Analysis of mobility network metrics in the spread of COVID-19 in the Metropolitan Region of Vale do Paraíba and Litoral Norte (RMVPLN)

In 2019, a pandemic of the so-called new coronavirus (SARS-COV-II) began, which causes the disease COVID-19. In a short time after the first case appeared, hundreds of countries began to register new cases every day. Mapping and analyzing the flow of people, regardless of the mode of transport, can help us to understand and prevent several phenomena that can affect our society in different ways. Graphs are complex networks made up of points and edges. The (geo)graphs are graphs with known spatial location and, in the case of our study, the edges represent the flow between them. The (geo)graphs proved to be a promising tool for such analyses. In the study region, municipalities that first registered their COVID-19 cases are also municipalities that have the highest mobility indices analyzed: degree, betweenness and weight of edges. © 2022 National Institute for Space Research, INPE. All rights reserved.

Metrics of Mobility: Assessing the Impact of COVID-19 on Travel Behavior

The COVID-19 pandemic disrupted typical travel behavior worldwide. In the United States (U.S.), government entities took action to limit its spread through public health messaging to encourage reduced mobility and thus reduce the spread of the virus. Within statewide responses to COVID-19, however, there were different responses locally. Likely some of these variations were a result of individual attitudes toward the government and health messaging, but there is also likely a portion of the effects that were because of the character of the communities. In this research, we summarize county-level characteristics that are known to affect travel behavior for 404 counties in the U.S., and we investigate correlates of mobility between April and September (2020). We do this through application of three metrics that are derived via changepoint analysis—initial post-disruption mobility index, changepoint on restoration of a ‘‘new normal,'' and recovered mobility index. We find that variables for employment sectors are significantly correlated and had large effects on mobility during the pandemic. The state dummy variables are significant, suggesting that counties within the same state behaved more similarly to one another than to counties in different states. Our findings indicate that few travel characteristics that typically correlate with travel behavior are related to pandemic mobility, and that the number of COVID-19 cases may not be correlated with mobility outcomes. © National Academy of Sciences: Transportation Research Board 2021.

Electromechanical Assisted Gait Training (EMGT) enables early mobilisation and improves mobility in people with acute stroke (preliminary findings)

Introduction: EMGT is an effective, evidence based intervention for improving mobility outcomes in people with stroke. It enables highly repetitive stepping practice, in patients who are unable to stand/step. Despite being recommended in national guidance, adoption within the UK is extremely limited. We report preliminary data from its implementation in an NHS stroke service. Method(s): We initiated use of EMGT in our Acute Stroke Unit in November 2021 - implementation has been phased, and use was limited at times due to the COVID-19 pandemic. Patient demographics, clinical outcome measures and discharge information are recorded pre- and post-treatment. Result(s): To date, 38 patients have used EMGT, accumulating 232 sessions of walking. 74% of patients were male. . Mean age was 73.5 years (range 51 - 91). 19 patients used EMGT for >=3 sessions;of those, 14 completed > 6 sessions. Median Functional Ambulatory Category (FAC) at baseline was 0 (range 0-1), rising post treatment to 2 (range 0-4). Mean modified Rivermead Mobility Index rose from 13.5 to 24.3. 57% of patients who used EMGT as part of their rehabilitation programme were able to mobilise at least 10 metres post intervention. Initially, only 1 patient could manage a step transfer and following treatment this increased to 8. Conclusion(s): Early results indicate that EMGT is feasible in an acute NHS setting, alongside conventional care. It enables early and highly intensive mobilisation, resulting in improved function. Further work is required to develop clinical protocols, establishing recommended dose, time after stroke for EMGT initiation, and recommended duration of treatment.

Mobility and economic activity around the world during the Covid-19 crisis

This paper traces the relationship between quarterly estimates of economic activity and people's mobility during the Covid-19 crisis in a sample of 53 economies. Over time, the estimates of elasticity of value added with respect to mobility have been declining, to below 0.2 at the start of 2021, attesting to the gradual adjustment of global economic activity to social distancing. Yet this adjustment appears to be modest, with economic recovery driven primarily by greater mobility. The study highlights the limit to the extent to which economic costs of restricted movement of people can be reduced, with implications for public policy. The estimated relationships can also be effectively applied to economic forecasting during periods of reduced mobility. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

Spatiotemporal heterogeneities of the associations between human mobility and close contacts with COVID-19 infections in the United States

It has been well-established that human mobility has an inseparable relationship with COVID-19 infections. As the COVID-19 pandemic progresses, our knowledge on how human behaviors including mobility and close contact associates with the pandemic also need to stay updated. In this paper, we examine the relationship of the effective reproduction number (Rt) of COVID-19 daily cases with the two indices that provide mobility insights: Mobility Index (CMI) and Contact Index (CCI). Both relationships are evaluated through Maximal Information Coefficient (MIC). Using the Bayesian Change Point Detection and the KShape clustering algorithms, we found significant temporal and spatial heterogeneities among the relationship between two indices and the daily confirmed COVID-19 cases. Although CMI has demonstrated high correlation with COVID-19 cases in 2020, CCI became much more correlated with COVID-19 cases than CMI in 2021. During the first wave in 2020, it is also shown that mobility has a high impact on states outside of Farwest and Southeast than those states within that region. © 2022 ACM.

Nowcasting unemployment rate during the COVID-19 pandemic using Twitter data: The case of South Africa.

The global economy has been hard hit by the COVID-19 pandemic. Many countries are experiencing a severe and destructive recession. A significant number of firms and businesses have gone bankrupt or been scaled down, and many individuals have lost their jobs. The main goal of this study is to support policy- and decision-makers with additional and real-time information about the labor market flow using Twitter data. We leverage the data to trace and nowcast the unemployment rate of South Africa during the COVID-19 pandemic. First, we create a dataset of unemployment-related tweets using certain keywords. Principal Component Regression (PCR) is then applied to nowcast the unemployment rate using the gathered tweets and their sentiment scores. Numerical results indicate that the volume of the tweets has a positive correlation, and the sentiments of the tweets have a negative correlation with the unemployment rate during and before the COVID-19 pandemic. Moreover, the now-casted unemployment rate using PCR has an outstanding evaluation result with a low Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Symmetric MAPE (SMAPE) of 0.921, 0.018, 0.018, respectively and a high R2-score of 0.929.

COVID-19 Vaccination and Public Health Countermeasures on Variants of Concern in Canada: Evidence From a Spatial Hierarchical Cluster Analysis.

BACKGROUND: There is mounting evidence that the third wave of COVID-19 incidence is declining, yet variants of concern (VOCs) continue to present public health challenges in Canada. The emergence of VOCs has sparked debate on how to effectively control their impacts on the Canadian population. OBJECTIVE: Provincial and territorial governments have implemented a wide range of policy measures to protect residents against community transmission of COVID-19, but research examining the specific impact of policy countermeasures on the VOCs in Canada is needed. Our study objective was to identify provinces with disproportionate prevalence of VOCs relative to COVID-19 mitigation efforts in provinces and territories in Canada. METHODS: We analyzed publicly available provincial- and territorial-level data on the prevalence of VOCs in relation to mitigating factors, summarized in 3 measures: (1) strength of public health countermeasures (stringency index), (2) the extent to which people moved about outside their homes (mobility index), and (3) the proportion of the provincial or territorial population that was fully vaccinated (vaccine uptake). Using spatial agglomerative hierarchical cluster analysis (unsupervised machine learning), provinces and territories were grouped into clusters by stringency index, mobility index, and full vaccine uptake. The Kruskal-Wallis test was used to compare the prevalence of VOCs (Alpha, or B.1.1.7; Beta, or B.1.351; Gamma, or P.1; and Delta, or B.1.617.2 variants) across the clusters. RESULTS: We identified 3 clusters of vaccine uptake and countermeasures. Cluster 1 consisted of the 3 Canadian territories and was characterized by a higher degree of vaccine deployment and fewer countermeasures. Cluster 2 (located in Central Canada and the Atlantic region) was typified by lower levels of vaccine deployment and moderate countermeasures. The third cluster, which consisted of provinces in the Pacific region, Central Canada, and the Prairies, exhibited moderate vaccine deployment but stronger countermeasures. The overall and variant-specific prevalences were significantly different across the clusters. CONCLUSIONS: This "up to the point" analysis found that implementation of COVID-19 public health measures, including the mass vaccination of populations, is key to controlling VOC prevalence rates in Canada. As of June 15, 2021, the third wave of COVID-19 in Canada is declining, and those provinces and territories that had implemented more comprehensive public health measures showed lower VOC prevalence. Public health authorities and governments need to continue to communicate the importance of sociobehavioural preventive measures, even as populations in Canada continue to receive their primary and booster doses of vaccines.

Understanding Risk Communication Effectiveness From Public Interest, Mobility, and COVID-19 Cases: A Case Study of COVID-19 in Nigeria

Since the emergence of COVID-19 in 2020, various actions have been taken by governments and agencies globally to curtail its spread and devastating effects. Risk communication is an essential component of such actions. Examination of public interest, risk perception and new cases of COVID-19 is vital to understanding the effectiveness of risk communication strategies implemented. With data paucity plaguing policymaking in Nigeria, there is a need to examine new data sources to support the enhancement of risk communication. The study explored Google Trends (GT) and Google Mobility Reports (GMR) in monitoring public restlessness and risk perception, respectively, toward COVID-19 in Nigeria. This is geared toward understanding the effectiveness of the national risk communication strategy. COVID-19 case statistics, stringency index, mobility, and search indices for selected terms were collated (February 28 to June 30, 2020). Temporal dynamics were examined while correlation analysis was carried out to examine the association. Public attention peaked just around the commencement of the nationwide lockdown and declined considerably afterwards despite increasing new cases. Mobility toward most place categories showed a sharp decline at the beginning of the pandemic, except for residential areas. This trend also reversed soon after the lockdown. COVID-19 case statistics were found to be negatively correlated with the public interest. Public interest had a weak but both negative and positive association with the stringency index, while mobility exhibited a weak negative association with the case statistics (except residential area mobility). The results indicated that the risk communication efforts were inadequate in providing a prolonged health behavior change. The initial risk communication and lockdown created a positive outcome, however, the impact soon faded out. The evidence suggests that risk perception may have been poorly targeted by risk communication interventions. It is recommended that continuous monitoring of public interest and risk perception is implemented during an emergency and risk communication adjusted accordingly. Copyright © 2022 Lawal.

Distributed lag interrupted time series model for unclear intervention timing: effect of a statement of emergency during COVID-19 pandemic.

BACKGROUND: Interrupted time series (ITS) analysis has become a popular design to evaluate the effects of health interventions. However, the most common formulation for ITS, the linear segmented regression, is not always adequate, especially when the timing of the intervention is unclear. In this study, we propose a new model to overcome this limitation. METHODS: We propose a new ITS model, ARIMAITS-DL, that combines (1) the Autoregressive Integrated Moving Average (ARIMA) model and (2) distributed lag functional terms. The ARIMA technique allows us to model autocorrelation, which is frequently observed in time series data, and the decaying cumulative effect of the intervention. By contrast, the distributed lag functional terms represent the idea that the intervention effect does not start at a fixed time point but is distributed over a certain interval (thus, the intervention timing seems unclear). We discuss how to select the distribution of the effect, the model construction process, diagnosing the model fitting, and interpreting the results. Further, our model is implemented as an example of a statement of emergency (SoE) during the coronavirus disease 2019 pandemic in Japan. RESULTS: We illustrate the ARIMAITS-DL model with some practical distributed lag terms to examine the effect of the SoE on human mobility in Japan. We confirm that the SoE was successful in reducing the movement of people (15.0-16.0% reduction in Tokyo), at least between February 20 and May 19, 2020. We also provide the R code for other researchers to easily replicate our method. CONCLUSIONS: Our model, ARIMAITS-DL, is a useful tool as it can account for the unclear intervention timing and distributed lag effect with autocorrelation and allows for flexible modeling of different types of impacts such as uniformly or normally distributed impact over time.

CARBON MONOXIDE EMISSIONS MONITORING during COVID-19 PANDEMIC in JAKARTA

Jakarta has been known as the capital city, has been dealing with air pollution since many years ago. The main contributor of air pollution in Jakarta is due to large population, and high numbers of transportation, of which still running on diesel fuels, emitting far higher levels of pollutants. Carbon monoxide pollution is associate with number of transportations run in the city. Mobility index provided by Facebook Data for Good has managed to identify people's movement during COVID-19 period (stay put and change in movement matrix's). Therefore, this study aims to analyze the dynamic of carbon monoxide emissions during the COVID-19 pandemic in Jakarta. The results showed the distribution of CO emission in Jakarta and its mobility index of each quarter showed a similar pattern. The quarter 2 of 2020) is presented the lowest CO emission distribution value compared to the other five quarters, ranging from 0.0309-0.0334 mol/m2. The low value of CO emission distribution during that period was related to the low community mobility index (index of go) in the same period. While in the next quarter which is quarter 3 2020, the CO emission was relatively increased ranging from 0.031-0.037 mol/m2, which associated with the rise of mobility index stay of go value. Therefore, in this study, there is a relationship between the distribution of CO emission with the mobility index provided by Facebook. © ACRS 2021.All right reserved.

Predicting Regional Economic Condition Using Human Mobility Index during the Pandemic

Coronavirus Disease (COVID-19) pandemic is causing both health and economic crises. The trade-off between economic activity and health puts the authorities in a serious dilemma. Real-time data can be a solution that provides an alternate approach to make dynamic policy scenarios. One of the real-time data that can represent economic activities is the COVID-19 community mobility report. This paper aims to analyse the relationship between human mobility and economic condition during the COVID-19 pandemic, using the COVID-19 community mobility report proxy. The binary logistic regression model result by using data in Indonesia shows that human mobility has a tendency towards economic improvement during the pandemic, simultaneously with the employment rate and geographical factor. © 2021 IEEE.

Telecommunication activity-derived public mobility indices as drivers of PM2.5 concentration in northwestern Croatia at the time of Covid-19 outbreak in the early 2020

The Covid-19 pandemic has emerged as a challenge for numerous socio-economic processes established in times prior the outbreak. Here we contribute to the subject with an analysis of the mobility pattern changes, derived from telecommunications location services data, and their effects on the air pollution with the PM2.5 particles during the imposed lockdown in Krapinsko-zagorska county (Županija krapinsko-zagorska) in the north-western Croatia in the first quarter of 2020. The study is conducted using the publicly available data. Statistical analysis reveals the complex cause-effect relationship between the PM2.5 concentration, as the measure of air quality, and the general public mobility. We continue our research to contribute to understanding of the Covid-19 epidemiological development, and the socio-economic impact on the climate change and environmental conditions © 2021 IEEE.

Assessing the impact of mobility reduction in the second wave of COVID-19

By February 2021, Uruguay was experiencing the first wave of the COVID-19 pandemic, while many countries were already suffering the second wave. Several countries took various measures to prevent the saturation of the health system, ranging from closure of restaurants and suspension of classes to nighttime traffic restrictions. In this paper, we explore the effect of mobility restriction measures on the infection incidence in countries that are in some way similar to Uruguay: they have between one and twelve million inhabitants, a reasonable testing effort and they had the epidemic under control at some point. For these countries, we study mobility indexes provided by Google, an index on governmental measures compiled by the University of Oxford, and the daily new cases per 100,000 inhabitants. First, we observed that the mobility reported by Google is directly related to government measures: the higher the level of restrictive measures, the lower the mobility index. Then, we analyze the influence of mobility reduction on the growth/decrease speed of the 7-day average of new cases per 100,000 inhabitants (P7) and show that high levels of mobility reduction lead to a decrease in the index. Finally, we related the required duration of mobility restrictions with the P7 maximum and also point out the risk of lifting the measures too early. ©2021 IEEE

Negative-Binomial and quasi-poisson regressions between COVID-19, mobility and environment in São Paulo, Brazil.

Brazil, the country most impacted by the coronavirus disease 2019 (COVID-19) on the southern hemisphere, use intensive care admissions per day, mobility and other indices to monitor quarantines and prevent the transmissions of SARS-CoV-2. In this study we quantified the associations between residential mobility index (RMI), air pollution, meteorology, and daily cases and deaths of COVID-19 in São Paulo, Brazil. We applied a semiparametric generalized additive model (GAM) to estimate: 1) the association between RMI and COVID-19, accounting for ambient particulate matter (PM2.5), ozone (O3), relative humidity, temperature and delayed exposure between 4 and 21 days, and 2) the association between COVID-19 and exposure to for ambient particulate matter (PM2.5), ozone (O3), accounting for relative humidity, temperature and mobility. We found that an RMI of 45.28% results in 1212 cases (95% CI: 1189 to 1235) and 44 deaths (95% CI: 40 to 47). Increasing the isolation from 45.28% to 50% would avoid 438 cases and 21 deaths. Also, we found that an increment of 10 µg⋅m-³ of PM2.5 results in a risk of 1.140 (95% CI: 1.021 to 1.274) for cases and 1.086 (95% CI: 1.008 to 1.170) for deaths, while O3 produces a relative risk of 1.075 (95% CI: 1.006 to 1.150) for cases and 1.063 (95% CI: 1.006 to 1.124) for deaths, respectively. We compared our results with observations and literature review, finding well agreement. Policymakers can use such mobility indices as tools to control social distance activities. Spatial distancing is an important factor to control COVID-19, however, measuring face-mask usage would enhance the understanding the pandemic dynamic. Small increments of air pollution result in an increased number of COVID-19 cases and deaths.

Temporal air quality (NO2, O3, and PM10) changes in urban and rural stations in Catalonia during COVID-19 lockdown: an association with human mobility and satellite data.

In this study, changes in air quality by NO2, O3, and PM10 in Barcelona metropolitan area and other parts of Catalonia during the COVID-19 lockdown with respect to pre-lockdown and to previous years (2018 and 2019) were evaluated. Selected air monitoring stations included 3 urban (Gràcia, Vall d'Hebron, and Granollers), 1 control site (Fabra Observatory), 1 semi-urban (Manlleu), and 3 rural (Begur, Bellver de Cerdanya, and Juneda). NO2 lockdown levels showed a diminution, which in relative terms was maximum in two rural stations (Bellver de Cerdanya, - 63% and Begur, - 61%), presumably due to lower emissions from the ceasing hotel and ski resort activities during eastern holidays. In absolute terms and from an epidemiologic perspective, decrease in NO2, also reinforced by the high amount of rainfall registered in April 2020, was more relevant in the urban stations around Barcelona. O3 levels increased in the transited urban stations (Gràcia, + 42%, and Granollers, + 64%) due to the lower titration effect by NOx. PM10 lockdown levels decreased, mostly in Gràcia, Vall d'Hebron, and Granollers (- 35, - 39%, and - 39%, respectively) due to traffic depletion (- 90% in Barcelona's transport). Correlation among mobility index in Barcelona (- 100% in retail and recreation) and contamination was positive for NO2 and PM10 and negative for O3 (P < 0.001). Satellite images evidenced two hotspots of NO2 in Spain (Madrid and Barcelona) in April 2018 and 2019 that disappeared in 2020. Overall, the benefits of lockdown on air quality in Catalonia were evidenced with NO2, O3 and PM10 levels below WHOAQG values in most of stations opposed to the excess registered in previous years.