Violations of mobility restrictions during COVID-19 in five Indonesian cities: A reflection of transport policy-practice gap

Since the detection of the first COVID-19 case in March 2020, the Indonesian government has implemented various mobility restrictions as a policy response to address the pandemic. To date, violations of mobility restrictions have been discussed in relation to public health risk, but rarely analyzed in terms of understanding the transport policy-practice gap. Using content analysis of news media from March 2020 to May 2021, this article identifies individual actions and institutional factors enabling violations of mobility restrictions. Our findings infer a policy-practice gap regarding operationalization, institutional issues, and lack of consideration of target groups' behavior. These findings provide insights for transport policy formulation in uncertain times, such as the post-pandemic, especially in the context of rapidly growing Asian cities. © 2023 The Authors

Justifying mobility restrictions during the COVID-19 pandemic: a test in multilevel governance

In public health crisis governance, effective communication has been shown to move people from awareness to compliance. This article examines the effectiveness of the communication strategy developed by stakeholders in the European multi-level governance during the COVID-19 pandemic. An original dataset of over 40,000 tweets from 65 actors in Switzerland, France, the UK, the EU and the UN is used to measure the timeliness, consistency and connectivity of tweets about mobility restrictions in the first phase of the pandemic. Analysis shows that the discourse surrounding mobility restrictions gradually becomes more politicised after an initial phase of high consistency and connectivity among actors. Additionally, low inter-level connectivity suggests a lack of coordination across governance levels, despite a strong consistency in the message. The study concludes that this pattern of communication could worsen the rising infodemic issue. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

The Vulnerability of Jobs to Mobility Restrictions: Malaysia's Experience during the COVID-19 Pandemic

Using the mobility restrictions implemented by Malaysia during the COVID-19 pandemic as a case study, this paper relies on detailed data on employment patterns and on the possibility to work from home and without physical proximity to estimate the extent and distribution of jobs most vulnerable to mobility restrictions. It finds that about 64.5 per cent of jobs in Malaysia cannot be performed from home, after adjusting for Internet access, while about 50.9 per cent of jobs require high levels of physical proximity. These are the jobs that are most vulnerable to strict mobility restrictions, such as those imposed during the pandemic. Workers most at risk are primarily those with relatively low education, low level of income and advanced or very young age. Jobs in less developed regions of Malaysia are also particularly vulnerable. Against this backdrop, the paper argues that Malaysia's experience during the COVID-19 pandemic provides some vital lessons in supporting those who are most vulnerable to job losses during mobility restrictions. These lessons include improving the targeting of cash transfers, scaling up wage subsidies in supporting worker retention and hiring and leveraging upskilling/reskilling initiatives with a focus on non-routine cognitive analytical and interpersonal skills. © 2022 ISEAS - Yusof Ishak Institute.

Health Anxiety, Information Anxiety, and Internet Self-Efficacy on Cyberchondria among Filipino Young Professionals during the COVID-19 Pandemic

The mobility restrictions due to COVID-19 lockdown impositions have forced people to stay at home in lieu of face-to-face activities. In effect, it has increased people's exposure to the Internet and its perils, brought by excessive information from different media that may lead to the development of health-related anxiety. This phenomenon is known as cyberchondria, where people may have experienced extreme anxiety about their physical health because of repeated internet searches concerning their medical conditions. This paper investigates the possible relationship between health anxiety, information anxiety, and computer self-efficacy toward cyberchondria. Data from a cross-sectional method using online surveys among fresh graduates aged 21-24 in several Philippine higher education institutions were analyzed. The results of the structural model test reveal that both health anxiety and information anxiety may contribute to cyberchondria. The study discusses the implication of the results and offers fruitful research directions for further studies. © ICCE 2022.All rights reserved.

Epidemiological Modeling with Sir and Arima Dynamic Models and Machine Learning to Predict the Future Behavior of Covid-19 in Panama

Mathematical models SIR and ARIMA were used, within an epidemiological approach, to adjust them to the COVID-19 pandemic data in Panama to establish a scientific criterion for taking decisions for the effects control that this pandemic has brought. Based on the predictions made from the adjustments of these models, it was concluded that they can be adjusted correctly to the data, allowing to make short-term predictions in a satisfactory way, however, if a more accurate model were to be carried out, independent variables could be included, besides time, such as mobility restrictions. This work lays down the foundations for future investigations of epidemiological models in Panama due to its exposition of mathematical model's comparison used to analyze the behavior of the COVID-19 Pandemic. Jupyter Notebook, GitHub, Machine Learning libraries and mathematical software such as Wolfram Mathematica were used. Adjustment of data was performed through statistical techniques and, for this prediction, statistical software Minitab and E-Views were also used. © 2022 IEEE.

Comparing Parallel Surrogate-Based and Surrogate-Free Multi-objective Optimization of COVID-19 Vaccines Allocation

The simulation-based and computationally expensive problem tackled in this paper addresses COVID-19 vaccines allocation in Malaysia. The multi-objective formulation considers simultaneously the total number of deaths, peak hospital occupancy and relaxation of mobility restrictions. Evolutionary algorithms have proven their capability to handle multi-to-many objectives but require a high number of computationally expensive simulations. The available techniques to raise the challenge rely on the joint use of surrogate-assisted optimization and parallel computing to deal with computational expensiveness. On the one hand, the simulation software is imitated by a cheap-to-evaluate surrogate model. On the other hand, multiple candidates are simultaneously assessed via multiple processing cores. In this study, we compare the performance of recently proposed surrogate-free and surrogate-based parallel multi-objective algorithms through the application to the COVID-19 vaccine distribution problem. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Gender equity and spatial variability of covid 19 impacts on mobility behavior: A comparison of two household-travel surveys

The impact of COVID-19 has been enormous, and the situation has been unprecedented. Consequently, most countries around the world impose mobility restrictions on their citizens. Society changes impact the demand for transportation, reducing the number of trips and altering modes of transportation, as well as affecting the price of living. Inequalities between various groups of the Soussian community have been further aggravated by the current health crisis. This is particularly relevant since there is already a gap between genders, and women are the most affected. In this paper, we attempt to identify the hierarchy of the travel patterns of females in the various delegations of the Grand Sousse, analyzing mobility behavior of women. Based on results from two household/travel surveys conducted before and during COVID (in 2019 and 2020), a descriptive analysis was conducted according to four categories: general mobility characteristics, travel rate, mobility characteristics (modal split and reasons for travel) and the trinomial;distance, time, and cost. The results of our analysis are consistent with similar analysis done by other researchers, with significant differences between genders, indicating that women are at greater risk of mobilizing in the 'Grand Sousse'. A large percentage of individuals impacted by this epidemic is females. By applying a detailed spatial reading of mobility characteristics, it was possible to clarify these differences under a significant variation in the daily mobility characteristics of women in favor of the best-equipped delegations. © 2022 IEEE.

Contesting views on mobility restrictions in urban green spaces amid COVID-19-Insights from Twitter in Latin America and Spain.

Positive sentiments towards urban green spaces (UGS) unequivocally increased worldwide amid COVID-19. In contrast, this paper documents that views on mobility restrictions applicable to UGS are of a contested nature. That is, while residents unambiguously report positive sentiments towards UGS, they do not share views on how to administer access to UGS-which is a matter of public policy. These contesting views reflect opposite demands that managers of UGS had to balance during the pandemic as they faced the challenge of reducing risk of spread while providing services that support physical and mental health of residents. The empirical analysis in this paper relies on views inferred through a text classification algorithm implemented on Twitter messages posted from January to October 2020, by urban residents in three Latin American countries-Argentina, Colombia, and Mexico-and Spain. The focus on Latin America is motivated by the documented lack of compliance with mobility restrictions; Spain works as a comparison point to learn differences with respect to other regions. Understanding and following in real-time the evolution of contesting views amid a pandemic is useful for managers and city planners to inform adaptation measures-e.g. communication strategies can be tailored to residents with specific views.

Examining post-pandemic behaviors influencing human mobility trends

COVID-19 unleashed a global pandemic that has resulted in human, economic, and social crises of unprecedented scale. While the efficacy of mobility restrictions in curbing contagion has been scientifically and empirically acknowledged, a deeper understanding of the human behavioral trends driving the mixed adoption of mobility restrictions will aid future policymaking. In this paper, we employ associative rule-mining and regression to pinpoint socioeconomic and demographic factors influencing the evolving mobility trends. We compare and contrast short-distance and long-distance trips by analyzing Chicago county-level and US state-level mobility. Our study yields rules that explain the changing propensity in trip length and the collective effect of population density, economic standing, COVID testing, and the number of infected cases on mobility decisions. Through regression and correlation analysis, we show the influence of ethnic and demographic factors and perception of infection on short and long-distance trips. We find that the new mobility rules correspond to reduced long-And short-distance trip frequencies. We graphically demonstrate a marked decline in the proportion of long county-level trips but a minor change in the distribution of state-level trips. Our correlation study highlights it is hard to characterize the effect of perception of infection spread on mobility decisions. We conclude the paper with a discussion on the overlap between the analysis in the existing literature on both during-And post-lockdown mobility trends and our findings. © 2022 ACM.

Simulating pandemic emergency scenarios considering mobility patterns through an agent-based model

One of the main policies to contain a pandemic spreading is to reduce people mobility. However, it is not easy to predict its actual impact, and this is a limitation for policy-makers who need to act effectively and timely to limit virus spreading. Data are fundamental for monitoring purposes;however, models are needed to predict the impact of different scenarios at a granular scale. Based on this premise, this paper presents the first results of an agent-based model (ABM) able to dynamically simulate a pandemic spreading under mobility restriction scenarios. The model is here used to reproduce the first wave of COVID-19 pandemic in Italy and considers factors that can be attributed to the diffusion and lethality of the virus and population mobility patterns. The model is calibrated with real data (considering the first wave), and it is based on a combination of static and dynamic parameters. First results show the ability of the model to reproduce the pandemic spreading considering the lockdown strategy adopted by the Italian Government and pave the way for scenario analysis of different mobility restrictions. This could be helpful to support policy-making by providing a strategic decision-tool to contrast pandemics. © 2021 Copyright for this paper by its authors.

City-Specific Effects of Lifting Mobility Restrictions - China, February-March 2020.

What is already known about this topic?: Government used mobility restrictions to help contain the first wave of coronavirus disease 2019 (COVID-19) across cities in China. The restrictions were lifted during times of non-zero incidence in response to a return to work order that went into effect on February 10, 2020. What is added by this report?: The effect of lifting mobility restrictions on COVID-19 death rate and incidence varied by city, with smaller increases or even reductions in cities with low community connectivity and small floating volume, and larger increases in cities with high community connectivity and large floating volume. Effects on recovery rates were similar across cities. What are the implications for public health practice?: City-specific mobility restriction lifting is likely to be beneficial. Two indexes, community connectivity and floating volume, can inform the design of city-specific mobility restriction lifting policies.

Effects of COVID-19 Non-Pharmacological Interventions on Dengue Infection: A Systematic Review and Meta-Analysis.

Non-pharmacological interventions (NPIs) implemented during the coronavirus disease 2019 (COVID-19) pandemic have demonstrated significant positive effects on other communicable diseases. Nevertheless, the response for dengue fever has been mixed. To illustrate the real implications of NPIs on dengue transmission and to determine the effective measures for preventing and controlling dengue, we performed a systematic review and meta-analysis of the available global data to summarize the effects comprehensively. We searched Embase, PubMed, and Web of Science in line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines from December 31, 2019, to March 30, 2022, for studies of NPI efficacy on dengue infection. We obtained the annual reported dengue cases from highly dengue-endemic countries in 2015-2021 from the European Centre for Disease Prevention and Control to determine the actual change in dengue cases in 2020 and 2021, respectively. A random-effects estimate of the pooled odds was generated with the Mantel-Haenszel method. Between-study heterogeneity was assessed using the inconsistency index (I2 ) and subgroup analysis according to country (dengue-endemic or non-endemic) was conducted. This review was registered with PROSPERO (CRD42021291487). A total of 17 articles covering 32 countries or regions were included in the review. Meta-analysis estimated a pooled relative risk of 0.39 (95% CI: 0.28-0.55), and subgroup revealed 0.06 (95% CI: 0.02-0.25) and 0.55 (95% CI: 0.44-0.68) in dengue non-endemic areas and dengue-endemic countries, respectively, in 2020. The majority of highly dengue-endemic countries in Asia and Americas reported 0-100% reductions in dengue cases in 2020 compared to previous years, while some countries (4/20) reported a dramatic increase, resulting in an overall increase of 11%. In contrast, there was an obvious reduction in dengue cases in 2021 in almost all countries (18/20) studied, with an overall 40% reduction rate. The overall effectiveness of NPIs on dengue varied with region and time due to multiple factors, but most countries reported significant reductions. Travel-related interventions demonstrated great effectiveness for reducing imported cases of dengue fever. Internal movement restrictions of constantly varying intensity and range are more likely to mitigate the entire level of dengue transmission by reducing the spread of dengue fever between regions within a country, which is useful for developing a more comprehensive and sustainable strategy for preventing and controlling dengue fever in the future.

Dynamic Linear Model for Urban Essential Traffic Congestion and Emissions

Congestion is a phenomenon that impacts most cities in the world. Due to car emissions, it is a significant source of pollution. Even though mobility restrictions can reduce congestion and emissions, essential activities still need cars. With lockdown measures during the global pandemic of Covid-19, measuring essential traffic data has been made possible. This paper concerns analysis and modelling of such essential traffic. It appears that congestion dynamics of essential traffic exhibits dynamics than can be represented with a linear model. This paper introduces such a model and provide a method to jointly estimate the parameters and the model input. The model is validated with data collected in Johannesburg, South Africa. Copyright © 2021 The Authors.

Decomposing the role of mobility restrictions in controlling COVID-19 outcomes: a regional level study of the Philippines

Purpose This paper aims to explore empirically the interactions between the coronavirus disease 2019 (COVID-19) pandemic, economic mobility and containment policy to test the effectiveness of mobility restrictions in controlling the spread of the disease. Design/methodology/approach This study used weekly regional data for the 17 Philippine regions and estimated the effect of shocks using a panel vector autoregression (VAR) model. Findings The authors conclude that COVID-19 deaths and incidence primarily respond to shocks that affect the lethality and transmissibility of the disease, and mobility restrictions and strict quarantine levels do not seem to have any impact on these outcomes. The movement of people during this pandemic period, on the other hand, seems to respond more to economic factors and government restrictions and less to the presence of and the characteristics of the disease. Originality/value Since the pandemic is a public bad, community cooperation is a must to address it. Clear government messaging that dispels doubts on the safety of the newly developed vaccines and that encourages public acceptance and trust might be a better nudge compared to a heavy-handed and threatening approach.

The Grocery Trolley Race in Times of Covid-19: Evidence from Italy

We study the effects of the containment measures imposed by the Italian Government during the first wave of the Covid-19 pandemic on sales of the retail trade sector, focusing on different types of grocery chain stores. We document a sustained growth in revenues for storable products, beginning right before restrictions on mobility were introduced, and lasting throughout the whole lockdown period. The surge has been driven by the dynamics of smaller outlets, located in urban areas and closer to the city centre, while hypermarkets experienced a drop, probably relating to their more peripheral position. Thanks to the remarkable granularity of the Nielsen scanner data and the staggered implementation of restrictions across regions, we causally identify the short-term effects of mobility constraints on outlets’ sales. According to our estimates, large grocery stores in areas subject to lockdown measures earned revenues around 10% lower than their control group did. In view of the protraction of the pandemic and the need for the Government to continue managing the containment of infections, our study helps quantifying the costs for the economy of mobility restrictions, also highlighting possible distortions arising among different categories of outlets.

Predicting the COVID-19 in the Metropolitan Region (Chile) using a GCN-LSTM neural network

COVID-19 is considered one of the largest pandemics in recent times. Predicting the number of future COVID-19 cases is extremely important for governments in order to make decisions about mobility restrictions, and for hospitals to be able to manage medical supplies, as well as health staff. Most of the predictions of COVID-19 cases are based on mathematical-epidemiological models such as the SEIR and SIR models. In our work, we propose a model of neural networks GCN-LSTM (Graph Convolutional Network - Long Short Term Memory) to predict the spatio-temporal rate incidence of COVID-19 in the Metropolitana Region, Chile. While the GCN network incorporates the spatial correlation in the nearby municipalities, the LSTM network considers the temporal correlation for the prediction over time. To interpolate the missing daily data for the network input, the use of the GAM (Generalized Additive Model) model is proposed. The results show better predictions for some municipalities with higher habitat density. © 2021 IEEE.

Heterogeneous impacts of mobility restrictions on air quality in the State of Sao Paulo during the COVID-19 pandemic.

Air quality in the State of Sao Paulo was evaluated during the first general State plan of mobility restrictions due to the COVID-19 pandemic (24th March to May 31, 2020). Nitrogen dioxide (NO2), ozone (O3), particulate matter PM10 and PM2.5 and sulphur dioxide (SO2) concentrations were assessed in cities of the Sao Paulo State with a monitoring station and compared to historical data. Linear regression models were built to investigate the relationship between the isolation of the population - determined using mobile phone monitoring data - and the concentration of each pollutant during the studied period. Although the reduction of pollutants such as NO2, SO2 and PM2.5 is very clear, the economic and climatic characteristics of each region were decisive in the general behaviour of O3 and PM10. It was not possible to establish a correlation between the pollutants and the isolation index, partly due to the lack of data, partly due to the compliance of the population to those measurements, which was variable over time. Another important limitation factor was the absence of data related to the pollutants of interest in many of the stations. However, the isolation measures carried out in the State opened the opportunity to individually assess the air quality measurements in each of the stations, enabling an understanding that will allow in the future the design of air quality policies together with local sanitary policies.

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

Lockdown strictness and mental health effects among older populations in Europe.

This paper investigates whether lockdown policies aggravated mental health problems of older populations (50 and over) in Europe during the first COVID-19 wave. Using data from the Survey of Health, Ageing and Retirement in Europe (SHARE COVID-19 questionnaire) and from the Oxford COVID-19 Government Response Tracker for 17 countries, we estimate the causal effect of lockdown policies on mental health by combining cross-country variability in the strictness of the policies with cross-individual variability in face-to-face contacts prior to the pandemic. We find that lockdown policies worsened insomnia, anxiety, and depression by 5, 7.2 and 5.1 percentage points, respectively. This effect was stronger for women and those aged between 50 and 65. Interestingly, lockdown policies notably damaged the mental health of healthy populations. We close with a discussion of lockdown policies targeted at individuals above 65 and/or with pre-existing conditions.

Impact of the COVID-19 Pandemic on Chronic Disease Care in India, China, Hong Kong, Korea, and Vietnam.

This study aims to provide evidence on how the COVID-19 pandemic has impacted chronic disease care in diverse settings across Asia. Cross-sectional surveys were conducted to assess the health, social, and economic consequences of the pandemic in India, China, Hong Kong, Korea, and Vietnam using standardized questionnaires. Overall, 5672 participants with chronic conditions were recruited from five countries. The mean age of the participants ranged from 55.9 to 69.3 years. A worsened economic status during the COVID-19 pandemic was reported by 19% to 59% of the study participants. Increased difficulty in accessing care was reported by 8% to 24% of participants, except Vietnam: 1.6%. The worsening of diabetes symptoms was reported by 5.6% to 14.6% of participants, except Vietnam: 3%. In multivariable regression analyses, increasing age, female participants, and worsened economic status were suggestive of increased difficulty in access to care, but these associations mostly did not reach statistical significance. In India and China, rural residence, worsened economic status and self-reported hypertension were statistically significantly associated with increased difficulty in access to care or worsening of diabetes symptoms. These findings suggest that the pandemic disproportionately affected marginalized and rural populations in Asia, negatively affecting population health beyond those directly suffering from COVID-19.