Identifying Crisis Response Communities in Online Social Networks for Compound Disasters: The Case of Hurricane Laura and Covid-19

Online social networks allow different agencies and the public to interact and share the underlying risks and protective actions during major disasters. This study revealed such crisis communication patterns during hurricane Laura compounded by the COVID-19 pandemic. Laura was one of the strongest (Category 4) hurricanes on record to make landfall in Cameron, Louisiana. Using the Application Programming Interface (API), this study utilizes large-scale social media data obtained from Twitter through the recently released academic track that provides complete and unbiased observations. The data captured publicly available tweets shared by active Twitter users from the vulnerable areas threatened by Laura. Online social networks were based on user influence feature ( mentions or tags) that allows notifying other users while posting a tweet. Using network science theories and advanced community detection algorithms, the study split these networks into twenty-one components of various sizes, the largest of which contained eight well-defined communities. Several natural language processing techniques (i.e., word clouds, bigrams, topic modeling) were applied to the tweets shared by the users in these communities to observe their risk-taking or risk-averse behavior during a major compounding crisis. Social media accounts of local news media, radio, universities, and popular sports pages were among those who involved heavily and interacted closely with local residents. In contrast, emergency management and planning units in the area engaged less with the public. The findings of this study provide novel insights into the design of efficient social media communication guidelines to respond better in future disasters

A Data-driven Resilience Framework of Directionality Configuration based on Topological Credentials in Road Networks

Roadway reconfiguration is a crucial aspect of transportation planning, aiming to enhance traffic flow, reduce congestion, and improve overall road network performance with existing infrastructure and resources. This paper presents a novel roadway reconfiguration technique by integrating optimization based Brute Force search approach and decision support framework to rank various roadway configurations for better performance. The proposed framework incorporates a multi-criteria decision analysis (MCDA) approach, combining input from generated scenarios during the optimization process. By utilizing data from optimization, the model identifies total betweenness centrality (TBC), system travel time (STT), and total link traffic flow (TLTF) as the most influential decision variables. The developed framework leverages graph theory to model the transportation network topology and apply network science metrics as well as stochastic user equilibrium traffic assignment to assess the impact of each roadway configuration on the overall network performance. To rank the roadway configurations, the framework employs machine learning algorithms, such as ridge regression, to determine the optimal weights for each criterion (i.e., TBC, STT, TLTF). Moreover, the network-based analysis ensures that the selected configurations not only optimize individual roadway segments but also enhance system-level efficiency, which is particularly helpful as the increasing frequency and intensity of natural disasters and other disruptive events underscore the critical need for resilient transportation networks. By integrating multi-criteria decision analysis, machine learning, and network science metrics, the proposed framework would enable transportation planners to make informed and data-driven decisions, leading to more sustainable, efficient, and resilient roadway configurations.Comment: 103rd Transportation Research Board (TRB) Annual Meetin

Seasonal intercomparison of observational rainfall datasets over India during the southwest monsoon season

The Indian monsoon is an important component of Earth's climate system, accurate forecasting of its mean rainfall being essential for regional food and water security. Accurate measurement of the rainfall is essential for various water-related applications, the evaluation of numerical models and detection and attribution of trends, but a variety of different gridded rainfall datasets are available for these purposes. In this study, six gridded rainfall datasets are compared against the India Meteorological Department (IMD) gridded rainfall dataset, chosen as the most representative of the observed system due to its high gauge density. The datasets comprise those based solely on rain gauge observations and those merging rain gauge data with satellite-derived products. Various skill scores and subjective comparisons are carried out for the Indian region during the south-west monsoon season (June to September). Relative biases and skill metrics are documented at all-India and sub-regional scales. In the gauge-based (land-only) category, Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of water resources (APHRODITE) and Global Precipitation Climatology Center (GPCC) datasets perform better relative to the others in terms of a variety of skill metrics. In the merged category, the Global Precipitation Climatology Project (GPCP) dataset is shown to perform better than the Climate Prediction Center Merged Analysis of Precipitation (CMAP) for the Indian monsoon in terms of various metrics, when compared with the IMD gridded data. Most of the datasets have difficulty in representing rainfall over orographic regions including the Western Ghats mountains, in north-east India and the Himalayan foothills. The wide range of skill scores seen among the datasets and even the change of sign of bias found in some years are causes of concern. This uncertainty between datasets is largest in north-east India. These results will help those studying the Indian monsoon region to select an appropriate dataset depending on their application and focus of research

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Indian Ocean simulation results from NEMO global ocean model

425-430A relatively newer version of Nucleus for European Modeling of the Ocean (NEMO) (v_3.2) ocean model was configured at NCMRWF high performance computing system at a coarser resolution. For initial study purposes, the global model resolution was kept at approximately 2o 2o latitude/longitude coarser resolution to study the mean large-scale ocean circulation related features from the model simulations. In this simulation 31 vertical layers were used in the model. Out of these 20 layers were kept in the upper 500 meters of the ocean to take care of the tropical air-sea interaction realistically. Initial model conditions of temperature and salinity were prescribed from the Levitus climatological value. Model was integrated from rest for 20 years with the monthly climatological data as forcing. Simulations were compared against observed climatological data. </span

Implementation of the ANISA Study in Karachi, Pakistan: Challenges and Solutions

Background: Aetiology of Neonatal Infection in South Asia (ANISA) is a multicenter study in Bangladesh, India and Pakistan exploring the incidence and etiology of neonatal infections. A periurban site in Karachi was selected for its representativeness of the general population in neonatal health indicators. An established demographic surveillance system and other infrastructure needed for conducting the study already existed at this site. ANISA presents a unique challenge because of the need to capture every birth outcome in the community within a few hours of delivery to reliably estimate the incidence and etiology of early-onset sepsis in a setting where home births and deaths are common.CONTEXTUAL CHALLENGES: Major challenges at the Karachi site are related to early birth reporting and newborn assessment for births outside the catchment areas, parental refusal to participate, diverse ethnicity of the population, collection of biological specimens from healthy controls, political instability and crime, power outages and blood culture contamination. Some of the remedial actions taken include prolonging working hours; developing counseling skills of field workers; hiring staff with different linguistic abilities from within the study community; liaising with health facilities, key community informants, Lady Health Workers and traditional birth attendants; hiring community mobilizers; enhancing community sensitization; developing contingency plans for field work interruptions and procuring backup generators. The specimen contamination rate has decreased through training, supervision and video monitoring of blood collection procedures with individualized counseling of phlebotomists.CONCLUSION: ANISA offers lessons for successful implementation of complex study protocols in areas of high child mortality and challenging social environments

Simplified antibiotic regimens for the management of clinically diagnosed severe infections in newborns and young infants in first-level facilities in Karachi, Pakistan: study design for an outpatient randomized controlled equivalence trial

Background: Infection in young infants is a major cause of morbidity and mortality in low-middle income countries, with high neonatal mortality rates. Timely case management is lifesaving, but the current standard of hospitalization for parenteral antibiotic therapy is not always feasible. Alternative, simpler antibiotic regimens that could be used in outpatient settings have the potential to save thousands of lives.Methods: This trial aims to determine whether 2 simplified antibiotic regimens are equivalent to the reference therapy with 7 days of once-daily (OD) intramuscular (IM) procaine penicillin and gentamicin for outpatient management of young infants with clinically presumed systemic bacterial infection treated in primary health-care clinics in 5 communities in Karachi, Pakistan. The reference regimen is close to the current recommendation of the hospital-based intravenous ampicillin and gentamicin therapy for neonatal sepsis. The 2 comparison arms are (1) IM gentamicin OD and oral amoxicillin twice daily for 7 days; and (2) IM penicillin and gentamicin OD for 2 days, followed by oral amoxicillin twice daily for 5 days; 2250 evaluable infants will be enrolled. The primary outcome of this trial is treatment failure (death, deterioration or lack of improvement) within 7 days of enrollment. Results are expected by early 2014.DISCUSSION: This trial will determine whether simplified antibiotic regimens with fewer injections in combination with high-dose amoxicillin are equivalent to 7 days of IM procaine penicillin and gentamicin in young infants with clinical severe infection. Results will have program and policy implications in countries with limited access to hospital care and high burden of neonatal deaths