Strategizing COVID-19 lockdowns using mobility patterns.

Objectives: Identifying factors that can lead to an outbreak in an orphanage is important. This study describes a COVID-19 outbreak in an orphanage in the Philippines. The clinicodemographic profile of the children and employees, safety protocols implemented and outbreak response are reported. Methodology: This is a descriptive study of the COVID-19 outbreak which occurred at a local orphanage in Metro Manila. The clinicodemographic profile of children and employees were obtained from the orphanage’s health records. Interview of employees and administrators and focused group discussions (FDG) were done. A questionnaire on how the orphanage dealt with the COVID-19 pandemic was administered. Results: A total of 128 children and 34 staff were in the orphanage during the COVID-19 outbreak. Majority (96%) of children were symptomatic and 15% of them have co-morbidities. While 82% of the employees were asymptomatic, majority had a positive rapid antigen test or RT-PCR for SARS-CoV2. All the children and employees had mild illness and recovered with no complications. The orphanage implemented strict infection control measures when the COVID-19 pandemic was declared. There was a halt in the adoption processes and in the movement of persons in the orphanage. With the decline of COVID-19 cases in Metro Manila, the orphanage resumed accepting new admissions and processing adoption papers. A few weeks later, a child became symptomatic but was not immediately considered as a case of COVID-19. This was followed by the majority of the children and the employees becoming symptomatic, thus the COVID-19 outbreak was called. Foremost in the response was to attend to the medical needs of the children. Strict lockdown was enforced by the City Health Office until all cases were deemed not contagious. Conclusion: Factors that can lead to an outbreak in an orphanage include missed opportunities for early diagnosis, contact tracing and intervention. Despite strict infection control measures, a single breach undermined all the efforts done to prevent an outbreak in a closed community.

PurposeThe objective of this paper was to review the functionalities and effectiveness of the free mobile health applications available in the Google Play and App stores used in Saudi Arabia, Italy, Singapore, the United Kingdom, USA, and India during the COVID-19 outbreak.MethodsThis study adopted a systematic search strategy to identify the free mobile applications available in the App and Google Play stores related to the COVID-19 outbreak. According to the PRISMA flowchart of the search, only 12 applications met the inclusion criterion.ResultsThe 12 mobile applications that met the inclusion criterion were: Mawid, Tabaud, Tawakkalna, Sehha, Aarogya setu, TraceTogether, COVID safe, Immuni, COVID symptom study, COVID watch, NHS COVID-19, and PathCheck. The following features and functionalities of the apps were described: app overview (price, ratings, android, iOS, developer/owner, country, status), health tools (user status-risk assessment, self-assessment, E-pass integration, test results reporting, online consultation, contact tracing), learning options (personalized notes, educational resources, COVID-19 information), communication tools (query resolution, appointments, social network, notifications), app design (data visualization, program plan), networking tools (location mapping – GPS, connectivity with other devices), and safety and security options (alerts, data protection). Also, the effectiveness of the apps was analyzed.ConclusionThe analysis revealed that various applications have been developed for different functions like contact tracing, awareness building, appointment booking, online consultation, etc. However, only a few applications have integrated various functions and features such as self-assessment, consultation, support and access to information. Also, most of the apps are focused on contact tracing, while very few are dedicated to raising awareness and sharing information about the COVID-19 pandemic. Likewise, the majority of applications rely on GPS and Bluetooth technologies for relevant functions. No apps were identified that had built-in social media features. It is suggested to design and develop an integrated mobile health application with most of the features and functionalities analyzed in this study.

The advancement of spatial information technology (SIT) boosts the location-based services (LBS) and smart urban applications. It brings the public into the era of smart society. Especially, by synthesizing of SIT, smartphones, and urban society, sharing economy mutualizing of goods and services to optimize the resource usage has boomed in recent years. In the transportation sector, transportation network companies (TNC) like Uber and Didi provide flexible and convenient on-demand sharing travel service for the public, thus have significantly changed urban mobility landscape. Taking the TNC vehicle GPS trajectories in Shenzhen as an example, this article presents a data-driven framework to uncover on-demand shared mobility pattern. The travels and stops are extracted from raw GPS trajectories using machine learning. Typical mobility patterns including sharing ridership, travel distance, and spatial communities are revealed. The results suggest that although most sharing travels are concentrated at downtown, a large proportional of LBS-based sharing travels appear at urban sub-centers. The distance of sharing travel follows the exponential distribution with the \(\lambda\) coefficient 0.15. The detected communities indicate the interaction derived from TNC vehicles are constrained by the administrative boundaries. These results suggest the advantages of travel sharing services in places where taxi services are not sufficient. These findings enable us to better understand the online sharing services and provide useful insights on the management of ride-sharing services in the city.

Population mobility patterns are an important reflection of the future distribution of migrant populations and the evolution trends of urbanization patterns. However, although research based on statistical data can reveal the pattern of population flow, it also shows a time lag. Most of the population flow network research based on location services data has failed to fully discuss the symmetry of directional outflows and inflows in the same place and the two-way symmetrical connections between places. This paper creatively proposes and constructs the concept and analysis framework of population flow asymmetry. We used the Yangtze River Delta (YRD) as a typical case and the results of our analysis reveal the temporal and spatial asymmetry of the population flow using complex network analysis methods based on the Spring Festival (SF) population migration big data. We found that the timing asymmetry manifested in such a way that the closer it was to the festival, the greater the scale and intensity of the population movement. This is a feature of the lack of scale and regional differences within China. The spatial asymmetry was manifested in three aspects, network, node, and link, and the core cities with administrative and economic hierarchical advantages dominated the asymmetric pattern of regional population mobility. In addition, distance and administrative boundaries are factors that cannot be ignored in population movements, and they were implicated in the degree of asymmetry by distance enhancement and administrative boundary blocking. The conclusions of this study can not only provide policy decision-making guidelines for population management and resource allocation in the YRD, but they can also provide a reference value for achieving the goal of regional, high-quality, integrated development. Future research will further the discussion and management of socio-economic attributes in order to develop a more detailed and microscopic understanding of the mechanisms of population mobility patterns.

Mathematical modeling of the COVID-19 pandemic with intervention strategies

Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.

Human mobility has been significantly affected by COVID-19 and associated travel restrictions imposed by government policies. This study examines changes in mobility patterns in Singapore during different stages of the pandemic using mobile phone data. Our results indicate that population mobility decreased over the COVID pandemic and is slowly increasing after the restrictions were lifted. However, there have been changes in the mobility patterns of the population. Despite the easing of COVID-19 measures, the population is making fewer trips and the trip distances are longer for some people. This change may be attributed to several factors. One of which is that the population has not quite come back to the pre-COVID working routine and has adopted a semi-work-from-home policy, where the staff is working in the office for a few days a week. The other factor is the habit of making more “purposeful” trips remains. Further, we investigate changes in mobility patterns among the classified explorers and returners. The results demonstrate that the change in mobility has a similar trend to the population as a whole. These observed changes in mobility patterns may become the new normal and should be taken into account for traffic management, business decision-making, policy-making, and urban or transport infrastructure planning.

Responding to COVID-19 pandemic: Why a strong health system is required.

Background:Migrant and seasonal farmworkers face enormous barriers to health and have been a particularly vulnerable population during the COVID-19 pandemic, but their pandemic experiences and potential inequities have not been well studied.Objectives:We aimed to assess the impact of COVID-19 in Immokalee, Florida, a community with a significant population of migrant and seasonal farmworkers. We evaluated for differences in pandemic experience by language, a known barrier to healthcare, to inform and strengthen future public health efforts.Methods:First, to estimate the burden of COVID in the area, we conducted a descriptive analysis of data on COVID-19 deaths for Collier County from May-August 2020. We then surveyed a cross-sectional, randomized representative sample of 318 adults living in Immokalee from March-November 2020 to assess socio-demographics, workplace conditions, sources of information, ability to follow guidelines, and experiences with testing and contact tracing programs. Results were compared across language groups.Findings:Average excess mortality in Collier County was 108%. The majority surveyed in Immokalee had socio-demographic factors associated with higher COVID risk. Non-English speakers had higher workplace risk due to less ability to work from home. Haitian Creole speakers were less likely to be tested, though all participants were willing to get symptomatic testing and quarantine. Those participants who tested positive or had COVID-19 exposures had low engagement with the contact tracing program, and Spanish-speakers reported lower quality of contact tracing than English speakers.Conclusions:The community of Immokalee, FL is a vulnerable population that suffered disproportionate deaths from COVID-19. This study reveals language inequities in COVID testing and contact tracing that should be targeted in future pandemic response in Immokalee and other migrant farmworker communities.

Outbreaks of communicable diseases such as COVID-19 can significantly threaten individual wellbeing and societal functioning. To contain such outbreaks, public health services (PHS) implement contact tracing (CT). In this process, a public health professional (PHP) interviews an infected individual (‘case’) to identify people they were in contact with during their infectious period (‘contacts’) and informs them about the necessary measures to prevent further transmission of the pathogen. However, CT can be challenging during large outbreaks due to the high numbers of cases and contacts, time and resource demands, and limited cooperation. In this thesis, we investigated whether and how CT can be improved by more actively involving cases and contacts in tasks typically performed by PHPs, supported by digital tools. We refer to this approach as ‘self-led’ CT. In part one of this thesis, we examined the use of web-based respondent-driven sampling (webRDS) in public health. A scoping review showed that webRDS has been successfully used to recruit participants for research on various topics, deliver health interventions, and support case finding. Peer recruitment can be stimulated by offering adequate incentives, conducting formative research into recruitment barriers, and thoroughly motivating ‘seeds’ to initiate recruitment. In part two, we explored PHPs’ perspectives on self-led CT through interviews and surveys. PHPs were generally positive, anticipating that self-led CT could make the process more efficient and enable more autonomous participation. However, they also expressed concerns about losing oversight and the ability to support cases and contacts. PHPs considered self-led CT appropriate when dealing with digitally skilled and motivated individuals, and during outbreaks involving relatively many cases and contacts. They considered self-led CT less suitable in high-risk or complex situations. PHPs emphasized that self-led CT should complement—not replace—provider-initiated CT and recommended maintaining options for personal support. In part three, we investigated citizens’ perspectives on self-led CT during the COVID-19 pandemic using interviews and surveys. Citizens generally indicated to be willing to participate in self-led CT. Their willingness depended on various factors, including previous experiences with CT, sense of responsibility, self-efficacy, perceived impact, trust in digital tools, and privacy concerns. Citizens recommended enabling early participation, minimizing data collection, and offering both autonomous and supported options within the CT process. In part four, we conducted an experimental online questionnaire study to compare different approaches to support citizens in recalling and reporting their contacts. We found that using context-specific recall cues and asking participants to list contacts separately for different situations increased the number of reported contacts, but also raised the risk of participant dropout. Furthermore, not all additionally elicited contacts may equally contribute to CT effectiveness. Our findings suggest that self-led CT may be less feasible for individuals with many or high-risk contacts. In the final part of the thesis, we discuss limitations, opportunities for future research, and propose a framework for integrating self-led and provider-initiated CT to maximize benefits while addressing potential challenges.

An analytical evaluation of contact tracing systems using real-world individual-level data.

As the most immediately impacted population, survivors’ mobility and distribution characteristics are closely linked to earthquake emergency response. Accurate population distribution and mobility data are vital foundational resources for post-disaster decision-making. With the help of mobile phone location data within the earthquake-stricken area, we explore novel rapid assessment approaches to identifying perceived impact area.The widespread adoption of smart mobile devices in China has led to the installation of numerous third-party applications. These applications rely on push notification services enabled by Push Software Development Kits (SDKs) provided by mobile service providers. These SDKs, compliant with data security standards, collection scopes, and transmission protocols, utilize built-in functional modules to periodically collect user-authorized geolocation data. This data encompasses device identifiers, GPS coordinates, WiFi signatures, cellular base station logs, and network connectivity metadata. After encryption, these multi-source data streams inputs are aggregated into structured mobile location datasets. They can provide high-resolution insights into population mobility patterns during disasters.At 9:05 (Beijing time) on 7 January 2025, an earthquake with magnitude Ms 6.8 hit Dingri County, Tibet Autonomous Region, which killed 126 people. We collected a mobile phone location dataset covering a 150 km radius from the epicenter, spanning 38 hours from 9:00 on 6 January to 1:00 on 8 January. It comprises 146,123 records. Each record includes four mobile location-based indicators, namely (1) Active Base Stations (base stations scanned and periodically reported by mobile devices), (2) Active Wi-Fi Hotspots (Wi-Fi hotspots scanned and periodically reported by mobile devices), (3) Mobile Devices (mobile devices that obtain services through multiple positioning methods) and (4) Wi-Fi-Connected Devices (mobile devices connected to Wi-Fi hotspots). Utilizing natural neighbor interpolation and Thiessen polygon interpolation methods, we analyzes changes in four mobile location-based indicators and their spatiotemporal distribution characteristics before and after the earthquake, summarizing crowd movement patterns and communication behaviors after the Dingri earthquake. The results indicate an uneven distribution of population and differing dynamics in mobile phone signal activity. This reflects different behavioral patterns and the potential perceived extent of the earthquake. Within 50 km of the epicenter, all four indicators showed varying degrees of decline post-earthquake, while areas beyond 100 km exhibited short-term surges, reflecting differentiated behavioral responses based on seismic impact severity. In areas experiencing strong shaking, risk avoidance behavior predominated, while in areas where shaking was noticeable but less severe, communication behavior was more prominent. Mobile data decline zones showed high spatial correlation with intensity VIII+ regions, proving their effectiveness as rapid indicators for identifying strongly affected areas. Notably, mobile location data enabled accurate identification of strongly affected zones within 30 min post-earthquake.The research establishes a theoretical-technical framework supporting three critical post-disaster applications: (1) dynamic population distribution sensing, (2) behavioral pattern analysis of affected populations, and (3) rapid evaluation of seismic perception zones.

Reconstructing mobility from smartphone data: Empirical evidence of the effects of COVID-19 pandemic crisis on working and leisure

A novel methodology for assessing contact tracing precision: Phylogenetic validation of a contact tracing program for COVID-19 in Belgium.

Early Response to COVID-19