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1.
PLoS One ; 16(9): e0256889, 2021.
Article in English | MEDLINE | ID: covidwho-1523421

ABSTRACT

Vaccinating individuals with more exposure to others can be disproportionately effective, in theory, but identifying these individuals is difficult and has long prevented implementation of such strategies. Here, we propose how the technology underlying digital contact tracing could be harnessed to boost vaccine coverage among these individuals. In order to assess the impact of this "hot-spotting" proposal we model the spread of disease using percolation theory, a collection of analytical techniques from statistical physics. Furthermore, we introduce a novel measure which we call the efficiency, defined as the percentage decrease in the reproduction number per percentage of the population vaccinated. We find that optimal implementations of the proposal can achieve herd immunity with as little as half as many vaccine doses as a non-targeted strategy, and is attractive even for relatively low rates of app usage.


Subject(s)
COVID-19 Vaccines/administration & dosage , COVID-19/prevention & control , COVID-19/transmission , Contact Tracing/statistics & numerical data , Mass Vaccination/statistics & numerical data , COVID-19/immunology , Contact Tracing/instrumentation , Humans , Immunity, Herd , Mobile Applications , Models, Statistical , SARS-CoV-2/pathogenicity
3.
Int J Surg ; 92: 106023, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1327011

ABSTRACT

Globally, digital contact tracing initiatives has been used as a tool to combat the COVID-19 pandemic. The Fijian Government and Ministry of Health are promoting the use of the "careFiji" app to help in contact tracing. This paper will discuss the rollout of the careFiji app which helps in combating COVID-19 in Fiji, and the challenges caused by the digital gap that has surfaced during the pandemic.


Subject(s)
COVID-19 , Contact Tracing/instrumentation , Mobile Applications , COVID-19/epidemiology , Fiji/epidemiology , Humans , Pandemics
4.
JAMA Netw Open ; 4(6): e2116425, 2021 06 01.
Article in English | MEDLINE | ID: covidwho-1281193

ABSTRACT

Importance: The COVID-19 pandemic has severely disrupted US educational institutions. Given potential adverse financial and psychosocial effects of campus closures, many institutions developed strategies to reopen campuses in the fall 2020 semester despite the ongoing threat of COVID-19. However, many institutions opted to have limited campus reopening to minimize potential risk of spread of SARS-CoV-2. Objective: To analyze how Boston University (BU) fully reopened its campus in the fall of 2020 and controlled COVID-19 transmission despite worsening transmission in Boston, Massachusetts. Design, Setting, and Participants: This multifaceted intervention case series was conducted at a large urban university campus in Boston, Massachusetts, during the fall 2020 semester. The BU response included a high-throughput SARS-CoV-2 polymerase chain reaction testing facility with capacity to deliver results in less than 24 hours; routine asymptomatic screening for COVID-19; daily health attestations; adherence monitoring and feedback; robust contact tracing, quarantine, and isolation in on-campus facilities; face mask use; enhanced hand hygiene; social distancing recommendations; dedensification of classrooms and public places; and enhancement of all building air systems. Data were analyzed from December 20, 2020, to January 31, 2021. Main Outcomes and Measures: SARS-CoV-2 diagnosis confirmed by reverse transcription-polymerase chain reaction of anterior nares specimens and sources of transmission, as determined through contact tracing. Results: Between August and December 2020, BU conducted more than 500 000 COVID-19 tests and identified 719 individuals with COVID-19, including 496 students (69.0%), 11 faculty (1.5%), and 212 staff (29.5%). Overall, 718 individuals, or 1.8% of the BU community, had test results positive for SARS-CoV-2. Of 837 close contacts traced, 86 individuals (10.3%) had test results positive for COVID-19. BU contact tracers identified a source of transmission for 370 individuals (51.5%), with 206 individuals (55.7%) identifying a non-BU source. Among 5 faculty and 84 staff with SARS-CoV-2 with a known source of infection, most reported a transmission source outside of BU (all 5 faculty members [100%] and 67 staff members [79.8%]). A BU source was identified by 108 of 183 undergraduate students with SARS-CoV-2 (59.0%) and 39 of 98 graduate students with SARS-CoV-2 (39.8%); notably, no transmission was traced to a classroom setting. Conclusions and Relevance: In this case series of COVID-19 transmission, BU used a coordinated strategy of testing, contact tracing, isolation, and quarantine, with robust management and oversight, to control COVID-19 transmission in an urban university setting.


Subject(s)
COVID-19/prevention & control , Infection Control/standards , Universities/trends , Urban Population/statistics & numerical data , Boston/epidemiology , COVID-19/epidemiology , COVID-19/transmission , Contact Tracing/instrumentation , Contact Tracing/methods , Hand Hygiene/methods , Humans , Infection Control/methods , Infection Control/statistics & numerical data , Quarantine/methods , Universities/organization & administration
5.
Nature ; 594(7863): 408-412, 2021 06.
Article in English | MEDLINE | ID: covidwho-1225509

ABSTRACT

The COVID-19 pandemic has seen the emergence of digital contact tracing to help to prevent the spread of the disease. A mobile phone app records proximity events between app users, and when a user tests positive for COVID-19, their recent contacts can be notified instantly. Theoretical evidence has supported this new public health intervention1-6, but its epidemiological impact has remained uncertain7. Here we investigate the impact of the National Health Service (NHS) COVID-19 app for England and Wales, from its launch on 24 September 2020 to the end of December 2020. It was used regularly by approximately 16.5 million users (28% of the total population), and sent approximately 1.7 million exposure notifications: 4.2 per index case consenting to contact tracing. We estimated that the fraction of individuals notified by the app who subsequently showed symptoms and tested positive (the secondary attack rate (SAR)) was 6%, similar to the SAR for manually traced close contacts. We estimated the number of cases averted by the app using two complementary approaches: modelling based on the notifications and SAR gave an estimate of 284,000 (central 95% range of sensitivity analyses 108,000-450,000), and statistical comparison of matched neighbouring local authorities gave an estimate of 594,000 (95% confidence interval 317,000-914,000). Approximately one case was averted for each case consenting to notification of their contacts. We estimated that for every percentage point increase in app uptake, the number of cases could be reduced by 0.8% (using modelling) or 2.3% (using statistical analysis). These findings support the continued development and deployment of such apps in populations that are awaiting full protection from vaccines.


Subject(s)
COVID-19/epidemiology , COVID-19/prevention & control , Contact Tracing/instrumentation , Contact Tracing/methods , Mobile Applications/statistics & numerical data , Basic Reproduction Number , COVID-19/mortality , COVID-19/transmission , England/epidemiology , Humans , Mortality , National Health Programs , Quarantine , Wales/epidemiology
6.
Epidemiol Infect ; 149: e77, 2021 03 25.
Article in English | MEDLINE | ID: covidwho-1203371

ABSTRACT

Control of the novel COronaVIrus Disease-2019 (COVID-19) in a hospital setting is a priority. A COVID-19-infected surgeon performed surgical activities before being tested. An exposure risk classification was applied to the identified exposed subjects and high- and medium-risk contacts underwent active symptom monitoring for 14 days at home. All healthcare professionals (HCPs) were tested for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) at the end of the quarantine and serological tests were performed. Three household contacts and 20 HCPs were identified as high- or medium-risk contacts and underwent a 14-day quarantine. Fourteen HCPs and 19 patients were instead classified as low risk. All the contacts remained asymptomatic and all HCPs tested negative for SARS-CoV-2. About 25-28 days after their last exposure, HCPs underwent serological testing and two of them had positive IgM but negative confirmatory swabs. In a low COVID-19 burden area, the in-hospital transmission of SARS-CoV-2 from an infectious doctor did not occur and, despite multiple and frequent contacts, a hospital outbreak was avoided. This may be linked to the adoption of specific recommendations and to the use of standard personal protective equipment by HCPs.


Subject(s)
COVID-19/diagnosis , Surgeons , COVID-19/etiology , COVID-19/psychology , Contact Tracing/instrumentation , Contact Tracing/methods , Epidemiology , Humans , Infection Control/standards , Pandemics/prevention & control , Personal Protective Equipment/standards
7.
Value Health ; 24(5): 658-667, 2021 05.
Article in English | MEDLINE | ID: covidwho-1126958

ABSTRACT

OBJECTIVES: Our study investigates the extent to which uptake of a COVID-19 digital contact-tracing (DCT) app among the Dutch population is affected by its configurations, its societal effects, and government policies toward such an app. METHODS: We performed a discrete choice experiment among Dutch adults including 7 attributes, that is, who gets a notification, waiting time for testing, possibility for shops to refuse customers who have not installed the app, stopping condition for contact tracing, number of people unjustifiably quarantined, number of deaths prevented, and number of households with financial problems prevented. The data were analyzed by means of panel mixed logit models. RESULTS: The prevention of deaths and financial problems of households had a very strong influence on the uptake of the app. Predicted app uptake rates ranged from 24% to 78% for the worst and best possible app for these societal effects. We found a strong positive relationship between people's trust in government and people's propensity to install the DCT app. CONCLUSIONS: The uptake levels we find are much more volatile than the uptake levels predicted in comparable studies that did not include societal effects in their discrete choice experiments. Our finding that the societal effects are a major factor in the uptake of the DCT app results in a chicken-or-the-egg causality dilemma. That is, the societal effects of the app are severely influenced by the uptake of the app, but the uptake of the app is severely influenced by its societal effects.


Subject(s)
COVID-19/diagnosis , Contact Tracing/instrumentation , Mobile Applications/standards , Social Change , COVID-19/epidemiology , Contact Tracing/statistics & numerical data , Health Policy , Humans , Netherlands , Public Health/instrumentation , Public Health/methods , Surveys and Questionnaires
10.
JMIR Public Health Surveill ; 7(1): e25701, 2021 01 06.
Article in English | MEDLINE | ID: covidwho-978994

ABSTRACT

BACKGROUND: Digital proximity tracing apps have been released to mitigate the transmission of SARS-CoV-2, the virus known to cause COVID-19. However, it remains unclear how the acceptance and uptake of these apps can be improved. OBJECTIVE: This study aimed to investigate the coverage of the SwissCovid app and the reasons for its nonuse in Switzerland during a period of increasing incidence of COVID-19 cases. METHODS: We collected data between September 28 and October 8, 2020, via a nationwide online panel survey (COVID-19 Social Monitor, N=1511). We examined sociodemographic and behavioral factors associated with app use by using multivariable logistic regression, whereas reasons for app nonuse were analyzed descriptively. RESULTS: Overall, 46.5% (703/1511) of the survey participants reported they used the SwissCovid app, which was an increase from 43.9% (662/1508) reported in the previous study wave conducted in July 2020. A higher monthly household income (ie, income >CHF 10,000 or >US $11,000 vs income ≤CHF 6000 or

Subject(s)
COVID-19/psychology , Contact Tracing/instrumentation , Mobile Applications/standards , Physical Distancing , Adult , Aged , COVID-19/complications , COVID-19/transmission , Contact Tracing/trends , Female , Humans , Male , Middle Aged , Mobile Applications/statistics & numerical data , Surveys and Questionnaires , Switzerland
11.
JMIR Mhealth Uhealth ; 8(10): e23148, 2020 10 29.
Article in English | MEDLINE | ID: covidwho-976119

ABSTRACT

BACKGROUND: Effective contact tracing is labor intensive and time sensitive during the COVID-19 pandemic, but also essential in the absence of effective treatment and vaccines. Singapore launched the first Bluetooth-based contact tracing app-TraceTogether-in March 2020 to augment Singapore's contact tracing capabilities. OBJECTIVE: This study aims to compare the performance of the contact tracing app-TraceTogether-with that of a wearable tag-based real-time locating system (RTLS) and to validate them against the electronic medical records at the National Centre for Infectious Diseases (NCID), the national referral center for COVID-19 screening. METHODS: All patients and physicians in the NCID screening center were issued RTLS tags (CADI Scientific) for contact tracing. In total, 18 physicians were deployed to the NCID screening center from May 10 to May 20, 2020. The physicians activated the TraceTogether app (version 1.6; GovTech) on their smartphones during shifts and urged their patients to use the app. We compared patient contacts identified by TraceTogether and those identified by RTLS tags within the NCID vicinity during physicians' 10-day posting. We also validated both digital contact tracing tools by verifying the physician-patient contacts with the electronic medical records of 156 patients who attended the NCID screening center over a 24-hour time frame within the study period. RESULTS: RTLS tags had a high sensitivity of 95.3% for detecting patient contacts identified either by the system or TraceTogether while TraceTogether had an overall sensitivity of 6.5% and performed significantly better on Android phones than iPhones (Android: 9.7%, iPhone: 2.7%; P<.001). When validated against the electronic medical records, RTLS tags had a sensitivity of 96.9% and specificity of 83.1%, while TraceTogether only detected 2 patient contacts with physicians who did not attend to them. CONCLUSIONS: TraceTogether had a much lower sensitivity than RTLS tags for identifying patient contacts in a clinical setting. Although the tag-based RTLS performed well for contact tracing in a clinical setting, its implementation in the community would be more challenging than TraceTogether. Given the uncertainty of the adoption and capabilities of contact tracing apps, policy makers should be cautioned against overreliance on such apps for contact tracing. Nonetheless, leveraging technology to augment conventional manual contact tracing is a necessary move for returning some normalcy to life during the long haul of the COVID-19 pandemic.


Subject(s)
Computer Systems , Contact Tracing/instrumentation , Coronavirus Infections/prevention & control , Mobile Applications , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Wearable Electronic Devices , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Cross-Sectional Studies , Electronic Health Records , Humans , Physician-Patient Relations , Pneumonia, Viral/epidemiology , Reproducibility of Results , Singapore/epidemiology
12.
ACS Nano ; 14(12): 16180-16193, 2020 12 22.
Article in English | MEDLINE | ID: covidwho-974870

ABSTRACT

The management of the COVID-19 pandemic has relied on cautious contact tracing, quarantine, and sterilization protocols while we await a vaccine to be made widely available. Telemedicine or mobile health (mHealth) is well-positioned during this time to reduce potential disease spread and prevent overloading of the healthcare system through at-home COVID-19 screening, diagnosis, and monitoring. With the rise of mass-fabricated electronics for wearable and portable sensors, emerging telemedicine tools have been developed to address shortcomings in COVID-19 diagnostics, monitoring, and management. In this Perspective, we summarize current implementations of mHealth sensors for COVID-19, highlight recent technological advances, and provide an overview on how these tools may be utilized to better control the COVID-19 pandemic.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , COVID-19/therapy , Disease Management , SARS-CoV-2/genetics , Telemedicine/methods , Antigens, Viral/analysis , Biosensing Techniques/instrumentation , COVID-19/pathology , COVID-19/virology , COVID-19 Testing/instrumentation , Contact Tracing/instrumentation , Humans , Mobile Applications/supply & distribution , Monitoring, Physiologic/instrumentation , Monitoring, Physiologic/methods , Nanotechnology/instrumentation , Nanotechnology/methods , Physical Distancing , Point-of-Care Systems/organization & administration , Point-of-Care Testing/organization & administration , Quarantine/organization & administration , SARS-CoV-2/immunology , Telemedicine/instrumentation
13.
Int J Infect Dis ; 101: 348-352, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-816546

ABSTRACT

AIM: Comprehensive case investigation and contact tracing are crucial to prevent community spread of COVID-19. We demonstrated a utility of using traditional contact tracing measures supplemented with symptom tracking and contact management system to assist public health workers with high efficiency. METHODS: A centralized contact tracing system was developed to support data linkage, cross-jurisdictional coordination, and follow-up of contacts' health status. We illustrated the process of how digital tools support contact tracing and management of COVID-19 cases and measured the timeliness from case detection to contact monitoring to evaluate system performance. RESULTS: Among the 8051 close contacts of the 487 confirmed cases (16.5 close contacts/case, 95% CI [13.9-19.1]), the median elapsed time from last exposure to quarantine was three days (IQR 1-5). By implementing the approach of self-reporting using automatic text-messages and web-app, the percentage of health status updates from self-reporting increased from 22.5% to 61.5%. The high proportion of secondary cases detected via contact tracing (88%) might reduce the R0 to under one and minimize the impact of local transmission in the community. CONCLUSION: Comprehensive contact tracing and management with complementary technology would still be a pillar of strategies for containing outbreaks during de-escalation or early in the next wave of COVID-19 pandemic.


Subject(s)
COVID-19/prevention & control , Contact Tracing/methods , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , COVID-19/transmission , COVID-19/virology , Child , Child, Preschool , Contact Tracing/instrumentation , Coronavirus Infections/epidemiology , Female , Humans , Infant , Male , Middle Aged , Pandemics/prevention & control , Pneumonia, Viral/diagnosis , Public Health , SARS-CoV-2/physiology , Taiwan/epidemiology , Telephone , Young Adult
14.
Cochrane Database Syst Rev ; 8: CD013699, 2020 08 18.
Article in English | MEDLINE | ID: covidwho-777340

ABSTRACT

BACKGROUND: Reducing the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global priority. Contact tracing identifies people who were recently in contact with an infected individual, in order to isolate them and reduce further transmission. Digital technology could be implemented to augment and accelerate manual contact tracing. Digital tools for contact tracing may be grouped into three areas: 1) outbreak response; 2) proximity tracing; and 3) symptom tracking. We conducted a rapid review on the effectiveness of digital solutions to contact tracing during infectious disease outbreaks. OBJECTIVES: To assess the benefits, harms, and acceptability of personal digital contact tracing solutions for identifying contacts of an identified positive case of an infectious disease. SEARCH METHODS: An information specialist searched the literature from 1 January 2000 to 5 May 2020 in CENTRAL, MEDLINE, and Embase. Additionally, we screened the Cochrane COVID-19 Study Register. SELECTION CRITERIA: We included randomised controlled trials (RCTs), cluster-RCTs, quasi-RCTs, cohort studies, cross-sectional studies and modelling studies, in general populations. We preferentially included studies of contact tracing during infectious disease outbreaks (including COVID-19, Ebola, tuberculosis, severe acute respiratory syndrome virus, and Middle East respiratory syndrome) as direct evidence, but considered comparative studies of contact tracing outside an outbreak as indirect evidence. The digital solutions varied but typically included software (or firmware) for users to install on their devices or to be uploaded to devices provided by governments or third parties. Control measures included traditional or manual contact tracing, self-reported diaries and surveys, interviews, other standard methods for determining close contacts, and other technologies compared to digital solutions (e.g. electronic medical records). DATA COLLECTION AND ANALYSIS: Two review authors independently screened records and all potentially relevant full-text publications. One review author extracted data for 50% of the included studies, another extracted data for the remaining 50%; the second review author checked all the extracted data. One review author assessed quality of included studies and a second checked the assessments. Our outcomes were identification of secondary cases and close contacts, time to complete contact tracing, acceptability and accessibility issues, privacy and safety concerns, and any other ethical issue identified. Though modelling studies will predict estimates of the effects of different contact tracing solutions on outcomes of interest, cohort studies provide empirically measured estimates of the effects of different contact tracing solutions on outcomes of interest. We used GRADE-CERQual to describe certainty of evidence from qualitative data and GRADE for modelling and cohort studies. MAIN RESULTS: We identified six cohort studies reporting quantitative data and six modelling studies reporting simulations of digital solutions for contact tracing. Two cohort studies also provided qualitative data. Three cohort studies looked at contact tracing during an outbreak, whilst three emulated an outbreak in non-outbreak settings (schools). Of the six modelling studies, four evaluated digital solutions for contact tracing in simulated COVID-19 scenarios, while two simulated close contacts in non-specific outbreak settings. Modelling studies Two modelling studies provided low-certainty evidence of a reduction in secondary cases using digital contact tracing (measured as average number of secondary cases per index case - effective reproductive number (R eff)). One study estimated an 18% reduction in R eff with digital contact tracing compared to self-isolation alone, and a 35% reduction with manual contact-tracing. Another found a reduction in R eff for digital contact tracing compared to self-isolation alone (26% reduction) and a reduction in R eff for manual contact tracing compared to self-isolation alone (53% reduction). However, the certainty of evidence was reduced by unclear specifications of their models, and assumptions about the effectiveness of manual contact tracing (assumed 95% to 100% of contacts traced), and the proportion of the population who would have the app (53%). Cohort studies Two cohort studies provided very low-certainty evidence of a benefit of digital over manual contact tracing. During an Ebola outbreak, contact tracers using an app found twice as many close contacts per case on average than those using paper forms. Similarly, after a pertussis outbreak in a US hospital, researchers found that radio-frequency identification identified 45 close contacts but searches of electronic medical records found 13. The certainty of evidence was reduced by concerns about imprecision, and serious risk of bias due to the inability of contact tracing study designs to identify the true number of close contacts. One cohort study provided very low-certainty evidence that an app could reduce the time to complete a set of close contacts. The certainty of evidence for this outcome was affected by imprecision and serious risk of bias. Contact tracing teams reported that digital data entry and management systems were faster to use than paper systems and possibly less prone to data loss. Two studies from lower- or middle-income countries, reported that contact tracing teams found digital systems simpler to use and generally preferred them over paper systems; they saved personnel time, reportedly improved accuracy with large data sets, and were easier to transport compared with paper forms. However, personnel faced increased costs and internet access problems with digital compared to paper systems. Devices in the cohort studies appeared to have privacy from contacts regarding the exposed or diagnosed users. However, there were risks of privacy breaches from snoopers if linkage attacks occurred, particularly for wearable devices. AUTHORS' CONCLUSIONS: The effectiveness of digital solutions is largely unproven as there are very few published data in real-world outbreak settings. Modelling studies provide low-certainty evidence of a reduction in secondary cases if digital contact tracing is used together with other public health measures such as self-isolation. Cohort studies provide very low-certainty evidence that digital contact tracing may produce more reliable counts of contacts and reduce time to complete contact tracing. Digital solutions may have equity implications for at-risk populations with poor internet access and poor access to digital technology. Stronger primary research on the effectiveness of contact tracing technologies is needed, including research into use of digital solutions in conjunction with manual systems, as digital solutions are unlikely to be used alone in real-world settings. Future studies should consider access to and acceptability of digital solutions, and the resultant impact on equity. Studies should also make acceptability and uptake a primary research question, as privacy concerns can prevent uptake and effectiveness of these technologies.


Subject(s)
Contact Tracing/methods , Disease Outbreaks/prevention & control , Mobile Applications/statistics & numerical data , Botswana/epidemiology , COVID-19/epidemiology , COVID-19/prevention & control , Cohort Studies , Contact Tracing/instrumentation , Coronavirus Infections/epidemiology , Hemorrhagic Fever, Ebola/epidemiology , Hemorrhagic Fever, Ebola/prevention & control , Humans , Models, Theoretical , Patient Isolation/statistics & numerical data , Privacy , Quarantine/statistics & numerical data , Secondary Prevention/methods , Secondary Prevention/statistics & numerical data , Sierra Leone/epidemiology , Tuberculosis/epidemiology , Tuberculosis/prevention & control , United States/epidemiology , Whooping Cough/epidemiology , Whooping Cough/prevention & control
15.
PLoS One ; 15(9): e0238973, 2020.
Article in English | MEDLINE | ID: covidwho-760709

ABSTRACT

New technological solutions play an important role in preventing the spread of Covid-19. Many countries have implemented tracking applications or other surveillance systems, which may raise concerns about privacy and civil rights violations but may be also perceived by citizens as a way to reduce threat and uncertainty. Our research examined whether feelings evoked by the pandemic (perceived threat and lack of control) as well as more stable ideological views predict the acceptance of such technologies. In two studies conducted in Poland, we found that perceived personal threat and lack of personal control were significantly positively related to the acceptance of surveillance technologies, but their predictive value was smaller than that of individual differences in authoritarianism and endorsement of liberty. Moreover, we found that the relationship between the acceptance of surveillance technologies and both perceived threat and lack of control was particularly strong among people high in authoritarianism. Our research shows that the negative feelings evoked by the unprecedented global crisis may inspire positive attitudes towards helpful but controversial surveillance technologies but that they do so to a lesser extent than ideological beliefs.


Subject(s)
Attitude , Contact Tracing/methods , Coronavirus Infections/psychology , Mobile Applications , Pneumonia, Viral/psychology , Quarantine/psychology , COVID-19 , Contact Tracing/instrumentation , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Culture , Humans , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Poland , Political Activism , Privacy
16.
JAMA Intern Med ; 180(12): 1614-1620, 2020 12 01.
Article in English | MEDLINE | ID: covidwho-738907

ABSTRACT

Importance: It is unknown how well cell phone location data portray social distancing strategies or if they are associated with the incidence of coronavirus disease 2019 (COVID-19) cases in a particular geographical area. Objective: To determine if cell phone location data are associated with the rate of change in new COVID-19 cases by county across the US. Design, Setting, and Participants: This cohort study incorporated publicly available county-level daily COVID-19 case data from January 22, 2020, to May 11, 2020, and county-level daily cell phone location data made publicly available by Google. It examined the daily cases of COVID-19 per capita and daily estimates of cell phone activity compared with the baseline (where baseline was defined as the median value for that day of the week from a 5-week period between January 3 and February 6, 2020). All days and counties with available data after the initiation of stay-at-home orders for each state were included. Exposures: The primary exposure was cell phone activity compared with baseline for each day and each county in different categories of place. Main Outcomes and Measures: The primary outcome was the percentage change in COVID-19 cases 5 days from the exposure date. Results: Between 949 and 2740 US counties and between 22 124 and 83 745 daily observations were studied depending on the availability of cell phone data for that county and day. Marked changes in cell phone activity occurred around the time stay-at-home orders were issued by various states. Counties with higher per-capita cases (per 100 000 population) showed greater reductions in cell phone activity at the workplace (ß, -0.002; 95% CI, -0.003 to -0.001; P < 0.001), areas classified as retail (ß, -0.008; 95% CI, -0.011 to -0.005; P < 0.001) and grocery stores (ß, -0.006; 95% CI, -0.007 to -0.004; P < 0.001), and transit stations (ß, -0.003, 95% CI, -0.005 to -0.002; P < 0.001), and greater increase in activity at the place of residence (ß, 0.002; 95% CI, 0.001-0.002; P < 0.001). Adjusting for county-level and state-level characteristics, counties with the greatest decline in workplace activity, transit stations, and retail activity and the greatest increases in time spent at residential places had lower percentage growth in cases at 5, 10, and 15 days. For example, counties in the lowest quartile of retail activity had a 45.5% lower growth in cases at 15 days compared with the highest quartile (SD, 37.4%-53.5%; P < .001). Conclusions and Relevance: Our findings support the hypothesis that greater reductions in cell phone activity in the workplace and retail locations, and greater increases in activity at the residence, are associated with lesser growth in COVID-19 cases. These data provide support for the value of monitoring cell phone location data to anticipate future trends of the pandemic.


Subject(s)
COVID-19 , Cell Phone Use/statistics & numerical data , Communicable Disease Control/organization & administration , Contact Tracing , Geographic Information Systems , COVID-19/epidemiology , COVID-19/prevention & control , Contact Tracing/instrumentation , Contact Tracing/methods , Contact Tracing/statistics & numerical data , Epidemiological Monitoring , Geographic Information Systems/instrumentation , Geographic Information Systems/statistics & numerical data , Government Regulation , Humans , Physical Distancing , Public Health , SARS-CoV-2 , United States/epidemiology
17.
Glob Health Res Policy ; 5: 36, 2020.
Article in English | MEDLINE | ID: covidwho-692453

ABSTRACT

Among the most critical strategies in the fight against the Corona Virus Disease (COVID-19) is the rapid tracing and notification of potentially infected persons. Several nations have implemented mobile software applications ("apps") to alert persons exposed to the coronavirus. The expected advantages of this new technology over the traditional method of contact tracing include speed, specificity, and mass reach. Beyond its use for mitigating and containing COVID-19, digital technology can complement or even augment the traditional approach to global health program implementation. However, as with any new system, strong regulatory frameworks are necessary to ensure that individual information is not used for surveillance purposes, and user privacy will be maintained. Having safeguarded this, perhaps the global health community will witness the beginning of a new era of implementing mass health programs through the medium of digital technology.


Subject(s)
COVID-19/epidemiology , Communicable Disease Control/instrumentation , Contact Tracing/instrumentation , Digital Technology/statistics & numerical data , Disease Outbreaks/statistics & numerical data , Public Health/methods , Humans
18.
Infect Dis Poverty ; 9(1): 93, 2020 Jul 13.
Article in English | MEDLINE | ID: covidwho-641146

ABSTRACT

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic has sparked attention in many countries, especially those that have experienced a steep spike in the number of identified cases. The continued spread of the coronavirus suggests that this situation may be here to stay for a while. Contact tracing is a time-consuming and resource-intensive process, which taps on the already lean healthcare resource in certain countries. Furthermore, the massive infodemic on COVID-19 on the Internet has also resulted in widespread circulation of misinformation online. This outbreak has evoked irrational fear and anxiety from the public, which has resulted in destabilizing of societal norms, such as panic buying and hoarding of daily necessities, and can potentially pose serious health risks to the public. The aim of this paper is to present a COVID-19 Symptom Monitoring and Contact Tracking Record (CoV-SCR) web-app as a bottom-up, proactive approach to supplement the current management strategies for COVID-19. MAIN TEXT: The CoV-SCR web-app ( http://bit.ly/covscrapp ) enables individuals to keep a personal record of their close contacts and monitor their symptoms on a daily basis, so that they can provide relevant and accurate details when they see the doctor and during the contact tracing process. Individuals can record their temperature and rate their symptoms on a 5-point severity scale, as well as record details of their travel and contact history for the last 14 days. The recorded information will be sent to their e-mail address for potential symptom monitoring and contact tracing purposes. In addition, this web-app consolidates evidence-based information on the coronavirus from credible sources, such as the World Health Organization, countries' health authorities, and PubMed literature. CONCLUSIONS: A COVID-19 Symptom Monitoring and Contact Tracking web-app has been developed to facilitate contact tracing efforts through public engagement. This app serves an additional purpose of providing information about COVID-19 from reliable resources.


Subject(s)
Betacoronavirus/physiology , Contact Tracing/methods , Coronavirus Infections/diagnosis , Epidemiological Monitoring , Health Records, Personal , Mobile Applications , Pneumonia, Viral/diagnosis , COVID-19 , Contact Tracing/instrumentation , Humans , Pandemics , SARS-CoV-2
19.
Int J Clin Pharmacol Ther ; 58(8): 417-425, 2020 Aug.
Article in English | MEDLINE | ID: covidwho-638831

ABSTRACT

AIMS OF THE STUDY: Published data show that the current progression of the COVID-19 pandemic in Heidelberg, Germany, despite the current lockdown, could continue into 2021 and become more severe. We have used the modified Bateman SIZ algorithm to predict the effects of interventional measures to control the COVID-19 pandemic. MATERIALS AND METHODS: Model parameters, e.g., doubling time and rate of decrease in the number of infectious persons were obtained from published reports. Predictions were made for the status quo on June 1, 2020, and for interventional measures obtained for 4 scenarios. These included vaccination of the whole population using a SARS-CoV-2 vaccine having an efficacy of 50% and 100%, mass-testing for COVID-19 coronavirus and application of the Corona-Warn-App. RESULTS: The principle findings were 1) without new measures to control the pandemic, the daily number of infectious persons could reach a peak of > 4,500 daily within 18 months when > 67,000 persons would have been infected. This could be prevented by using a vaccine with 50% efficacy which was almost equally effective as a vaccine with 100% efficacy. Application of the Corona-Warn-App was the most effective method and more effective than testing for COVID-19. The methodology used has been described in detail to enable other researchers to apply the modified Bateman SIZ model to obtain predictions for COVID-19 outbreaks in other regions. Application of the model has been verified by independent investigators using different commercial software packages. CONCLUSION: The modified Bateman SIZ model has been verified and used to predict the course of the COVID-19 pandemic in Heidelberg. Lockdown measures alone are insufficient to control the pandemic during 2021. Vaccination, diagnostic tests, and use of the Corona-Warn-App with quarantine could successfully control the spread of the coronavirus infection in the community. The Corona-Warn-App applied correctly may be the most effective. The model showed that vaccination with 50% efficacy is almost as effective as vaccination with 100% efficacy.


Subject(s)
Coronavirus Infections/epidemiology , Models, Statistical , Pneumonia, Viral/epidemiology , Algorithms , Betacoronavirus , COVID-19 , COVID-19 Testing , COVID-19 Vaccines , Clinical Laboratory Techniques , Communicable Disease Control , Contact Tracing/instrumentation , Coronavirus Infections/diagnosis , Coronavirus Infections/prevention & control , Germany , Humans , Mass Screening , Mobile Applications , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , SARS-CoV-2 , Vaccination , Viral Vaccines
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