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The extensive history of pandemics has spanned many centuries, profoundly impacting societies, economies, and public health, and thereby shaping the course of history in various ways. Advances in medicine, science, and public health practices have played a pivotal role in mitigating the effects of pandemics over time. This review explores the scientific landscape of contemporary pandemics, examining their diverse and complex nature. It goes beyond the biological aspects of pandemics to consider socioeconomic, environmental, and technological factors. Through a scientific lens, this study aims to understand the complexities of pandemics and contribute to the expanding knowledge base that helps humanity strengthen its defenses against global health threats. By elucidating the enigmas of pandemics, the study hopes to foster a more resilient and prepared global health environment. Highlighting the importance of a multidisciplinary, cross-disciplinary, and transdisciplinary approach, this exploration emphasizes the critical need to integrate biological, socioeconomic, environmental, and technological domains to develop more robust defenses against these global health challenges. [ABSTRACT FROM AUTHOR]

This work proposes a non-smooth epidemic model with a two-threshold control strategy. Under the two-threshold policy, social distancing is suggested when the infection level exceeds a certain threshold, and contact tracing is implemented and isolation rate is improved when the infection level exceeds a higher threshold. The global dynamics of the non-smooth system is investigated and it reveals that the disease-free equilibrium, or one of the three endemic equilibria of the subsystems, or one of the two pseudo-equilibria may be globally asymptotically stable, which depends on the two threshold levels and the intensity of control measures. The global stability of the pseudo-equilibrium indicates that the disease could be maintained at a previously given level by choosing the two thresholds properly. It is also obtained that at low thresholds and high intensity of control measures, the peak value is low and peak time is late for the unisolated infections and daily new infections, and the number of cumulative infections is small. However, the effects of control measures are very limited both on the final trend of the epidemic and the cumulative infections if threshold levels are too high. • A non-smooth epidemic model with a two-threshold control strategy is proposed. • Disease-free equilibrium, or endemic equilibrium, or pseudo-equilibrium may be globally asymptotically stable. • The disease could be maintained at a previously given level for proper thresholds. • Low thresholds and high implementation intensity of control measures are beneficial for disease control. • Effects of control measures are very limited if thresholds levels are too high. [ABSTRACT FROM AUTHOR]

Background: Pakistan witnessed five waves of COVID-19 infections during the pandemic. Punjab, the largest province of Pakistan, remained the epicentre due to a high infection rate. Administrative data for five waves of the pandemic was analyzed to determine the rate of infections and the significance of pharmacological and non-pharmacological interventions on the severity and duration of infection. Methodology: COVID-19 data from March 2020 to May 2023 was obtained from the Provincial Public Health Reference Laboratory (PPHRL), Punjab AIDS Control Program, Lahore. The data included samples from index cases, contacts, and recovered patients. A total of 36,252,48 cases were screened for COVID-19, and 90,923 (2.50%) were detected positive by RT-PCR, accounting for 5.69% of the cases reported positive throughout the country. Results: Among the positive cases, 50.86% (n = 46,244) cases were new cases (registered for the first time), 40.41% (n = 36751) were the contact cases traced from the newly identified cases and 8.62% (n = 7842) repeated cases. The positivity rates among index cases were reported to be 2.37%, 2.34%, 4.61%, 2.09%, and 1.19%, respectively, for the five respective COVID-19 pandemic waves. Distribution by gender indicated that 64% of males and 35% of females were infected during the pandemic. The age factor demonstrated the most susceptibility to infection in women aged 19-29 years, whereas most males between the ages of 29-39 had an infection. Susceptibility to COVID-19 infection was observed to be equally likely between males and females; however, clinical outcomes indicated that infections in males were more severe and often resulted in fatalities as compared to those in females. This trend was also reflected in the viral titer as measured by the Ct values, where 40% of males had Ct values < 25 (an indicator of high viral titers) compared to 30% of females with Ct values < 25. Conclusion: Overall, our data indicated that infection rates remained stable throughout the pandemic except for 3rd wave, which showed a higher incidence of infection rate of 4%. Additionally, data showed a positive impact of masking, social distancing, and immunization, as indicated by the shorter window of high infection rates. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic is an active epidemic disease and is evolving in various variants of SAR-COV-2. The most critical point for COVID-19 is to break the transmission chain by early detection and isolation. It necessitates monitoring human interactions using remote technologies as a proactive strategy to minimize the spread. This paper presents a framework for user behaviour recognition and localization in a heterogeneous environment leveraging artificial intelligence for COVID-19 contact tracing. This approach designed and developed a mobile application to acquire the mobile sensors (received signal strength indicator (RSSI), accelerometer, gyroscope, and speed), sensors response, and machine learning-based user behaviour classification to assess the spread of COVID-19 with better proximity detection. This paper presents a machine learning-based free-space path loss (FSPL) model to estimate the distance between users holding mobile devices. The framework considers comprehensive user interaction scenarios depending upon multiple parameters, including the randomness of a user's interaction (device-device communication), device position, environmental obstacles, and device type. This platform classifies user behaviour on a train, bus, walking, and sitting at one, two, and three-meter from the suspected patient. The proposed system outperforms existing models, estimates the distance up to three meters, and accurately classifies each user behaviour. This system can send alert notifications to all users within three meters of a person whose COVID-19 test is positive. This approach uses machine learning models and finds that the random forest classifier shows higher accuracy of 97.95% for user behaviour classification and uses the LSTM model for distance estimation from one to three meters between users with the highest accuracy of 98.07%. [ABSTRACT FROM AUTHOR]

A contact tracing app can positively support the requirement of social and physical distancing during a pandemic. However, there are aspects of the user's intention to download the app that remain under-researched. To address this, we investigate the role of perceived privacy risks, social empowerment, perceived information transparency and control, and attitudes towards government, in influencing the intention to download the contact tracing app. Using fuzzy set qualitative comparative analysis (fsQCA), we found eight different configurations of asymmetrical relationships of conditions that lead to the presence or absence of an intention to download. In our study, social empowerment significantly influences the presence of an intention to download. We also found that perceived information transparency significantly influences the absence of an intention to download the app. [ABSTRACT FROM AUTHOR]

We study contact tracing in a new macro-epidemiological model with asymptomatic transmission and limited testing capacity. Contact tracing is a testing strategy that aims to reconstruct the infection chain of newly symptomatic agents. This strategy may be unsuccessful because of an externality leading agents to expand their interactions at rates exceeding policymakers' ability to test all the traced contacts. Complementing contact tracing with timely deployed containment measures (e.g. social distancing or a tighter quarantine policy) corrects this externality and delivers outcomes that are remarkably similar to the benchmark case where tests are unlimited. We provide theoretical underpinnings to the risk of becoming infected in macro-epidemiological models. Our methodology to reconstruct infection chains is not affected by curse-of-dimensionality problems. [ABSTRACT FROM AUTHOR]

The Corona pandemic has necessitated the implementation of preventive measures, with social distancing emerging as a crucial strategy to curb the spread of the virus. However, ensuring compliance with social distancing guidelines remains a challenge. This study aimed to explore innovative preventive methods that effectively facilitate and enforce social distancing measures during the Corona pandemic. A comprehensive literature review was conducted to identify existing preventive methods and innovative approaches employed worldwide. Various sources were systematically searched and screened. The identified methods were categorized and analysed based on their implementation, effectiveness, and feasibility in promoting social distancing. The review revealed a range of preventive methods, including technological solutions, environmental modifications, communication strategies, public awareness campaigns, and incentivization programs. Technological advancements such as contact tracing apps and wearable devices have shown promise in reducing viral transmission. Environmental modifications such as crowd management systems and physical barriers have effectively promoted social distancing. Communication strategies, public awareness campaigns, and incentivization programs have played a critical role in encouraging compliance with social distancing guidelines. In conclusions, implementing innovative preventive methods can significantly contribute to achieving and sustaining social distancing during the Corona pandemic. Technological advancements, coupled with effective communication strategies and incentivization programs, can foster behavioural changes and enhance compliance. Policymakers, public health officials, and community leaders should consider the feasibility and adaptability of these methods to ensure successful implementation. Embracing these preventive approaches can help mitigate the impact of the Corona pandemic and safeguard public health. [ABSTRACT FROM AUTHOR]

This paper studies the suppression of an infectious disease in the canonical susceptible-infectious-recovered model. It derives three results. First, if technically feasible, the optimal response to a sufficiently small outbreak is halting transmissions instead of building up immunity through infections. Second, the crucial trade-off is not between health and economic costs, but between the intensity and duration of control measures. A simple formula of observables characterises the optimum. Third, the total cost depends critically on the efficiency of contact tracing, since it allows relaxing costly social distancing without increasing transmissions. A calibration to the COVID-19 pandemic illustrates the theoretical findings. [ABSTRACT FROM AUTHOR]

Pandemics have always been a nightmare for humanity, especially in developing countries. Forced lockdowns are considered one of the effective ways to deal with spreading such pandemics. Still, developing countries cannot afford such solutions because these may severely damage the country’s economy. Therefore, this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations smoothly. The novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things (IIoT) and blockchain-enabled technologies. Compared to existing studies, the immutable and tamper-proof contact tracing and quarantine management solution is proposed. The use of advanced technologies and information security is a critical area for practitioners in the internet of things (IoT) and corresponding solutions. Therefore, this study also emphasizes information security, end-to-end solution, and experimental results. Firstly, a wearable wristband is proposed, incorporating 4G-enabled ultra-wideband (UWB) technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization (WHO). Secondly, distributed ledger technology (DLT) omits the centralized dependency for transmitting contact tracing data. Thirdly, a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication mechanism. Lastly, based on geofencing techniques, blockchain-enabled machine-to-machine (M2M) technology is proposed for quarantine management. The step-by-step methodology and test results are proposed to ensure contact tracing and quarantine management. Unlike existing research studies, the security aspect is also considered in the realm of blockchain. The practical implementation of the proposed solution also obtains the results. The results indicate the successful implementation of blockchain-enabled contact tracing and isolation management using IoT and geo-fencing techniques, which could help battle pandemic situations. Researchers can also consider the 5G-enabled narrowband internet of things (NB-IoT) technologies to implement contact tracing solutions. [ABSTRACT FROM AUTHOR]

Non-pharmaceutical intervention measures, such as social distancing, have so far been the only means to slow the spread of SARS-CoV-2. In the United States, strict social distancing during the first wave of virus spread has resulted in different types of infection dynamics. In some states, such as New York, extensive infection spread was followed by a pronounced decline of infection levels. In other states, such as California, less infection spread occurred before strict social distancing, and a different pattern was observed. Instead of a pronounced infection decline, a long-lasting plateau is evident, characterized by similar daily new infection levels. Here we show that network models, in which individuals and their social contacts are explicitly tracked, can reproduce the plateau if network connections are cut due to social distancing measures. The reason is that in networks characterized by a 2D spatial structure, infection tends to spread quadratically with time, but as edges are randomly removed, the infection spreads along nearly one-dimensional infection "corridors", resulting in plateau dynamics. Further, we show that plateau dynamics are observed only if interventions start sufficiently early; late intervention leads to a "peak and decay" pattern. Interestingly, the plateau dynamics are predicted to eventually transition into an infection decline phase without any further increase in social distancing measures. Additionally, the models suggest that a second wave becomes significantly less pronounced if social distancing is only relaxed once the dynamics have transitioned to the decline phase. The network models analyzed here allow us to interpret and reconcile different infection dynamics during social distancing observed in various US states.

Regarding the problem of epidemic outbreak prevention and control, infectious disease dynamics models cannot support urban managers in reducing urban-scale healthcare costs through community-scale control measures, as they usually have difficulty meeting the requirements for simulation at different scales. In this paper, we propose combining contact networks at different spatial scales to study the COVID-19 outbreak in Shanghai from March to July 2022, calculate the initial R t through the number of cases at the beginning of the outbreak, and evaluate the effectiveness of dynamic non-pharmaceutical interventions (NPIs) adopted at different time periods in Shanghai using our proposed approach. In particular, our proposed contact network is a three-layer multi-scale network that is used to distinguish social interactions occurring in areas of different sizes, as well as to distinguish between intensive and non-intensive population contacts. This susceptible–exposure–infection–quarantine–recovery (SEIQR) epidemic model constructed based on a multi-scale network can more effectively assess the feasibility of small-scale control measures, such as assessing community quarantine measures and mobility restrictions at different moments and phases of an epidemic. Our experimental results show that this model can meet the simulation needs at different scales, and our further discussion and analysis show that the spread of the epidemic in Shanghai from March to July 2022 can be successfully controlled by implementing a strict long-term dynamic NPI strategy. [ABSTRACT FROM AUTHOR]

The objective of this study was to assess COVID-19 classroom transmission in the university setting when physical distancing was eliminated. Data was collected in fall 2021 at a private university. Universal masking, robust contact tracing, vaccination requirement, and enforced testing were in place. Exposures were classified as classroom versus non-classroom. ANOVA and chi-squared tests were used to identify significant relationships between predictors and COVID-19 test result. Logistic regression was conducted to investigate the relationship between exposure type and test result. A total of 162 student cases were identified with 1,658 associated close contacts. One-third of contacts (31.1%, n = 516) only had a non-classroom exposure, 63.8% (n = 1,057) only had a classroom exposure, and 5.1% (n = 85) had both. Close contacts were significantly more likely to test positive if they had a non-classroom exposure (60 of 601; 10.0%) compared to a classroom exposure (1 of 1057; 0.1%) (OR 58.8, CI 18.5–333.3, p < 0.001). Removing physical distancing in classrooms that had universal masking did not result in high rates of COVID-19 transmission. This has policy implications because eliminating physical distancing does not greatly increase transmission risk when universal masking is in place. [ABSTRACT FROM AUTHOR]

This manuscript uses competitive college football as a lens into the complexities of decision-making amid the Covid-19 pandemic. Pulling together what is known about the decision-makers, the decision-making processes, the social and political context, the risks and benefits, and the underlying obligations of institutions to these athletes, we conduct an ethical analysis of the decisions surrounding the 2020 fall football season. Based on this ethical analysis, we provide key recommendations to improve similar decision processes moving forward. [ABSTRACT FROM AUTHOR]

Background The use of non-pharmaceutical interventions (NPI) is one of the main tools used in the coronavirus disease 2019 (COVID-19) pandemic response, including physical distancing, frequent hand washing, face mask use, respiratory hygiene and use of contact tracing apps. Literature on compliance with NPI during the COVID-19 pandemic is limited. Methods We studied this compliance and associated factors in Portugal, between 28th October 2020 and 11th January 2021 (Portuguese second and third waves of the pandemic), using logistic regressions. Data were collected through a web-based survey and included questions regarding NPI compliance, COVID-19-related concerns, perception of institutions' performance, agreement with the measures implemented and socio-demographic characteristics. Results From the 1263 eligible responses, we found high levels of compliance among all COVID-19 related NPI, except for the contact tracing app. Females and older participants showed the highest compliance levels, whereas the opposite was observed for previously infected participants. There was heterogeneity of COVID-19 NPI compliance across Portuguese regions and a clear gradient between concern, government performance's perception or agreement and compliance. Conclusions Results suggested areas for further study with important implications for pandemic management and communication, for future preparedness, highlighting other factors to be accounted for when recommending policy measures during public health threats. [ABSTRACT FROM AUTHOR]

Contact tracing is a non-pharmaceutical intervention (NPI) widely used in the control of the COVID-19 pandemic. Its effectiveness may depend on a number of factors including the proportion of contacts traced, delays in tracing, the mode of contact tracing (e.g. forward, backward or bidirectional contact training), the types of contacts who are traced (e.g. contacts of index cases or contacts of contacts of index cases), or the setting where contacts are traced (e.g. the household or the workplace). We performed a systematic review of the evidence regarding the comparative effectiveness of contact tracing interventions. 78 studies were included in the review, 12 observational (ten ecological studies, one retrospective cohort study and one pre-post study with two patient cohorts) and 66 mathematical modelling studies. Based on the results from six of the 12 observational studies, contact tracing can be effective at controlling COVID-19. Two high quality ecological studies showed the incremental effectiveness of adding digital contact tracing to manual contact tracing. One ecological study of intermediate quality showed that increases in contact tracing were associated with a drop in COVID-19 mortality, and a pre-post study of acceptable quality showed that prompt contact tracing of contacts of COVID-19 case clusters / symptomatic individuals led to a reduction in the reproduction number R. Within the seven observational studies exploring the effectiveness of contact tracing in the context of the implementation of other non-pharmaceutical interventions, contact tracing was found to have an effect on COVID-19 epidemic control in two studies and not in the remaining five studies. However, a limitation in many of these studies is the lack of description of the extent of implementation of contact tracing interventions. Based on the results from the mathematical modelling studies, we identified the following highly effective policies: (1) manual contact tracing with high tracing coverage and either medium-term immunity, highly efficacious isolation/quarantine and/ or physical distancing (2) hybrid manual and digital contact tracing with high app adoption with highly effective isolation/ quarantine and social distancing, (3) secondary contact tracing, (4) eliminating contact tracing delays, (5) bidirectional contact tracing, (6) contact tracing with high coverage in reopening educational institutions. We also highlighted the role of social distancing to enhance the effectiveness of some of these interventions in the context of 2020 lockdown reopening. While limited, the evidence from observational studies shows a role for manual and digital contact tracing in controlling the COVID-19 epidemic. More empirical studies accounting for the extent of contact tracing implementation are required. [ABSTRACT FROM AUTHOR]

We examine the effect of testing and social distancing measures on the severity of COVID19 across Indian states during the 68th day nationwide lockdown period. We also explore whether pre‐existing socio‐economic factors such as quality of health care and the ability to practice social distancing influences the effect of these policy measures across states. Using daily level data between April 1 and May 31 for 18 of the major states, we find that both testing and social distancing have a negative effect on COVID‐19 fatalities in India. Further, testing is more helpful in reducing CFR for states with lower per capita health expenditure and weaker medical infrastructure. This highlights how ramping up testing can aid states that have a weak health care system through the detection of infection, contact tracing and isolation. In contrast, social distancing measures are more effective in states that are less populous and have lesser people dwelling in single‐room houses. Our results confirm the role of pre‐existing institutional factors in shaping the effect of policy actions on health outcomes. [ABSTRACT FROM AUTHOR]

For over a century, contact tracing has been an integral public health strategy for infectious disease control when there is no pharmaceutical treatment. Contact tracing for the coronavirus disease COVID-19 introduced a variety of automated methods deployed across several countries. The present paper examines privacy risk to infected persons and their physical contacts in contact tracing systems. Automated contact tracing systems implemented during the early months of COVID-19 are compared to conventional manual methods. Solove's taxonomy of privacy is applied to examine privacy risks in both conventional and automated contact tracing systems. As a method of epidemiological surveillance, all contact tracing systems inherently incur privacy risk. However, compared to conventional methods, automated contact tracing systems amplify privacy risk with pre-emptive data collection on all app users, regardless of exposure to an infectious disease; continuous, granular data collection on all users' location and proximity contacts; insecurities in proximity app technologies and interconnectivity; and in many cases, the use of centralised systems. Reducing these risk factors can reduce privacy harms, such as identification, distortion, secondary use, stigma, and social control. [ABSTRACT FROM AUTHOR]

Objectives: During June–July 2021, an outbreak of SARS-CoV-2 occurred among attendees of a summer youth camp in Nebraska. We assessed the factors that contributed to onward transmission of disease. Methods: The Four Corners Health Department conducted an outbreak investigation and recorded both laboratory-confirmed and self-reported cases of SARS-CoV-2 and mitigation measures employed. We generated sequences on positive specimens, created an epidemic curve to assist with outbreak visualization, and examined epidemiologic, genomic, and laboratory outcomes. Results: Evaluation of 3 index cases led to the identification of 25 people with COVID-19 who interacted directly with the camp. Contact tracing revealed an additional 18 cases consistent with onward community transmission. Most (24 of 35, 68.5%) vaccine-eligible community cases were not vaccinated. We sequenced 8 positive specimens; all were identified as the Delta variant. Precamp planning incorporated local health officials who recommended wearing face masks, practicing social distancing, and using attendee cohorts to limit mixing of people involved in various activities. Conclusion: Low vaccination levels and poor face mask–wearing habits among attendees resulted in secondary and tertiary spread of SARS-CoV-2 and severe outcomes among young adults. This outbreak of COVID-19 at a youth camp highlights the importance of vaccination and use of other measures to interrupt opportunities for SARS-CoV-2 spread in the community and shows that vaccinated people remain vulnerable to infection when in an environment of high exposure to SARS-CoV-2. Proactive case identification and interruption of chains of transmission can help decrease the number of cases and avoid further severe outcomes. [ABSTRACT FROM AUTHOR]

Introduction: Healthcare workers (HCWs) are at higher risk of getting infected with COVID-19 infection due to their close proximity to COVID-19-positive patients. We studied the risk stratification and positivity rate in HCWs at risk of getting COVID-19 infection as well as the possible factors responsible for their being at risk of COVID-19 infection during the study period. Material and Methods: This prospective study was conducted after approval by the institutional ethics committee. The data regarding demographic variables, risk stratification, COVID-19 (reverse-transcription polymerase chain reaction) report, and possible sources of exposure for HCWs were recorded in a proforma by personal/telephonic interviews as well as from hospital records from March 2020 to June 2021. The data generated were entered into Microsoft Excel® software and analyzed using percentages, proportions, and Chi-square tests for qualitative variables. Results: COVID-19 infection's positivity rate was 19.5% among high-risk and 0.6% among low-risk HCW contacts. HCWs working in non-COVID-19 areas (67.9%) were more at risk than those working in COVID-19 areas (32.1%). In contrast, the COVID-19 positivity rate was significantly higher among high-risk contact HCWs from COVID-19 areas (34.2%) than in non-COVID-19 areas (12.6%). The maximum COVID-19 positivity rate was seen in high-risk contacts with body fluid exposure (21%), performing aerosol-generating procedures (20%), and close exposure in operation theaters (18%). Conclusions: Risk stratification is an important tool to contain infection among HCWs who had unprotected close contact with a COVID-19-positive case. With appropriate contact tracing, we were able to avoid over- and under-quarantine, save many man-hours as well as contain the spread of infection. HCWs should not only wear appropriate personal protective equipment (PPE) during work hours but should also practice mask-wearing and social distancing while they are in the community. [ABSTRACT FROM AUTHOR]

Physical distancing was recommended to break the chain of transmission, with 761402,282 confirmed cases worldwide. The Bio-secure bubble system consisted of routine COVID-19 testing and cleaning of tournament locations. PESTEL analysis is a technique for examining external macro-environmental variables that have an impact on a company or sector. Difficulties in enforcing laws and cultural variations in adherence to safety precautions, technology can be used for contact tracing and testing, as well as for monitoring and maintaining the bio-secure bubble environment. Cost associated with deploying and maintaining the technology, while adopting a safe bubble approach, and carbon emissions while protecting the environment. INTRODUCTION The COVID-19 epidemic caused novel and unusual stress on a variety of social groupings, including athletic codes. [1,2] The entire schedule of sporting events for the year 2020 has been impacted by the coronavirus disease-2019 (COVID-19) pandemic. [3,4] Sports events were postponed or cancelled due to the high risk of COVID-19 transmission through close contact and droplets, and physical separation was recommended to break the chain of transmission. [3,4,5] According to the information that is now available, a total of 761,402,282 confirmed cases have been documented globally, and 6,887,000 individuals. [ABSTRACT FROM AUTHOR]

This study conceptually explores the relationship between a nation's culture and the success of utilizing various digital technologies to mitigate the spread of a pandemic, such as novel coronavirus (COVID-19). In the absence of a cure or vaccine of COVID-19, the national governments and public health authorities have been aggressively utilizing digital technologies to mitigate the pandemic spread. Given the urgency caused by COVID-19, this study highlights the importance of considering a country's national culture in evaluating the efficacy of a given digital technology, despite how promising or groundbreaking it may sound, in combating the spread of an infectious disease. Relying on the two critical dimensions of national culture, power distance and individualism/collectivism, this study proposes a framework that describes how people from different countries, depending on their prevalent national cultural values, would be receptive (or intolerant) to using government-run technology solutions meant for curbing the pandemic spread. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has shed light on the challenges of complying with public health guidance to isolate or quarantine without access to adequate income, housing, food, and other resources. When people cannot safely isolate or quarantine during an outbreak of infectious disease, a critical public health strategy fails. This article proposes integrating sociolegal needs screening and services into contact tracing as a way to mitigate public health harms and pandemic-related health inequities. [ABSTRACT FROM AUTHOR]

Background: In an unparalleled global response, during the COVID-19 pandemic, 90 countries asked 3.9 billion people to stay home. Yet other countries avoided lockdowns and focused on other strategies, like contact tracing. How effective and cost-effective are these strategies? We aimed to provide a comprehensive summary of the evidence on past pandemic controls, with a focus on cost-effectiveness. Methods: Following PRISMA guidelines, MEDLINE (1946 to April week 2, 2020) and EMBASE (1974 to April 17, 2020) were searched using a range of terms related to pandemic control. Articles reporting on the effectiveness or cost-effectiveness of at least one intervention were included. Results: We found 1653 papers; 62 were included. The effectiveness of hand-washing and face masks was supported by randomized trials. These measures were highly cost-effective. For other interventions, only observational and modelling studies were found. They suggested that (1) the most cost-effective interventions are swift contact tracing and case isolation, surveillance networks, protective equipment for healthcare workers, and early vaccination (when available); (2) home quarantines and stockpiling antivirals are less cost-effective; (3) social distancing measures like workplace and school closures are effective but costly, making them the least cost-effective options; (4) combinations are more cost-effective than single interventions; and (5) interventions are more cost-effective when adopted early. For 2009 H1N1 influenza, contact tracing was estimated to be 4363 times more cost-effective than school closure ($2260 vs. $9,860,000 per death prevented). Conclusions and contributions: For COVID-19, a cautious interpretation suggests that (1) workplace and school closures are effective but costly, especially when adopted late, and (2) scaling up as early as possible a combination of interventions that includes hand-washing, face masks, ample protective equipment for healthcare workers, and swift contact tracing and case isolation is likely to be the most cost-effective strategy. [ABSTRACT FROM AUTHOR]

In this study, we aim to determine the effect of discharge education provided with the Pecha Kucha method using smartphones on the levels of feeling of being ready for postpartum discharge and anxiety. This is a randomized controlled experimental study. ClinicalTrials.gov Identifier: NCT04696991. We assigned the participants to the experimental (70) and control (70) groups through the permuted block randomization method. We collected the data using Personal Information Form, State-Trait Anxiety Inventory (STAI) and the Readiness for Hospital Discharge Scale–New Mother Form (RHDS-NMF). The experimental group obtained significantly higher scores from the STAI and RHDS-NMF (p < 0.05). Today especially during the period of the pandemic in which contact tracing and social distancing are highly important, such a type of discharge education is also of importance for reducing the duration spent and the physical contact established by people indoors. According to the results of our research, this method can be a good alternative to routine discharge education. Supplemental data for this article is available online at https://doi.org/10.1080/07399332.2022.2043860. [ABSTRACT FROM AUTHOR]

Physical distancing and contact tracing are two key components in controlling the COVID-19 epidemics. Understanding their interaction at local level is important for policymakers. We propose a flexible modeling framework to assess the effect of combining contact tracing with different physical distancing strategies. Using scenario tree analyses, we compute the probability of COVID-19 detection using passive surveillance, with and without contact tracing, in metropolitan Barcelona. The estimates of detection probability and the frequency of daily social contacts are fitted into an age-structured susceptible-exposed-infectious-recovered compartmental model to simulate the epidemics considering different physical distancing scenarios over a period of 26 weeks. With the original Wuhan strain, the probability of detecting an infected individual without implementing physical distancing would have been 0.465, 0.515, 0.617, and 0.665 in designated age groups (0e14, 15e49, 50e64, and >65), respectively. As the physical distancing measures were reinforced and the disease circulation decreased, the interaction between the two interventions resulted in a reduction of the detection probabilities; however, despite this reduction, active contact tracing and isolation remained an effective supplement to physical distancing. If we relied solely on passive surveillance for diagnosing COVID-19, the model required a minimal 50% (95% credible interval, 39e69%) reduction of daily social contacts to keep the infected population under 5%, as compared to the 36% (95% credible interval, 22e56%) reduction with contact tracing systems. The simulation with the B.1.1.7 and B.1.167.2 strains shows similar results. Our simulations showed that a functioning contact tracing program would reduce the need for physical distancing and mitigate the COVID-19 epidemics. [ABSTRACT FROM AUTHOR]

COVID-19 (Coronavirus disease of 2019) is caused by SARS-CoV2 (Severe Acute Respiratory Syndrome Coronavirus 2) and it was first diagnosed in December 2019 in China. As of 25th Aug 2021, there are 165 million confirmed COVID-19 positive cases and 4.4 million deaths globally. As of today, though there are approved COVID-19 vaccine candidates only 4 billion doses have been administered. Until 100% of the population is safe, no one is safe. Even though these vaccines can provide protection against getting seriously ill and dying from the disease, it does not provide 100% protection from getting infected and passing it on to others. The more the virus spreads; it has more opportunity to mutate. So, it is mandatory to follow all precautions like maintaining social distance, wearing mask, washing hands frequently irrespective of whether a person is vaccinated or not. To prevent spread of the virus, contact tracing based on social distance also becomes equally important. The work proposes a solution that can help with contact tracing/identification, knowing the infected persons recent travel history (even within the city) for few days before being assessed positive. While the person would be able to give the known contacts with whom he/she has interacted with, he/she will not be aware of who all were in proximity if he/she had been in public places. The proposed solution is to get the CCTV (Closed-Circuit Television) video clips from those public places for the specific date and time and identify the people who were in proximity--i.e., not followed the safe distance to the infected person. The approach uses YOLO V3 (You Only Look Once) which uses darknet framework for people detection. Once the infected person is located from the video frames, the distance from that person to the other people in the frame is found, to check if there is a violation of social distance guideline. If there is, then the people violating the distance are extracted and identified using Facial detection and recognition algorithms. Two different solutions for Face detection and Recognition are implemented and results compared--Dlib based models and OpenCV (Open Source Computer Vision Library) based models. The solutions were studied for two different CCTV footages and the results for Dlib based models were better than OpenCV based models for the studied videos. [ABSTRACT FROM AUTHOR]

Objectives: Classroom layout plays a central role in maintaining physical distancing as part of a multicomponent prevention strategy for safe in-person learning during the COVID-19 pandemic. We conducted a school investigation to assess layouts and physical distancing in classroom settings with and without in-school SARS-CoV-2 transmission. Methods: We assessed, measured, and mapped 90 K-12 (kindergarten through grade 12) classrooms in 3 Missouri public school districts during January–March 2021, prior to widespread prevalence of the Delta variant; distances between students, teachers, and people with COVID-19 and their contacts were analyzed. We used whole-genome sequencing to further evaluate potential transmission events. Results: The investigation evaluated the classrooms of 34 students and staff members who were potentially infectious with COVID-19 in a classroom. Of 42 close contacts (15 tested) who sat within 3 ft of possibly infectious people, 1 (2%) probable transmission event occurred (from a symptomatic student with a longer exposure period [5 days]); of 122 contacts (23 tested) who sat more than 3 ft away from possibly infectious people with shorter exposure periods, no transmission events occurred. Conclusions: Reduced student physical distancing is one component of mitigation strategies that can allow for increased classroom capacity and support in-person learning. In the pre–Delta variant period, limited physical distancing (<6 ft) among students in K-12 schools was not associated with increased SARS-CoV-2 transmission. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Data for COVID-19 incidence and the source of infection among health care workers (HCWs) in Indian population are limited. The main objective of the study was to assess the incidence of COVID-19 infection and identify the source of infection among the HCWs in a tertiary teaching hospital. Methods: A prospective cohort study was conducted among the 2134 HCWs recruited by purposive sampling from a tertiary teaching hospital from May to August 2020 (4-month period--123 days). Over the 4-month period, all the HCWs who had symptoms or those were close contacts of COVID positive patients were traced and tested using validated COVID diagnostic test (reverse transcription-polymerase chain reaction [RT-PCR] test). A semi-structured questionnaire was used to interview each positive HCW to identify the source of exposure of the infection. Results: Incidence proportion was 9.3% among HCWs and was two times higher among males compared to females. Hazard ratio was found to be higher among males and HCWs working in the non-COVID areas. Test positivity rate was found to highest (around 57.8%) among those aged less than 30 years. The most common source of infection was infected HCW colleagues (40.9%) followed by exposure to patients in non-COVID areas (27.3%). Only 5.1% of total infection was found in HCWs who had worked in COVID zones. Conclusions: People working in non-COVID areas, those using shared workplace, dining halls, and staff hostels, must follow strict COVID protocols by using appropriate Personal Protective Equipment (PPE) and following social distancing measures. [ABSTRACT FROM AUTHOR]

The spread of the COVID-19 pandemic has been considered as a global issue. Based on the reported cases and clinical data, there are still required international efforts and more preventative measures to control the pandemic more effectively. Physical contact between individuals plays an essential role in spreading the coronavirus more widely. Mathematical models with computational simulations are effective tools to study and discuss this virus and minimize its impact on society. These tools help to determine more relevant factors that influence the spread of the virus. In this work, we developed two computational tools by using the R package and Python to simulate the COVID-19 transmissions. Additionally, some computational simulations were investigated that provide critical questions about global control strategies and further interventions. Accordingly, there are some computational model results and control strategies. First, we identify the model critical factors that helps us to understand the key transmission elements. Model transmissions can significantly be changed for primary tracing with delay to isolation. Second, some types of interventions, including case isolation, no intervention, quarantine contacts and quarantine contacts together with contacts of contacts are analyzed and discussed. The results show that quarantining contacts is the best way of intervening to minimize the spread of the virus. Finally, the basic reproduction number R0 is another important factor which provides a great role in understanding the transmission of the pandemic. Interestingly, the current computational simulations help us to pay more attention to critical model transmissions and minimize their impact on spreading this disease. They also help for further interventions and control strategies. [ABSTRACT FROM AUTHOR]

The rapidly growing global Coronavirus Disease 2019 (COVID-19) pandemic crisis affects the labor-intensive mining industry. The industry is characterized by high worker mobility and 24-hour operations; thus, this continuous, mobile workforce increases the transmission risk of COVID-19 and has been a challenge for the mining industry during the pandemic. PT X, one of the largest mineral mining locations in Mimika District, Central Papua Province, Indonesia, was challenged to face the COVID-19 pandemic crisis. Therefore, this qualitative study aimed to analyze the COVID-19 handling efforts by PT X Concentrating Division using a descriptive-analytical method to describe the completion of PT X. The assessment aimed to examine nine critical factors formulated by the International Labour Organization and evaluated as practical steps to prevent and mitigate the virus spread in the division. Furthermore, an analysis was conducted on data related to COVID-19 handling. The results showed that the nine critical factors had an average value of 89.41%. These results indicated that the pandemic handling efforts in the division had been implemented well. [ABSTRACT FROM AUTHOR]

Human interactions and perceptions about health risk are essential to understand the evolution over the course of a pandemic. We present a Susceptible-Exposed-Asymptomatic-Infectious-Recovered-Susceptible mathematical model with quarantine and social-distance-dependent transmission rates, to study COVID-19 dynamics. Human activities are split across different location settings: home, work, school, and elsewhere. Individuals move from home to the other locations at rates dependent on their epidemiological conditions and maintain a social distancing behavior, which varies with their location. We perform simulations and analyze how distinct social behaviors and restrictive measures affect the dynamic of the disease within a population. The model proposed in this study revealed that the main focus on the transmission of COVID-19 is attributed to the "home" location setting, which is understood as family gatherings including relatives and close friends. Limiting encounters at work, school and other locations will only be effective if COVID-19 restrictions occur simultaneously at all those locations and/or contact tracing or social distancing measures are effectively and strictly implemented, especially at the home setting. [ABSTRACT FROM AUTHOR]

The world is experiencing the unprecedented time of a pandemic caused by the coronavirus disease (i.e., COVID-19). As a countermeasure, contact tracing and social distancing are essential to prevent the transmission of the virus, which can be achieved using indoor location analytics. Based on the indoor location analytics, the human mobility on a site can be monitored and planned to minimize human's contact and enforce social distancing to contain the transmission of COVID-19. Given the indoor location data, the clustering can be applied to cluster spatial data, spatio-temporal data and movement behavior features for proximity detection or contact tracing applications. More specifically, we propose the Coherent Moving Cluster (CMC) algorithm for contact tracing, the density-based clustering (DBScan) algorithm for identification of hotspots and the trajectory clustering (TRACLUS) algorithm for clustering indoor trajectories. The feature extraction mechanism is then developed to extract useful and valuable features that can assist the proposed system to construct the network of users based on the similarity of the movement behaviors of the users. The network of users is used to model an optimization problem to manage the human mobility on a site. The objective function is formulated to minimize the probability of contact between the users and the optimization problem is solved using the proposed effective scheduling solution based on OR-Tools. The simulation results show that the proposed indoor location analytics system outperforms the existing clustering methods by about 30% in terms of accuracy of clustering trajectories. By adopting this system for human mobility management, the count of close contacts among the users within a confined area can be reduced by 80% in the scenario where all users are allowed to access the site. [ABSTRACT FROM AUTHOR]

Background: The complete contact tracing of coronavirus disease-19 (COVID-19) cases in South Korea allows a unique opportunity to investigate cluster characteristics. This study aimed to investigate all reported COVID-19 clusters in the Seoul metropolitan area from January 23 to September 24, 2020. Methods: Publicly available COVID-19 data was collected from the Seoul Metropolitan City and Gyeonggi Province. Community clusters with ≥5 cases were characterized by size and duration, categorized using K-means clustering, and the correlation between the types of clusters and the level of social distancing investigated. Results: A total of 134 clusters comprised of 4033 cases were identified. The clusters were categorized into small (type I and II), medium (type III), and large (type IV) clusters. A comparable number of daily reported cases in different time periods were composed of different types of clusters. Increased social distancing was related to a shift from large to small-sized clusters. Conclusions: Classification of clusters may provide opportunities to understand the pattern of COVID-19 outbreaks better and implement more effective suppression strategies. Social distancing administered by the government may effectively suppress large clusters but may not effectively control small and sporadic clusters.

• Developing a time-fractional compartmental model that incorporates the effect of contact tracing for Covid-19. • Describe the effective reproduction number in terms of observable quantities. • Discuss effects of tracking, reporting and monitoring in contact tracing. • Monitoring proportion of reported cases which must be tracked to ensure that the reproduction number is below one. • Numerical simulations are done to discuss the impact of contact tracing on the pandemic. Contact Tracing (CT) is one of the measures taken by government and health officials to reduce the spread of the novel coronavirus. In this paper, we investigate its efficacy by developing a compartmental model for assessing its impact on mitigating the spread of the virus. We describe the impact on the reproduction number R 0 of COVID-19. In particular, we discuss the importance and relevance of parameters of the model such as the number of reported cases, effectiveness of tracking and monitoring policy, and the transmission rates to contact tracing. We describe the terms "perfect tracking", "perfect monitoring" and "perfect reporting" to indicate that traced contacts will be tracked while incubating, tracked contacts are efficiently monitored so that they do not cause secondary infections, and all infected persons are reported, respectively. We consider three special scenarios: (1) perfect monitoring and perfect tracking of contacts of a reported case, (2) perfect reporting of cases and perfect monitoring of tracked reported cases and (3) perfect reporting and perfect tracking of contacts of reported cases. Furthermore, we gave a lower bound on the proportion of contacts to be traced to ensure that the effective reproduction, R c , is below one and describe R c in terms of observable quantities such as the proportion of reported and traced cases. Model simulations using the COVID-19 data obtained from John Hopkins University for some selected states in the US suggest that even late intervention of CT may reasonably reduce the transmission of COVID-19 and reduce peak hospitalizations and deaths. In particular, our findings suggest that effective monitoring policy of tracked cases and tracking of traced contacts while incubating are more crucial than tracing more contacts. The use of CT coupled with other measures such as social distancing, use of face mask, self-isolation or quarantine and lockdowns will greatly reduce the spread of the epidemic as well as peak hospitalizations and total deaths. [ABSTRACT FROM AUTHOR]

The test-trace-isolate-quarantine (TTIQ) strategy, where confirmed-positive pathogen carriers are isolated from the community and their recent close contacts are identified and pre-emptively quarantined, is used to break chains of transmission during a disease outbreak. The protocol is frequently followed after an individual presents with disease symptoms, at which point they will be tested for the pathogen. This TTIQ strategy, along with hygiene and social distancing measures, make up the non-pharmaceutical interventions that are utilised to suppress the ongoing COVID-19 pandemic. Here we develop a tractable mathematical model of disease transmission and the TTIQ intervention to quantify how the probability of detecting and isolating a case following symptom onset, the fraction of contacts that are identified and quarantined, and the delays inherent to these processes impact epidemic growth. In the model, the timing of disease transmission and symptom onset, as well as the frequency of asymptomatic cases, is based on empirical distributions of SARS-CoV-2 infection dynamics, while the isolation of confirmed cases and quarantine of their contacts is implemented by truncating their respective infectious periods. We find that a successful TTIQ strategy requires intensive testing: the majority of transmission is prevented by isolating symptomatic individuals and doing so in a short amount of time. Despite the lesser impact, additional contact tracing and quarantine increases the parameter space in which an epidemic is controllable and is necessary to control epidemics with a high reproductive number. TTIQ could remain an important intervention for the foreseeable future of the COVID-19 pandemic due to slow vaccine rollout and highly-transmissible variants with the potential for vaccine escape. Our results can be used to assess how TTIQ can be improved and optimised, and the methodology represents an improvement over previous quantification methods that is applicable to future epidemic scenarios. [ABSTRACT FROM AUTHOR]

Background: The Kingdom of Saudi Arabia (KSA) quickly controlled the spread of SARS-CoV-2 by implementing several non-pharmaceutical interventions (NPIs), including suspension of international and national travel, local curfews, closing public spaces (i.e., schools and universities, malls and shops), and limiting religious gatherings. The KSA also mandated all citizens to respect physical distancing and to wear face masks. However, after relaxing some restrictions during June 2020, the KSA is now planning a strategy that could allow resuming in-person education and international travel. The aim of our study was to evaluate the effect of NPIs on the spread of the COVID-19 and test strategies to open schools and resume international travel.Methods: We built a spatial-explicit individual-based model to represent the whole KSA population (IBM-KSA). The IBM-KSA was parameterized using country demographic, remote sensing, and epidemiological data. A social network was created to represent contact heterogeneity and interaction among age groups of the population. The IBM-KSA also simulated the movement of people across the country based on a gravity model. We used the IBM-KSA to evaluate the effect of different NPIs adopted by the KSA (physical distancing, mask-wearing, and contact tracing) and to forecast the impact of strategies to open schools and resume international travels.Results: The IBM-KSA results scenarios showed the high effectiveness of mask-wearing, physical distancing, and contact tracing in controlling the spread of the disease. Without NPIs, the KSA could have reported 4,824,065 (95% CI: 3,673,775-6,335,423) cases by June 2021. The IBM-KSA showed that mandatory mask-wearing and physical distancing saved 39,452 lives (95% CI: 26,641-44,494). In-person education without personal protection during teaching would have resulted in a high surge of COVID-19 cases. Compared to scenarios with no personal protection, enforcing mask-wearing and physical distancing in schools reduced cases, hospitalizations, and deaths by 25% and 50%, when adherence to these NPIs was set to 50% and 70%, respectively. The IBM-KSA also showed that a quarantine imposed on international travelers reduced the probability of outbreaks in the country.Conclusions: This study showed that the interventions adopted by the KSA were able to control the spread of SARS-CoV-2 in the absence of a vaccine. In-person education should be resumed only if NPIs could be applied in schools and universities. International travel can be resumed but with strict quarantine rules. The KSA needs to keep strict NPIs in place until a high fraction of the population is vaccinated in order to reduce hospitalizations and deaths. [ABSTRACT FROM AUTHOR]

Many large nations have failed to adequately protect their indigenous populations from COVID-19, a novel disease which has challenged overarching nation-states to develop plans that are politically acceptable to and effective for heterogeneous populations. These failures of the nation-state to adequately protect citizens have endangered indigenous communities but also opened a space for indigenous solidarity, organization, and institution building. The residents of Spiti Valley of Northern India initially attempted to organize against COVID-19 through the secular mechanisms of government in their district. When residents lost trust in their ability to work through official channels, monasteries transcended traditional and contemporary boundaries to cooperate with each other in developing the Committee for Preventive Measures and Sustainable Development (CPMSD). The CPMSD has successfully warded off COVID-19 through a variety of measures, including limiting incoming traffic, quarantine, education, contact tracing, and social distancing. In the process, the Spiti Valley has revitalized traditional structures of governance. Public health practitioners should carefully document how indigenous (and other) communities organize to combat COVID-19. This data – which may reveal practical as opposed to formal networks of authority – may assist current and future public health practitioners to decide how and with whom to invest scarce human resources. [ABSTRACT FROM AUTHOR]

Governments around the world are taking different measures to deal with the novel severe respiratory syndrome coronavirus 2. In this sense, realistic mathematical models are important tools to explore the effect of different control strategies. In this work, we developed an agent-based model for COVID-19 disease dynamics which incorporates a basic social structure to simulate different control strategies in mid-size cities. We evaluated the impact of combinations of social distancing measures, such as contact tracing/case isolation, school closures and partial lockdowns for workplaces on the evolution of hospital beds occupancy. The contact tracing/case isolation modeled in most cases cannot prevent hospital beds saturation by itself. Our results suggest that schools, without strong social distancing measures, may be an important driver of the epidemic. Household and workplace people distribution is also an important factor to consider when studying the impact of control measures. [ABSTRACT FROM AUTHOR]

The control of the initial outbreak and spread of SARS-CoV-2/COVID-19 via the application of population-wide non-pharmaceutical mitigation measures have led to remarkable successes in dampening the pandemic globally. However, with countries beginning to ease or lift these measures fully to restart activities, concern is growing regarding the impacts that such reopening of societies could have on the subsequent transmission of the virus. While mathematical models of COVID-19 transmission have played important roles in evaluating the impacts of these measures for curbing virus transmission, a key need is for models that are able to effectively capture the effects of the spatial and social heterogeneities that drive the epidemic dynamics observed at the local community level. Iterative forecasting that uses new incoming epidemiological and social behavioral data to sequentially update locally-applicable transmission models can overcome this gap, potentially resulting in better predictions and policy actions. Here, we present the development of one such data-driven iterative modelling tool based on publicly available data and an extended SEIR model for forecasting SARS-CoV-2 at the county level in the United States. Using data from the state of Florida, we demonstrate the utility of such a system for exploring the outcomes of the social measures proposed by policy makers for containing the course of the pandemic. We provide comprehensive results showing how the locally identified models could be employed for accessing the impacts and societal tradeoffs of using specific social protective strategies. We conclude that it could have been possible to lift the more disruptive social interventions related to movement restriction/social distancing measures earlier if these were accompanied by widespread testing and contact tracing. These intensified social interventions could have potentially also brought about the control of the epidemic in low- and some medium-incidence county settings first, supporting the development and deployment of a geographically-phased approach to reopening the economy of Florida. We have made our data-driven forecasting system publicly available for policymakers and health officials to use in their own locales, so that a more efficient coordinated strategy for controlling SARS-CoV-2 region-wide can be developed and successfully implemented. [ABSTRACT FROM AUTHOR]

In response to the COVID19 pandemic, many countries have implemented lockdowns in multiple phases to ensure social distancing and quarantining of the infected subjects. Subsequent unlocks to reopen the economies started next waves of infection and imposed an extra burden on quarantine to keep the reproduction number ( R 0 ) < 1. However, most countries could not effectively contain the infection spread, suggesting identification of the potential sources weakening the effect of lockdowns could help design better informed lockdown-unlock cycles in the future. Here, through building quantitative epidemic models and analyzing the metadata of 50 countries from across the continents we first found that the estimated value of R 0 , adjusted w.r.t the distribution of medical facilities and virus clades correlates strongly with the testing rates in a country. Since the testing capacity of a country is limited by its medical resources, we investigated if a cost–benefit trade-off can be designed connecting testing rate and extent of unlocking. We present a strategy to optimize this trade-off in a country specific manner by providing a quantitative estimate of testing and quarantine rates required to allow different extents of unlocks while aiming to maintain R 0 < 1 . We further show that a small fraction of superspreaders can dramatically increase the number of infected individuals even during strict lockdowns by strengthening the positive feedback loop driving infection spread. Harnessing the benefit of optimized country-specific testing rates would critically require minimizing the movement of these superspreaders via strict social distancing norms, such that the positive feedback driven switch-like exponential spread phase of infection can be avoided/delayed. [ABSTRACT FROM AUTHOR]

Background: With the global spreading of Coronavirus disease (COVID-19), many primary care medical workers have been infected, particularly in the early stages of this pandemic. Although extensive studies have explored the COVID-19 transmission patterns and (non-) pharmaceutical intervention to protect the general public, limited research has analysed the measures to prevent nosocomial transmission based upon detailed interpersonal contacts between medical staff and patients. Aim: This paper aims to develop and evaluate proactive prevention measures to contain the nosocomial transmission of COVID-19. The specific objectives are (1) to understand the virus transmission via interpersonal contacts among medical staff and patients; (2) to define proactive measures to reduce the risk of infection of medical staff and (3) evaluate the effectiveness of these measures to control the COVID-19 epidemic in hospitals. Methods: We observed the operation of a typical primary hospital in China to understand the interpersonal contacts among medical staff and patients. We defined effective distance as the indicator for risk of transmission. Then three proactive measures were proposed based upon the observations, including a medical staff rotation system, the establishment of a separate fever clinic and medical staff working alone. Finally, the impacts of these measures are evaluated with a modified Susceptible-Exposure-Infected-Removed model accommodating the situation of hospitals and asymptomatic and latent infection of COVID-19. The case study was conducted with the hospital observed in December 2019 and February 2020. Findings: The implementation of the medical staff rotation system has the most significant impact on containing the epidemic. The establishment of a separate fever clinic and medical staff working alone also benefits from inhibiting the epidemic outbreak. The simulation finds that if effective prevention and control measures are not taken in time, it will lead to a surge of infection cases in all asymptomatic probabilities and incubation periods. [ABSTRACT FROM AUTHOR]

Background Understanding the role of children in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is critical to guide decision-making for schools in the pandemic. We aimed to describe the transmission of SARS-CoV-2 among children and adult staff in summer schools. Methods During July 2020, we prospectively recruited children and adult staff attending summer schools in Barcelona who had SARS-CoV-2 infection. Primary SARS-CoV-2 infections were identified through (1) a surveillance program in 22 summer schools of 1905 participants, involving weekly saliva sampling for SARS-CoV-2 reverse-transcription polymerase chain reaction (RT-PCR) during 2–5 weeks; and (2) cases identified through the Catalonian Health Surveillance System of children diagnosed with SARS-CoV-2 infection by nasopharyngeal RT-PCR. All centers followed prevention protocols: bubble groups, handwashing, face masks, and conducting activities mostly outdoors. Contacts of a primary case within the same bubble were evaluated by nasopharyngeal RT-PCR. Secondary attack rates and the effective reproduction number in summer schools (Re*) were calculated. Results Among the >2000 repeatedly screened participants, 30 children and 9 adults were identified as primary cases. A total of 253 close contacts of these primary cases were studied (median, 9 [interquartile range, 5–10] for each primary case), among which 12 new cases (4.7%) were positive for SARS-CoV-2. The Re* was 0.3, whereas the contemporary rate in the general population from the same areas in Barcelona was 1.9. Conclusions The transmission rate of SARS-CoV-2 infection among children attending school-like facilities under strict prevention measures was lower than that reported for the general population. This suggests that under preventive measures schools are unlikely amplifiers of SARS-CoV-2 transmission, supporting current recommendations for school opening. [ABSTRACT FROM AUTHOR]