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1.
PLoS One ; 16(12): e0260931, 2021.
Article in English | MEDLINE | ID: covidwho-1632675

ABSTRACT

During the COVID-19 pandemic, US populations have experienced elevated rates of financial and psychological distress that could lead to increases in suicide rates. Rapid ongoing mental health monitoring is critical for early intervention, especially in regions most affected by the pandemic, yet traditional surveillance data are available only after long lags. Novel information on real-time population isolation and concerns stemming from the pandemic's social and economic impacts, via cellular mobility tracking and online search data, are potentially important interim surveillance resources. Using these measures, we employed transfer function model time-series analyses to estimate associations between daily mobility indicators (proportion of cellular devices completely at home and time spent at home) and Google Health Trends search volumes for terms pertaining to economic stress, mental health, and suicide during 2020 and 2021 both nationally and in New York City. During the first pandemic wave in early-spring 2020, over 50% of devices remained completely at home and searches for economic stressors exceeded 60,000 per 10 million. We found large concurrent associations across analyses between declining mobility and increasing searches for economic stressor terms (national proportion of devices at home: cross-correlation coefficient (CC) = 0.6 (p-value <0.001)). Nationally, we also found strong associations between declining mobility and increasing mental health and suicide-related searches (time at home: mood/anxiety CC = 0.53 (<0.001), social stressor CC = 0.51 (<0.001), suicide seeking CC = 0.37 (0.006)). Our findings suggest that pandemic-related isolation coincided with acute economic distress and may be a risk factor for poor mental health and suicidal behavior. These emergent relationships warrant ongoing attention and causal assessment given the potential for long-term psychological impact and suicide death. As US populations continue to face stress, Google search data can be used to identify possible warning signs from real-time changes in distributions of population thought patterns.


Subject(s)
COVID-19/psychology , Cell Phone/statistics & numerical data , Search Engine/statistics & numerical data , Socioeconomic Factors , Suicide/psychology , Geographic Information Systems , Humans , Mental Health/statistics & numerical data , New York City , Quarantine/statistics & numerical data , Search Engine/trends , Stress, Psychological , Time Factors , United States
2.
Sci Rep ; 11(1): 24171, 2021 12 17.
Article in English | MEDLINE | ID: covidwho-1593554

ABSTRACT

The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3 weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3 weeks in advance.


Subject(s)
Algorithms , COVID-19/transmission , Cell Phone/statistics & numerical data , Hospitalization/statistics & numerical data , Models, Theoretical , Patient Admission/statistics & numerical data , COVID-19/epidemiology , COVID-19/virology , Disease Transmission, Infectious/statistics & numerical data , Forecasting/methods , Geography , Hospitalization/trends , Humans , Pandemics/prevention & control , Patient Admission/trends , Retrospective Studies , SARS-CoV-2/physiology , Sweden/epidemiology , Travel/statistics & numerical data
4.
Nat Microbiol ; 6(10): 1271-1278, 2021 10.
Article in English | MEDLINE | ID: covidwho-1402078

ABSTRACT

Genomics, combined with population mobility data, used to map importation and spatial spread of SARS-CoV-2 in high-income countries has enabled the implementation of local control measures. Here, to track the spread of SARS-CoV-2 lineages in Bangladesh at the national level, we analysed outbreak trajectory and variant emergence using genomics, Facebook 'Data for Good' and data from three mobile phone operators. We sequenced the complete genomes of 67 SARS-CoV-2 samples (collected by the IEDCR in Bangladesh between March and July 2020) and combined these data with 324 publicly available Global Initiative on Sharing All Influenza Data (GISAID) SARS-CoV-2 genomes from Bangladesh at that time. We found that most (85%) of the sequenced isolates were Pango lineage B.1.1.25 (58%), B.1.1 (19%) or B.1.36 (8%) in early-mid 2020. Bayesian time-scaled phylogenetic analysis predicted that SARS-CoV-2 first emerged during mid-February in Bangladesh, from abroad, with the first case of coronavirus disease 2019 (COVID-19) reported on 8 March 2020. At the end of March 2020, three discrete lineages expanded and spread clonally across Bangladesh. The shifting pattern of viral diversity in Bangladesh, combined with the mobility data, revealed that the mass migration of people from cities to rural areas at the end of March, followed by frequent travel between Dhaka (the capital of Bangladesh) and the rest of the country, disseminated three dominant viral lineages. Further analysis of an additional 85 genomes (November 2020 to April 2021) found that importation of variant of concern Beta (B.1.351) had occurred and that Beta had become dominant in Dhaka. Our interpretation that population mobility out of Dhaka, and travel from urban hotspots to rural areas, disseminated lineages in Bangladesh in the first wave continues to inform government policies to control national case numbers by limiting within-country travel.


Subject(s)
COVID-19/transmission , Cell Phone/statistics & numerical data , Genome, Viral/genetics , SARS-CoV-2/genetics , Social Media/statistics & numerical data , Bangladesh/epidemiology , Bayes Theorem , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/virology , Disease Outbreaks/prevention & control , Disease Outbreaks/statistics & numerical data , Genomics , Health Policy/legislation & jurisprudence , Humans , Phylogeny , Population Dynamics/statistics & numerical data , SARS-CoV-2/classification , Travel/legislation & jurisprudence , Travel/statistics & numerical data
6.
Sci Rep ; 11(1): 4150, 2021 02 18.
Article in English | MEDLINE | ID: covidwho-1091455

ABSTRACT

We employ the Google and Apple mobility data to identify, quantify and classify different degrees of social distancing and characterise their imprint on the first wave of the COVID-19 pandemic in Europe and in the United States. We identify the period of enacted social distancing via Google and Apple data, independently from the political decisions. Our analysis allows us to classify different shades of social distancing measures for the first wave of the pandemic. We observe a strong decrease in the infection rate occurring two to five weeks after the onset of mobility reduction. A universal time scale emerges, after which social distancing shows its impact. We further provide an actual measure of the impact of social distancing for each region, showing that the effect amounts to a reduction by 20-40% in the infection rate in Europe and 30-70% in the US.


Subject(s)
COVID-19/epidemiology , Cell Phone Use/statistics & numerical data , Quarantine/statistics & numerical data , COVID-19/prevention & control , COVID-19/transmission , Cell Phone/statistics & numerical data , Cell Phone/trends , Cell Phone Use/trends , Data Mining/methods , Europe/epidemiology , Humans , Mobile Applications/statistics & numerical data , Mobile Applications/trends , Pandemics , Physical Distancing , Quarantine/trends , SARS-CoV-2/isolation & purification , United States/epidemiology
7.
PLoS One ; 15(11): e0240526, 2020.
Article in English | MEDLINE | ID: covidwho-1067387

ABSTRACT

In-person (face-to-face) data collection methods offer many advantages but can also be time-consuming and expensive, particularly in areas of difficult access. We take advantage of the increasing mobile phone penetration rate in rural areas to evaluate the feasibility of using cell phones to monitor the provision of key health and nutrition interventions linked to the first 1,000 days of life, a critical period of growth and development. We examine response rates to calendarized text messages (SMS) and phone calls sent to 1,542 households over a period of four months. These households have children under two years old and pregnant women and are located across randomly selected communities in Quiche, Guatemala. We find that the overall (valid) response rate to phone calls is over 5 times higher than to text messages (75.8% versus 14.4%). We also test whether simple SMS reminders improve the timely reception of health services but do not find any effects in this regard. Language, education, and age appear to be major barriers to respond to text messages as opposed to phone calls, and the rate of response is not correlated with a household's geographic location (accessibility). Moreover, response veracity is high, with an 84-91% match between household responses and administrative records. The costs per monitored intervention are around 1.12 US dollars using text messages and 85 cents making phone calls, with the costs per effective answer showing a starker contrast, at 7.76 and 1.12 US dollars, respectively. Our findings indicate that mobile phone calls can be an effective, low-cost tool to collect reliable information remotely and in real time. In the current context, where in-person contact with households is not possible due to the COVID-19 crisis, phone calls can be a valuable instrument for collecting information, monitoring development interventions, or implementing brief surveys.


Subject(s)
Cell Phone/statistics & numerical data , Coronavirus Infections/epidemiology , Monitoring, Physiologic/statistics & numerical data , Nutritional Status/physiology , Pandemics , Pneumonia, Viral/epidemiology , Rural Population/statistics & numerical data , Adult , COVID-19 , Cell Phone/economics , Child, Preschool , Female , Guatemala/epidemiology , Humans , Infant , Infant, Newborn , Male , Monitoring, Physiologic/economics , Pregnancy , Reminder Systems/economics , Reminder Systems/statistics & numerical data , Surveys and Questionnaires , Telemedicine/economics , Telemedicine/statistics & numerical data , Text Messaging/economics , Text Messaging/statistics & numerical data
8.
Nature ; 589(7840): 82-87, 2021 01.
Article in English | MEDLINE | ID: covidwho-917538

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1. Here we introduce a metapopulation susceptible-exposed-infectious-removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2-8 solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19.


Subject(s)
COVID-19/epidemiology , COVID-19/prevention & control , Computer Simulation , Locomotion , Physical Distancing , Socioeconomic Factors , COVID-19/transmission , Cell Phone/statistics & numerical data , Data Analysis , Humans , Mobile Applications/statistics & numerical data , Religion , Restaurants/organization & administration , Risk Assessment , Time Factors
10.
Lancet Digit Health ; 2(11): e622-e628, 2020 11.
Article in English | MEDLINE | ID: covidwho-738321

ABSTRACT

A surge of interest has been noted in the use of mobility data from mobile phones to monitor physical distancing and model the spread of severe acute respiratory syndrome coronavirus 2, the virus that causes COVID-19. Despite several years of research in this area, standard frameworks for aggregating and making use of different data streams from mobile phones are scarce and difficult to generalise across data providers. Here, we examine aggregation principles and procedures for different mobile phone data streams and describe a common syntax for how aggregated data are used in research and policy. We argue that the principles of privacy and data protection are vital in assessing more technical aspects of aggregation and should be an important central feature to guide partnerships with governments who make use of research products.


Subject(s)
COVID-19/prevention & control , Cell Phone/statistics & numerical data , Epidemiological Monitoring , Physical Distancing , Travel/statistics & numerical data , COVID-19/epidemiology , Geographic Information Systems , Humans , Information Dissemination , Models, Statistical , Spatio-Temporal Analysis
11.
Int J Clin Pharm ; 42(4): 1197-1206, 2020 Aug.
Article in English | MEDLINE | ID: covidwho-593419

ABSTRACT

Background An infectious disease caused by a novel coronavirus (later called COVID-19) reached pandemic levels in 2020 and community pharmacists were involved in responding to this pandemic, also in Kosovo. Objectives To explore the experiences of community pharmacists in relation to provision of community pharmacy services during COVID-19 pandemic. Setting Community pharmacists in Kosovo. Methods This was a cross-sectional study where data was collected via a self-administered online questionnaire, from 264 pharmacists actively practicing in Kosovo during the pandemic. The questionnaire consisted of a combination of closed and open-ended questions, optional statements and statements on a five-point Likert scale, derived at least in part from the Transtheoretical Model. One-way analysis of variance was used to analyze differences in responses to Likert-type items whereas categorical variables were analyzed using Chi square testing. Main outcome measures Community pharmacists' perceptions on COVID-19 related preventative measures. Results A response rate of 40.6% was achieved. Sufficient and adequate COVID-19-related preventative measures were being implemented by a majority of pharmacies (n = 232; 87.9%), and over two-thirds of respondents agreed/strongly agreed that their pharmacies were sufficiently prepared with protective equipment for their personnel. Implementation of preventative measures was associated with respondents' perception that pharmacists and the pharmacy profession were valued more by patients during the pandemic and to a lesser degree, by other health professionals. Most commonly stated pros dealt with employee and patient safety, while key cons dealt with increased costs and running out of the necessary protective equipment. Key barriers to pharmacy activities were price increases by wholesalers, and patients' panic and excessive buying, whereas drivers dealt with professional obligation to assist and opportunity to prove inseparable to other health professionals. The most popular means of accessing COVID-19 related information by pharmacists was via mobile devices and information from professional organizations was considered most useful by pharmacists. Conclusions Community pharmacies actively implemented various measures as precautions to mitigate the spread of COVID-19. Our findings highlight the value of continuous provision of information by professional organizations and use of mobile devices as key means to access information by pharmacists.


Subject(s)
Community Pharmacy Services/organization & administration , Coronavirus Infections/prevention & control , Pandemics/prevention & control , Pharmacists/organization & administration , Pneumonia, Viral/prevention & control , COVID-19 , Cell Phone/statistics & numerical data , Coronavirus Infections/epidemiology , Cross-Sectional Studies , Female , Humans , Kosovo , Male , Pneumonia, Viral/epidemiology , Professional Role , Societies, Pharmaceutical/statistics & numerical data , Surveys and Questionnaires
12.
Ir J Med Sci ; 189(4): 1145-1146, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-15734

ABSTRACT

We reported several personal-oriented and mobile phone-based information technologies which were recently developed and widely used during the outbreak of COVID-19 in China. These technologies help reduce the transmission of COVID-19 and maintain normal social order.


Subject(s)
Betacoronavirus , Cell Phone/statistics & numerical data , Coronavirus Infections/prevention & control , Disease Outbreaks/prevention & control , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , COVID-19 , China , Humans , SARS-CoV-2
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