement, with a correlation value of 0.97 and 0.98, respectively. However, a slightly lower correlation value of 0.89 was found at the Pakse station. The root mean square errors (RMSEs) were also computed to show the measurement’s overall accuracy. This study also demonstrated the sensitivity of altimetry to dam operations by examining the impact of the Pak Mun dam on water levels. Additionally, this study shows that factors such as limited observations and complex topography are crucial for analyzing altimetry data. In conclusion, Water level tracking using satellite radar altimetry is an effective and reliable method in even complex river systems like the Mekong River, enabling informed decision-making and efficient water resource management. This approach also holds broader significance for transboundary water management and addressing climate change. Incorporating multiple data sources and expanding virtual stations can improve water level insight and long-term climate change implications.

Background: The COVID-19 epidemic had spread rapidly through China and subsequently has proliferated globally leading to a pandemic situation around the globe. Human-to-human transmissions, as well as asymptomatic transmissions of the infection, have been confirmed. As of April 3^rd, public health crisis in China due to COVID-19 is potentially under control. Methods: We compiled a daily dataset of case counts, mortality, recovery, temperature, population density, and demographic information for each prefecture during the period of January 11 to April 07, 2020 (excluding Wuhan from our analysis due to missing data). Understanding the characteristics of spatiotemporal clustering of the COVID-19 epidemic and R₀ is critical in effectively preventing and controlling the ongoing global pandemic. The prefectures were grouped based on several relevant features using unsupervised machine learning techniques. We performed a computational analysis utilizing the reported cases in China to estimate the revised R₀ among different regions for prevention planning in an ongoing global pandemic. Results: Finally, our results indicate that the impact of temperature and demographic (different age group percentage compared to the total population) factors on virus transmission may be characterized using a stochastic transmission model. Conclusions: Such predictions will help prioritize segments of a given community/ region for action and provide a visual aid in designing prevention strategies for a specific geographic region. Furthermore, revised estimation and our methodology will aid in improving the human health consequences of COVID-19 elsewhere.