Impact of human mobility and networking on spread of COVID-19 at the time of the 1st and 2nd epidemic waves in Japan: An effective distance approach.

BACKGROUND: The influence of human mobility to the domestic spread of COVID-19 in Japan using the approach of effective distance has not yet been assessed. METHODS: We calculated the effective distance between prefectures using the data on laboratory-confirmed cases of COVID-19 from January 16 to August 23, 2020, that were times in the 1st and the 2nd epidemic waves in Japan. We also used the aggregated data on passenger volume by transportation mode for the 47 prefectures, as well as those in the private railway, bus, ship, and aviation categories. The starting location (prefecture) was defined as Kanagawa and as Tokyo for the 1st and the 2nd waves, respectively. The accuracy of the spread models was evaluated using the correlation between time of arrival and effective distance, calculated according to the different starting locations. RESULTS: The number of cases in the analysis was 16,226 and 50,539 in the 1st and 2nd epidemic waves, respectively. The relationship between arrival time and geographical distance shows that the coefficient of determination was R2 = 0.0523 if geographical distance Dgeo and time of arrival Ta set to zero at Kanagawa and was R2 = 0.0109 if Dgeo and Ta set to zero at Tokyo. The relationship between arrival time and effective distance shows that the coefficient of determination was R2 = 0.3227 if effective distance Deff and Ta set to zero at Kanagawa and was R2 = 0.415 if Deff and time of arrival Ta set to zero at Tokyo. In other words, the effective distance taking into account the mobility network shows the spatiotemporal characteristics of the spread of infection better than geographical distance. The correlation of arrival time to effective distance showed the possibility of spreading from multiple areas in the 1st epidemic wave. On the other hand, the correlation of arrival time to effective distance showed the possibility of spreading from a specific area in the 2nd epidemic wave. CONCLUSIONS: The spread of COVID-19 in Japan was affected by the mobility network and the 2nd epidemic wave is more affected than those of the 1st epidemic. The effective distance approach has the impact to estimate the domestic spreading COVID-19.

Spatiotemporal distribution of COVID-19 during the first 7 months of the epidemic in Vietnam.

BACKGROUND: Understanding the spatiotemporal distribution of emerging infectious diseases is crucial for implementation of control measures. In the first 7 months from the occurrence of COVID-19 pandemic, Vietnam has documented comparatively few cases of COVID-19. Understanding the spatiotemporal distribution of these cases may contribute to development of global countermeasures. METHODS: We assessed the spatiotemporal distribution of COVID-19 from 23 January to 31 July 2020 in Vietnam. Data were collected from reports of the World Health Organization, the Vietnam Ministry of Health, and related websites. Temporal distribution was assessed via the transmission classification (local or quarantined cases). Geographical distribution was assessed via the number of cases in each province along with their timelines. The most likely disease clusters with elevated incidence were assessed via calculation of the relative risk (RR). RESULTS: Among 544 observed cases of COVID-19, the median age was 35 years, 54.8% were men, and 50.9% were diagnosed during quarantine. During the observation period, there were four phases: Phase 1, COVID-19 cases occurred sporadically in January and February 2020; Phase 2, an epidemic wave occurred from the 1st week of March to the middle of April (Wave 1); Phase 3, only quarantining cases were involved; and Phase 4, a second epidemic wave began on July 25th, 2020 (Wave 2). A spatial cluster in Phase 1 was detected in Vinh Phuc Province (RR, 38.052). In Phase 2, primary spatial clusters were identified in the areas of Hanoi and Ha Nam Province (RR, 6.357). In Phase 4, a spatial cluster was detected in Da Nang, a popular coastal tourist destination (RR, 70.401). CONCLUSIONS: Spatial disease clustering of COVID-19 in Vietnam was associated with large cities, tourist destinations, people's mobility, and the occurrence of nosocomial infections. Past experiences with outbreaks of emerging infectious diseases led to quick implementation of governmental countermeasures against COVID-19 and a general acceptance of these measures by the population. The behaviors of the population and the government, as well as the country's age distribution, may have contributed to the low incidence and small number of severe COVID-19 cases.