Contact Tracing for Imported Case of Middle East Respiratory Syndrome, China, 2015

The effectiveness of intervention measures on MERS-CoV transmission by using the contact networks reconstructed from link prediction data

Introduction

Mitigating the spread of infectious diseases is of paramount concern for societal safety, necessitating the development of effective intervention measures. Epidemic simulation is widely used to evaluate the efficacy of such measures, but realistic simulation environments are crucial for meaningful insights. Despite the common use of contact-tracing data to construct realistic networks, they have inherent limitations. This study explores reconstructing simulation networks using link prediction methods as an alternative approach.

Methods

The primary objective of this study is to assess the effectiveness of intervention measures on the reconstructed network, focusing on the 2015 MERS-CoV outbreak in South Korea. Contact-tracing data were acquired, and simulation networks were reconstructed using the graph autoencoder (GAE)-based link prediction method. A scale-free (SF) network was employed for comparison purposes. Epidemic simulations were conducted to evaluate three intervention strategies: Mass Quarantine (MQ), Isolation, and Isolation combined with Acquaintance Quarantine (AQ + Isolation).

Results

Simulation results showed that AQ + Isolation was the most effective intervention on the GAE network, resulting in consistent epidemic curves due to high clustering coefficients. Conversely, MQ and AQ + Isolation were highly effective on the SF network, attributed to its low clustering coefficient and intervention sensitivity. Isolation alone exhibited reduced effectiveness. These findings emphasize the significant impact of network structure on intervention outcomes and suggest a potential overestimation of effectiveness in SF networks. Additionally, they highlight the complementary use of link prediction methods.

Discussion

This innovative methodology provides inspiration for enhancing simulation environments in future endeavors. It also offers valuable insights for informing public health decision-making processes, emphasizing the importance of realistic simulation environments and the potential of link prediction methods.

Limited impact of contact tracing in a University setting for COVID-19 due to asymptomatic transmission and social distancing

Contact tracing is an important tool for controlling the spread of infectious diseases, including COVID-19. Here, we investigate the spread of COVID-19 and the effectiveness of contact tracing in a university population, using a data-driven ego-centric network model constructed with social contact data collected during 2020 and similar data collected in 2010. We find that during 2020, university staff and students consistently reported fewer social contacts than in 2010, however those contacts occurred more frequently and were of longer duration. We find that contact tracing in the presence of social distancing is less impactful than without social distancing. By combining multiple data sources, we show that University-aged populations are likely to develop asymptomatic COVID-19 infections. We find that asymptomatic index cases cannot be reliably discovered through contact tracing and consequently transmission in their social network is not significantly reduced through contact tracing. In summary, social distancing restrictions had a large impact on limiting COVID-19 outbreaks in universities; to reduce transmission further contact tracing should be used in conjunction with alternative interventions.

Risk factors of infection among close contacts of COVID-19: A systematic review and meta-analysis

BACKGROUND

Coronavirus disease 2019 (COVID-19) was first detected in December 2019, and declared as a pandemic by the World Health Organization (WHO) on March 11, 2020. The current management of COVID-19 is based generally on supportive therapy and treatment to prevent respiratory failure.

METHODS

PubMed, Web of Science, Embase, CNKI, and other databases were searched by computer, and relevant literature published from December 2019 to November 2022 on the influencing factors of infection in close contacts with novel coronavirus pneumonia was collected. Meta-analysis was carried out after literature screening, quality assessment, and data extraction.

RESULTS

A total of 425 articles were retrieved and 11 were included. Meta-analysis showed that there were 6 risk factors, and the combined OR value and 95% CI of each influencing factor were 5.23 (3.20, 8.57) for family members, 1.63 (0.56, 4.77) for regular contact, 2.14 (0.62, 7.32) for the elderly, 0.58 (0.001569.89) for cohabitation, 1.97 (1.02, 3.82) for women and 0.75 (0.01, 54.07) for others. The Deeks' funnel diagram indicates that there is no potential publication bias among the included studies.

CONCLUSION

Family members and gender differences are the risk factors of infection among close contacts, and it cannot be proved that there are differences in infection among frequent contact, advanced age, and living together.

Modeling and multi-objective optimal control of reaction-diffusion COVID-19 system due to vaccination and patient isolation

In this paper, a reaction-diffusion COVID-19 model is proposed to explore how vaccination-isolation strategies affect the development of the epidemic. First, the basic dynamical properties of the system are explored. Then, the system's asymptotic distributions of endemic equilibrium under different conditions are studied. Further, the global sensitivity analysis of R 0 is implemented with the aim of determining the sensitivity for these parameters. In addition, the optimal vaccination-isolation strategy based on the optimal path is proposed. Meantime, social cost C ( m , σ ) , social benefit B ( m , σ ) , threshold R 0 ( m , σ ) three objective optimization problem based on vaccination-isolation strategy is explored, and the maximum social cost ( M S C ) and maximum social benefit ( M S B ) are obtained. Finally, the instance prediction of the Lhasa epidemic in China on August 7, 2022, is made by using the piecewise infection rates β 1 ( t ) , β 2 ( t ) , and some key indicators are obtained as follows: (1) The basic reproduction numbers of each stage in Lhasa, China are R 0 ( 1 : 8 ) = 0.4678 , R 0 ( 9 : 20 ) = 2.7655 , R 0 ( 21 : 30 ) = 0.3810 and R 0 ( 31 : 100 ) = 0.7819 ; (2) The daily new cases of this epidemic will peak at 43 on the 20th day (August 26, 2022); (3) The cumulative cases in Lhasa, China will reach about 640 and be cleared about the 80th day (October 28, 2022). Our research will contribute to winning the war on epidemic prevention and control.

Does 2x2 airplane passenger contact tracing for infectious respiratory pathogens work? A systematic review of the evidence

We critically appraised the literature regarding in-flight transmission of a range of respiratory infections to provide an evidence base for public health policies for contact tracing passengers, given the limited pathogen-specific data for SARS-CoV-2 currently available. Using PubMed, Web of Science, and other databases including preprints, we systematically reviewed evidence of in-flight transmission of infectious respiratory illnesses. A meta-analysis was conducted where total numbers of persons on board a specific flight was known, to calculate a pooled Attack Rate (AR) for a range of pathogens. The quality of the evidence provided was assessed using a bias assessment tool developed for in-flight transmission investigations of influenza which was modelled on the PRISMA statement and the Newcastle-Ottawa scale. We identified 103 publications detailing 165 flight investigations. Overall, 43.7% (72/165) of investigations provided evidence for in-flight transmission. H1N1 influenza A virus had the highest reported pooled attack rate per 100 persons (AR = 1.17), followed by SARS-CoV-2 (AR = 0.54) and SARS-CoV (AR = 0.32), Mycobacterium tuberculosis (TB, AR = 0.25), and measles virus (AR = 0.09). There was high heterogeneity in estimates between studies, except for TB. Of the 72 investigations that provided evidence for in-flight transmission, 27 investigations were assessed as having a high level of evidence, 23 as medium, and 22 as low. One third of the investigations that reported on proximity of cases showed transmission occurring beyond the 2x2 seating area. We suggest that for emerging pathogens, in the absence of pathogen-specific evidence, the 2x2 system should not be used for contact tracing. Instead, alternate contact tracing protocols and close contact definitions for enclosed areas, such as the same cabin on an aircraft or other forms of transport, should be considered as part of a whole of journey approach.

Potential reduction in transmission of COVID-19 by digital contact tracing systems: a modelling study

BACKGROUND

Digital tools are being developed to support contact tracing as part of the global effort to control the spread of COVID-19. These include smartphone apps, Bluetooth-based proximity detection, location tracking and automatic exposure notification features. Evidence on the effectiveness of alternative approaches to digital contact tracing is so far limited.

METHODS

We use an age-structured branching process model of the transmission of COVID-19 in different settings to estimate the potential of manual contact tracing and digital tracing systems to help control the epidemic. We investigate the effect of the uptake rate and proportion of contacts recorded by the digital system on key model outputs: the effective reproduction number, the mean outbreak size after 30 days and the probability of elimination.

RESULTS

Effective manual contact tracing can reduce the effective reproduction number from 2.4 to around 1.5. The addition of a digital tracing system with a high uptake rate over 75% could further reduce the effective reproduction number to around 1.1. Fully automated digital tracing without manual contact tracing is predicted to be much less effective.

CONCLUSIONS

For digital tracing systems to make a significant contribution to the control of COVID-19, they need be designed in close conjunction with public health agencies to support and complement manual contact tracing by trained professionals.

Risk Assessment for the Transmission of Middle East Respiratory Syndrome Coronavirus (MERS-Cov) on Aircraft: A Systematic Review

Middle East respiratory syndrome coronavirus (MERS-CoV) causes a potentially fatal respiratory disease. Although it is most common in the Arabian Peninsula, it has been exported to 17 countries outside the Middle East, mostly through air travel. The Risk Assessment Guidelines for Infectious Diseases transmitted on Aircraft (RAGIDA) advise authorities on measures to take when an infected individual travelled by air. The aim of this systematic review was to gather all available information on documented MERS-CoV cases that had travelled by air, to update RAGIDA. The databases used were PubMed, Embase, Scopus and Global Index Medicus; Google was searched for grey literature and hand searching was performed on the EU Early Warning and Response System and the WHO Disease Outbreak News. Forty-seven records were identified, describing 21 cases of MERS that had travelled on 31 flights. Contact tracing was performed for 17 cases. Most countries traced passengers sitting in the same row and the two rows in front and behind the case. Only one country decided to trace all passengers and crew. No cases of in-flight transmission were observed; thus, considering the resources it requires, a conservative approach may be appropriate when contact tracing passengers and crew where a case of MERS has travelled by air.

Successful contact tracing systems for COVID-19 rely on effective quarantine and isolation

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.

Projecting the criticality of COVID-19 transmission in India using GIS and machine learning methods

There is a new public health catastrophe forbidding the world. With the advent and spread of 2019 novel coronavirus (2019-nCoV). Learning from the experiences of various countries and the World Health Organization (WHO) guidelines, social distancing, use of sanitizers, thermal screening, quarantining, and provision of lockdown in the cities being the effective measure that can contain the spread of the pandemic. Though complete lockdown helps in containing the spread, it generates complexity by breaking the economic activity chain. Besides, laborers, farmers, and workers may lose their daily earnings. Owing to these detrimental effects, the government has to open the lockdown strategically. Prediction of the COVID-19 spread and analyzing when the cases would stop increasing helps in developing a strategy. An attempt is made in this paper to predict the time after which the number of new cases stops rising, considering the strong implementation of lockdown conditions using three different techniques such as Decision Tree, Support Vector Machine, and Gaussian Process Regression algorithm are used to project the number of cases. Thus, the projections are used in identifying inflection points, which would help in planning the easing of lockdown in a few of the areas strategically. The criticality in a region is evaluated using the criticality index (CI), which is proposed by authors in one of the past of research works. This research work is made available in a dashboard to enable the decision-makers to combat the pandemic.

Containment of COVID-19: Simulating the impact of different policies and testing capacities for contact tracing, testing, and isolation

Efficient contact tracing and testing are fundamental tools to contain the transmission of SARS-CoV-2. We used multi-agent simulations to estimate the daily testing capacity required to find and isolate a number of infected agents sufficient to break the chain of transmission of SARS-CoV-2, so decreasing the risk of new waves of infections. Depending on the non-pharmaceutical mitigation policies in place, the size of secondary infection clusters allowed or the percentage of asymptomatic and paucisymptomatic (i.e., subclinical) infections, we estimated that the daily testing capacity required to contain the disease varies between 0.7 and 9.1 tests per thousand agents in the population. However, we also found that if contact tracing and testing efficacy dropped below 60% (e.g. due to false negatives or reduced tracing capability), the number of new daily infections did not always decrease and could even increase exponentially, irrespective of the testing capacity. Under these conditions, we show that population-level information about geographical distribution and travel behaviour could inform sampling policies to aid a successful containment, while avoiding concerns about government-controlled mass surveillance.

Effectiveness of Intervention Strategies on MERS-CoV Transmission Dynamics in South Korea, 2015: Simulations on the Network Based on the Real-World Contact Data

The MERS-CoV spread in South Korea in 2015 was not only the largest outbreak of MERS-CoV in the region other than the Middle East but also a historic epidemic in South Korea. Thus, investigation of the MERS-CoV transmission dynamics, especially by agent-based modeling, would be meaningful for devising intervention strategies for novel infectious diseases. In this study, an agent-based model on MERS-CoV transmission in South Korea in 2015 was built and analyzed. The prominent characteristic of this model was that it built the simulation environment based on the real-world contact tracing network, which can be characterized as being scale-free. In the simulations, we explored the effectiveness of three possible intervention scenarios; mass quarantine, isolation, and isolation combined with acquaintance quarantine. The differences in MERS-CoV transmission dynamics by the number of links of the index case agent were examined. The simulation results indicate that isolation combined with acquaintance quarantine is more effective than others, and they also suggest the key role of super-spreaders in MERS-CoV transmission.

Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

BACKGROUND

Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification.

OBJECTIVE

We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms.

METHODS

Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily.

RESULTS

Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19-related symptom compared to all other symptom-free days (P=.01).

CONCLUSIONS

Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19-related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

Risk factors associated with COVID-19 infection: a retrospective cohort study based on contacts tracing

This study aimed to estimate the attack rates, and identify the risk factors of COVID-19 infection. Based on a retrospective cohort study, we investigated 11,580 contacts of COVID-19 cases in Guangdong Province from 10 January to 15 March 2020. All contacts were tested by RT-PCR to detect their infection of SARS-COV-2. Attack rates by characteristics were calculated. Logistic regression was used to estimate the risk factors of infection for COVID-19. A total of 515 of 11,580 contacts were identified to be infected with SARS-COV-2. Compared to young adults aged 20-29 years, the infected risk was higher in children (RR: 2.59, 95%CI: 1.79-3.76), and old people aged 60-69 years (RR: 5.29, 95%CI: 3.76-7.46). Females also had higher infected risk (RR: 1.66, 95%CI: 1.39-2.00). People having close relationship with index cases encountered higher infected risk (RR for spouse: 20.68, 95%CI: 14.28-29.95; RR for non-spouse family members: 9.55, 95%CI: 6.73-13.55; RR for close relatives: 5.90, 95%CI: 4.06-8.59). Moreover, contacts exposed to index case in symptomatic period (RR: 2.15, 95%CI: 1.67-2.79), with critically severe symptoms (RR: 1.61, 95%CI: 1.00-2.57), with symptoms of dizzy (RR: 1.58, 95%CI: 1.08-2.30), myalgia (RR: 1.49, 95%CI: 1.15-1.94), and chill (RR: 1.42, 95%CI: 1.05-1.92) had higher infected risks. Children, old people, females, and family members are susceptible of COVID-19 infection, while index cases in the incubation period had lower contagiousness. Our findings will be helpful for developing targeted prevention and control strategies to combat the worldwide pandemic.

Post COVID-19 lockdown: measures and practices for dental institutes

Resuming regular clinical activities at dental premises after the COVID-19 lockdown period or post COVID-19 is likely to be a challenge for all dental institutes. When returning to the dental practice or training, staff and students alike should abide by the new rules and regulations. In the process of controlling viral spread, clinical dental facilities face a higher risk of disease transmission among patients as well as clinical and non-clinical staff. Aerosols formation and diffusion into the surrounding air can be a real concern of viral transmission, if no protective measures are established. We aim in this review to present the currently implemented measures and propose changes in clinical dental facilities to minimize the risk of transmission. Dental professionals should be prepared to treat every patient as a suspected COVID-19 carrier and be ready to receive and manage an overwhelming number of patients. We suggest that dental practices establish a sensible workforce shift schedule, improve ventilation levels, reduce dental aerosol generating procedures, and develop a comprehensive guidance to Healthcare Workers to reduce the risk of COVID-19 transmission.

A Mathematical Model to Investigate the Transmission of COVID-19 in the Kingdom of Saudi Arabia

Since the first confirmed case of SARS-CoV-2 coronavirus (COVID-19) on March 02, 2020, Saudi Arabia has not reported quite a rapid COVD-19 spread as seen in America and many European countries. Possible causes include the spread of asymptomatic COVID-19 cases. To characterize the transmission of COVID-19 in Saudi Arabia, a susceptible, exposed, symptomatic, asymptomatic, hospitalized, and recovered dynamical model was formulated, and a basic analysis of the model is presented including model positivity, boundedness, and stability around the disease-free equilibrium. It is found that the model is locally and globally stable around the disease-free equilibrium when R 0 < 1. The model parameterized from COVID-19 confirmed cases reported by the Ministry of Health in Saudi Arabia (MOH) from March 02 till April 14, while some parameters are estimated from the literature. The numerical simulation showed that the model predicted infected curve is in good agreement with the real data of COVID-19-infected cases. An analytical expression of the basic reproduction number R 0 is obtained, and the numerical value is estimated as R 0 ≈ 2.7.

Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID

This study has two goals. The first is to explain the geo-environmental determinants of the accelerated diffusion of COVID-19 that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats similar to COVID-19 having an accelerated viral infectivity in society. Using data on sample of N = 55 Italian province capitals, and data of infected individuals at as of April 7th, 2020, results reveal that the accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution of cities measured with days exceeding the limits set for PM10 (particulate matter 10 μm or less in diameter) or ozone. In particular, hinterland cities with average high number of days exceeding the limits set for PM10 (and also having a low wind speed) have a very high number of infected people on 7th April 2020 (arithmetic mean is about 2200 infected individuals, with average polluted days greater than 80 days per year), whereas coastal cities also having days exceeding the limits set for PM10 or ozone but with high wind speed have about 944.70 average infected individuals, with about 60 average polluted days per year; moreover, cities having more than 100 days of air pollution (exceeding the limits set for PM10), they have a very high average number of infected people (about 3350 infected individuals, 7th April 2020), whereas cities having less than 100 days of air pollution per year, they have a lower average number of infected people (about 1014 individuals). The findings here also suggest that to minimize the impact of future epidemics similar to COVID-19, the max number of days per year that Italian provincial capitals or similar industrialized cities can exceed the limits set for PM10 or for ozone, considering their meteorological conditions, is about 48 days. Moreover, results here reveal that the explanatory variable of air pollution in cities seems to be a more important predictor in the initial phase of diffusion of viral infectivity (on 17th March 2020, b1 = 1.27, p < 0.001) than interpersonal contacts (b2 = 0.31, p < 0.05). In the second phase of maturity of the transmission dynamics of COVID-19, air pollution reduces intensity (on 7th April 2020 with b'1 = 0.81, p < 0.001) also because of the indirect effect of lockdown, whereas regression coefficient of transmission based on interpersonal contacts has a stable level (b'2 = 0.31, p < 0.01). This result reveals that accelerated transmission dynamics of COVID-19 is due to mainly to the mechanism of "air pollution-to-human transmission" (airborne viral infectivity) rather than "human-to-human transmission". Overall, then, transmission dynamics of viral infectivity, such as COVID-19, is due to systemic causes: general factors that are the same for all regions (e.g., biological characteristics of virus, incubation period, etc.) and specific factors which are different for each region and/or city (e.g., complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity) and health level of individuals (habits, immune system, age, sex, etc.). Lessons learned for COVID-19 in the case study here suggest that a proactive strategy to cope with future epidemics is also to apply especially an environmental and sustainable policy based on reduction of levels of air pollution mainly in hinterland and polluting cities- (having low wind speed, high percentage of moisture and number of fog days) -that seem to have an environment that foster a fast transmission dynamics of viral infectivity in society. Hence, in the presence of polluting industrialization in regions that can trigger the mechanism of air pollution-to-human transmission dynamics of viral infectivity, this study must conclude that a comprehensive strategy to prevent future epidemics similar to COVID-19 has to be also designed in environmental and socioeconomic terms, that is also based on sustainability science and environmental science, and not only in terms of biology, medicine, healthcare and health sector.

Containment of future waves of COVID-19: simulating the impact of different policies and testing capacities for contact tracing, testing, and isolation

We used multi-agent simulations to estimate the testing capacity required to find and isolate a number of infections sufficient to break the chain of transmission of SARS-CoV-2. Depending on the mitigation policies in place, a daily capacity between 0.7 to 3.6 tests per thousand was required to contain the disease. However, if contact tracing and testing efficacy dropped below 60% (e.g. due to false negatives or reduced tracing capability), the number of infections kept growing exponentially, irrespective of any testing capacity. Under these conditions, the population's geographical distribution and travel behaviour could inform sampling policies to aid a successful containment.

Two mechanisms for accelerated diffusion of COVID-19 outbreaks in regions with high intensity of population and polluting industrialization: the air pollution-to-human and human-to-human transmission dynamics (Preprint).. [DOI: 10.2196/preprints.19331]

BACKGROUND

Coronavirus disease 2019 (COVID-19) is viral infection that generates a severe acute respiratory syndrome with serious pneumonia that may result in progressive respiratory failure and death.

OBJECTIVE

This study has two goals. The first is to explain the main factors determining the diffusion of COVID-19 that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats with of accelerated viral infectivity in society.

METHODS

Correlation and regression analyses on on data of N=55 Italian province capitals, and data of infected individuals at as of April 2020.

RESULTS

The main results are: o The accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution. o Hinterland cities have average days of exceeding the limits set for PM10 (particulate matter 10 micrometers or less in diameter) equal to 80 days, and an average number of infected more than 2,000 individuals as of April 1st, 2020, coastal cities have days of exceeding the limits set for PM10 equal to 60 days and have about 700 infected in average. o Cities that average number of 125 days exceeding the limits set for PM10, last year, they have an average number of infected individual higher than 3,200 units, whereas cities having less than 100 days (average number of 48 days) exceeding the limits set for PM10, they have an average number of about 900 infected individuals. o The results reveal that accelerated transmission dynamics of COVID-19 in specific environments is due to two mechanisms given by: air pollution-to-human transmission and human-to-human transmission; in particular, the mechanisms of air pollution-to-human transmission play a critical role rather than human-to-human transmission. o The finding here suggests that to minimize future epidemic similar to COVID-19, the max number of days per year in which cities can exceed the limits set for PM10 or for ozone, considering their meteorological condition, is less than 50 days. After this critical threshold, the analytical output here suggests that environmental inconsistencies because of the combination between air pollution and meteorological conditions (with high moisture%, low wind speed and fog) trigger a take-off of viral infectivity (accelerated epidemic diffusion) with damages for health of population, economy and society.

CONCLUSIONS

Considering the complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity, lessons learned for COVID-19 have to be applied for a proactive socioeconomic strategy to cope with future epidemics, especially an environmental policy based on reduction of air pollution mainly in hinterland zones of countries, having low wind speed, high percentage of moisture and fog that create an environment that can damage immune system of people and foster a fast transmission of viral infectivity similar to the COVID-19.

CLINICALTRIAL

not applicable

Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts

BACKGROUND

Isolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19.

METHODS

We developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (R₀), the delay from symptom onset to isolation, the probability that contacts were traced, the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort.

FINDINGS

Simulated outbreaks starting with five initial cases, an R₀ of 1·5, and 0% transmission before symptom onset could be controlled even with low contact tracing probability; however, the probability of controlling an outbreak decreased with the number of initial cases, when R₀ was 2·5 or 3·5 and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R₀ of 1·5 were controllable with less than 50% of contacts successfully traced. To control the majority of outbreaks, for R₀ of 2·5 more than 70% of contacts had to be traced, and for an R₀ of 3·5 more than 90% of contacts had to be traced. The delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable when R₀ was 1·5. For R₀ values of 2·5 or 3·5, if there were 40 initial cases, contact tracing and isolation were only potentially feasible when less than 1% of transmission occurred before symptom onset.

INTERPRETATION

In most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts.

FUNDING

Wellcome Trust, Global Challenges Research Fund, and Health Data Research UK.

Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts

BACKGROUND

Isolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19.

METHODS

We developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (R0), the delay from symptom onset to isolation, the probability that contacts were traced, the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort.

FINDINGS

Simulated outbreaks starting with five initial cases, an R0 of 1·5, and 0% transmission before symptom onset could be controlled even with low contact tracing probability; however, the probability of controlling an outbreak decreased with the number of initial cases, when R0 was 2·5 or 3·5 and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R0 of 1·5 were controllable with less than 50% of contacts successfully traced. To control the majority of outbreaks, for R0 of 2·5 more than 70% of contacts had to be traced, and for an R0 of 3·5 more than 90% of contacts had to be traced. The delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable when R0 was 1·5. For R0 values of 2·5 or 3·5, if there were 40 initial cases, contact tracing and isolation were only potentially feasible when less than 1% of transmission occurred before symptom onset.

INTERPRETATION

In most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts.

FUNDING

Wellcome Trust, Global Challenges Research Fund, and Health Data Research UK.

Conveyance Contact Investigation for Imported Middle East Respiratory Syndrome Cases, United States, May 2014

In 2014, the Centers for Disease Control and Prevention conducted conveyance contact investigations for 2 Middle East respiratory syndrome cases imported into the United States, comprising all passengers and crew on 4 international and domestic flights and 1 bus. Of 655 contacts, 78% were interviewed; 33% had serologic testing. No secondary cases were identified.