Time series cross-correlation between home range and number of infected people during the COVID-19 pandemic in a suburban city

Daily walking time effects of the opening of a multifunctional facility "ONIKURU" using propensity score matching and GPS tracking techniques

Urban design focused on improving walkability has received attention as a method of increasing physical activity among the population. However, only a few studies have examined the effect of walking time of opening multifunctional facilities as an architecture-scale intervention. This study aimed to clarify the effect of opening a multifunctional facility on residents' daily walking time. In addition, this study analyzed the gender and age subgroups. The natural experiment was conducted using the case of the Ibaraki City Cultural and Childcare Complex "ONIKURU," a public multifunctional facility. This study used GPS-trajectory data based on GPS tracking techniques, which is anonymized location data for smartphone users. The causal relationship was analyzed using propensity score matching and difference-in-differences analysis. The results showed that the opening of ONIKURU significantly increased the average walking time of visitors to 3.165 [- 1.697, 8.027] min/day compared with that of non-visitors. Specifically, visitors' average daily walking time improved to a level comparable to that of non-visitors after the opening of ONIKURU. In addition, opening ONIKURU significantly increased female young adults' average walking time to 3.385 [- 4.906, 11.676] min/day. Therefore, this study provides theoretical contributions to a health-promoting built environment significantly affecting walking at an architecture-scale intervention.

Cross-correlation and time series analysis of rabies in different animal species in Nepal from 2005 to 2018

Rabies is a fatal zoonotic disease, resulting in human and livestock deaths. In Nepal, animal rabies has posed a significant challenge to public health. Because animals are the primary source of rabies in humans, a better understanding of rabies epidemiology in animals is necessary. The objectives of this study were to determine the correlation between rabies occurrences in dogs and livestock animals and to detect the trends and change points of the disease using longitudinal data. The nationwide rabies dataset from 2005 to 2018 was analyzed using cross-correlation, multiple change points, and time series methods. Autoregressive Integrated Moving Average (ARIMA) and Neural Network Autoregression (NNAR) were applied to the time series data. The results show a positive correlation between canine rabies and livestock rabies occurrences. Three significant change points were detected in the time series data, demonstrating that the occurrences were high in the initial years but stabilized before peaking to an upward trend in the final years of the study period. Nonetheless, there was no seasonality pattern in rabies occurrences. The most suitable models were ARIMA (2,1,2) and NNAR (5,1,4) (12). Based on the study findings, both locals and tourists in Nepal need to have enhanced awareness of the potential dangers posed by rabies in canines and livestock. This study offers much-needed insight into the patterns and epidemiology of animal rabies which will be helpful for policymakers in drafting rabies control plans for Nepal.