Probabilistic Tsunami Hazard and Risk Analysis: A Review of Research Gaps

Tsunamis are unpredictable and infrequent but potentially large impact natural disasters. To prepare, mitigate and prevent losses from tsunamis, probabilistic hazard and risk analysis methods have been developed and have proved useful. However, large gaps and uncertainties still exist and many steps in the assessment methods lack information, theoretical foundation, or commonly accepted methods. Moreover, applied methods have very different levels of maturity, from already advanced probabilistic tsunami hazard analysis for earthquake sources, to less mature probabilistic risk analysis. In this review we give an overview of the current state of probabilistic tsunami hazard and risk analysis. Identifying research gaps, we offer suggestions for future research directions. An extensive literature list allows for branching into diverse aspects of this scientific approach. © Copyright © 2021 Behrens, Løvholt, Jalayer, Lorito, Salgado-Gálvez, Sørensen, Abadie, Aguirre-Ayerbe, Aniel-Quiroga, Babeyko, Baiguera, Basili, Belliazzi, Grezio, Johnson, Murphy, Paris, Rafliana, De Risi, Rossetto, Selva, Taroni, Del Zoppo, Armigliato, Bureš, Cech, Cecioni, Christodoulides, Davies, Dias, Bayraktar, González, Gritsevich, Guillas, Harbitz, Kânoǧlu, Macías, Papadopoulos, Polet, Romano, Salamon, Scala, Stepinac, Tappin, Thio, Tonini, Triantafyllou, Ulrich, Varini, Volpe and Vyhmeister

The Making of the NEAM Tsunami Hazard Model 2018 (NEAMTHM18)

The NEAM Tsunami Hazard Model 2018 (NEAMTHM18) is a probabilistic hazard model for tsunamis generated by earthquakes. It covers the coastlines of the North-eastern Atlantic, the Mediterranean, and connected seas (NEAM). NEAMTHM18 was designed as a three-phase project. The first two phases were dedicated to the model development and hazard calculations, following a formalized decision-making process based on a multiple-expert protocol. The third phase was dedicated to documentation and dissemination. The hazard assessment workflow was structured in Steps and Levels. There are four Steps: Step-1) probabilistic earthquake model; Step-2) tsunami generation and modeling in deep water; Step-3) shoaling and inundation; Step-4) hazard aggregation and uncertainty quantification. Each Step includes a different number of Levels. Level-0 always describes the input data; the other Levels describe the intermediate results needed to proceed from one Step to another. Alternative datasets and models were considered in the implementation. The epistemic hazard uncertainty was quantified through an ensemble modeling technique accounting for alternative models’ weights and yielding a distribution of hazard curves represented by the mean and various percentiles. Hazard curves were calculated at 2,343 Points of Interest (POI) distributed at an average spacing of ∼20 km. Precalculated probability maps for five maximum inundation heights (MIH) and hazard intensity maps for five average return periods (ARP) were produced from hazard curves. In the entire NEAM Region, MIHs of several meters are rare but not impossible. Considering a 2% probability of exceedance in 50 years (ARP≈2,475 years), the POIs with MIH >5 m are fewer than 1% and are all in the Mediterranean on Libya, Egypt, Cyprus, and Greece coasts. In the North-East Atlantic, POIs with MIH >3 m are on the coasts of Mauritania and Gulf of Cadiz. Overall, 30% of the POIs have MIH >1 m. NEAMTHM18 results and documentation are available through the TSUMAPS-NEAM project website (http://www.tsumaps-neam.eu/), featuring an interactive web mapper. Although the NEAMTHM18 cannot substitute in-depth analyses at local scales, it represents the first action to start local and more detailed hazard and risk assessments and contributes to designing evacuation maps for tsunami early warning. © Copyright © 2021 Basili, Brizuela, Herrero, Iqbal, Lorito, Maesano, Murphy, Perfetti, Romano, Scala, Selva, Taroni, Tiberti, Thio, Tonini, Volpe, Glimsdal, Harbitz, Løvholt, Baptista, Carrilho, Matias, Omira, Babeyko, Hoechner, Gürbüz, Pekcan, Yalçıner, Canals, Lastras, Agalos, Papadopoulos, Triantafyllou, Benchekroun, Agrebi Jaouadi, Ben Abdallah, Bouallegue, Hamdi, Oueslati, Amato, Armigliato, Behrens, Davies, Di Bucci, Dolce, Geist, Gonzalez Vida, González, Macías Sánchez, Meletti, Ozer Sozdinler, Pagani, Parsons, Polet, Power, Sørensen and Zaytsev

Hepatitis C Virus Infection in San Francisco's HIV-infected Urban Poor: High Prevalence but Low Treatment Rates

OBJECTIVE: To measure Hepatitis C Virus (HCV) prevalence, incidence, and initiation of HCV therapy in a representative HIV-infected cohort of the urban poor. DESIGN: Cohort analysis. SETTING: The Research and Access to Care for the Homeless (REACH) Cohort is a systematic sample of HIV-infected marginally housed individuals identified from single-room occupancy hotels, homeless shelters, and free lunch programs in San Francisco. PARTICIPANTS: Two hundred forty-nine participants with 28.9 months (median) of follow-up were studied. Mean age was 44 (range 24 to 75, standard deviation 8.4) years. Eighty-two percent were male, 43% were African-American, 64% were lifetime injection drug users, and 24% had been on the street or in a shelter in the prior month. INTERVENTIONS: We measured HCV testing and treatment history with structured interviews; additionally, participants were tested for HCV antibodies (EIA-2) with RNA viral load confirmation. MAIN RESULTS: At baseline, 172 (69.1%) were HCV-positive and 182 (73.1%) were HCV-positive at follow-up, including 155 (62.2%) with viremia. HCV-positive status was associated with having injected drugs, elevated serum alanine aminotransferase, homelessness in the last 1 year, and more severe depressive symptoms. The incidence of new HCV infection was 4.63% per person-year (ppy; 95% confidence interval, 2.31 to 8.13) in the entire cohort and 16.77% ppy among injection drug users. The prevalence of HCV antibody-negative HCV-viremia was 13.2% (10/76). Nonwhites were less likely to receive HCV testing and subspecialty referral, controlled for drug use and other confounders. Sixty-eight percent (123/182) were aware treatment was available; however, only 3.8% (7/182) or 1.16% ppy received HCV treatment. CONCLUSIONS: While HCV infection is common, HCV treatment is rare in the HIV-HCV coinfected urban poor. Urban poor, nonwhite individuals are less likely to receive HCV testing and subspecialty referral than their white counterparts. Antibody-negative infection may complicate screening and diagnosis in HIV-infected persons