This paper presents the results of a cross-sectional study on adolescents’ behavioral and emotional reactions during and immediately after the 2017 Puebla earthquake that occurred in Mexico City. Convenience sampling was used to achieve a sample size of n=362. Some of the key findings are as follows: a) some 57.0% of adolescent women and over 60.0% of men “ran out of the building” during the earthquake; only 17.4% of women and 14.2% of men took shelter. Early and late adolescent women were significantly associated with building “evacuation” immediately after the earthquake. b) Early and late adolescent men were “surprised” by the earthquake occurrence. c) The perceived vulnerability of the city (PVC) was significantly associated with men but not with women. d) Adolescents exhibited strong information-seeking behavior regarding seismic risk. The results of the study highlight a lack of adolescents’ preparedness for earthquakes in terms of the protective actions taken during the ground shaking and knowledge of the functioning of the Mexican seismic alert system. An educational program aimed at better educating and preparing adolescents for a seismic emergency should be implemented in preparatory schools.

Concepts and approaches of earthquake risk reduction have evolved over time. The Great Kanto Earthquake of 1923 has paved the pathway of this long journey. 1995 the Great Hanshin-Awaji Earthquake has taught the importance of self help and mutual help in terms of community participation, volunteer activities, and role of civil society. In the first international decade of disaster risk reduction, two landmark earthquake risk reduction projects, namely risk assessment tools for diagnosis of urban areas against seismic disaster (RADIUS) and global earthquake safety initiative (GESI) have made significant impacts to scenraio-based risk assessment, and raising awareness of local governments mainly in developing countries to undertake decisive actions. The Kutch Earthquake of 2001 of India has prompted the need of owner driven reconstruction (ODR), which is considered inclusive as well as sustainable in long run. While the pre-disaster preventive measures for earthquake risks are challenging, the new complex global risk landscape poses additional challenges which need to be addressed to reposition earthquake risk reduction. It is argued that addressing systemic risks, active involvement of private sectors, role of new and emerging technologies, understanding priorities of new generation, and role of new entrepreneurship (in the form of Science-preneurship) are some of the future pathways to address the complexity of earthquake risk reduction.

The outbreak of COVID-19 poses a serious threat to global public health, and vaccination is an effective means of prevention. Studying the spatial layout and accessibility of COVID-19 vaccination sites is of great significance. The study analyzes the spatial distribution characteristics and accessibility of vaccination sites in the early stage of mass vaccination in Tianhe District, Guangzhou, based on GIS technology and combines three location allocation models: the p-median model, maximum covering location problem (MCLP) model, and location set covering problem (LSCP) model to identify candidate COVID-19 vaccination sites for the proposed public service facilities. The study found that only 47 COVID-19 vaccination sites exist in the early stage, with a small overall number, uneven spatial distribution, and trend of high accessibility in the central but low accessibility in the north and south; after the proposed addition of 31 vaccination sites, the overall distribution showed an even and dense distribution in the central and western regions, sporadic distribution in other regions, consistent with the distribution characteristics of residential communities. The areas where the accessibility of vaccination sites increased by more than 500 m accounted for 41% of the total area, and the area served by vaccination sites increased by 18%. Therefore, using the existing public service facilities to reasonably add the vaccination sites can improve the efficiency of vaccination and safeguard the establishment of a herd immunity barrier.

The restoration period of the water supply system in Lima, Peru, after a scenario earthquake was estimated in this study. To achieve the objective, the probabilistic assessment model for post-earthquake residual capacity of the utility lifeline system initially proposed by Nojima and Sugito (2005) and revised by following related studies was employed. The dataset of water distribution pipelines was provided by Potable Water and Sewer System Service in Lima, Peru (SEDAPAL), and the spatial distribution of ground motion with a moment magnitude of 8.6 was considered as a scenario earthquake in this study. The water disruption was anticipated to continue for approximately one month in certain districts of Lima, Peru. The estimated smallest water supplying ratio was 21.1% in Villa El Salvador after the scenario earthquake.

Earthquakes and tsunami disasters happen suddenly, devastating cities in just a few minutes. For decision makers to manage such disasters, they need current information on the status of their cities, but all information is collected separately and on paper now. All disaster-related information should be integrated to support decision making. To develop such an integrated system, Peru and Japan launched a new SATREPS project, “Development of Integrated Expert System for Estimation and Observation of Damage Level of Infrastructure in Lima Metropolitan Area,” in 2021. The project consists of four groups. Group 1 is related to earthquake and tsunami hazards, estimating the level of shaking and predicting the damage due to tsunami. Group 2 is related to damage detection/estimation, using sensing devices to estimate the level of damage to buildings and other infrastructure. Group 3 is related to understanding the damage over a wide area through satellite images, and also to the integration of all information from Groups 1 to 3. Group 4 is related to developing the capacity to utilize the integrated expert system. This special issue has been organized to present the progress accomplished in the first two years and to share the project’s achievements. It covers the fields of earthquake hazards, building and infrastructure damage estimation, damage estimation with satellite imagery, and human loss estimation.

We are very honored to receive the prestigious JDR Award for the Most Cited Paper 2022. The winning paper, “Real-Time Tsunami Prediction System Using DONET,” discusses a system that uses data from the Dense Oceanfloor Network system for Earthquakes and Tsunamis (DONET), which was installed in the rupture areas of the 1944 Tonankai and 1946 Nankai earthquakes in order to instantly generate and visualize tsunami prediction information. The forecast information from this system consists of tsunami arrival time, maximum tsunami height, tsunami inundation area, and inundation depth distribution. The system, which visualizes and distributes forecast information for areas where users need it as a supplement to the tsunami information provided nationwide by the Japan Meteorological Agency, has been introduced in Wakayama Prefecture, Mie Prefecture, Chiba Prefecture, and the city of Owase, as well as by Chubu Electric Power Co., Inc. After DONET was installed in the Tonankai rupture area, we were considering the possibility of using DONET data regionally, and we developed the system as results of discussions with Wakayama Prefecture and Chubu Electric Power Co., Inc. Under the concept that the users themselves would be operating the system, we intended to make the system as simple and easy to understand as possible, and to reduce costs by minimizing the number of hardware units. We also considered making the system flexible and scalable, recognizing that each user has a different way of how to use the tsunami forecast information. We were able to receive this award thanks to the cooperation of the people who were involved in many discussions with us during the process of establishing the concept. We will not become complacent going forward, but will continue to improve our system in ways that reflect the opinions of the users. We would like to thank everyone who has cited this paper with their interests.

Based on the lessons learned from past disasters, the Japanese Ministry of Health, Labour and Welfare has notified the establishment of disaster base hospitals and ensured medical care continuity based on business continuity plan (BCP). Following these, we created a disaster management training program with 45 items, the Disaster Mitigation (Gensai) Calendar for Healthcare Disaster Management Guide (HDMG)* (“Gensai Calendar”) in 2018. Thereafter, a three-year multicenter project was launched at the Tokyo Metropolitan Hospital Organization to introduce the “Gensai Calendar” and assess its merits as a self-learning material through tests based on it. The project also aimed at establishing a common operating procedure (COOP) for medical continuity at affected healthcare facilities using annual comprehensive disaster drills. The numbers of participants and hospitals recruited for the test over the three-year period were 4,281 in six hospitals, 6,179 in ten hospitals, and 6,216 in seven hospitals. The total of 55 questions were asked in the e-learning format in the first and second years, and 11 in the third year. Questions with less than 70% correct rate were same for three years. Through the drills, a COOP flow diagram was constructed based on the initial responses in the wards. Furthermore, the crisis management system of each hospital was unified, and each BCP was revised into a standard document along the “COOP Flow Diagram.” A COOP system combining the “Gensai Calendar” learnings, annual “COOP Flow Diagram” drill, and standard documentation can be a viable system for medical continuity. This system could also be versatile and introduced to many healthcare facilities. * This is also the abbreviation for the Hiroo Disaster Management Group, which has a double meaning. The group consists of disaster prevention staff from the Tokyo Metropolitan Hiroo Hospital, Komagome Hospital, and Bokutoh Hospital, and is responsible for planning and managing disaster mitigation measures.

I am very honored to receive the JDR Award for the Most Cited Paper 2022, and I am very grateful to the editorial board and staff of the Journal of Disaster Research. The paper, “Total Electron Content Observations by Dense Regional and Worldwide International Networks of GNSS,” reviews our research related to ionospheric observations using GNSS receiver networks. Since the late 1990s, the rapid development of GNSS receiver networks has made it possible to observe the ionosphere in two dimensions with high temporal and spatial resolution, revealing new features of various ionospheric phenomena. Ionospheric observation using GNSS receiver networks has become an indispensable method of observation in the space weather field. After the 2011 Tohoku Earthquake, it was revealed that various atmospheric waves excited by earthquakes and tsunamis propagated up to the ionosphere and caused ionospheric variations, suggesting the possibility of tsunami monitoring using ionospheric observations. We are also working on the standardization of ionospheric data formatting to achieve even higher spatial resolution and wider coverage of ionospheric observation using GNSS receiver networks through international cooperation. Encouraged by this award, we will continue our research and development efforts with a view to applying this technology not only to space weather but also to natural disasters such as earthquakes and tsunamis.

Since 2020, the outbreak of the coronavirus disease 2019 (COVID-19) pandemic has affected the entire world, and networks of human connections were identified as a factor that had potentially impacted the geographical spread of COVID-19. With the help of social media platforms, these networks have connected populations across the word and allowed people to view each other in close virtual proximity. Consequently, the Social Connectedness Index (SCI) is used to measure the strength of social connectivity across geographical regions through friendship ties on Facebook. The importance of social networks—and their relation to human connections—may correlate with the spread of COVID-19. Since these networks can have a potential effect on the spread of COVID-19, it is crucial to identify the factors that were associated with its spread during the pandemic. In order to analyze SCI data, a social network analysis was conducted to define the network parameters and perform calculations using graph theory. A correlation analysis was also performed to identify factors that correlated with the spread of COVID-19 cases using the data in the United States (US). Finally, the machine learning model was used to create a case prediction paradigm from the network parameters. The results showed that SCI can be used as a parameter to create a pandemic prediction model. Multiple linear regression also yielded satisfactory results that predicted the total number of positive cases measured by adjusted R2. In terms of the time frame, this study suggested that the parameters from the previous week can be used to predict the number of weekly infections. The findings showed that social networks had a greater impact on the prediction of current active cases than total positive cases. The social networks between counties within a state also held more importance than those across states.

Background: Earlier studies have indicated the BA.5 sublineage of Omicron variant strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as more infective than BA.2. Object: This study estimated BA.5 infectivity while controlling other factors possibly affecting BA.5 infectivity including vaccine effectiveness, waning effectiveness, other mutated strains, Olympic Games, and countermeasures. Method: The effective reproduction number R(t) was regressed on shares of BA.5 and vaccine coverage, vaccine coverage with some delay, temperature, humidity, mobility, shares of other mutated strains, countermeasures including the Go to Travel Campaign, and the Olympic Games and associated countermeasures. The study period was February 2020–July 22, 2022, using data available on August 12, 2022. Results: A 120 day lag was assumed to assess waning. Mobility, some states of emergency, vaccine coverage and those with lag, and the Delta and Omicron BA.2 proportions were found to be significant. The omicron BA.1 proportion was significant, but with an unexpected sign. The estimated coefficient of BA.5 was negative but not significant. The Go to Travel Campaign was significantly negative, indicating reduced infectivity. The Olympic Games were negative but not significant, indicating that they did not raise infectivity. Discussion: The obtained estimated results show that BA.5 did not have higher infectivity than the original strain. It was lower than either Delta or Omicron BA.2 variant strains. That finding might be inconsistent with results obtained from earlier studies. This study controlled several factors potentially affecting R(t), though the earlier studies did not. Therefore, results from this study might be more reliable than those of earlier studies.