Mobility and the effective reproduction rate of COVID-19

The Mediating Role of Human Mobility in Temporal-Lagged Relationships Between Risk Perception and COVID-19 Dynamics in Taiwan: Statistical Modeling for Comparing the Pre-Omicron and Omicron Eras

Background

The COVID-19 pandemic has profoundly impacted all aspects of human life for over 3 years. Understanding the evolution of public risk perception during these periods is crucial. Few studies explore the mechanisms for reducing disease transmission due to risk perception. Thus, we hypothesize that changes in human mobility play a mediating role between risk perception and the progression of the pandemic.

Objective

The study aims to explore how various forms of human mobility, including essential, nonessential, and job-related behaviors, mediate the temporal relationships between risk perception and pandemic dynamics.

Methods

We used distributed-lag linear structural equation models to compare the mediating impact of human mobility across different virus variant periods. These models examined the temporal dynamics and time-lagged effects among risk perception, changes in mobility, and virus transmission in Taiwan, focusing on two distinct periods: (1) April-August 2021 (pre-Omicron era) and (2) February-September 2022 (Omicron era).

Results

In the pre-Omicron era, our findings showed that an increase in public risk perception correlated with significant reductions in COVID-19 cases across various types of mobility within specific time frames. Specifically, we observed a decrease of 5.59 (95% CI -4.35 to -6.83) COVID-19 cases per million individuals after 7 weeks in nonessential mobility, while essential mobility demonstrated a reduction of 10.73 (95% CI -9.6030 to -11.8615) cases after 8 weeks. Additionally, job-related mobility resulted in a decrease of 3.96 (95% CI -3.5039 to -4.4254) cases after 11 weeks. However, during the Omicron era, these effects notably diminished. A reduction of 0.85 (95% CI -1.0046 to -0.6953) cases through nonessential mobility after 10 weeks and a decrease of 0.69 (95% CI -0.7827 to -0.6054) cases through essential mobility after 12 weeks were observed.

Conclusions

This study confirms that changes in mobility serve as a mediating factor between heightened risk perception and pandemic mitigation in both pre-Omicron and Omicron periods. This suggests that elevating risk perception is notably effective in impeding virus progression, especially when vaccines are unavailable or their coverage remains limited. Our findings provide significant value for health authorities in devising policies to address the global threats posed by emerging infectious diseases.

A Modeling Study on the Effect of Interstate Mobility Restrictions on the SARS-CoV-2 Pandemic

Mobility is a crucial element in comprehending the possible expansion of the transmission chain in an epidemic. In the initial phases, strategies for containing cases can be directly linked to population mobility restrictions, especially when only non-pharmaceutical measures are available. During the pandemic of COVID-19 in Brazil, mobility limitation measures were strongly opposed by a large portion of the population. Hypothetically, if the population had supported such measures, the sharp rise in the number of cases could have been suppressed. In this context, computational modeling offers systematic methods for analyzing scenarios about the development of the epidemiological situation taking into account specific conditions. In this study, we examine the impacts of interstate mobility in Brazil. To do so, we develop a metapopulational model that considers both intra and intercompartmental dynamics, utilizing graph theory. We use a parameter estimation technique that allows us to infer the effective reproduction number in each state and estimate the time-varying transmission rate. This makes it possible to investigate scenarios related to mobility and quantify the effect of people moving between states and how certain measures to limit movement might reduce the impact of the pandemic. Our results demonstrate a clear association between the number of cases and mobility, which is heightened when states are closer to each other. This serves as a proof of concept and shows how reducing mobility in more heavily trafficked areas can be more effective.

The day-of-the-week effect is resilient to routine change

Temporal landmarks are salient events that structure the way humans think about time. They may be personal events, such as one's birthday, or shared cultural events, such as the COVID-19 pandemic. Due to societal habits, the cyclical weekly structure - for example, working on weekdays, resting on the weekends - helps individuals orient themselves in time. In the "day-of-the-week effect," individuals are faster at reporting which day of the week it is on weekends than they are on weekdays. Herein, we hypothesized that the disruption of social habits during the COVID-19 pandemic lockdowns may have weakened this effect, thereby accounting for the "Blursday" phenomenon. In the current study, speeded responses to the question "What day of the week is it?" were collected online from 1,742 French participants, during and after the lockdown periods. We found that reaction times for days of the weekends remained faster than for weekdays during the lockdown, although the overall reaction times were significantly slower during lockdown. We also found that responses were slower as governmental stringency rules and restrictions in mobility increased. Our results suggest that the weekend landmark remains a stable temporal anchor in French culture despite the experienced temporal distortions induced by the disruption of social habits during the pandemic. We conclude that cultural temporal landmarks shape socially shared temporal cognitive maps.

The effect of mobility reductions on infection growth is quadratic in many cases

Stay-at-home orders were introduced in many countries during the COVID-19 pandemic, limiting the time people spent outside their home and the attendance of gatherings. In this study, we argue from a theoretical model that in many cases the effect of such stay-at-home orders on incidence growth should be quadratic, and that this statement should also hold beyond COVID-19. That is, a reduction of the out-of-home duration to, say, 70% of its original value should reduce incidence growth and thus the effective R-value to 70 % · 70 % = 49 % of its original value. We then show that this hypothesis can be substantiated from data acquired during the COVID-19 pandemic by using a multiple regression model to fit a combination of the quadratic out-of-home duration and temperature to the COVID-19 growth multiplier. We finally demonstrate that many other models, when brought to the same scale, give similar reductions of the effective R-value, but that none of these models extend plausibly to an out-of-home duration of zero.

A mixture of mobility and meteorological data provides a high correlation with COVID-19 growth in an infection-naive population: a study for Spanish provinces

Introduction

We use Spanish data from August 2020 to March 2021 as a natural experiment to analyze how a standardized measure of COVID-19 growth correlates with asymmetric meteorological and mobility situations in 48 Spanish provinces. The period of time is selected prior to vaccination so that the level of susceptibility was high, and during geographically asymmetric implementation of non-pharmacological interventions.

Methods

We develop reliable aggregated mobility data from different public sources and also compute the average meteorological time series of temperature, dew point, and UV radiance in each Spanish province from satellite data. We perform a dimensionality reduction of the data using principal component analysis and investigate univariate and multivariate correlations of mobility and meteorological data with COVID-19 growth.

Results

We find significant, but generally weak, univariate correlations for weekday aggregated mobility in some, but not all, provinces. On the other hand, principal component analysis shows that the different mobility time series can be properly reduced to three time series. A multivariate time-lagged canonical correlation analysis of the COVID-19 growth rate with these three time series reveals a highly significant correlation, with a median R-squared of 0.65. The univariate correlation between meteorological data and COVID-19 growth is generally not significant, but adding its two main principal components to the mobility multivariate analysis increases correlations significantly, reaching correlation coefficients between 0.6 and 0.98 in all provinces with a median R-squared of 0.85. This result is robust to different approaches in the reduction of dimensionality of the data series.

Discussion

Our results suggest an important effect of mobility on COVID-19 cases growth rate. This effect is generally not observed for meteorological variables, although in some Spanish provinces it can become relevant. The correlation between mobility and growth rate is maximal at a time delay of 2-3 weeks, which agrees well with the expected 5?10 day delays between infection, development of symptoms, and the detection/report of the case.

Predicting Covid-19 pandemic waves with biologically and behaviorally informed universal differential equations

During the COVID-19 pandemic, it became clear that pandemic waves and population responses were locked in a mutual feedback loop in a classic example of a coupled behavior-disease system. We demonstrate for the first time that universal differential equation (UDE) models are able to extract this interplay from data. We develop a UDE model for COVID-19 and test its ability to make predictions of second pandemic waves. We find that UDEs are capable of learning coupled behavior-disease dynamics and predicting second waves in a variety of populations, provided they are supplied with learning biases describing simple assumptions about disease transmission and population response. Though not yet suitable for deployment as a policy-guiding tool, our results demonstrate potential benefits, drawbacks, and useful techniques when applying universal differential equations to coupled systems.

Disparities and effectiveness of COVID-19 vaccine policies in three representative European countries

OBJECTIVE

The aim of this study was to examine the Coronavirus disease 2019(COVID-19) vaccine policies disparities and effectiveness in Germany, Denmark and Bulgaria, with a view to providing lessons for global vaccination and response to possible outbreak risks.

METHODS

This study analyzed big data through public information on the official websites of the Ministries of Health of the European Union, Germany, Denmark and Bulgaria and the official websites of the World Health Organization. We systematically summarized the COVID-19 vaccine policies of the three countries, and selected the following six indicators for cross-cutting vaccination comparisons: COVID-19 vaccine doses administered per 100 people, COVID-19 vaccination rate, the share of people with fully vaccinated, the share of people only partly vaccinated, cumulative confirmed COVID-19 cases per million, cumulative confirmed COVID-19 deaths per million. Meanwhile, we selected the following four indicators for measuring the effectiveness of COVID-19 vaccine policy implementation: daily cases per million, daily deaths per million, the effective reproduction rate (Rt), the moving-average case fatality rate (CFR).

RESULTS

Although these three EU countries had the same start time for vaccination, and the COVID-19 vaccine supply was coordinated by the EU, there are still differences in vaccination priorities, vaccination types, and vaccine appointment methods. Compared to Germany and Denmark, Bulgaria had the least efficient vaccination efforts and the worst vaccination coverage, with a vaccination rate of just over 30% as of June 2023, and the maximum daily deaths per million since vaccination began in the country was more than three times that of the other two countries. From the perspective of implementation effect, vaccination has a certain effect on reducing infection rate and death rate, but the spread of new mutant strains obviously aggravates the severity of the epidemic and reduces the effectiveness of the vaccine. Among them, the spread of the Omicron mutant strain had the most serious impact on the three countries, showing an obvious epidemic peak.

CONCLUSIONS

Expanding vaccination coverage has played a positive role in reducing COVID-19 infection and mortality rates and stabilizing Rt. Priority vaccination strategies targeting older people and at-risk groups have been shown to be effective in reducing COVID-19 case severity and mortality in the population. However, the emergence and spread of new variant strains, and the relaxation of epidemic prevention policies, still led to multiple outbreaks peaking. In addition, vaccine hesitancy, mistrust in government and ill-prepared health systems are hampering vaccination efforts. Among the notable ones are divergent types of responses to vaccine safety issue could fuel mistrust and hesitancy around vaccination. At this stage, it is also necessary to continue to include COVID-19 vaccination in priority vaccination plans and promote booster vaccination to prevent severe illness and death. Improving the fairness of vaccine distribution and reducing the degree of vaccine hesitancy are the focus of future vaccination work.

Investigating the potential of aggregated mobility indices for inferring public transport ridership changes

Aggregated mobility indices (AMIs) derived from information and communications technologies have recently emerged as a new data source for transport planners, with particular value during periods of major disturbances or when other sources of mobility data are scarce. Particularly, indices estimated on the aggregate user concentration in public transport (PT) hubs based on GPS of smartphones, or the number of PT navigation queries in smartphone applications have been used as proxies for the temporal changes in PT aggregate demand levels. Despite the popularity of these indices, it remains largely untested whether they can provide a reasonable characterisation of actual PT ridership changes. This study aims to address this research gap by investigating the reliability of using AMIs for inferring PT ridership changes by offering the first rigorous benchmarking between them and ridership data derived from smart card validations and tickets. For the comparison, we use monthly and daily ridership data from 12 cities worldwide and two AMIs shared globally by Google and Apple during periods of major change in 2020-22. We also explore the complementary role of AMIs on traditional ridership data. The comparative analysis revealed that the index based on human mobility (Google) exhibited a notable alignment with the trends reported by ridership data and performed better than the one based on PT queries (Apple). Our results differ from previous studies by showing that AMIs performed considerably better for similar periods. This finding highlights the huge relevance of dealing with methodological differences in datasets before comparing. Moreover, we demonstrated that AMIs can also complement data from smart card records when ticketing is missing or of doubtful quality. The outcomes of this study are particularly relevant for cities of developing countries, which usually have limited data to analyse their PT ridership, and AMIs may offer an attractive alternative.

The effectiveness of vaccination, testing, and lockdown strategies against COVID-19

The ability of various policy activities to reduce the reproduction rate of the COVID-19 disease is widely discussed. Using a stringency index that comprises a variety of lockdown levels, such as school and workplace closures, we analyze the effectiveness of government restrictions. At the same time, we investigate the capacity of a range of lockdown measures to lower the reproduction rate by considering vaccination rates and testing strategies. By including all three components in an SIR (Susceptible, Infected, Recovery) model, we show that a general and comprehensive test strategy is instrumental in reducing the spread of COVID-19. The empirical study demonstrates that testing and isolation represent a highly effective and preferable approach towards overcoming the pandemic, in particular until vaccination rates have risen to the point of herd immunity.

Comparison of COVID-19 vaccine policies and their effectiveness in Korea, Japan, and Singapore

BACKGROUND

This study aimed to analyze coronavirus disease 2019 (COVID-19)vaccine policies and effectiveness in Korea, Japan, and Singapore, thereby providing empirical experience for vaccination and response to similar public health emergencies.

METHODS

The study systematically summarized the COVID-19 vaccine policies in Korea, Japan, and Singapore through public information from the Our World in Data website and the official websites of the Ministries of Health in these three countries.Total vaccinations, COVID-19 vaccination rates, rates of fully vaccinated, rates of boostervaccinated, and total confifirmed cases were selected for cross-sectional comparison of COVID-19 vaccination in these three countries. Combining the basic characteristics of these three countries, daily cases per million, daily deaths per million, and the effective reproduction rate were calculated to measure the effectiveness of COVID-19 vaccine policies implementation in each of these three countries RESULTS: The countermeasures against the COVID-19 in Korea, Japan, and Singapore, although seemingly different on the surface, have all taken an aggressive approach. There are large similarities in the timing of the start of COVID-19 vaccination, the type of vaccine, how vaccine appointments are made, and whether vaccination are free, and all had high vaccination rates. A systematic comparison of the anti-epidemic practices in the three East Asian countries revealed that all three countries experienced more than one outbreak spike due to the spread of new mutant strains after the start of mass vaccination with COVID-19 vaccination, but that vaccination played a positive role in reducing the number of deaths and stabilizing the effective reproduction rate.

CONCLUSIONS

This study comparatively analyzed the COVID-19 vaccine policies and their effects in South Korea, Japan, and Singapore, and found that there is a common set of logical combinations behind the seemingly different strategies of these three countries. Therefore, in the process of combating COVID-19, countries can learn from the successful experience of combating the epidemic and continue to strengthen the implementation of vaccination programs, as well as adjusting public perceptions to reduce the level of vaccine hesitancy, enhance the motivation for vaccination, and improve the coverage of COVID-19 vaccine based on different cultural factors, which remains the direction for future development.

Multi-scale risk assessment and mitigations comparison for COVID-19 in urban public transport: A combined field measurement and modeling approach

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused an unparalleled disruption to daily life. Given that COVID-19 primarily spreads in densely populated indoor areas, urban public transport (UPT) systems pose significant risks. This study presents an analysis of the air change rate in buses, subways, and high speed trains based on measured CO2 concentrations and passenger behaviors. The resulting values were used as inputs for an infection risk assessment model, which was used to quantitatively evaluate the effects of various factors, including ventilation rates, respiratory activities, and viral variants, on the infection risk. The findings demonstrate that ventilation has a negligible impact on reducing average risks (less than 10.0%) for short-range scales, but can result in a reduction of average risks by 32.1%-57.4% for room scales. When all passengers wear masks, the average risk reduction ranges from 4.5-folds to 7.5-folds. Based on our analysis, the average total reproduction numbers (R) of subways are 1.4-folds higher than buses, and 2-folds higher than high speed trains. Additionally, it is important to note that the Omicron variant may result in a much higher R value, estimated to be approximately 4.9-folds higher than the Delta variant. To reduce disease transmission, it is important to keep the R value below 1. Thus, two indices have been proposed: time-scale based exposure thresholds and spatial-scale based upper limit warnings. Mask wearing provides the greatest protection against infection in the face of long exposure duration to the omicron epidemic.

The impact of containment policy and mobility on COVID-19 cases through structural equation model in Chile, Singapore, South Korea and Israel

Objectives

The study aims to understand the impact of containment policy and mobility on COVID-19 cases in Chile, Singapore, South Korea and Israel. To provide experience in epidemic prevention and control.

Methods

Structural equation modeling (SEM) of containment policies, mobility, and COVID-19 cases were used to test and analyze the proposed hypotheses.

Results

Chile, Israel and Singapore adopted containment strategies, focusing on closure measures. South Korea adopted a mitigation strategy with fewer closure measures, focusing on vaccination and severe case management. There was a significant negative relationship among containment policies, mobility, and COVID-19 cases.

Conclusion

To control the COVID-19 and slow down the increase of COVID-19 cases, countries can increase the stringency of containment policies when COVID-19 epidemic is more severe. Thus, countries can take measures from the following three aspects: strengthen the risk monitoring, and keep abreast of the COVID-19 risk; adjust closure measures in time and reduce mobility; and strengthen public education on COVID-19 prevention to motivate citizen to consciously adhere to preventive measures.

Fine scale human mobility changes within 26 US cities in 2020 in response to the COVID-19 pandemic were associated with distance and income

Human mobility patterns changed greatly due to the COVID-19 pandemic. Despite many analyses investigating general mobility trends, there has been less work characterising changes in mobility on a fine spatial scale and developing frameworks to model these changes. We analyse zip code-level within-city mobility data from 26 US cities between February 2 -August 31, 2020. We use Bayesian models to characterise the initial decrease in mobility and mobility patterns between June-August at this fine spatial scale. There were similar temporal trends across cities but large variations in the magnitude of mobility reductions. Long-distance routes and higher-income subscribers, but not age, were associated with greater mobility reductions. At the city level, mobility rates around early April, when mobility was lowest, and over summer showed little association with non-pharmaceutical interventions or case rates. Changes in mobility patterns lasted until the end of the study period, despite overall numbers of trips recovering to near baseline levels in many cities.

Nonpharmaceutical Interventions in Georgia: Public Health Implications

OBJECTIVES

As coronavirus disease 2019 (COVID-19) spread, many states implemented nonpharmaceutical interventions in the absence of effective therapies with varying degrees of success. Our aim was to evaluate restrictions comparing two regions of Georgia and their impact on outcomes as measured by confirmed illness and deaths.

METHODS

Using The New York Times COVID-19 incidence data and mandate information from various web sites, we examined trends in cases and deaths using joinpoint analysis at the region and county level before and after the implementation of a mandate.

RESULTS

We found that rates of cases and deaths showed the greatest decrease in acceleration after the simultaneous implementation of a statewide shelter-in-place for vulnerable populations combined with social distancing for businesses and limiting gatherings to <10 people. County-level shelters-in-place, business closures, limits on gatherings to <10, and mask mandates showed significant case rate decreases after a county implemented them. School closures had no consistent effect on either outcome.

CONCLUSIONS

Our findings indicate that protecting vulnerable populations, implementing social distancing, and mandating masks may be effective countermeasures to containment while mitigating the economic and psychosocial effects of strict shelters-in-place and business closures. In addition, states should consider allowing local municipalities the flexibility to enact nonpharmaceutical interventions that are more or less restrictive than the state-level mandates under some conditions in which the data indicate it is necessary to protect communities from disease or undue economic burden.

Ten Takeaways from the COVID-19 Pandemic for Transportation Planners

The COVID-19 pandemic has created significant challenges but also unprecedented opportunities for transportation researchers and practitioners. In this article, the major lessons and gaps in knowledge for those working in the transportation sector are identified, including the following: (1) integration between public health and transportation; (2) technology to support contact tracing and tracking of travelers; (3) focus on vulnerable, at-risk operators, patrons, and underserved members of society; (4) re-engineering of travel demand models to support social distancing, quarantine, and public health interventions; (5) challenges with Big Data and information technologies; (6) trust relationships between the general public, government, private sector, and others in disaster management; (7) conflict management during disasters; (8) complexities of transdisciplinary knowledge and engagement; (9) demands for training and education; and (10) transformative change to support community resilience. With a focus on transportation planning and community resilience, the lessons from the pandemic need to be shared and customized for different systems, services, modalities, and users. While many of the interventions during the pandemic have been based on public health, the management, response, recovery, adaptation, and transformation of transportation systems resulting from the crisis require multi-disciplinary, multi-jurisdictional communications and coordination, and resource sharing. Further research to support knowledge to action is needed.

The Effectiveness of Mobility Restrictions on Controlling the Spread of COVID-19 in a Resistant Population

Human mobility plays an important role in the spread of COVID-19. Given this knowledge, countries implemented mobility-restricting policies. Concomitantly, as the pandemic progressed, population resistance to the virus increased via natural immunity and vaccination. We address the question: "What is the impact of mobility-restricting measures on a resistant population?" We consider two factors: different types of points of interest (POIs)-including transit stations, groceries and pharmacies, retail and recreation, workplaces, and parks-and the emergence of the Delta variant. We studied a group of 14 countries and estimated COVID-19 transmission based on the type of POI, the fraction of population resistance, and the presence of the Delta variant using a Pearson correlation between mobility and the growth rate of cases. We find that retail and recreation venues, transit stations, and workplaces are the POIs that benefit the most from mobility restrictions, mainly if the fraction of the population with resistance is below 25-30%. Groceries and pharmacies may benefit from mobility restrictions when the population resistance fraction is low, whereas in parks, there is little advantage to mobility-restricting measures. These results are consistent for both the original strain and the Delta variant; Omicron data were not included in this work.

A decade of child pedestrian safety in England: a bayesian spatio-temporal analysis

BACKGROUND

Child pedestrian injury is a public health and health equality challenge worldwide, including in high-income countries. However, child pedestrian safety is less-understood, especially over long time spans. The intent of this study is to understand factors affecting child pedestrian safety in England over the period 2011-2020.

METHODS

We conducted an area-level study using a Bayesian space-time interaction model to understand the association between the number of road crashes involving child pedestrians in English Local Authorities and a host of socio-economic, transport-related and built-environment variables. We investigated spatio-temporal trends in child pedestrian safety in England over the study period and identified high-crash local authorities.

RESULTS

We found that child pedestrian crash frequencies increase as child population, unemployment-related claimants, road density, and the number of schools increase. Nevertheless, as the number of licensed vehicles per capita and zonal-level walking/cycling increase, child pedestrian safety increases. Generally, child pedestrian safety has improved in England since 2011. However, the socio-economic inequality gap in child pedestrian safety has not narrowed down. In addition, we found that after adjusting for the effect of covariates, the rate of decline in crashes varies between local authorities. The presence of localised risk factors/mitigation measures contributes to variation in the spatio-temporal patterns of child pedestrian safety.

CONCLUSIONS

Overall, southern England has experienced more improvement in child pedestrian safety over the last decade than the northern regions. Our study revealed socio-economic inequality in child pedestrian safety in England. To better inform safety and public health policy, our findings support the importance of a targeted system approach, considering the identification of high-crash areas while keeping track of how child pedestrian safety evolves over time.

Passenger flow analysis and emergency response simulation in a metro network using virus transmission model

OBJECTIVES

The potential virus in transportation facilities poses a serious risk to travelers. This research focus on the commuting by metro on the risk of the coronavirus disease 2019 (COVID-19). The main purpose is to explore the trajectory of virus transmission and the effectiveness of various control measures.

METHODS

A transmission model was established on the basis of the susceptible-infected-recovered (SIR) model, combined with the spatial and temporal characteristics of the metro passenger flow. The implementation effects of the emergency strategies were analyzed through a series of simulation experiments. The changes in passenger flow affected by the virus transmission were analyzed both under the single intervention condition of the disinfection or off-peak travel policy and their double interventions.

RESULTS

The results of the experiments show that disinfection and off-peak travel can effectively reduce the number of the infected people. To promote the disinfection is better than the off-peak travel strategy. The optimal solution is the combination of these two strategies, thereby reducing the infection rate in the stations effectively. In particular, it can reduce the number of potential infected people in high-traffic stations by 50%.

CONCLUSIONS

This study provides a scientific basis for the prevention of COVID-19 in the urban transportation system and the formulation of public emergency strategies. It can also be applied to other epidemic diseases such as the seasonal flu, for public health prevention.

Usefulness of open data to determine the incidence of COVID-19 and its relationship with atmospheric variables in Spain during the 2020 lockdown

The SARS-CoV-2 pandemic and the spread of the COVID-19 disease led to a lockdown being imposed in Spain to minimise contagion from 16 March 2020 to 1 May 2020. Over this period, measures were taken to reduce population mobility (a key factor in disease transmission). The scenario thus created enabled us to examine the impact of factors other than mobility (in this case, meteorological conditions) on the incidence of the disease, and thus to identify which environmental variables played the biggest role in the pandemic's evolution. Worthy of note, the data required to perform the study was entirely extracted from governmental open data sources. The present work therefore demonstrates the utility of such data to conduct scientific research of interest to society, leading to studies that are also fully reproducible. The results revealed a relationship between temperatures and the spread of COVID-19. The trend was that of a slightly lower disease incidence as the minimum temperature rises, i.e. the lower the minimum temperature, the greater the number of cases. Furthermore, a link was found between the incidence of the disease and other variables, such as altitude and proximity to the sea. There were no indications, however, in the study's data, of a relationship between incidence and precipitation or wind.

Modeling mobility, risk, and pandemic severity during the first year of COVID

During the COVID-19 pandemic, most US states have taken measures of varying strength, enforcing social and physical distancing in the interest of public safety. These measures have enabled counties and states, with varying success, to slow down the propagation and mortality of the disease by matching the propagation rate to the capacity of medical facilities. However, each state's government was making its decisions based on limited information and without the benefit of being able to look retrospectively at the problem at large and to analyze the commonalities and the differences among the states and the counties across the country. We developed models connecting people's mobility, socioeconomic, and demographic factors with severity of the COVID pandemic in the US at the County level. These models can be used to inform policymakers and other stakeholders on measures to be taken during a pandemic. They also enable in-depth analysis of factors affecting the relationship between mobility and the severity of the disease. With the exception of one model, that of COVID recovery time, the resulting models accurately predict the vulnerability and severity metrics and rank the explanatory variables in the order of statistical importance. We also analyze and explain why recovery time did not allow for a good model.

Do the determinants of COVID-19 transmission differ by epidemic wave? Evidence from U.S. counties

This paper uses data from the United States to examine determinants of the spread of COVID-19 during three different epidemic waves. We address how sociodemographic and economic attributes, industry composition, density, crowding in housing, and COVID-19-related variables are associated with the transmission of COVID-19. After controlling for spatial autocorrelation, our findings indicate that the percentage of people in poverty, number of restaurants, and percentage of workers teleworking were associated with the COVID-19 incidence rate during all three waves. Our results also show that dense areas were more vulnerable to the transmission of COVID-19 after the first epidemic wave. Regarding the density of supermarkets, our study elaborates the negative aspects of wholesale retail stores, which likely provide a vulnerable place for virus transmission. Our results suggest that sociodemographic and economic attributes were the determinants of the early phase of the pandemic, while density showed positive association with the transmission during subsequent waves. We provide implications for regions serving as gateway cities with high density and number of population. To add, we further provide evidence that non-pharmaceutical interventions in the early stage may mitigate the virus transmission.

Fine scale human mobility changes in 26 US cities in 2020 in response to the COVID-19 pandemic were associated with distance and income

Human mobility patterns changed greatly due to the COVID-19 pandemic. Despite many analyses investigating general mobility trends, there has been less work characterising changes in mobility on a fine spatial scale and developing frameworks to model these changes. We analyse zip code-level mobility data from 26 US cities between February 2 â€" August 31, 2020. We use Bayesian models to characterise the initial decrease in mobility and mobility patterns between June - August at this fine spatial scale. There were similar temporal trends across cities but large variations in the magnitude of mobility reductions. Long-distance routes and higher-income subscribers, but not age, were associated with greater mobility reductions. At the city level, mobility rates around early April, when mobility was lowest, and over summer showed little association with non-pharmaceutical interventions or case rates. Changes in mobility patterns lasted until the end of the study period, despite overall numbers of trips recovering to near baseline levels in many cities.

Determinants of voluntary compliance: COVID-19 mitigation

During the pre-vaccine period, the success of containing the spread of COVID-19 depends upon how communities respond to non-pharmaceutical mitigation policies such as social distancing, wearing of masks, retail and dining constraints, crowd limitation, and shelter-in-place orders. Of these policies, shelter-in-place and social distancing are of central importance. By using county-level mobility data as a measure of a community's voluntary compliance with social distancing policies, this study found that counties who received strong state social distancing policy directives and who had a high pro-social character showed lower mobility on retail and recreation mobility and grocery and pharmacy mobility (better social distancing) after states reopened from shelter-in-place orders. Counties that experienced a longer duration of shelter-in-place orders showed higher mobility (less social distancing), implying that the duration of the shelter-in-place order deteriorated social distancing response after reopening. This may be because reopening sent a "safe" signal to these counties or resulted in a response to the pent-up demand inducing higher mobility. The results indicate that implementing shelter-in-place and social distancing policies to slow down the transmission of COVID-19 were not necessarily effective in motivating a county to reduce mobility voluntarily. A county's pro-social character and the duration of shelter-in-place order should be considered when designing COVID-19 mitigation policies.

COVID-19 forecasting using new viral variants and vaccination effectiveness models

Recently, a high number of daily positive COVID-19 cases have been reported in regions with relatively high vaccination rates; hence, booster vaccination has become necessary. In addition, infections caused by the different variants and correlated factors have not been discussed in depth. With large variabilities and different co-factors, it is difficult to use conventional mathematical models to forecast the incidence of COVID-19. Machine learning based on long short-term memory was applied to forecasting the time series of new daily positive cases (DPC), serious cases, hospitalized cases, and deaths. Data acquired from regions with high rates of vaccination, such as Israel, were blended with the current data of other regions in Japan such that the effect of vaccination was considered in efficient manner. The protection provided by symptomatic infection was also considered in terms of the population effectiveness of vaccination as well as the vaccination protection waning effect and ratio and infectivity of different viral variants. To represent changes in public behavior, public mobility and interactions through social media were also included in the analysis. Comparing the observed and estimated new DPC in Tel Aviv, Israel, the parameters characterizing vaccination effectiveness and the waning protection from infection were well estimated; the vaccination effectiveness of the second dose after 5 months and the third dose after two weeks from infection by the delta variant were 0.24 and 0.95, respectively. Using the extracted parameters regarding vaccination effectiveness, DPC in three major prefectures of Japan were replicated. The key factor influencing the prevention of COVID-19 transmission is the vaccination effectiveness at the population level, which considers the waning protection from vaccination rather than the percentage of fully vaccinated people. The threshold of the efficiency at the population level was estimated as 0.3 in Tel Aviv and 0.4 in Tokyo, Osaka, and Aichi. Moreover, a weighting scheme associated with infectivity results in more accurate forecasting by the infectivity model of viral variants. Results indicate that vaccination effectiveness and infectivity of viral variants are important factors in future forecasting of DPC. Moreover, this study demonstrate a feasible way to project the effect of vaccination using data obtained from other country.

Comparison of COVID-19 Vaccine Policies in Italy, India, and South Africa

(1) Purpose: This study aimed to analyze coronavirus disease 2019 (COVID-19) vaccine policies and their effectiveness in Italy, India, and South Africa to provide empirical experience for vaccination and COVID-19 pandemic control. (2) Methods: The study systematically summarized the COVID-19 vaccine policies in Italy, India, and South Africa through public information available on the official websites of the World Health Organization and the ministries of health in these three countries. Total vaccinations, COVID-19 vaccination rates, rates of fully vaccinated, rates of booster-vaccinated, and total confirmed cases were selected for cross-sectional comparison of COVID-19 vaccination in these three countries. Daily cases per million, daily deaths per million, and the effective reproduction rate were calculated to measure the effectiveness of COVID-19 vaccine policies implementation in each of these three countries. (3) Results: Italy, India, and South Africa differ in the start date of COVID-19 vaccination, vaccine types, vaccine appointments, and whether vaccinations are free. The COVID-19 vaccination rates in these three countries varied widely, with Italy having the highest and South Africa the lowest. COVID-19 vaccination has had a positive effect on reducing daily deaths and stabilizing the effective reproduction rate. The three countries had experienced more than one outbreak spike due to the spread of new mutated strains since the start of COVID-19 vaccination. (4) Conclusions: This study concluded that responding to the COVID-19 pandemic requires active promotion of basic and booster vaccinations to comprehensively build up the population immune barrier. Promoting equitable distribution of COVID-19 vaccine internationally and solidarity and cooperation among countries maximizes global common interests. By combining vaccination with non-pharmaceutical interventions, the pandemic can be prevented and controlled comprehensively and systematically in three aspects: detection of the source of infection, reduction of transmission routes, and protection of susceptible populations.

Simulating the effects of mobility restrictions in the spread of SARS-CoV-2 in metropolitan areas in Portugal

Commuting flows and long-distance travel are important spreading factors of viruses and particularly airborne ones. Therefore, it is relevant to examine the association among diverse mobility scenarios and the spatial dissemination of SARS-CoV-2 cases. We intended to analyze the patterns of virus spreading linked to different mobility scenarios, in order to better comprehend the effect of the lockdown measures, and how such measures can be better informed. We simulated the effects of mobility restrictions in the spread of SARS-CoV-2 amongst the municipalities of two metropolitan areas, Lisbon (LMA) and Porto (PMA). Based on an adapted SEIR (Suscetible-Exposed-Infected-Removed) model, we estimated the number of new daily infections during one year, according to different mobility scenarios: restricted to essential activities, industrial activities, public transport use, and a scenario with unrestricted mobility including all transport modes. The trends of new daily infections were further explored using time-series clustering analysis, using dynamic time warping. Mobility restrictions resulted in lower numbers of new daily infections when compared to the unrestricted mobility scenario, in both metropolitan areas. Between March and September 2020, the official number of new infections followed overall a similar timeline to the one simulated considering only essential activities. At the municipal level, trends differ amongst the two metropolitan areas. The analysis of the effects of mobility in virus spread within different municipalities and regions could help tailoring future strategies and increase the public acceptance of eventual restrictions.

Effects of tourism promotion on COVID-19 spread: The case of the "Go To Travel" campaign in Japan

Introduction

On July 22, 2020, the Japanese government launched the "Go to Travel" campaign that subsidizes 50% of personal travel expenditure to support the tourism industry under the COVID-19 pandemic. This policy was controversial from the viewpoint of infection spread and was temporarily cancelled in December 2020, though there was no statistical evidence.

Methods

This is the first study that measures the extent to which this campaign increased COVID-19 cases. This study regards the campaign as a natural experiment: although Tokyo and its commuting areas experienced the same time-series trends of COVID-19 cases before the "Go To Travel" campaign, this campaign was implemented in areas outside Tokyo, but not in Tokyo. Then, the comparison (difference-in-differences) yields the campaign's effect.

Results

The estimation shows that the "Go To Travel" campaign significantly raised the increment rate of cases by 23.7%-34.4% during July 30-August 4. There is no significant effect after August 5. In addition, our simulation identified the number of campaign-related cases in each city.

Conclusions

Although the campaign significantly spread COVID-19, the effect was not continuous to permanently change the time-series trend.

Determinants of voluntary compliance: COVID-19 mitigation

During the pre-vaccine period, the success of containing the spread of COVID-19 depends upon how communities respond to non-pharmaceutical mitigation policies such as social distancing, wearing of masks, retail and dining constraints, crowd limitation, and shelter-in-place orders. Of these policies, shelter-in-place and social distancing are of central importance. By using county-level mobility data as a measure of a community's voluntary compliance with social distancing policies, this study found that counties who received strong state social distancing policy directives and who had a high pro-social character showed lower mobility on retail and recreation mobility and grocery and pharmacy mobility (better social distancing) after states reopened from shelter-in-place orders. Counties that experienced a longer duration of shelter-in-place orders showed higher mobility (less social distancing), implying that the duration of the shelter-in-place order deteriorated social distancing response after reopening. This may be because reopening sent a "safe" signal to these counties or resulted in a response to the pent-up demand inducing higher mobility. The results indicate that implementing shelter-in-place and social distancing policies to slow down the transmission of COVID-19 were not necessarily effective in motivating a county to reduce mobility voluntarily. A county's pro-social character and the duration of shelter-in-place order should be considered when designing COVID-19 mitigation policies.

Examining the COVID-19 case growth rate due to visitor vs. local mobility in the United States using machine learning

Travel patterns and mobility affect the spread of infectious diseases like COVID-19. However, we do not know to what extent local vs. visitor mobility affects the growth in the number of cases. This study evaluates the impact of state-level local vs. visitor mobility in understanding the growth with respect to the number of cases for COVID spread in the United States between March 1, 2020, and December 31, 2020. Two metrics, namely local and visitor transmission risk, were extracted from mobility data to capture the transmission potential of COVID-19 through mobility. A combination of the three factors: the current number of cases, local transmission risk, and the visitor transmission risk, are used to model the future number of cases using various machine learning models. The factors that contribute to better forecast performance are the ones that impact the number of cases. The statistical significance of the forecasts is also evaluated using the Diebold-Mariano test. Finally, the performance of models is compared for three waves across all 50 states. The results show that visitor mobility significantly impacts the case growth by improving the prediction accuracy by 33.78%. We also observe that the impact of visitor mobility is more pronounced during the first peak, i.e., March-June 2020.

Browsing for food: Will COVID‐induced online grocery delivery persist?

The COVID‐19 pandemic altered daily activities. Many consumers reverted to online grocery shopping and home delivery. We analyze factors associated with the decision to grocery shop online and whether this will persist post‐COVID using data collected via a representative online Qualtrics panel in the State of New Jersey (N = 1,419). Around half of respondents either decreased in‐person shopping, increased online shopping, or pursued a combination of both. We used factor analysis to decompose attitudes towards the pandemic, finding that attitudinal responses broke down into ‘fearful’, ‘believers’, and ‘deniers’. Binomial regressions were used to analyze patterns of frequency of grocery shopping during the pandemic and changes in behavior during the pandemic. Results suggest that age, gender, ethnicity, educational attainment, having children at home, and attitudes towards COVID‐19 are likely to influence frequency of online and in‐person grocery shopping. Specifically, being 50 years or older is negatively associated with online grocery shopping. Those who deny COVID‐19 were less likely to decrease in‐person grocery shopping. People who had children at home, who had advanced degrees, or who were of Hispanic origin were more likely to increase online shopping and decrease in‐person shopping during the pandemic. While our results suggest that in‐person grocery shopping will return to prepandemic levels, we found that respondents report some increased persistence in online grocery shopping post‐COVID.

Novel mobility index tracks COVID-19 transmission following stay-at-home orders

Considering the emergence of SARS-CoV-2 variants and low vaccine access and uptake, minimizing human interactions remains an effective strategy to mitigate the spread of SARS-CoV-2. Using a functional principal component analysis, we created a multidimensional mobility index (MI) using six metrics compiled by SafeGraph from all counties in Illinois, Ohio, Michigan and Indiana between January 1 to December 8, 2020. Changes in mobility were defined as a time-updated 7-day rolling average. Associations between our MI and COVID-19 cases were estimated using a quasi-Poisson hierarchical generalized additive model adjusted for population density and the COVID-19 Community Vulnerability Index. Individual mobility metrics varied significantly by counties and by calendar time. More than 50% of the variability in the data was explained by the first principal component by each state, indicating good dimension reduction. While an individual metric of mobility was not associated with surges of COVID-19, our MI was independently associated with COVID-19 cases in all four states given varying time-lags. Following the expiration of stay-at-home orders, a single metric of mobility was not sensitive enough to capture the complexity of human interactions. Monitoring mobility can be an important public health tool, however, it should be modelled as a multidimensional construct.

COVID-19 and suburban public transport in the conditions of the Czech Republic

This article focuses on possible approaches to safe regional public transport during the COVID-19 pandemic. The purposes of the research are examination the conditions for ensuring safe transport and the impact on the planning of transport services. The result is an assessment of the operation of regional public transport, consisting of the possibility of maintaining safe distances in public transport. Authors work on suburban transport cases in selected regions of the Czech Republic (Prague and Moravian-Silesian Region). Census devices in public transport, periodical transport surveys, Google mobility reports and data on fare sales from regional transport were used as data sources. Emphasis is placed on a safe distance between commuters, this condition leads to lower occupancy of the vehicle while maintaining the capacity of the vehicles. The value of this new occupancy is determined for selected vehicles and the coefficient that represents the maximum occupancy level to ensure safe transport is established. The capacity of the connections is examined in the period before and during the COVID-19 pandemic. Compared to the period before COVID-19, the daily variation of passengers is expected to change significantly, leading to different occupancy rates during the day.

The lockdown, mobility, and spatial health disparities in COVID-19 pandemic: A case study of New York City

The world has adopted unprecedented lockdown as the key method to mitigate COVID-19; yet its effect on pandemic outcomes and health disparities remains largely unknown. Adopting a multilevel conceptual framework, this research investigates how city-level lockdown policy and public transit system shape mobility and thus intra-city health disparities, using New York City as a case study. With a spatial method and multiple sources of data, this research demonstrates the uneven impact of the lockdown policy and public transit system in shaping local pandemic outcomes. Census tracts with people spending more time at home have lower infection and death rates, while those with a higher density of transit stations have higher infection and death rates. Residential profile matters and census tracts with a higher concentration of disadvantaged population, such as Blacks, Hispanics, poor and elderly people, and people with no health insurance, have higher infection and death rates. Spatial analyses identify clusters where the lockdown policy was not effective and census tracts that share similar pandemic characteristics. Through the lens of mobility, this research advances knowledge of health disparities by focusing on institutional causes for health disparities and the role of the government through intervention policy and public transit system.

Estimating the effect of mobility on SARS-CoV-2 transmission during the first and second wave of the COVID-19 epidemic, Switzerland, March to December 2020

IntroductionHuman mobility was considerably reduced during the COVID-19 pandemic. To support disease surveillance, it is important to understand the effect of mobility on transmission.AimWe compared the role of mobility during the first and second COVID-19 wave in Switzerland by studying the link between daily travel distances and the effective reproduction number (Rt) of SARS-CoV-2.MethodsWe used aggregated mobile phone data from a representative panel survey of the Swiss population to measure human mobility. We estimated the effects of reductions in daily travel distance on Rt via a regression model. We compared mobility effects between the first (2 March-7 April 2020) and second wave (1 October-10 December 2020).ResultsDaily travel distances decreased by 73% in the first and by 44% in the second wave (relative to February 2020). For a 1% reduction in average daily travel distance, Rt was estimated to decline by 0.73% (95% credible interval (CrI): 0.34-1.03) in the first wave and by 1.04% (95% CrI: 0.66-1.42) in the second wave. The estimated mobility effects were similar in both waves for all modes of transport, travel purposes and sociodemographic subgroups but differed for movement radius.ConclusionMobility was associated with SARS-CoV-2 Rt during the first two epidemic waves in Switzerland. The relative effect of mobility was similar in both waves, but smaller mobility reductions in the second wave corresponded to smaller overall reductions in Rt. Mobility data from mobile phones have a continued potential to support real-time surveillance of COVID-19.

In-Depth Correlation Analysis of SARS-CoV-2 Effective Reproduction Number and Mobility Patterns: Three Groups of Countries

Objectives

Many governments have imposed—and are still imposing—mobility restrictions to contain the coronavirus disease 2019 (COVID-19) pandemic. However, there is no consensus on whether policy-induced reductions of human mobility effectively reduce the effective reproduction number (Rt) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several studies based on country-restricted data reported conflicting trends in the change of the SARS-CoV-2 Rt following mobility restrictions. The objective of this study was to examine, at the global scale, the existence of regional specificities in the correlations between Rt and human mobility.

Methods

We computed the Rt of SARS-CoV-2 using data on worldwide infection cases reported by the Johns Hopkins University, and analyzed the correlation between Rt and mobility indicators from the Google COVID-19 Community Mobility Reports in 125 countries, as well as states/regions within the United States, using the Pearson correlation test, linear modeling, and quadratic modeling.

Results

The correlation analysis identified countries where Rt negatively correlated with residential mobility, as expected by policymakers, but also countries where Rt positively correlated with residential mobility and countries with more complex correlation patterns. The correlations between Rt and residential mobility were non-linear in many countries, indicating an optimal level above which increasing residential mobility is counterproductive.

Conclusions

Our results indicate that, in order to effectively reduce viral circulation, mobility restriction measures must be tailored by region, considering local cultural determinants and social behaviors. We believe that our results have the potential to guide differential refinement of mobility restriction policies at a country/regional resolution.

Mobility as a driver of severe acute respiratory syndrome coronavirus 2 in cancer patients during the second coronavirus disease 2019 pandemic wave

We retrospectively analyzed the epidemiological characteristics of cancer patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and their correlations with publicly available mobility data. Between 19 October 2020 and 28 February 2021, 4754 patient visits were carried out, and 1454 treatments have been applied at the Haemato-Oncology Day Hospital Merano. Additional measures to prevent local SARS-CoV-2 transmission included a specific questionnaire for coronavirus disease 2019 (COVID-19) symptoms as well as a SARS-CoV-2 real-time polymerase-chain reaction (RT-PCR) 2 days prior to any intravenous or subcutaneous therapy. Community mobility was assessed through publicly available mobile phone tracking data from Google; 106/719 (14.7%) cancer patients have been tested positive for SARS-CoV-2 by PCR during the second wave compared to 5/640 (0.8%) within the first wave (P < .001); 66/106 (62%) had solid tumors, and 40/106 (38%) had hematological malignancies; 90/106 (85%) patients received ongoing antitumor therapies. Mortality rate of COVID-19 positive cancer patients (7/106; 6.6%) was higher compared to the overall population (731/46 421; 1.6%; P < .001). Strict control measures at our department led to a significantly lower test positivity rate compared to the general population, resulting in a reduction of 58.5% of new SARS-CoV-2 cases. Over time, infection rates and community mobility correlated in the first and second wave after initiating and lifting restrictions. Our findings underscore the importance of strict preventive control measures including testing and contact tracing in vulnerable subpopulations such as cancer patients, particularly if social restriction policies are being lifted. Smartphone-based mobility data may help to guide policy makers to prevent a vulnerable population like cancer patients from virus transmission.

Percolation of temporal hierarchical mobility networks during COVID-19

Percolation theory is essential for understanding disease transmission patterns on the temporal mobility networks. However, the traditional approach of the percolation process can be inefficient when analysing a large-scale, dynamic network for an extended period. Not only is it time-consuming but it is also hard to identify the connected components. Recent studies demonstrate that spatial containers restrict mobility behaviour, described by a hierarchical topology of mobility networks. Here, we leverage crowd-sourced, large-scale human mobility data to construct temporal hierarchical networks composed of over 175 000 block groups in the USA. Each daily network contains mobility between block groups within a Metropolitan Statistical Area (MSA), and long-distance travels across the MSAs. We examine percolation on both levels and demonstrate the changes of network metrics and the connected components under the influence of COVID-19. The research reveals the presence of functional subunits even with high thresholds of mobility. Finally, we locate a set of recurrent critical links that divide components resulting in the separation of core MSAs. Our findings provide novel insights into understanding the dynamical community structure of mobility networks during disruptions and could contribute to more effective infectious disease control at multiple scales. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

Associations between mobility and socio-economic indicators vary across the timeline of the Covid-19 pandemic

Covid-19 interventions are greatly affecting patterns of human mobility. Changes in mobility during Covid-19 have differed across socio-economic gradients during the first wave. We use fine-scale network mobility data in Ontario, Canada to study the association between three different mobility measures and four socio-economic indicators throughout the first and second wave of Covid-19 (January to December 2020). We find strong associations between mobility and the socio-economic indicators and that relationships between mobility and other socio-economic indicators vary over time. We further demonstrate that understanding how mobility has changed in response to Covid-19 varies considerably depending on how mobility is measured. Our findings have important implications for understanding how mobility data should be used to study interventions across space and time. Our results support that Covid-19 non-pharmaceutical interventions have resulted in geographically disparate responses to mobility and quantifying mobility changes at fine geographical scales is crucial to understanding the impacts of Covid-19.

Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis

Dense urban areas are especially hardly hit by the Covid-19 crisis due to the limited availability of public transport, one of the most efficient means of mass mobility. In light of the Covid-19 pandemic, public transport operators are experiencing steep declines in demand and fare revenues due to the perceived risk of infection within vehicles and other facilities. The purpose of this paper is to explore the possibilities of implementing social distancing in public transport in line with epidemiological advice. Social distancing requires effective demand management to keep vehicle occupancy rates under a predefined threshold, both spatially and temporally. We review the literature of five demand management methods enabled by new information and ticketing technologies: (i) inflow control with queueing, (ii) time and space dependent pricing, (iii) capacity reservation with advance booking, (iv) slot auctioning, and (v) tradeable travel permit schemes. Thus the paper collects the relevant literature into a single point of reference, and provides interpretation from the viewpoint of practical applicability during and after the pandemic.

Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection

At the beginning of 2022 the global daily count of new cases of COVID-19 exceeded 3.2 million, a tripling of the historical peak value reported between the initial outbreak of the pandemic and the end of 2021. Aerosol transmission through interpersonal contact is the main cause of the disease's spread, although control measures have been put in place to reduce contact opportunities. Mobility pattern is a basic mechanism for understanding how people gather at a location and how long they stay there. Due to the inherent dependencies in disease transmission, models for associating mobility data with confirmed cases need to be individually designed for different regions and time periods. In this paper, we propose an autoregressive count data model under the framework of a generalized linear model to illustrate a process of model specification and selection. By evaluating a 14-day-ahead prediction from Sweden, the results showed that for a dense population region, using mobility data with a lag of 8 days is the most reliable way of predicting the number of confirmed cases in relative numbers at a high coverage rate. It is sufficient for both of the autoregressive terms, studied variable and conditional expectation, to take one day back. For sparsely populated regions, a lag of 10 days produced the lowest error in absolute value for the predictions, where weekly periodicity on the studied variable is recommended for use. Interventions were further included to identify the most relevant mobility categories. Statistical features were also presented to verify the model assumptions.

Examining spatiotemporal evolution of racial/ethnic disparities in human mobility and COVID-19 health outcomes: Evidence from the contiguous United States

Social distancing has become a key countermeasure to contain the dissemination of COVID-19. This study examined county-level racial/ethnic disparities in human mobility and COVID-19 health outcomes during the year 2020 by leveraging geo-tracking data across the contiguous US. Sets of generalized additive models were fitted under cross-sectional and time-varying settings, with percentage of mobility change, percentage of staying home, COVID-19 infection rate, and case-fatality ratio as dependent variables, respectively. After adjusting for spatial effects, built environment, socioeconomics, demographics, and partisanship, we found counties with higher Asian populations decreased most in travel, counties with higher White and Asian populations experienced the least infection rate, and counties with higher African American populations presented the highest case-fatality ratio. Control variables, particularly partisanship and education attainment, significantly influenced modeling results. Time-varying analyses further suggested racial differences in human mobility varied dramatically at the beginning but remained stable during the pandemic, while racial differences in COVID-19 outcomes broadly decreased over time. All conclusions hold robust with different aggregation units or model specifications. Altogether, our analyses shine a spotlight on the entrenched racial segregation in the US as well as how it may influence the mobility patterns, urban forms, and health disparities during the COVID-19.

Parks and the Pandemic: A Scoping Review of Research on Green Infrastructure Use and Health Outcomes during COVID-19

Green infrastructure (GI) has long been known to impact human health, and many academics have used past research to argue for the potential importance of GI as a mechanism for maintaining or improving health within the context of the COVID-19 pandemic. This scoping review addresses the question: What evidence, if any, have researchers found of a relationship between green infrastructure use and health during the COVID-19 pandemic? Specifically, evaluating the (a) association of GI use with COVID-19 disease outcomes and (b) association of GI use with other health outcomes as impacted by the COVID-19 pandemic. Twenty-two studies were identified that measured GI use and studied it in relation to health outcomes during the pandemic. The studies were reviewed for the specific measures and types of GI use, level of analysis, specific types of health outcomes, and the conclusions reached with regard to GI use and health. Studies exploring COVID-19-specific health outcomes showed mixed results, while non-COVID health outcomes were more consistently improved through GI use, particularly with regard to improved mental health. While the evidence strongly suggests that GI use has played a protective role in non-COVID-19 physical and mental health during the pandemic, questions remain with regard to possible impacts on COVID transmission and mortality.

Effect of the COVID-19 pandemic on bike-sharing demand and hire time: Evidence from Santander Cycles in London

The COVID-19 pandemic has been influencing travel behaviour in many urban areas around the world since the beginning of 2020. As a consequence, bike-sharing schemes have been affected-partly due to the change in travel demand and behaviour as well as a shift from public transit. This study estimates the varying effect of the COVID-19 pandemic on the London bike-sharing system (Santander Cycles) over the period March-December 2020. We employed a Bayesian second-order random walk time-series model to account for temporal correlation in the data. We compared the observed number of cycle hires and hire time with their respective counterfactuals (what would have been if the pandemic had not happened) to estimate the magnitude of the change caused by the pandemic. The results indicated that following a reduction in cycle hires in March and April 2020, the demand rebounded from May 2020, remaining in the expected range of what would have been if the pandemic had not occurred. This could indicate the resiliency of Santander Cycles. With respect to hire time, an important increase occurred in April, May, and June 2020, indicating that bikes were hired for longer trips, perhaps partly due to a shift from public transit.

Reproducibility of Research During COVID-19: Examining the Case of Population Density and the Basic Reproductive Rate from the Perspective of Spatial Analysis

The emergence of the novel SARS-CoV-2 coronavirus and the global COVID-19 pandemic in 2019 led to explosive growth in scientific research. Alas, much of the research in the literature lacks conditions to be reproducible, and recent publications on the association between population density and the basic reproductive number of SARS-CoV-2 are no exception. Relatively few papers share code and data sufficiently, which hinders not only verification but additional experimentation. In this article, an example of reproducible research shows the potential of spatial analysis for epidemiology research during COVID-19. Transparency and openness means that independent researchers can, with only modest efforts, verify findings and use different approaches as appropriate. Given the high stakes of the situation, it is essential that scientific findings, on which good policy depends, are as robust as possible; as the empirical example shows, reproducibility is one of the keys to ensure this.

Association of Public Health Measures During the COVID-19 Pandemic With the Incidence of Infectious Conjunctivitis

Importance

Infectious conjunctivitis is highly transmissible and a public health concern. While mitigation strategies have been successful on a local level, population-wide decreases in spread are rare.

Objective

To evaluate whether internet search interest and emergency department visits for infectious conjunctivitis were associated with public health interventions adopted during the COVID-19 pandemic.

Design, Setting, and Participants

Internet search data from the US and emergency department data from a single academic center in the US were used in this study. Publicly available smartphone mobility data were temporally aligned to quantify social distancing. Internet search term trends for nonallergic conjunctivitis, corneal abrasions, and posterior vitreous detachments were obtained. Additionally, all patients who presented to a single emergency department from February 2015 to February 2021 were included in a review. Physician notes for emergency department visits at a single academic center with the same diagnoses were extracted. Causal inference was performed using a bayesian structural time-series model. Data were compared from before and after April 2020, when the US Centers for Disease Control and Prevention recommended members of the public wear masks, stay at least 6 feet from others who did not reside in the same home, avoid crowds, and quarantine if experiencing flulike symptoms or exposure to persons with COVID-19 symptoms.

Exposures

Symptoms of or interest in conjunctivitis in the context of the COVID-19 pandemic.

Main Outcome and Measures

The hypothesis was that there would be a decrease in internet search interest and emergency department visits for infectious conjunctivitis after the adaptation of public health measures targeted to curb COVID-19.

Results

A total of 1156 emergency department encounters with a diagnosis of conjunctivitis were noted from January 2015 to February 2021. Emergency department encounters for nonallergic conjunctivitis decreased by 37.3% (95% CI, -12.9% to -60.6%; P < .001). In contrast, encounters for corneal abrasion (1.1% [95% CI, -29.3% to 29.1%]; P = .47) and posterior vitreous detachments (7.9% [95% CI, -46.9% to 66.6%]; P = .39) remained stable after adjusting for total emergency department encounters. Search interest in conjunctivitis decreased by 34.2% (95% CI, -30.6% to -37.6%; P < .001) after widespread implementation of public health interventions to mitigate COVID-19.

Conclusions and Relevance

Public health interventions, such as social distancing, increased emphasis on hygiene, and travel restrictions during the COVID-19 pandemic, were associated with decreased search interest in nonallergic conjunctivitis and conjunctivitis-associated emergency department encounters. Mobility data may provide novel metrics of social distancing. These data provide evidence of a sustained population-wide decrease in infectious conjunctivitis.

Non-linear spatial linkage between COVID-19 pandemic and mobility in ten countries: A lesson for future wave

BACKGROUND

Restrictive measures enacted in response to the COVID-19 pandemic have resulted in dramatic and substantial variations in people's travel habits and behaviors worldwide. This paper empirically examines the asymmetric inter-linkages between transportation mobility and COVID-19.

METHODS

Using daily data from 1st March 2020 to 15th July 2020, this study draws the dynamic and causal relationships between transportation mobility and COVID-19 in ten selected countries (i.e., USA, Brazil, Mexico, UK, Spain, Italy, France, Germany, Canada, and Belgium). To systematically analyze how the quantiles of COVID-19 (transportation mobility) affect the quantiles of transportation mobility (COVID-19), a complete set of non-linear modeling including the quantile-on-quantile (QQ) regression and quantile Granger causality in mean is applied.

RESULTS

Our preliminary findings strictly reject the preposition of data normality and highlight that the observed relationship is highly correlated and quantile-dependent. The empirical results demonstrate the heterogeneous dependence between COVID-19 and transportation mobility across quantiles. The findings acclaim the presence of a significant positive association between COVID-19 and transportation mobility in the USA, UK, Spain, Italy, Canada, France, Germany and Belgium, predominantly at upper quantiles, but results are contrasting in the case of Brazil and Mexico. In addition, either lower or upper quantiles of both variables indicate a declining negative effect of transportation mobility on COVID-19. Furthermore, the outcomes of quantile Granger causality in mean conclude a bidirectional causal link between COVID-19 and transportation mobility for almost all sample countries. Unlike them, France has found unidirectional causality that extends from COVID-19 to transportation mobility.

CONCLUSIONS

We may conclude that COVID-19 leads to a reduction in transportation mobility. On the other hand, the empirical results quantify that excessive transportation mobility levels stimulate pandemic cases, and social distancing is one of the primary measures to encounter infection transmission. Imperative country-specific policy implications pertaining to public health, potential virus spread, transportation, and the environment may be drawn from these findings.

Transportation, the pathogen vector to rule them all: Evidence from the recent coronavirus pandemic

Introduction

It is common knowledge that mobility refers to a distinct vector for pathogens, but the importance of prevention and the infusion of public health practices within transportation systems is not manifest. Replication studies of this effect are important because transportation remains veiled in modern societies, since its demand is not direct, but derived.

Methods

Variables mirroring transportation and logistics' systems intensity (trade data, the logistics performance index, and investment in transportation) are cross-tabulated with epidemiological data from the recent coronavirus pandemic. As the samples of the data pertain to a dependent commonality, the statistical hypothesis test applicable is McNemar's test. In addition, the statistical power of the test(s) is calculated as a marker of methodological validity and reliability. To further strengthen the analytical methodology, a plethora of descriptive statistics have been calculated and multiple correspondence analysis (MCA) has been conducted.

Results

This work confirms that the domain of transportation bears a strong association with not only mortality of a disease, but its recovery rates as well. All crosstabs provide statistically significant results and the statistical power calculated is very high, signifying the appropriateness of the methodology and the very low probability of Type II error. The MCA results are significant, as well.

Conclusions

The impact, or even the presence of transportation is veiled, as transportation comprises of derived demand dynamics. As such, its activities and even the prerequisites for its efficient operations many times go unnoticed. This work replicates a known effect, that mobility exacerbates the presence of a pathogen. The significance of this research lies on the fact that distinct indicators that reflect transportation and logistics are (though a robust calculatory methodology) statistically associated with epidemiological data.

Spatial association of mobility and COVID-19 infection rate in the USA: A county-level study using mobile phone location data

INTRODUCTION

Human mobility has been a central issue in the discussion from the beginning of COVID-19. While the body of literature on the relationship of COVID transmission and mobility is large, studies mostly captured a relatively short timeframe. Moreover, spatial non-stationarity has garnered less attention in these explorative models. Therefore, the major concern of this study is to see the relationship of mobility and COVID on a broader temporal scale and after mitigating this methodological gap.

OBJECTIVE

In response to this concern, this study first explores the spatiotemporal pattern of mobility indicators. Secondly, it attempts to understand how mobility is related to COVID infection rate and how this relationship has been changed over time and space after controlling several sociodemographic characteristics, spatial heterogeneity, and policy-related changes during different phases of Coronavirus.

DATA AND METHOD

This study uses GPS-based mobility data for a wider time frame of six months (March 20-August'20) divided into four tiers and carries analysis for all the US counties (N = 3142). Space-time cube is used to generate the spatiotemporal pattern. For the second objective, Ordinary Least Square (OLS), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR) were used.

RESULT

The spatial-temporal pattern suggests that the trip rate, out-of-county trip rate, and miles/person traveled were mostly plummeted till the first wave reached its peak, and subsequently, all of these mobility matrices started to rise. From spatial models, infection rates were found negatively correlated with miles traveled and out-of-county trips. Highly COVID infected areas mostly had more people working from home, low percentages of aged people and educated people, and high percentages of poor people.

CONCLUSION

This study, with necessary policy implications, provides a comprehensive understanding of the shifting pattern of mobility and COVID. Spatial models outperform OLS with better fits and non-clustered residuals.

Non-Intrusive Assessment of COVID-19 Lockdown Follow-Up and Impact Using Credit Card Information: Case Study in Chile

In this paper, we propose and validate with data extracted from the city of Santiago, capital of Chile, a methodology to assess the actual impact of lockdown measures based on the anonymized and geolocated data from credit card transactions. Using unsupervised Latent Dirichlet Allocation (LDA) semantic topic discovery, we identify temporal patterns in the use of credit cards that allow us to quantitatively assess the changes in the behavior of the people under the lockdown measures because of the COVID-19 pandemic. An unsupervised latent topic analysis uncovers the main patterns of credit card transaction activity that explain the behavior of the inhabitants of Santiago City. The approach is non-intrusive because it does not require the collaboration of people for providing the anonymous data. It does not interfere with the actual behavior of the people in the city; hence, it does not introduce any bias. We identify a strong downturn of the economic activity as measured by credit card transactions (down to 70%), and thus of the economic activity, in city sections (communes) that were subjected to lockdown versus communes without lockdown. This change in behavior is confirmed by independent data from mobile phone connectivity. The reduction of activity emerges before the actual lockdowns were enforced, suggesting that the population was spontaneously implementing the required measures for slowing virus propagation.

Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis

Dense urban areas are especially hardly hit by the Covid-19 crisis due to the limited availability of public transport, one of the most efficient means of mass mobility. In light of the Covid-19 pandemic, public transport operators are experiencing steep declines in demand and fare revenues due to the perceived risk of infection within vehicles and other facilities. The purpose of this paper is to explore the possibilities of implementing social distancing in public transport in line with epidemiological advice. Social distancing requires effective demand management to keep vehicle occupancy rates under a predefined threshold, both spatially and temporally. We review the literature of five demand management methods enabled by new information and ticketing technologies: (i) inflow control with queueing, (ii) time and space dependent pricing, (iii) capacity reservation with advance booking, (iv) slot auctioning, and (v) tradeable travel permit schemes. Thus the paper collects the relevant literature into a single point of reference, and provides interpretation from the viewpoint of practical applicability during and after the pandemic.