What can urban mobility data reveal about the spatial distribution of infection in a single city?

The Apple Mobility Trends Data in Human Mobility Patterns during Restrictions and Prediction of COVID-19: A Systematic Review and Meta-Analysis

The objective of this systematic review with PRISMA guidelines is to discover how population movement information has epidemiological implications for the spread of COVID-19. In November 2022, the Web of Science and Scopus databases were searched for relevant reports for the review. The inclusion criteria are: (1) the study uses data from Apple Mobility Trends Reports, (2) the context of the study is about COVID-19 mobility patterns, and (3) the report is published in a peer-reviewed venue in the form of an article or conference paper in English. The review included 35 studies in the period of 2020-2022. The main strategy used for data extraction in this review is a matrix proposal to present each study from a perspective of research objective and outcome, study context, country, time span, and conducted research method. We conclude by pointing out that these data are not often used in studies and it is better to study a single country instead of doing multiple-country research. We propose topic classifications for the context of the studies as transmission rate, transport policy, air quality, re-increased activities, economic activities, and financial markets.

Urban mobility patterns and the spatial distribution of infections in Santiago de Chile

The process of a virus spread is inherently spatial. Even though Latin America became the epicenter of the COVID-19 pandemic in May 2020, there is still little evidence of the relationship between urban mobility and virus propagation in the region. This paper combines network analysis of mobility patterns in public transportation with a spatial error correction model for Santiago de Chile. Results indicate that a 10% higher number of daily public transportation trips received by an administrative unit in the city was associated with a 1.3% higher number of confirmed COVID-19 cases per 100,000 inhabitants. Following these findings, we propose an empirical method to identify and classify neighborhoods according to the level and type of risk for COVID-19-like disease propagation, helping policymakers manage mobility during the initial stages of an epidemic outbreak.

The Unequal Effects of Social Distancing Policy on Subway Ridership during the COVID-19 Pandemic in Seoul, South Korea

In the Republic of Korea, social distancing policies relied on voluntary participation by citizens and exhibited short-term changes. In this situation, the effects of such policies varied depending on each community's capacity to comply. Here, we collected subway ridership data for 294 stations on nine Seoul Metro lines and aggregated the data for each station to the 184 smallest administrative areas. We found that the mean percent change in subway ridership was fitted by an additive model of the log-transformed percent ratio of the restaurant industry (estimated degrees of freedom (EDF) = 3.24, P < 0.001), the Deprivation Index (DI) (EDF = 3.66, P = 0.015), and the proportion of essential workers (β =  - 0.10 (95% confidence interval - 0.15 to - 0.05, P < 0.001). We found a distinct decrease in subway ridership only in the least deprived areas, suggesting that social distancing is costly.

Using big data for evaluating development outcomes: A systematic map

BACKGROUND

Policy makers need access to reliable data to monitor and evaluate the progress of development outcomes and targets such as sustainable development outcomes (SDGs). However, significant data and evidence gaps remain. Lack of resources, limited capacity within governments and logistical difficulties in collecting data are some of the reasons for the data gaps. Big data-that is digitally generated, passively produced and automatically collected-offers a great potential for answering some of the data needs. Satellite and sensors, mobile phone call detail records, online transactions and search data, and social media are some of the examples of big data. Integrating big data with the traditional household surveys and administrative data can complement data availability, quality, granularity, accuracy and frequency, and help measure development outcomes temporally and spatially in a number of new ways.The study maps different sources of big data onto development outcomes (based on SDGs) to identify current evidence base, use and the gaps. The map provides a visual overview of existing and ongoing studies. This study also discusses the risks, biases and ethical challenges in using big data for measuring and evaluating development outcomes. The study is a valuable resource for evaluators, researchers, funders, policymakers and practitioners in their effort to contributing to evidence informed policy making and in achieving the SDGs.

OBJECTIVES

Identify and appraise rigorous impact evaluations (IEs), systematic reviews and the studies that have innovatively used big data to measure any development outcomes with special reference to difficult contexts.

SEARCH METHODS

A number of general and specialised data bases and reporsitories of organisations were searched using keywords related to big data by an information specialist.

SELECTION CRITERIA

The studies were selected on basis of whether they used big data sources to measure or evaluate development outcomes.

DATA COLLECTION AND ANALYSIS

Data collection was conducted using a data extraction tool and all extracted data was entered into excel and then analysed using Stata. The data analysis involved looking at trends and descriptive statistics only.

MAIN RESULTS

The search yielded over 17,000 records, which we then screened down to 437 studies which became the foundation of our systematic map. We found that overall, there is a sizable and rapidly growing number of measurement studies using big data but a much smaller number of IEs. We also see that the bulk of the big data sources are machine-generated (mostly satellites) represented in the light blue. We find that satellite data was used in over 70% of the measurement studies and in over 80% of the IEs.

AUTHORS' CONCLUSIONS

This map gives us a sense that there is a lot of work being done to develop appropriate measures using big data which could subsequently be used in IEs. Information on costs, ethics, transparency is lacking in the studies and more work is needed in this area to understand the efficacies related to the use of big data. There are a number of outcomes which are not being studied using big data, either due to the lack to applicability such as education or due to lack of awareness about the new methods and data sources. The map points to a number of gaps as well as opportunities where future researchers can conduct research.

Risk mapping for COVID-19 outbreaks in Australia using mobility data

COVID-19 is highly transmissible and containing outbreaks requires a rapid and effective response. Because infection may be spread by people who are pre-symptomatic or asymptomatic, substantial undetected transmission is likely to occur before clinical cases are diagnosed. Thus, when outbreaks occur there is a need to anticipate which populations and locations are at heightened risk of exposure. In this work, we evaluate the utility of aggregate human mobility data for estimating the geographical distribution of transmission risk. We present a simple procedure for producing spatial transmission risk assessments from near-real-time population mobility data. We validate our estimates against three well-documented COVID-19 outbreaks in Australia. Two of these were well-defined transmission clusters and one was a community transmission scenario. Our results indicate that mobility data can be a good predictor of geographical patterns of exposure risk from transmission centres, particularly in outbreaks involving workplaces or other environments associated with habitual travel patterns. For community transmission scenarios, our results demonstrate that mobility data add the most value to risk predictions when case counts are low and spatially clustered. Our method could assist health systems in the allocation of testing resources, and potentially guide the implementation of geographically targeted restrictions on movement and social interaction.

Effect of lockdown on wastewater characteristics: a comparison of two large urban areas

The effect of the lockdown imposed to limit the spread of SARS-CoV-2 in France between March 14 and May 11, 2020 on the wastewater characteristics of two large urban areas (with between 250,000 and 300,000 inhabitants) was studied. The number of outward and inward daily commuters was extracted from national census databases related to the population and their commuting habits. For urban area A, with the larger number of daily inward commuters (110,000, compared to 53,000 for B), lockdown was observed to have an effect on the monthly load averages of chemical oxygen demand, biochemical oxygen demand, total Kjeldahl nitrogen, total suspended solids and total phosphorus, all of which decreased (confidence level of 95%). This decrease, which varied between 20% and 40% and reached 45% for COD, can be related to the cessation of catering and activities such as hairdressing, which generate large amounts of graywater. The ammonium loads, due to the use of toilets before leaving for work and after returning from work, remained constant. In the case of urban area B, lockdown had no noticeable effect. More data would be necessary in the long term to analyze the effect of changes in the balance between ammonia and carbon sources on the operation of wastewater treatment plants.

Risk assessment of the step-by-step return-to-work policy in Beijing following the COVID-19 epidemic peak

Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. As the virus spread worldwide causing a global pandemic, China reduced transmission at considerable social and economic cost. Post-lockdown, resuming work safely, that is, while avoiding a second epidemic outbreak, is a major challenge. Exacerbating this challenge, Beijing hosts many residents and workers with origins elsewhere, making it a relatively high-risk region in which to resume work. Nevertheless, the step-by-step approach taken by Beijing appears to have been effective so far. To learn from the epidemic progression and return-to-work measures undertaken in Beijing, and to inform efforts to avoid a second outbreak of COVID-19, we simulated the epidemiological progression of COVID-19 in Beijing under the real scenario of multiple stages of resuming work. A new epidemic transmission model was developed from a modified SEIR model for SARS, tailored to the situation of Beijing and fitted using multi-source data. Because of strong spatial heterogeneity amongst the population, socio-economic factors and medical capacity of Beijing, the risk assessment was undertaken spatiotemporally with respect to each district of Beijing. The epidemic simulation confirmed that the policy of resuming work step-by step, as implemented in Beijing, was sufficient to avoid a recurrence of the epidemic. Moreover, because of the structure of the model, the simulation provided insights into the specific factors at play at different stages of resuming work, allowing district-specific recommendations to be made with respect to monitoring at different stages of resuming work . As such, this research provides important lessons for other cities and regions dealing with outbreaks of COVID-19 and implementing return-to-work policies.