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1.
Proc Natl Acad Sci U S A ; 119(34): e2200652119, 2022 08 23.
Article in English | MEDLINE | ID: covidwho-1991763

ABSTRACT

Although testing, contact tracing, and case isolation programs can mitigate COVID-19 transmission and allow the relaxation of social distancing measures, few countries worldwide have succeeded in scaling such efforts to levels that suppress spread. The efficacy of test-trace-isolate likely depends on the speed and extent of follow-up and the prevalence of SARS-CoV-2 in the community. Here, we use a granular model of COVID-19 transmission to estimate the public health impacts of test-trace-isolate programs across a range of programmatic and epidemiological scenarios, based on testing and contact tracing data collected on a university campus and surrounding community in Austin, TX, between October 1, 2020, and January 1, 2021. The median time between specimen collection from a symptomatic case and quarantine of a traced contact was 2 days (interquartile range [IQR]: 2 to 3) on campus and 5 days (IQR: 3 to 8) in the community. Assuming a reproduction number of 1.2, we found that detection of 40% of all symptomatic cases followed by isolation is expected to avert 39% (IQR: 30% to 45%) of COVID-19 cases. Contact tracing is expected to increase the cases averted to 53% (IQR: 42% to 58%) or 40% (32% to 47%), assuming the 2- and 5-day delays estimated on campus and in the community, respectively. In a tracing-accelerated scenario, in which 75% of contacts are notified the day after specimen collection, cases averted increase to 68% (IQR: 55% to 72%). An accelerated contact tracing program leveraging rapid testing and electronic reporting of test results can significantly curtail local COVID-19 transmission.


Subject(s)
COVID-19 Testing , COVID-19 , Contact Tracing , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/transmission , COVID-19 Testing/standards , COVID-19 Testing/statistics & numerical data , Contact Tracing/statistics & numerical data , Humans , Quarantine , SARS-CoV-2 , Texas/epidemiology
2.
BMC Med ; 20(1): 199, 2022 05 23.
Article in English | MEDLINE | ID: covidwho-1862132

ABSTRACT

BACKGROUND: As we are confronted with more transmissible/severe variants with immune escape and the waning of vaccine efficacy, it is particularly relevant to understand how the social contacts of individuals at greater risk of COVID-19 complications evolved over time. We described time trends in social contacts of individuals according to comorbidity and vaccination status before and during the first three waves of the COVID-19 pandemic in Quebec, Canada. METHODS: We used data from CONNECT, a repeated cross-sectional population-based survey of social contacts conducted before (2018/2019) and during the pandemic (April 2020 to July 2021). We recruited non-institutionalized adults from Quebec, Canada, by random digit dialling. We used a self-administered web-based questionnaire to measure the number of social contacts of participants (two-way conversation at a distance ≤2 m or a physical contact, irrespective of masking). We compared the mean number of contacts/day according to the comorbidity status of participants (pre-existing medical conditions with symptoms/medication in the past 12 months) and 1-dose vaccination status during the third wave. All analyses were performed using weighted generalized linear models with a Poisson distribution and robust variance. RESULTS: A total of 1441 and 5185 participants with and without comorbidities, respectively, were included in the analyses. Contacts significantly decreased from a mean of 6.1 (95%CI 4.9-7.3) before the pandemic to 3.2 (95%CI 2.5-3.9) during the first wave among individuals with comorbidities and from 8.1 (95%CI 7.3-9.0) to 2.7 (95%CI 2.2-3.2) among individuals without comorbidities. Individuals with comorbidities maintained fewer contacts than those without comorbidities in the second wave, with a significant difference before the Christmas 2020/2021 holidays (2.9 (95%CI 2.5-3.2) vs 3.9 (95%CI 3.5-4.3); P<0.001). During the third wave, contacts were similar for individuals with (4.1, 95%CI 3.4-4.7) and without comorbidities (4.5, 95%CI 4.1-4.9; P=0.27). This could be partly explained by individuals with comorbidities vaccinated with their first dose who increased their contacts to the level of those without comorbidities. CONCLUSIONS: It will be important to closely monitor COVID-19-related outcomes and social contacts by comorbidity and vaccination status to inform targeted or population-based interventions (e.g., booster doses of the vaccine).


Subject(s)
COVID-19 , Contact Tracing , Vaccination Coverage , Adult , COVID-19/epidemiology , COVID-19/prevention & control , Comorbidity , Contact Tracing/statistics & numerical data , Contact Tracing/trends , Cross-Sectional Studies , Humans , Pandemics/prevention & control , SARS-CoV-2 , Social Behavior , Time Factors , Vaccination/statistics & numerical data , Vaccination/trends , Vaccination Coverage/statistics & numerical data , Vaccination Coverage/trends
3.
Lancet Public Health ; 7(3): e259-e273, 2022 03.
Article in English | MEDLINE | ID: covidwho-1683803

ABSTRACT

BACKGROUND: Contact tracing is used for multiple infectious diseases, most recently for COVID-19, but data regarding its effectiveness in disease control are scarce. To address this knowledge gap and inform public health decision making for COVID-19, we systematically reviewed the existing literature to determine the effectiveness of contact tracing in the control of communicable illness. METHODS: We searched PubMed, Embase, and the Cochrane Library from database inception up to Nov 22, 2021, for published studies evaluating associations between provider-initiated contact tracing for transmissible infectious diseases and one of three outcomes of interest: case detection rates among contacts or at the community level, overall forward transmission, or overall disease incidence. Clinical trials and observational studies were eligible, with no language or date restrictions. Reference lists of reviews were searched for additional studies. We excluded studies without a control group, using only mathematical modelling, not reporting a primary outcome of interest, or solely examining patient-initiated contact tracing. One reviewer applied eligibility criteria to each screened abstract and full-text article, and two reviewers independently extracted summary effect estimates and additional data from eligible studies. Only data reported in published manuscripts or supplemental material was extracted. Risk of bias for each included study was assessed with the Cochrane Risk of Bias 2 tool (randomised studies) or the Newcastle-Ottawa Scale (non-randomised studies). FINDINGS: We identified 9050 unique citations, of which 47 studies met the inclusion criteria: six were focused on COVID-19, 20 on tuberculosis, eight on HIV, 12 on curable sexually transmitted infections (STIs), and one on measles. More than 2 million index patients were included across a variety of settings (both urban and rural areas and low-resource and high-resource settings). Of the 47 studies, 29 (61·7%) used observational designs, including all studies on COVID-19, and 18 (38·3%) were randomised controlled trials. 40 studies compared provider-initiated contact tracing with other interventions or evaluated expansions of provider-initiated contact tracing, and seven compared programmatic adaptations within provider-initiated contact tracing. 29 (72·5%) of the 40 studies evaluating the effect of provider-initiated contact tracing, including four (66·7%) of six COVID-19 studies, found contact tracing interventions were associated with improvements in at least one outcome of interest. 23 (48·9%) studies had low risk of bias, 22 (46·8%) studies had some risk of bias, and two (4·3%) studies (both randomised controlled trials on curable STIs) had high risk of bias. INTERPRETATION: Provider-initiated contact tracing can be an effective public health tool. However, the ability of authorities to make informed choices about its deployment might be limited by heterogenous approaches to contact tracing in studies, a scarcity of quantitative evidence on its effectiveness, and absence of specificity of tracing parameters most important for disease control. FUNDING: The Sullivan Family Foundation, Massachusetts General Hospital Executive Committee on Research, and US National Institutes of Health.


Subject(s)
COVID-19/epidemiology , Communicable Diseases/epidemiology , Contact Tracing/statistics & numerical data , Public Health , Humans , Sexually Transmitted Diseases/epidemiology , Tuberculosis/epidemiology
4.
Lancet Public Health ; 7(3): e250-e258, 2022 03.
Article in English | MEDLINE | ID: covidwho-1665603

ABSTRACT

BACKGROUND: Digital proximity tracing apps were rolled out early in the COVID-19 pandemic in many countries to complement conventional contact tracing. Empirical evidence about their benefits for pandemic response remains scarce. We evaluated the effectiveness and usefulness of COVIDSafe, Australia's national smartphone-based proximity tracing app for COVID-19. METHODS: In this prospective study, done in New South Wales (NSW), Australia, we included all individuals in the state who were older than 12 years with confirmed, locally acquired SARS-CoV-2 infection between May 4 and Nov 4, 2020. We used data from the NSW Notifiable Conditions Information Management System, the national COVIDSafe database, and information from case interviews, including information on app usage, the number of app-suggested contacts, and the number of app-suggested contacts determined by public health staff to be actual close contacts. We calculated the positive predictive value and sensitivity of COVIDSafe, its additional contact yield, and the number of averted public exposure events. Semi-structured interviews with public health staff were done to assess the app's perceived usefulness. FINDINGS: There were 619 confirmed COVID-19 cases with more than 25 300 close contacts identified by conventional contact tracing during the study period. COVIDSafe was used by 137 (22%) cases and detected 205 contacts, 79 (39%) of whom met the close contact definition. Its positive predictive value was therefore 39%. 35 (15%) of the 236 close contacts who could have been expected to have been using the app during the study period were identified by the app, making its estimated sensitivity 15%. 79 (0·3%) of the estimated 25 300 contacts in NSW were app-suggested and met the close contact definition. The app detected 17 (<0·1%) additional close contacts who were not identified by conventional contact tracing. COVIDSafe generated a substantial additional perceived workload for public health staff and was not considered useful. INTERPRETATION: The low uptake of the app among cases probably led to a reduced sensitivity estimate in our study, given that only contacts who were using the app could be detected. COVIDSafe was not sufficiently effective to make a meaningful contribution to the COVID-19 response in Australia's most populous state over a 6 month period. We provide an empirical evaluation of this digital contact tracing app that questions the potential benefits of digital contact tracing apps to the public health response to COVID-19. Effectiveness evaluations should be integrated into future implementations of proximity contact tracing systems to justify their investment. FUNDING: New South Wales Ministry of Health (Australia); National Health and Medical Research Council (Australia).


Subject(s)
COVID-19/epidemiology , Contact Tracing/statistics & numerical data , Mobile Applications , Public Health , Adult , Australia/epidemiology , Humans , Interviews as Topic , New South Wales , Prospective Studies , Surveys and Questionnaires
5.
PLoS One ; 16(12): e0261330, 2021.
Article in English | MEDLINE | ID: covidwho-1638355

ABSTRACT

Coronavirus disease 2019 (COVID-19) is an infectious disease of humans caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the first case was identified in China in December 2019 the disease has spread worldwide, leading to an ongoing pandemic. In this article, we present an agent-based model of COVID-19 in Luxembourg, and use it to estimate the impact, on cases and deaths, of interventions including testing, contact tracing, lockdown, curfew and vaccination. Our model is based on collation, with agents performing activities and moving between locations accordingly. The model is highly heterogeneous, featuring spatial clustering, over 2000 behavioural types and a 10 minute time resolution. The model is validated against COVID-19 clinical monitoring data collected in Luxembourg in 2020. Our model predicts far fewer cases and deaths than the equivalent equation-based SEIR model. In particular, with R0 = 2.45, the SEIR model infects 87% of the resident population while our agent-based model infects only around 23% of the resident population. Our simulations suggest that testing and contract tracing reduce cases substantially, but are less effective at reducing deaths. Lockdowns are very effective although costly, while the impact of an 11pm-6am curfew is relatively small. When vaccinating against a future outbreak, our results suggest that herd immunity can be achieved at relatively low coverage, with substantial levels of protection achieved with only 30% of the population fully immune. When vaccinating in the midst of an outbreak, the challenge is more difficult. In this context, we investigate the impact of vaccine efficacy, capacity, hesitancy and strategy. We conclude that, short of a permanent lockdown, vaccination is by far the most effective way to suppress and ultimately control the spread of COVID-19.


Subject(s)
COVID-19/epidemiology , Pandemics/prevention & control , Quarantine/statistics & numerical data , Vaccination/statistics & numerical data , Adolescent , Adult , Aged , Child , Child, Preschool , Contact Tracing/statistics & numerical data , Humans , Immunity, Herd , Infant , Infant, Newborn , Luxembourg/epidemiology , Masks/statistics & numerical data , Middle Aged , Young Adult
7.
PLoS One ; 16(11): e0260041, 2021.
Article in English | MEDLINE | ID: covidwho-1533420

ABSTRACT

BACKGROUND: In response to the COVID-19 pandemic, governments have implemented a range of non-pharmaceutical interventions (NPIs) and pharmaceutical interventions (PIs) to reduce transmission and minimise morbidity and mortality, whilst maintaining social and economic activities. The perceptions of public health workers (PHWs) and healthcare workers (HCWs) are essential to inform future COVID-19 strategies as they are viewed as trusted sources and are at the forefront of COVID-19 response. The objectives of this study were to 1) describe the practicality of implementing NPIs and PIs and 2) identify potential barriers to implementation, as perceived by HCWs and PHWs. METHODS: We conducted a cross-sectional study of PHWs and HCWs perceptions of the implementation, practicality of, and barriers to implementation of NPIs and PIs using an online survey (28/9/2020-1/11/2020) available in English, French and Portuguese. We used descriptive statistics and thematic analysis to analyse quantitative and qualitative responses. RESULTS: In total, 226 respondents (67 HCWs and 159 PHWs) from 52 countries completed the survey and 222 were included in the final analysis. Participants from low and middle-income countries (LMICs) accounted for 63% of HCWs and 67% of PHWs, with the remaining from high-income (HICs). There was little difference between the perceptions of PHWs and HCWs in HICs and LMICs, with the majority regarding a number of common NPIs as difficult to implement. However, PHWs in HICs perceived restrictions on schools and educational institutions to be more difficult to implement, with a lack of childcare support identified as the main barrier. Additionally, most contact tracing methods were perceived to be more difficult to implement in HICs than LMICs, with a range of barriers reported. A lack of public support was the most commonly reported barrier to NPIs overall across both country income and professional groups. Similarly, public fear of vaccine safety and lack of vaccine supply were the main reported barriers to implementing a COVID-19 vaccine. However, PHWs and HCWs in LMICs perceived a lack of financial support and the vaccine being manufactured in another country as additional barriers. CONCLUSION: This snapshot provides insight into the difficulty of implementing interventions as perceived by PHWs and HCWs. There is no one-size-fits-all solution to implementing interventions, and barriers in different contexts do vary. Barriers to implementing a vaccine programme expressed here by HCWs and PHCWs have subsequently come to the fore internationally.


Subject(s)
COVID-19 Vaccines/administration & dosage , COVID-19/prevention & control , Contact Tracing/statistics & numerical data , Health Knowledge, Attitudes, Practice , Health Personnel/psychology , Practice Guidelines as Topic/standards , SARS-CoV-2/physiology , Adolescent , Adult , Aged , COVID-19/transmission , COVID-19/virology , Cross-Sectional Studies , Developing Countries , Female , Humans , Immunization Programs/statistics & numerical data , Male , Middle Aged , Surveys and Questionnaires , Young Adult
8.
PLoS One ; 16(11): e0259970, 2021.
Article in English | MEDLINE | ID: covidwho-1526691

ABSTRACT

The COVID-19 pandemic has been particularly threatening to patients with end-stage kidney disease (ESKD) on intermittent hemodialysis and their care providers. Hemodialysis patients who receive life-sustaining medical therapy in healthcare settings, face unique challenges as they need to be at a dialysis unit three or more times a week, where they are confined to specific settings and tended to by dialysis nurses and staff with physical interaction and in close proximity. Despite the importance and critical situation of the dialysis units, modelling studies of the SARS-CoV-2 spread in these settings are very limited. In this paper, we have used a combination of discrete event and agent-based simulation models, to study the operations of a typical large dialysis unit and generate contact matrices to examine outbreak scenarios. We present the details of the contact matrix generation process and demonstrate how the simulation calculates a micro-scale contact matrix comprising the number and duration of contacts at a micro-scale time step. We have used the contacts matrix in an agent-based model to predict disease transmission under different scenarios. The results show that micro-simulation can be used to estimate contact matrices, which can be used effectively for disease modelling in dialysis and similar settings.


Subject(s)
COVID-19/transmission , Contact Tracing/statistics & numerical data , Disease Transmission, Infectious/statistics & numerical data , Hemodialysis Units, Hospital/statistics & numerical data , Computer Simulation , Humans , Models, Statistical
9.
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
10.
Math Biosci ; 338: 108645, 2021 08.
Article in English | MEDLINE | ID: covidwho-1492387

ABSTRACT

With more than 1.7 million COVID-19 deaths, identifying effective measures to prevent COVID-19 is a top priority. We developed a mathematical model to simulate the COVID-19 pandemic with digital contact tracing and testing strategies. The model uses a real-world social network generated from a high-resolution contact data set of 180 students. This model incorporates infectivity variations, test sensitivities, incubation period, and asymptomatic cases. We present a method to extend the weighted temporal social network and present simulations on a network of 5000 students. The purpose of this work is to investigate optimal quarantine rules and testing strategies with digital contact tracing. The results show that the traditional strategy of quarantining direct contacts reduces infections by less than 20% without sufficient testing. Periodic testing every 2 weeks without contact tracing reduces infections by less than 3%. A variety of strategies are discussed including testing second and third degree contacts and the pre-exposure notification system, which acts as a social radar warning users how far they are from COVID-19. The most effective strategy discussed in this work was combining the pre-exposure notification system with testing second and third degree contacts. This strategy reduces infections by 18.3% when 30% of the population uses the app, 45.2% when 50% of the population uses the app, 72.1% when 70% of the population uses the app, and 86.8% when 95% of the population uses the app. When simulating the model on an extended network of 5000 students, the results are similar with the contact tracing app reducing infections by up to 79%.


Subject(s)
COVID-19/prevention & control , Contact Tracing/statistics & numerical data , Disease Notification/standards , Models, Theoretical , Social Network Analysis , Adult , Computer Simulation , Humans , Medical Informatics Applications , Mobile Applications , Quarantine/statistics & numerical data , Students , Young Adult
11.
Proc Natl Acad Sci U S A ; 118(43)2021 10 26.
Article in English | MEDLINE | ID: covidwho-1483204

ABSTRACT

Contact tracing is a pillar of COVID-19 response, but language access and equity have posed major obstacles. COVID-19 has disproportionately affected minority communities with many non-English-speaking members. Language discordance can increase processing times and hamper the trust building necessary for effective contact tracing. We demonstrate how matching predicted patient language with contact tracer language can enhance contact tracing. First, we show how to use machine learning to combine information from sparse laboratory reports with richer census data to predict the language of an incoming case. Second, we embed this method in the highly demanding environment of actual contact tracing with high volumes of cases in Santa Clara County, CA. Third, we evaluate this language-matching intervention in a randomized controlled trial. We show that this low-touch intervention results in 1) significant time savings, shortening the time from opening of cases to completion of the initial interview by nearly 14 h and increasing same-day completion by 12%, and 2) improved engagement, reducing the refusal to interview by 4%. These findings have important implications for reducing social disparities in COVID-19; improving equity in healthcare access; and, more broadly, leveling language differences in public services.


Subject(s)
COVID-19/prevention & control , COVID-19/transmission , Contact Tracing/methods , Language , SARS-CoV-2 , Algorithms , COVID-19/epidemiology , California/epidemiology , Communication Barriers , Contact Tracing/statistics & numerical data , Female , Humans , Machine Learning , Male , Pandemics/prevention & control , Surveys and Questionnaires , Trust
12.
JAMA Pediatr ; 176(1): 59-67, 2022 01 01.
Article in English | MEDLINE | ID: covidwho-1460123

ABSTRACT

Importance: Data about the risk of SARS-CoV-2 infection among children compared with adults are needed to inform COVID-19 risk communication and prevention strategies, including COVID-19 vaccination policies for children. Objective: To compare incidence rates and clinical characteristics of SARS-CoV-2 infection among adults and children and estimated household infection risks within a prospective household cohort. Design, Setting, and Participants: Households with at least 1 child aged 0 to 17 years in selected counties in Utah and New York City, New York, were eligible for enrollment. From September 2020 through April 2021, participants self-collected midturbinate nasal swabs for reverse transcription-polymerase chain reaction testing for SARS-CoV-2 and responded to symptom questionnaires each week. Participants also self-collected additional respiratory specimens with onset of COVID-19-like illness. For children unable to self-collect respiratory specimens, an adult caregiver collected the specimens. Main Outcomes and Measures: The primary outcome was incident cases of any SARS-CoV-2 infection, including asymptomatic and symptomatic infections. Additional measures were the asymptomatic fraction of infection calculated by dividing incidence rates of asymptomatic infection by rates of any infection, clinical characteristics of infection, and household infection risks. Primary outcomes were compared by participant age group. Results: A total of 1236 participants in 310 households participated in surveillance, including 176 participants (14%) who were aged 0 to 4 years, 313 (25%) aged 5 to 11 years, 163 (13%) aged 12 to 17 years, and 584 (47%) 18 years or older. Overall incidence rates of SARS-CoV-2 infection were 3.8 (95% CI, 2.4-5.9) and 7.7 (95% CI, 4.1-14.5) per 1000 person-weeks among the Utah and New York City cohorts, respectively. Site-adjusted incidence rates per 1000 person-weeks were similar by age group: 6.3 (95% CI, 3.6-11.0) for children 0 to 4 years, 4.4 (95% CI, 2.5-7.5) for children 5 to 11 years, 6.0 (95% CI, 3.0-11.7) for children 12 to 17 years, and 5.1 (95% CI, 3.3-7.8) for adults (≥18 years). The asymptomatic fractions of infection by age group were 52%, 50%, 45%, and 12% among individuals aged 0 to 4 years, 5 to 11 years, 12 to 17 years, and 18 years or older, respectively. Among 40 households with 1 or more SARS-CoV-2 infections, the mean risk of SARS-CoV-2 infection among all enrolled household members was 52% (range, 11%-100%), with higher risks in New York City compared with Utah (80% [95% CI, 64%-91%] vs 44% [95% CI, 36%-53%]; P < .001). Conclusions and Relevance: In this study, children had similar incidence rates of SARS-CoV-2 infection compared with adults, but a larger proportion of infections among children were asymptomatic.


Subject(s)
Asymptomatic Infections/epidemiology , COVID-19 Testing/statistics & numerical data , COVID-19/transmission , Adolescent , Adult , COVID-19/epidemiology , Child , Child, Preschool , Contact Tracing/statistics & numerical data , Disease Susceptibility , Family Characteristics , Humans , Incidence , Infant , Infant, Newborn , Male , Middle Aged , New York City/epidemiology , Prospective Studies , Utah/epidemiology , Young Adult
14.
Elife ; 102021 09 27.
Article in English | MEDLINE | ID: covidwho-1441362

ABSTRACT

The relationship between SARS-CoV-2 viral load and infectiousness is poorly known. Using data from a cohort of cases and high-risk contacts, we reconstructed viral load at the time of contact and inferred the probability of infection. The effect of viral load was larger in household contacts than in non-household contacts, with a transmission probability as large as 48% when the viral load was greater than 1010 copies per mL. The transmission probability peaked at symptom onset, with a mean probability of transmission of 29%, with large individual variations. The model also projects the effects of variants on disease transmission. Based on the current knowledge that viral load is increased by two- to eightfold with variants of concern and assuming no changes in the pattern of contacts across variants, the model predicts that larger viral load levels could lead to a relative increase in the probability of transmission of 24% to 58% in household contacts, and of 15% to 39% in non-household contacts.


Subject(s)
COVID-19 Vaccines/administration & dosage , COVID-19/transmission , SARS-CoV-2/pathogenicity , Viral Load , Adult , COVID-19/immunology , COVID-19/prevention & control , COVID-19/virology , Cohort Studies , Contact Tracing/statistics & numerical data , Female , Humans , Logistic Models , Male , Middle Aged , Risk Factors , SARS-CoV-2/immunology , SARS-CoV-2/isolation & purification , Virus Replication/immunology , Young Adult
15.
Ghana Med J ; 54(4 Suppl): 77-85, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-1436198

ABSTRACT

BACKGROUND: A novel coronavirus, SARS-CoV-2 is currently causing a worldwide pandemic. The first cases of SARS-CoV-2 infection were recorded in Ghana on March 12, 2020. Since then, the country has been combatting countrywide community spread. This report describes how the Virology Department, Noguchi Memorial Institute for Medical Research (NMIMR) is supporting the Ghana Health Service (GHS) to diagnose infections with this virus in Ghana. METHODS: The National Influenza Centre (NIC) in the Virology Department of the NMIMR, adopted real-time Polymerase Chain Reaction (rRT-PCR) assays for the diagnosis of the SARS-CoV-2 in January 2020. Samples from suspected cases and contact tracing across Ghana were received and processed for SARS-CoV-2. Samples were 'pooled' to enable simultaneous batch testing of samples without reduced sensitivity. OUTCOMES: From February 3 to August 21, the NMIMR processed 283 946 (10%) samples. Highest number of cases were reported in June when the GHS embarked on targeted contact tracing which led to an increase in number of samples processed daily, peaking at over 7,000 samples daily. There were several issues to overcome including rapid consumption of reagents and consumables. Testing however continued successfully due to revised procedures, additional equipment and improved pipeline of laboratory supplies. Test results are now provided within 24 to 48 hours of sample submission enabling more effective response and containment. CONCLUSION: Following the identification of the first cases of SARS-CoV-2infection by the NMIMR, the Institute has trained other centres and supported the ramping up of molecular testing capacity in Ghana. This provides a blueprint to enable Ghana to mitigate further epidemics and pandemics. FUNDING: The laboratory work was supported with materials from the Ghana Health Service Ministry of Health, the US Naval Medical Research Unit #3, the World Health Organization, the Jack Ma Foundation and the University of Ghana Noguchi Memorial Institute for Medical Research. Other research projects hosted by the Noguchi Memorial Institute for Medical Research contributed reagents and laboratory consumables. The funders had no role in the preparation of this manuscript.


Subject(s)
COVID-19 Nucleic Acid Testing/methods , COVID-19/diagnosis , Infection Control/methods , Population Surveillance , SARS-CoV-2/isolation & purification , COVID-19/epidemiology , Contact Tracing/methods , Contact Tracing/statistics & numerical data , Ghana/epidemiology , Humans , National Health Programs , SARS-CoV-2/genetics
17.
JAMA Netw Open ; 4(4): e218184, 2021 04 01.
Article in English | MEDLINE | ID: covidwho-1384070

ABSTRACT

Importance: Digital contact tracing (DCT) apps have been released in several countries to help interrupt SARS-CoV-2 transmission chains. However, the effect of DCT on pandemic mitigation remains to be demonstrated. Objective: To estimate key populations and performance indicators along the exposure notification cascade of the SwissCovid DCT app in a clearly defined regional and temporal context. Design, Setting, and Participants: This comparative effectiveness study was based on a simulation informed by measured data from issued quarantine recommendations and positive SARS-CoV-2 test results after DCT exposure notifications in the canton of Zurich. A stochastic model was developed to re-create the DCT notification cascade for Zurich. Population sizes at each cascade step were estimated using triangulation based on publicly available administrative and observational research data for the study duration from September 1 to October 31, 2020. The resultant estimates were checked for internal consistency and consistency with upstream or downstream estimates in the cascade. Stochastic sampling from data-informed parameter distributions was performed to explore the robustness of results. Subsequently, key performance indicators were evaluated to assess the potential contribution of DCT compared with manual contact tracing. Main Outcomes and Measures: Receiving a voluntary quarantine recommendation and/or a positive SARS-CoV-2 test result after exposure notification. Results: In September 2020, 537 app users received a positive SARS-CoV-2 test result in Zurich, 324 of whom received and entered an upload authorization code. This code triggered an app notification for an estimated 1374 (95% simulation interval [SI], 932-2586) proximity contacts and led to 722 information hotline calls, with an estimated 170 callers (95% SI, 154-186) receiving a quarantine recommendation. An estimated 939 (95% SI, 720-1127) notified app users underwent testing for SARS-CoV-2, of whom 30 (95% SI, 23-36) had positive results after an app notification. Key indicator evaluations revealed that the DCT app triggered quarantine recommendations for the equivalent of 5% of all exposed contacts placed in quarantine by manual contact tracing. For every 10.9 (95% SI, 7.6-15.6) upload authorization codes entered in the app, 1 contact had positive test results for SARS-CoV-2 after app notification. Longitudinal indicator analyses demonstrated bottlenecks in the notification cascade, because capacity limits were reached owing to an increased incidence of SARS-CoV-2 infection in October 2020. Conclusions and Relevance: In this simulation study of the notification cascade of the SwissCovid DCT app, receipt of exposure notifications was associated with quarantine recommendations and identification of SARS-CoV-2-positive cases. These findings in notified proximity contacts reflect important intermediary steps toward transmission prevention.


Subject(s)
COVID-19 , Computer Simulation , Contact Tracing , Disease Notification , Disease Transmission, Infectious , Mobile Applications , Adult , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/transmission , Communicable Disease Control/methods , Communicable Disease Control/organization & administration , Contact Tracing/methods , Contact Tracing/statistics & numerical data , Disease Notification/methods , Disease Notification/statistics & numerical data , Disease Transmission, Infectious/prevention & control , Disease Transmission, Infectious/statistics & numerical data , Female , Humans , Male , Quarantine , SARS-CoV-2/isolation & purification , Switzerland/epidemiology
19.
PLoS One ; 16(8): e0256180, 2021.
Article in English | MEDLINE | ID: covidwho-1362090

ABSTRACT

Contact tracing and quarantine are well established non-pharmaceutical epidemic control tools. The paper aims to clarify the impact of these measures in evolution of epidemic. The proposed deterministic model defines a simple rule on the reproduction number [Formula: see text] in terms of ratio of diagnosed cases and, quarantine and transmission parameters. The model is applied to the early stage of Covid19 crisis in Poland. We investigate 3 scenarios corresponding to different ratios of diagnosed cases. Our results show that, depending on the scenario, contact tracing prevented from 50% to over 90% of cases. The effects of quarantine are limited by fraction of undiagnosed cases. The key conclusion is that under realistic assumptions the epidemic can not be controlled without any social distancing measures.


Subject(s)
Algorithms , COVID-19/epidemiology , Contact Tracing/statistics & numerical data , Quarantine/statistics & numerical data , COVID-19/prevention & control , COVID-19/transmission , Computer Simulation , Contact Tracing/methods , Humans , Physical Distancing , Poland/epidemiology , Quarantine/methods , SARS-CoV-2/pathogenicity
20.
Proc Natl Acad Sci U S A ; 118(33)2021 08 17.
Article in English | MEDLINE | ID: covidwho-1356601

ABSTRACT

Contact tracing has for decades been a cornerstone of the public health approach to epidemics, including Ebola, severe acute respiratory syndrome, and now COVID-19. It has not yet been possible, however, to causally assess the method's effectiveness using a randomized controlled trial of the sort familiar throughout other areas of science. This study provides evidence that comes close to that ideal. It exploits a large-scale natural experiment that occurred by accident in England in late September 2020. Because of a coding error involving spreadsheet data used by the health authorities, a total of 15,841 COVID-19 cases (around 20% of all cases) failed to have timely contact tracing. By chance, some areas of England were much more severely affected than others. This study finds that the random breakdown of contact tracing led to more illness and death. Conservative causal estimates imply that, relative to cases that were initially missed by the contact tracing system, cases subject to proper contact tracing were associated with a reduction in subsequent new infections of 63% and a reduction insubsequent COVID-19-related deaths of 66% across the 6 wk following the data glitch.


Subject(s)
COVID-19/epidemiology , Contact Tracing/statistics & numerical data , Pandemics , SARS-CoV-2 , COVID-19/mortality , COVID-19/prevention & control , COVID-19 Testing/statistics & numerical data , Contact Tracing/methods , Cooperative Behavior , Data Accuracy , Data Collection , England/epidemiology , Humans , Incidence , Information Storage and Retrieval , Program Evaluation , Software , Time Factors
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