Following the first triple-drug MDA for lymphatic filariasis in Samoa in 2018, unexpected persistence of Mf-positivity in 18 (15%) of 121 antigen-positive persons was observed in a nationwide household survey 1-2 months later, raising concerns about MDA efficacy. In 2019, a monitored treatment study was done before and 7 days after directly observed weight-based treatment. Mf presence and density were evaluated using 1 mL membrane filtered venous blood, and 60uL thick blood films on slides prepared from venous or fingerprick blood. All 14 participants were still Mf positive on filters from venous blood pre-treatment samples, but two were negative by slide made from the same samples. Mf were cleared completely by day 7 in 12 of 13 participants followed up, and by day 30 in the remaining participant. Filtered blood using EDTA samples (to reduce clumping of Mf) is preferred over slides alone for improving the likelihood of detecting Mf and estimating their density. The triple-drug MDA strategy was effective at clearing Mf by day 30 when given and taken at the correct dose.

Recent advancements in cloud-computing and social-networking are influencing how we communicate professionally, work collaboratively, and approach data-science tasks. Here we show how the groundwater modeling field is well positioned to benefit from these advancements. We present a case study detailing a vertically-integrated, collaborative modeling framework jointly developed by participants at the American Samoa Power Authority and at the University of Hawaii Water Resources Research Center. The framework components include direct collection and analysis of climatic and streamflow data, development of a water budget model, and initiation of a dynamic groundwater modeling process. The framework is entirely open-source and applies newly available data-science infrastructure using Python-based tools compiled with Jupyter Notebooks and cloud computing services such as GitHub. These resources allow for seamless integration of multiple computational components into a dynamic cloud-based workflow that is immediately accessible to stakeholders, resource managers, or anyone with an internet connection

Coastal flooding exacerbated by climate change is recognised as a major global threat which is expected to impact more than a quarter of people currently residing in Pacific Islands countries. While most research in the last decade has focused on understanding the dynamics and impacts of future coastal flooding from extreme sea levels, relative sea level rise (RSLR) effects on tsunami hazards are not well understood. Far-field or distant-sourced tsunamis tend to have relatively lower impacts in Pacific Island states compared with locally sourced events, but there is limited understanding on how the impact of far-field tsunamis changes over time due to RSLR. Using the hydrodynamics software BG-Flood, we modelled the Tōhoku tsunami from propagation to inundation in Samoa under incremental SLR to examine the effects that RSLR has on changing the exposure of the built environment (e.g., buildings) to a far-field tsunami. Outputs of maximum tsunami inundation and flow depth intensities which incorporate incremental SLR were then combined with digital representations of buildings and depth-damage functions in the RiskScape multi-hazard risk modelling software to assess the changes in building exposure over time. Results indicate that present day buildings exposure in Samoa to a Tōhoku-oki type far-field tsunami will increase by approx. 600% with 1 m RSLR by 2080–2130, and approx. 2,350% with 2 m RSLR by 2130–2140. These findings provide a useful baseline for tsunami hazard risk assessment under changing sea level conditions in analogous island environments.