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1.
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
3.
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
4.
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
5.
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
6.
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
7.
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
9.
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
11.
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
12.
Proc Natl Acad Sci U S A ; 118(32)2021 08 10.
Article in English | MEDLINE | ID: covidwho-1327246

ABSTRACT

Contact tracing is an essential tool to mitigate the impact of a pandemic, such as the COVID-19 pandemic. In order to achieve efficient and scalable contact tracing in real time, digital devices can play an important role. While a lot of attention has been paid to analyzing the privacy and ethical risks of the associated mobile applications, so far much less research has been devoted to optimizing their performance and assessing their impact on the mitigation of the epidemic. We develop Bayesian inference methods to estimate the risk that an individual is infected. This inference is based on the list of his recent contacts and their own risk levels, as well as personal information such as results of tests or presence of syndromes. We propose to use probabilistic risk estimation to optimize testing and quarantining strategies for the control of an epidemic. Our results show that in some range of epidemic spreading (typically when the manual tracing of all contacts of infected people becomes practically impossible but before the fraction of infected people reaches the scale where a lockdown becomes unavoidable), this inference of individuals at risk could be an efficient way to mitigate the epidemic. Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact. Such communication may be encrypted and anonymized, and thus, it is compatible with privacy-preserving standards. We conclude that probabilistic risk estimation is capable of enhancing the performance of digital contact tracing and should be considered in the mobile applications.


Subject(s)
Contact Tracing/methods , Epidemics/prevention & control , Algorithms , Bayes Theorem , COVID-19/epidemiology , COVID-19/prevention & control , Contact Tracing/statistics & numerical data , Humans , Mobile Applications , Privacy , Risk Assessment , SARS-CoV-2
13.
Philos Trans R Soc Lond B Biol Sci ; 376(1829): 20200270, 2021 07 19.
Article in English | MEDLINE | ID: covidwho-1309689

ABSTRACT

Contact tracing is an important tool for allowing countries to ease lockdown policies introduced to combat SARS-CoV-2. For contact tracing to be effective, those with symptoms must self-report themselves while their contacts must self-isolate when asked. However, policies such as legal enforcement of self-isolation can create trade-offs by dissuading individuals from self-reporting. We use an existing branching process model to examine which aspects of contact tracing adherence should be prioritized. We consider an inverse relationship between self-isolation adherence and self-reporting engagement, assuming that increasingly strict self-isolation policies will result in fewer individuals self-reporting to the programme. We find that policies which increase the average duration of self-isolation, or that increase the probability that people self-isolate at all, at the expense of reduced self-reporting rate, will not decrease the risk of a large outbreak and may increase the risk, depending on the strength of the trade-off. These results suggest that policies to increase self-isolation adherence should be implemented carefully. Policies that increase self-isolation adherence at the cost of self-reporting rates should be avoided. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.


Subject(s)
COVID-19/epidemiology , Contact Tracing/statistics & numerical data , Models, Theoretical , Pandemics , Basic Reproduction Number/statistics & numerical data , COVID-19/transmission , COVID-19/virology , Communicable Disease Control/statistics & numerical data , Disease Outbreaks , Humans , SARS-CoV-2/pathogenicity
14.
Virol J ; 18(1): 138, 2021 07 03.
Article in English | MEDLINE | ID: covidwho-1295469

ABSTRACT

BACKGROUND: The COVID-19 pandemic is a catastrophic global phenomenon, affecting human life in a way unseen since the 1918 influenza pandemic. Effective management of this threat requires halting transmission, a strategy requiring accurate knowledge of SARS-CoV-2 transmission patterns. METHODS: This was a retrospective contact study aiming to estimate the transmission rate of COVID-19 by tracing contacts in symptomatic, pre-symptomatic, and asymptomatic patients. History of patients' contacts during 24 h before appearance of symptoms or infection confirmation was traced for disease transmission. RESULTS: Overall, a total of 201 COVID-19 patients had contact with 7168 people in 24 h with an average of 35.66 contacts per patient, ranging from a minimum of 4 to maximum of 87 contacts (meetings). Out of 7168 persons met, infection was detected in 64 (0.89%). For the 155 symptomatic patients, a total of 5611 contacted persons were traced before appearance of symptoms (pre-symptomatic) in last 24 h with an average of 36.20 meetings per patient. The infection was transmitted in 63 (1.12%) people with 5548 (98.88%) remaining uninfected. Out of the 63 transmissions, 62 (98.4%) were traced within 6 h before symptom onset, while only 1 was identified in the 6-12 h timeframe before symptoms. A total of 1557 persons were traced having meeting/contacts with asymptomatic cases in last 24 h before infection confirmation. Out of these 1557 persons, only 1 was found to be infected and the infection rate was calculated to be 0.06%. Statistically, the transmission rate by pre-symptomatic patients was found to be significantly higher than the transmission rate by asymptomatic individuals (P < 0.05). CONCLUSION: In the studied population, the risk of pre-symptomatic and asymptomatic transmission of COVID-19 was low, with transmission risks of 1.12% and 0.06% respectively. Pre-symptomatic infection becomes very rare in contacts made longer than 6 h before onset of symptoms. The infection transmission is traced as long as about 9 h before the appearance of clear symptoms in the patients, but the incidence rate was as low as about 0.02% of the total contacts in that period.


Subject(s)
Asymptomatic Infections/epidemiology , COVID-19/transmission , Contact Tracing/statistics & numerical data , Adult , Aged , COVID-19/epidemiology , COVID-19/prevention & control , Contact Tracing/methods , Female , Humans , Incidence , Male , Middle Aged , Pakistan , Retrospective Studies , Risk Factors , Young Adult
15.
Sci Rep ; 11(1): 11984, 2021 06 07.
Article in English | MEDLINE | ID: covidwho-1260947

ABSTRACT

The present novel coronavirus (COVID-19) infection has engendered a worldwide crisis on an enormous scale within a very short period. The effective solution for this pandemic is to recognize the nature and spread of the disease so that appropriate policies can be framed. Mathematical modelling is always at the forefront to understand and provide an adequate description of the transmission of any disease. In this research work, we have formulated a deterministic compartmental model (SEAMHCRD) including various stages of infection, such as Mild, Moderate, Severe and Critical to study the spreading of COVID-19 and estimated the model parameters by fitting the model with the reported data of ongoing pandemic in Oman. The steady-state, stability and final pandemic size of the model has been proved mathematically. The various transmission as well as transition parameters are estimated during the period from June 4th to July 30th, 2020. Based on the currently estimated parameters, the pandemic size is also predicted for another 100 days. Sensitivity analysis is performed to identify the key model parameters, and the parameter gamma due to contact with the symptomatic moderately infected is found to be more significant in spreading the disease. Accordingly, the corresponding basic reproduction number has also been computed using the Next Generation Matrix (NGM) method. As the value of the basic reproduction number (R0) is 0.9761 during the period from June 4th to July 30th, 2020, the disease-free equilibrium is stable. Isolation and tracing the contact of infected individuals are recommended to control the spread of disease.


Subject(s)
COVID-19/epidemiology , Contact Tracing/statistics & numerical data , Models, Theoretical , SARS-CoV-2/pathogenicity , Basic Reproduction Number , Humans , Oman , Quarantine
16.
JAMA Netw Open ; 4(6): e2115850, 2021 06 01.
Article in English | MEDLINE | ID: covidwho-1251884

ABSTRACT

Importance: Contact tracing is a multistep process to limit SARS-CoV-2 transmission. Gaps in the process result in missed opportunities to prevent COVID-19. Objective: To quantify proportions of cases and their contacts reached by public health authorities and the amount of time needed to reach them and to compare the risk of a positive COVID-19 test result between contacts and the general public during 4-week assessment periods. Design, Setting, and Participants: This cross-sectional study took place at 13 health departments and 1 Indian Health Service Unit in 11 states and 1 tribal nation. Participants included all individuals with laboratory-confirmed COVID-19 and their named contacts. Local COVID-19 surveillance data were used to determine the numbers of persons reported to have laboratory-confirmed COVID-19 who were interviewed and named contacts between June and October 2020. Main Outcomes and Measures: For contacts, the numbers who were identified, notified of their exposure, and agreed to monitoring were calculated. The median time from index case specimen collection to contact notification was calculated, as were numbers of named contacts subsequently notified of their exposure and monitored. The prevalence of a positive SARS-CoV-2 test among named and tested contacts was compared with that jurisdiction's general population during the same 4 weeks. Results: The total number of cases reported was 74 185. Of these, 43 931 (59%) were interviewed, and 24 705 (33%) named any contacts. Among the 74 839 named contacts, 53 314 (71%) were notified of their exposure, and 34 345 (46%) agreed to monitoring. A mean of 0.7 contacts were reached by telephone by public health authorities, and only 0.5 contacts per case were monitored. In general, health departments reporting large case counts during the assessment (≥5000) conducted smaller proportions of case interviews and contact notifications. In 9 locations, the median time from specimen collection to contact notification was 6 days or less. In 6 of 8 locations with population comparison data, positive test prevalence was higher among named contacts than the general population. Conclusions and Relevance: In this cross-sectional study of US local COVID-19 surveillance data, testing named contacts was a high-yield activity for case finding. However, this assessment suggests that contact tracing had suboptimal impact on SARS-CoV-2 transmission, largely because 2 of 3 cases were either not reached for interview or named no contacts when interviewed. These findings are relevant to decisions regarding the allocation of public health resources among the various prevention strategies and for the prioritization of case investigations and contact tracing efforts.


Subject(s)
COVID-19/prevention & control , Contact Tracing , Public Health , COVID-19/complications , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19 Testing , Contact Tracing/statistics & numerical data , Cost-Benefit Analysis , Cross-Sectional Studies , Disclosure/statistics & numerical data , Health Services, Indigenous , Humans , Incidence , Prevalence , SARS-CoV-2 , Telephone , United States/epidemiology
17.
PLoS One ; 16(6): e0252499, 2021.
Article in English | MEDLINE | ID: covidwho-1256040

ABSTRACT

Models of contact tracing often over-simplify the effects of quarantine and isolation on disease transmission. We develop a model that allows us to investigate the importance of these factors in reducing the effective reproduction number. We show that the reduction in onward transmission during quarantine and isolation has a bigger effect than tracing coverage on the reproduction number. We also show that intuitively reasonable contact tracing performance indicators, such as the proportion of contacts quarantined before symptom onset, are often not well correlated with the reproduction number. We conclude that provision of support systems to enable people to quarantine and isolate effectively is crucial to the success of contact tracing.


Subject(s)
COVID-19/transmission , Contact Tracing/methods , Basic Reproduction Number , COVID-19/metabolism , Contact Tracing/statistics & numerical data , Disease Outbreaks , Humans , Models, Theoretical , Patient Isolation , Quarantine/methods , Quarantine/psychology , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity , Social Isolation/psychology
18.
Sci Prog ; 104(2): 368504211017800, 2021.
Article in English | MEDLINE | ID: covidwho-1255793

ABSTRACT

Accurate modeling of viral outbreaks in living populations and computer networks is a prominent research field. Many researchers are in search for simple and realistic models to manage preventive resources and implement effective measures against hazardous circumstances. The ongoing Covid-19 pandemic has revealed the fact about deficiencies in health resource planning of some countries having relatively high case count and death toll. A unique epidemic model incorporating stochastic processes and queuing theory is presented, which was evaluated by computer simulation using pre-processed data obtained from an urban clinic providing family health services. Covid-19 data from a local corona-center was used as the initial model parameters (e.g. R0, infection rate, local population size, number of contacts with infected individuals, and recovery rate). A long-run trend analysis for 1 year was simulated. The results fit well to the current case data of the sample corona center. Effective preventive and reactive resource planning basically depends on accurately designed models, tools, and techniques needed for the prediction of feature threats, risks, and mitigation costs. In order to sufficiently analyze the transmission and recovery dynamics of epidemics it is important to choose concise mathematical models. Hence, a unique stochastic modeling approach tied to queueing theory and computer simulation has been chosen. The methods used here can also serve as a guidance for accurate modeling and classification of stages (or compartments) of epidemics in general.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Hospitals, Urban , Models, Statistical , Pandemics , SARS-CoV-2/pathogenicity , Antiviral Agents/therapeutic use , COVID-19/drug therapy , COVID-19/mortality , Computer Simulation , Contact Tracing/statistics & numerical data , Family Practice , Humans , Incidence , Models, Immunological , Population Density , Quarantine/organization & administration , SARS-CoV-2/immunology , Stochastic Processes , Survival Analysis , Turkey/epidemiology
19.
Nat Commun ; 12(1): 2586, 2021 05 10.
Article in English | MEDLINE | ID: covidwho-1253934

ABSTRACT

High impact epidemics constitute one of the largest threats humanity is facing in the 21st century. In the absence of pharmaceutical interventions, physical distancing together with testing, contact tracing and quarantining are crucial in slowing down epidemic dynamics. Yet, here we show that if testing capacities are limited, containment may fail dramatically because such combined countermeasures drastically change the rules of the epidemic transition: Instead of continuous, the response to countermeasures becomes discontinuous. Rather than following the conventional exponential growth, the outbreak that is initially strongly suppressed eventually accelerates and scales faster than exponential during an explosive growth period. As a consequence, containment measures either suffice to stop the outbreak at low total case numbers or fail catastrophically if marginally too weak, thus implying large uncertainties in reliably estimating overall epidemic dynamics, both during initial phases and during second wave scenarios.


Subject(s)
COVID-19 Testing/statistics & numerical data , COVID-19/epidemiology , COVID-19/prevention & control , Disease Outbreaks/statistics & numerical data , Epidemics/prevention & control , COVID-19/diagnosis , Contact Tracing/statistics & numerical data , Disease Outbreaks/prevention & control , Epidemics/statistics & numerical data , Humans , Italy/epidemiology , Models, Statistical , Models, Theoretical , Physical Distancing , Quarantine , Social Isolation
20.
PLoS One ; 16(5): e0252019, 2021.
Article in English | MEDLINE | ID: covidwho-1238774

ABSTRACT

Against the current COVID-19 pandemic, governments worldwide have devised a variety of non-pharmaceutical interventions to mitigate it. However, it is generally difficult to estimate the joint impact of different control strategies. In this paper, we tackle this question with an extended epidemic SEIR model, informed by a socio-political classification of different interventions. First, we inquire the conceptual effect of mitigation parameters on the infection curve. Then, we illustrate the potential of our model to reproduce and explain empirical data from a number of countries, to perform cross-country comparisons. This gives information on the best synergies of interventions to control epidemic outbreaks while minimising impact on socio-economic needs. For instance, our results suggest that, while rapid and strong lockdown is an effective pandemic mitigation measure, a combination of social distancing and early contact tracing can achieve similar mitigation synergistically, while keeping lower isolation rates. This quantitative understanding can support the establishment of mid- and long-term interventions, to prepare containment strategies against further outbreaks. This paper also provides an online tool that allows researchers and decision makers to interactively simulate diverse scenarios with our model.


Subject(s)
COVID-19/epidemiology , Contact Tracing/statistics & numerical data , Quarantine/statistics & numerical data , COVID-19/prevention & control , COVID-19/transmission , Contact Tracing/methods , Humans , Models, Statistical , Physical Distancing , Quarantine/methods
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