The impact of declaring the state of emergency on human mobility during COVID-19 pandemic in Japan

Trends in Group A Streptococcus Pharyngitis and Co-Infection with Severe Acute Respiratory Syndrome Coronavirus 2: A Retrospective Observational Study

Background and Objectives: Group A Streptococcus (GAS) is a leading cause of acute pharyngitis with seasonal outbreaks. The coronavirus disease 2019 (COVID-19) pandemic significantly altered respiratory infection trends; however, its impact on GAS pharyngitis (GAS-P) incidence remains unclear. Additionally, data on co-infections with GAS and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are limited. In this study, temporal trends in GAS-P incidence and characteristics of GAS-SARS-CoV-2 co-infections in Japan were examined. Materials and Methods: In this observational study, data from patients who visited the Tokyo Shinagawa Hospital between January 2019 and December 2024 were retrospectively analyzed. Data on GAS and SARS-CoV-2 test results and patient demographics were extracted from medical records. The study period was categorized based on COVID-19-related public health measures as follows: pre-COVID-19 social period (January 2019-April 2020), restricted social period (May 2020-April 2023), and post-restriction period (May 2023-December 2024). GAS incidence stratified by sex, age, and period was calculated. Clinical characteristics of patients co-infected with GAS and SARS-CoV-2 were analyzed. Results: Among 4837 GAS tests, 463 (9.6%) were positive. GAS positivity rates varied significantly: 11.4% (pre-COVID-19), 7.1% (restricted social period), and 12.6% (post-restriction period; p < 0.001). The proportion of pediatric cases decreased significantly during the restricted social period (24.8-5.3%) before rising sharply in the post-restriction period (47.1%, p < 0.001). Among 151 patients tested for GAS and SARS-CoV-2, 14 (9.3%) had co-infections, which were identified exclusively after July 2022. Most patients exhibited mild symptoms, primarily fever and sore throat, with decreased lymphocyte counts despite normal white blood cell counts. Conclusions: In our cohort, the incidence of GAS pharyngitis temporarily declined during COVID-19-related public health measures and subsequently increased, particularly among children, after restrictions were lifted. Limited testing may contribute to the underdiagnosis of GAS-SARS-CoV-2 co-infections. Further large-scale studies are warranted to assess microbial interactions, disease severity, and long-term outcomes.

Local effects of non-pharmaceutical interventions on mitigation of COVID-19 spread through decreased human mobilities in Japan: a prefecture-level mediation analysis

To control the COVID-19 epidemic, the Japanese government and the local governments have repeatedly implemented non-pharmaceutical interventions (NPIs) throughout 2020-2022. Using Bayesian state-space mediation models, we examined the effect of repeated NPIs on infection spread mitigation, mediated by human mobility changes in each prefecture during three epidemic phases: from April 1, 2020 to February 28, 2021; from March 1, 2021 to December 16, 2021; and from December 17, 2021 to December 31, 2022. In the first phase, controlling downtown populations at nighttime was effective in mitigating the infection spread in almost all prefectures. In the second and third phases, the effect was not clear, especially in metropolitan prefectures. Controlling visitors from the central prefectures of metropolitan areas was effective in mitigating infection spread in the surrounding prefectures during all phases. These results suggest that the local spread of infection can be mitigated by focusing on nighttime human mobility control in downtown areas before the epidemic spreads widely and transmission routes become more diverse, and that the geospatial spread of infection can be prevented by controlling the flows of people from large cities to other areas.

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.

Surveillance of seasonal influenza viruses during the COVID-19 pandemic in Tokyo, Japan, 2018-2023, a single-center study

Introduction

COVID-19 pandemic led to significant reductions in influenza detection worldwide, fueling debates on whether influenza truly ceased circulating in communities. The number of influenza cases decreased significantly in Japan, raising concerns about the potential risk of decreased immunity to influenza in the population. Our single-center study aimed to investigate influenza trends before and during the COVID-19 pandemic in Tokyo, Japan.

Materials and Methods

This cross-sectional study included patients of all ages who visited Tokyo Shinagawa Hospital between April 1, 2018, and March 31, 2023. Influenza and COVID-19 tests were conducted using Quick Navi-Flu2 and polymerase chain reaction (PCR). We analyzed data from before and during the COVID-19 epidemic, based on patient background, hospitalization, and deaths, collected from medical records.

Results

A total of 12 577 influenza tests were conducted, with approximately 100 tests consistently performed each month even in the influenza off-season. Throughout the observation period, 962 positive cases were identified. However, no cases were observed for 27 months between March 2020 and November 2022. Influenza A cases were reobserved in December 2022, followed by influenza B cases in March 2023, similar to the influenza incidence reports from Tokyo. The positivity rate during the 2022-2023 winter season was lower than before the COVID-19 epidemic and decreased in elderly patients, with no hospitalizations or deaths observed.

Conclusion

This single-center study provided actual trend data for influenza patients before and during COVID-19 outbreaks in Tokyo, which could offer insights into the potential impact and likelihood of influenza virus infection in Japan.

Epidemiological impact of travel enhancement on the inter-prefectural importation dynamics of COVID-19 in Japan, 2020

Mobility restrictions were widely practiced to reduce contact with others and prevent the spatial spread of COVID-19 infection. Using inter-prefectural mobility and epidemiological data, a statistical model was devised to predict the number of imported cases in each Japanese prefecture. The number of imported cases crossing prefectural borders in 2020 was predicted using inter-prefectural mobility rates based on mobile phone data and prevalence estimates in the origin prefectures. The simplistic model was quantified using surveillance data of cases with an inter-prefectural travel history. Subsequently, simulations were carried out to understand how imported cases vary with the mobility rate and prevalence at the origin. Overall, the predicted number of imported cases qualitatively captured the observed number of imported cases over time. Although Hokkaido and Okinawa are the northernmost and the southernmost prefectures, respectively, they were sensitive to differing prevalence rate in Tokyo and Osaka and the mobility rate. Additionally, other prefectures were sensitive to mobility change, assuming that an increment in the mobility rate was seen in all prefectures. Our findings indicate the need to account for the weight of an inter-prefectural mobility network when implementing countermeasures to restrict human movement. If the mobility rates were maintained lower than the observed rates, then the number of imported cases could have been maintained at substantially lower levels than the observed, thus potentially preventing the unnecessary spatial spread of COVID-19 in late 2020.

Novel indicator for the spread of new coronavirus disease 2019 and its association with human mobility in Japan

The Japanese government adopted policies to control human mobility in 2020 to prevent the spread of severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). The present study examined the impact of human mobility on COVID-19 cases at the prefectural level in Japan by devising an indicator to have a relationship between the number of infected people and on human mobility. We calculated origin-destination travel mobility within prefectures in Japan from March 1st to December 31st, 2020, using mobile phone data. A cross-correlation function (CCF) was used to examine the relationship between human mobility and a COVID-19 infection acceleration indicator (IAI), which represents the rate of change in the speed of COVID-19 infection. The CCF of intraprefectural human mobility and the IAI in Tokyo showed a maximum value of 0.440 at lag day 12, and the IAI could be used as an indicator to predict COVID-19 cases. Therefore, the IAI and human mobility during the COVID-19 pandemic were useful for predicting infection status. The number of COVID-19 cases was associated with human mobility at the prefectural level in Japan in 2020. Controlling human mobility could help control infectious diseases in a pandemic, especially prior to starting vaccination.