Are mega-events super spreaders of infectious diseases similar to COVID-19? A look into Tokyo 2020 Olympics and Paralympics to improve preparedness of next international events

Analyzing factors of daily travel distances in Japan during the COVID-19 pandemic

The global impact of the COVID-19 pandemic is widely recognized as a significant concern, with human flow playing a crucial role in its propagation. Consequently, recent research has focused on identifying and analyzing factors that can effectively regulate human flow. However, among the multiple factors that are expected to have an effect, few studies have investigated those that are particularly associated with human flow during the COVID-19 pandemic. In addition, few studies have investigated how regional characteristics and the number of vaccinations for these factors affect human flow. Furthermore, increasing the number of verified cases in countries and regions with insufficient reports is important to generalize conclusions. Therefore, in this study, a group-level analysis was conducted for Narashino City, Chiba Prefecture, Japan, using a human flow prediction model based on machine learning. High-importance groups were subdivided by regional characteristics and the number of vaccinations, and visual and correlation analyses were conducted at the factor level. The findings indicated that tree-based models, especially LightGBM, performed better in terms of prediction. In addition, the cumulative number of vaccinated individuals and the number of newly infected individuals are likely explanatory factors for changes in human flow. The analyses suggested a tendency to move with respect to the number of newly infected individuals in Japan or Tokyo, rather than the number of new infections in the area where they lived when vaccination had not started. With the implementation of vaccination, attention to the number of newly infected individuals in their residential areas may increase. However, after the spread of vaccination, the perception of infection risk may decrease. These findings can contribute to the proposal of new measures for efficiently controlling human flows and determining when to mitigate or reinforce specific measures.

Effect of Long-Distance Domestic Travel Ban Policies in Japan on COVID-19 Outbreak Dynamics During Dominance of the Ancestral Strain: Ex Post Facto Retrospective Observation Study

BACKGROUND

In Japan, long-distance domestic travel was banned while the ancestral SARS-CoV-2 strain was dominant under the first declared state of emergency from March 2020 until the end of May 2020. Subsequently, the "Go To Travel" campaign travel subsidy policy was activated, allowing long-distance domestic travel, until the second state of emergency as of January 7, 2021. The effects of this long-distance domestic travel ban on SARS-CoV-2 infectivity have not been adequately evaluated.

OBJECTIVE

We evaluated the effects of the long-distance domestic travel ban in Japan on SARS-CoV-2 infectivity, considering climate conditions, mobility, and countermeasures such as the "Go To Travel" campaign and emergency status.

METHODS

We calculated the effective reproduction number R(t), representing infectivity, using the epidemic curve in Kagoshima prefecture based on the empirical distribution of the incubation period and procedurally delayed reporting from an earlier study. Kagoshima prefecture, in southern Japan, has several resorts, with an airport commonly used for transportation to Tokyo or Osaka. We regressed R(t) on the number of long-distance domestic travelers (based on the number of airport limousine bus users provided by the operating company), temperature, humidity, mobility, and countermeasures such as state of emergency declarations and the "Go To Travel" campaign in Kagoshima. The study period was June 20, 2020, through February 2021, before variant strains became dominant. A second state of emergency was not declared in Kagoshima prefecture but was declared in major cities such as Tokyo and Osaka.

RESULTS

Estimation results indicated a pattern of declining infectivity with reduced long-distance domestic travel volumes as measured by the number of airport limousine bus users. Moreover, infectivity was lower during the "Go To Travel" campaign and the second state of emergency. Regarding mobility, going to restaurants, shopping malls, and amusement venues was associated with increased infectivity. However, going to grocery stores and pharmacies was associated with decreased infectivity. Climate conditions showed no significant association with infectivity patterns.

CONCLUSIONS

The results of this retrospective analysis suggest that the volume of long-distance domestic travel might reduce SARS-CoV-2 infectivity. Infectivity was lower during the "Go To Travel" campaign period, during which long-distance domestic travel was promoted, compared to that outside this campaign period. These findings suggest that policies banning long-distance domestic travel had little legitimacy or rationale. Long-distance domestic travel with appropriate infection control measures might not increase SARS-CoV-2 infectivity in tourist areas. Even though this analysis was performed much later than the study period, if we had performed this study focusing on the period of April or May 2021, it would likely yield the same results. These findings might be helpful for government decision-making in considering restarting a "Go To Travel" campaign in light of evidence-based policy.

High potential of technology to face new respiratory viruses: mechanical ventilation devices for effective healthcare to next pandemic emergencies

Some countries in the presence of unforeseen Coronavirus Disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have experienced lower total deaths, though higher numbers of COVID-19 related infections. Results here suggest that one of the explanations is the critical role of ventilator technology in clinical health environment to cope with the initial stage of COVID-19 pandemic crisis. Statistical evidence shows that a large number of ventilators or breathing devices in countries (26.76 units per 100,000 inhabitants) is associated with a fatality rate of 1.44% (December 2020), whereas a higher fatality rate given by 2.46% is in nations with lower numbers of ventilator devices (10.38 average units per 100,000 people). These findings suggest that a large number of medical ventilators in clinical setting has a high potential for more efficient healthcare and improves the effective preparedness of crisis management to cope with new respiratory pandemic diseases in society. Hence, a forward-thinking and technology-oriented strategy in healthcare sector, based on investments in high-tech ventilator devices and other new medical technologies, can help clinicians deliver effective care and reduce negative effects of present and future respiratory infectious diseases, in particular when new drugs and appropriate treatments are missing in clinical environment to face unknown respiratory viral agents .

Spatio-temporal heterogeneity in the international trade resilience during COVID-19

The COVID-19 pandemic and subsequent lockdowns have created immeasurable health and economic crises, leading to unprecedented disruptions to world trade. The COVID-19 pandemic shows diverse impacts on different economies that suffer and recover at different rates and degrees. This research aims to evaluate the spatio-temporal heterogeneity of international trade network vulnerabilities in the current crisis to understand the global production resilience and prepare for the future crisis. We applied a series of complex network analysis approaches to the monthly international trade networks at the world, regional, and country scales for the pre- and post- COVID-19 outbreak period. The spatio-temporal patterns indicate that countries and regions with an effective COVID-19 containment such as East Asia show the strongest resilience, especially Mainland China, followed by high-income countries with fast vaccine roll-out (e.g., U.S.), whereas low-income countries (e.g., Africa) show high vulnerability. Our results encourage a comprehensive strategy to enhance international trade resilience when facing future pandemic threats including effective non-pharmaceutical measures, timely development and rollout of vaccines, strong governance capacity, robust healthcare systems, and equality via international cooperation. The overall findings elicit the hidden global trading disruption, recovery, and growth due to the adverse impact of the COVID-19 pandemic.

Sources, diffusion and prediction in COVID-19 pandemic: lessons learned to face next health emergency

Scholars and experts argue that future pandemics and/or epidemics are inevitable events, and the problem is not whether they will occur, but when a new health emergency will emerge. In this uncertain scenario, one of the most important questions is an accurate prevention, preparedness and prediction for the next pandemic. The main goal of this study is twofold: first, the clarification of sources and factors that may trigger pandemic threats; second, the examination of prediction models of on-going pandemics, showing pros and cons. Results, based on in-depth systematic review, show the vital role of environmental factors in the spread of Coronavirus Disease 2019 (COVID-19), and many limitations of the epidemiologic models of prediction because of the complex interactions between the new viral agent SARS-CoV-2, environment and society that have generated variants and sub-variants with rapid transmission. The insights here are, whenever possible, to clarify these aspects associated with public health in order to provide lessons learned of health policy that may reduce risks of emergence and diffusion of new pandemics having negative societal impact.

From Heroes to Scoundrels: Exploring the effects of online campaigns celebrating frontline workers on COVID-19 outcomes

This paper examines the effects of online campaigns celebrating frontline workers on COVID-19 outcomes regarding new cases, deaths, and vaccinations, using the United Kingdom as a case study. We implement text and sentiment analysis on Twitter data and feed the result into random regression forests and cointegration analysis. Our combined machine learning and econometric approach shows very weak effects of both the volume and the sentiment of Twitter discussions on new cases, deaths, and vaccinations. On the other hand, established relationships (such as between stringency measures and cases/deaths and between vaccinations and deaths) are confirmed. On the contrary, we find adverse lagged effects from negative sentiment to vaccinations and from new cases to negative sentiment posts. As we assess the knowledge acquired from the COVID-19 crisis, our findings can be used by policy makers, particularly in public health, and prepare for the next pandemic.

The Impact of Preventive Strategies Adopted during Large Events on the COVID-19 Pandemic: A Case Study of the Tokyo Olympics to Provide Guidance for Future Large Events

This study aimed to analyze the impact of hosting large events on the spread of pandemics, taking Tokyo Olympics 2020 as a case study. A risk assessment method for the whole organization process was established, which could be used to evaluate the effectiveness of various risk mitigation measures. Different scenarios for Games participants and Japanese residents during the Tokyo Olympics were designed based on the infection control protocols proposed by the Olympic Committee and local governments. A modified Wells-Riley model considering the influence of social distance, masking and vaccination, and an SIQRV model that introduced the effect of quarantine and vaccination strategies on the pandemic spread were developed in this study. Based on the two models, our predicted results of daily confirmed cases and cumulative cases were obtained and compared with reported data, where good agreement was achieved. The results show that the two core infection control strategies of the bubble scheme and frequent testing scheme curbed the spread of the COVID-19 pandemic during the Tokyo Olympics. Among Games participants, Japanese local staff accounted for more than 60% of the total in positive cases due to their large population and most relaxed travel restrictions. The surge in positive cases was mainly attributed to the high transmission rate of the Delta variant and the low level of immunization in Japan. Based on our simulation results, the risk management flaws for the Tokyo Olympics were identified and improvement measures were investigated. Moreover, a further analysis was carried out on the impact of different preventive measures with respect to minimizing the transmission of new variants with higher transmissibility. Overall, the findings in this study can help policymakers to design scientifically based and practical countermeasures to cope with pandemics during the hosting of large events.

COVID-19 cases with a contact history: A modeling study of contact history-stratified data in Japan

The purpose of the present study was to develop a transmission model of COVID-19 cases with and without a contact history to understand the meaning of the proportion of infected individuals with a contact history over time. We extracted epidemiological information regarding the proportion of coronavirus disease 2019 (COVID-19) cases with a contact history and analyzed incidence data stratified by the presence of a contact history in Osaka from January 15 to June 30, 2020. To clarify the relationship between transmission dynamics and cases with a contact history, we used a bivariate renewal process model to describe transmission among cases with and without a contact history. We quantified the next-generation matrix as a function of time; thus, the instantaneous (effective) reproduction number was calculated for different periods of the epidemic wave. We objectively interpreted the estimated next-generation matrix and replicated the proportion of cases with a contact p(t) over time, and we examined the relevance to the reproduction number. We found that p(t) does not take either the maximum or minimum value at a threshold level of transmission with R(t)=1.0. With R(t) < 1 (subcritical level), p(t) was a decreasing function of R(t). Qualitatively, the minimum p(t) was seen in the domain with R(t) > 1. An important future implication for use of the proposed model is to monitor the success of ongoing contact tracing practice. A decreasing signal of p(t) reflects the increasing difficulty of contact tracing. The present study findings indicate that monitoring p(t) would be a useful addition to surveillance.

Improving Public Health Policy by Comparing the Public Response during the Start of COVID-19 and Monkeypox on Twitter in Germany: A Mixed Methods Study

Little is known about monkeypox public concerns since its widespread emergence in many countries. Tweets in Germany were examined in the first three months of COVID-19 and monkeypox to examine concerns and issues raised by the public. Understanding views and positions of the public could help to shape future public health campaigns. Few qualitative studies reviewed large datasets, and the results provide the first instance of the public thinking comparing COVID-19 and monkeypox. We retrieved 15,936 tweets from Germany using query words related to both epidemics in the first three months of each one. A sequential explanatory mixed methods research joined a machine learning approach with thematic analysis using a novel rapid tweet analysis protocol. In COVID-19 tweets, there was the selfing construct or feeling part of the emerging narrative of the spread and response. In contrast, during monkeypox, the public considered othering after the fatigue of the COVID-19 response, or an impersonal feeling toward the disease. During monkeypox, coherence and reconceptualization of new and competing information produced a customer rather than a consumer/producer model. Public healthcare policy should reconsider a one-size-fits-all model during information campaigns and produce a strategic approach embedded within a customer model to educate the public about preventative measures and updates. A multidisciplinary approach could prevent and minimize mis/disinformation.

COVID-19 Vaccination is not a Sufficient Public Policy to face Crisis Management of next Pandemic Threats

This study reveals that a vast vaccination campaign is a necessary but not sufficient public policy to reduce the negative impact of Coronavirus Disease 2019 (COVID-19) pandemic crisis because manifold factors guide the spread of this new infectious disease and related mortality in society. Statistical evidence here, based on a worldwide sample of countries, shows a positive correlation between people fully vaccinated and COVID-19 mortality (r = + 0.65, p-value < 0.01). Multivariate regression, controlling income per capita, confirms this finding. Results suggest that the increasing share of people vaccinated against COVID-19 seems to be a necessary but not sufficient health policy to reduce mortality of COVID-19. The findings here can be explained with the role of Peltzman effect, new variants, environmental and socioeconomic factors that affect the diffusion and negative impact of COVID-19 pandemic in society. This study extends the knowledge in this research field to design effective public policies of crisis management for facing next pandemic threats.