Model-based evaluation of alternative reactive class closure strategies against COVID-19

Assessing the effectiveness of test-trace-isolate interventions using a multi-layered temporal network

In the early stage of an infectious disease outbreak, public health strategies tend to gravitate towards non-pharmaceutical interventions (NPIs) given the time required to develop targeted treatments and vaccines. One of the most common NPIs is Test-Trace-Isolate (TTI). One of the factors determining the effectiveness of TTI is the ability to identify contacts of infected individuals. In this study, we propose a multi-layer temporal contact network to model transmission dynamics and assess the impact of different TTI implementations, using SARS-CoV-2 as a case study. The model was used to evaluate TTI effectiveness both in containing an outbreak and mitigating the impact of an epidemic. We estimated that a TTI strategy based on home isolation and testing of both primary and secondary contacts can contain outbreaks only when the reproduction number is up to 1.3, at which the epidemic prevention potential is 88.2% (95% CI: 87.9%-88.5%). On the other hand, for higher value of the reproduction number, TTI is estimated to noticeably mitigate disease burden but at high social costs (e.g., over a month in isolation/quarantine per person for reproduction numbers of 1.7 or higher). We estimated that strategies considering quarantine of contacts have a larger epidemic prevention potential than strategies that either avoid tracing contacts or require contacts to be tested before isolation. Combining TTI with other social distancing measures can improve the likelihood of successfully containing an outbreak but the estimated epidemic prevention potential remains lower than 50% for reproduction numbers higher than 2.1. In conclusion, our model-based evaluation highlights the challenges of relying on TTIs to contain an outbreak of a novel pathogen with characteristics similar to SARS-CoV-2, and that the estimated effectiveness of TTI depends on the way contact patterns are modeled, supporting the relevance of obtaining comprehensive data on human social interactions to improve preparedness.

Epidemic Dynamics and Intervention Measures in Campus Settings Based on Multilayer Temporal Networks

This study simulates the spread of epidemics on university campuses using a multilayer temporal network model combined with the SEIR (Susceptible-Exposed-Infectious-Recovered) transmission model. The proposed approach explicitly captures the time-varying contact patterns across four distinct layers (Rest, Dining, Activity, and Academic) to reflect realistic student mobility driven by class schedules and spatial constraints. It evaluates the impact of various intervention measures on epidemic spreading, including subnetwork closure and zoned management. Our analysis reveals that the Academic and Activity layers emerge as high-risk transmission hubs due to their dynamic, high-density contact structures. Intervention measures exhibit layer-dependent efficacy: zoned management is highly effective in high-contact subnetworks, its impact on low-contact subnetworks remains limited. Consequently, intervention measures must be dynamically adjusted based on the characteristics of each subnetwork and the epidemic situations, with higher participation rates enhancing the effectiveness of these measures. This work advances methodological innovation in temporal network epidemiology by bridging structural dynamics with SEIR processes, offering actionable insights for campus-level pandemic preparedness. The findings underscore the necessity of layer-aware policies to optimize resource allocation in complex, time-dependent contact systems.

MetaCity: Data-driven sustainable development of complex cities

Cities are complex systems that develop under complicated interactions among their human and environmental components. Urbanization generates substantial outcomes and opportunities while raising challenges including congestion, air pollution, inequality, etc., calling for efficient and reasonable solutions to sustainable developments. Fortunately, booming technologies generate large-scale data of complex cities, providing a chance to propose data-driven solutions for sustainable urban developments. This paper provides a comprehensive overview of data-driven urban sustainability practice. In this review article, we conceptualize MetaCity, a general framework for optimizing resource usage and allocation problems in complex cities with data-driven approaches. Under this framework, we decompose specific urban sustainable goals, e.g., efficiency and resilience, review practical urban problems under these goals, and explore the probability of using data-driven technologies as potential solutions to the challenge of complexity. On the basis of extensive urban data, we integrate urban problem discovery, operation of urban systems simulation, and complex decision-making problem solving into an entire cohesive framework to achieve sustainable development goals by optimizing resource allocation problems in complex cities.

A modeling study to define guidelines for antigen screening in schools and workplaces to mitigate COVID-19 outbreaks

BACKGROUND

In-person interaction offers invaluable benefits to people. To guarantee safe in-person activities during a COVID-19 outbreak, effective identification of infectious individuals is essential. In this study, we aim to analyze the impact of screening with antigen tests in schools and workplaces on identifying COVID-19 infections.

METHODS

We assess the effectiveness of various screening test strategies with antigen tests in schools and workplaces through quantitative simulations. The primary outcome of our analyses is the proportion of infected individuals identified. The transmission process at the population level is modeled using a deterministic compartmental model. Infected individuals are identified through screening tests or symptom development. The time-varying sensitivity of antigen tests and infectiousness is determined by a viral dynamics model. Screening test strategies are characterized by the screening schedule, sensitivity of antigen tests, screening duration, timing of screening initiation, and available tests per person.

RESULTS

Here, we show that early and frequent screening is the key to maximizing the effectiveness of the screening program. For example, 44.5% (95% CI: 40.8-47.5) of infected individuals are identified by daily testing, whereas it is only 33.7% (95% CI: 30.5-37.3) when testing is performed at the end of the program duration. If high sensitivity antigen tests (Detection limit: 6.3 × 10 4 copies/mL) are deployed, it reaches 69.3% (95% CI: 66.5-72.5).

CONCLUSIONS

High sensitivity antigen tests, high frequency screening tests, and immediate initiation of screening tests are important to safely restart educational and economic activities in-person. Our computational framework is useful for assessing screening programs by incorporating situation-specific factors.

Perceived stress and influencing factors for the people at high risk to COVID-19 in centralized quarantine camps in Wenzhou, China

BACKGROUND

This study was designed to assess stress levels and related factors during the coronavirus disease 2019 (COVID-19) epidemic among individuals in centralized quarantine camps in Wenzhou, China.

METHODS

The survey was conducted using a questionnaire. The questionnaire included questions on sociodemographic characteristics, life events related to the COVID-19 and stressful situations, as well as Perceived Stress Scale-14. Participants included close contacts of patients with COVID-19 or at-risk individuals in quarantine camps. Multivariate logistic regression was used to analyze different factors affecting perceived stress.

RESULTS

The prevalence of high stress among quarantine camp participants was 37.45%. Of the 881 respondents, 51.99% were concerned about the difficulty of controlling the epidemic, 46.20% were concerned about the health of themselves and their family members and 39.61% were concerned about not being able to leave their homes. Multivariate logistic regression analysis revealed statistically significant differences in the prevalence of stress among different groups for certain variables, including occupation, education level and knowledge of COVID-19 (all P < 0.05). Our study found that at-risk individuals and close contacts experienced high levels of stress in quarantine camps during the COVID-19 pandemic.

CONCLUSIONS

These findings suggest that centralized quarantine policies should be adapted and optimized to minimize negative psychological effects on quarantined individuals.

Decisions and Influential Factors Regarding Class-Specific School Closures Against Seasonal Influenza Outbreak

Background One of the characteristics of school closure in Japan is class-specific school closure, which involves a reactive, short-term closure in the event of an infectious disease outbreak. These closures are implemented at each school in reaction to the annual seasonal influenza outbreaks. Very little research has addressed the formation of class-specific school closures to combat infectious diseases in elementary schools. We carried out a survey on factors involved in the decision to close classes and the determination of the timing and duration of class closures in elementary schools in Japan. Methods A mail-based questionnaire survey of elementary schools from four prefectures in western Japan was conducted between August and September 2021. The questions addressed the criteria for school closures (the timing and duration of class closure), various considerations, and confusion regarding class closures, with answers analyzed using descriptive statistical methods. Results In total, 714 elementary schools responded to the survey (37.9%). Furthermore, 398 (55.7%) schools established criteria for class closures during seasonal influenza. Class closure was most frequently initiated in schools with criteria when either 20% or 30% of class pupils were absent; the most common duration was three days. The duration of class closures was decided upon depending on the outbreak in some schools (69.8%), depending on the circumstances of the outbreak. Regarding class closure decisions, schools viewed school physicians' opinions as a priority, followed by school events, adjustments for Saturdays and Sundays, and Yogo teachers' opinions. Schools answering "no criteria for class closure" or "adjustments for Saturdays and Sundays" had difficulty determining class closure duration. Conclusion To guarantee the continuation of children's education and improve the effectiveness of preventive efforts against seasonal influenza, the following were considered important and helpful in class closure decision-making in elementary schools: scientific evidence, the school physician's opinion, and Yogo teachers' analysis of children's health information.

Impact of different nucleic acid testing scenarios on COVID-19 transmission

In the past three years, waves of COVID-19 infections have emerged one after another, and may enter a small-scale wave-like recurrent epidemic pattern in the future. When COVID-19 infections occur in small-scale, how to efficiently detect and prevent the disease has become the main problem. In this study, based on the characteristics of the Omicron variant and China's pandemic prevention and control strategies, the following three nucleic acid testing scenarios were simulated: scenario 1 (baseline scenario) included conducting nucleic acid testing at administrative region; scenario 2 included conducting nucleic acid testing at the community; and scenario 3 included conducting nucleic acid testing at the health facility closest to households. The model calibration showed that the baseline scenario was consistent with the actual transmission scenario of the disease. The simulation results revealed that compared with scenario 1, the cumulative cases in scenarios 2 and 3 were reduced by 9.52 % and 46.83 %, respectively. Compared with scenario 2, the cumulative cases in scenario 3 were reduced by 41.23 %. Thus, adopting nucleic acid testing measures at the household level can effectively limit the spread of COVID-19 and should be given a priority when local emergency occurs in the future.

Recovering Zipf's law in intercontinental scientific cooperation

Scientific cooperation on an international level has been well studied in the literature. However, much less is known about this cooperation on an intercontinental level. In this paper, we address this issue by creating a collection of approximately 13.8×106 publications around the papers by one of the highly cited authors working in complex networks and their applications. The obtained rank-frequency distribution of the probability of sequences describing continents and the number of countries-with which authors of papers are affiliated-follows the power law with an exponent -1.9108(15). Such dependence is known in the literature as Zipf's law, and it has been originally observed in linguistics; later, it turned out that it is very commonly observed in various fields. The number of distinct "continent (number of countries)" sequences in a function of the number of analyzed papers grows according to power law with exponent 0.527(14); i.e., it follows Heap's law.

Evaluating the impact of test-trace-isolate for COVID-19 management and alternative strategies

There are many contrasting results concerning the effectiveness of Test-Trace-Isolate (TTI) strategies in mitigating SARS-CoV-2 spread. To shed light on this debate, we developed a novel static-temporal multiplex network characterizing both the regular (static) and random (temporal) contact patterns of individuals and a SARS-CoV-2 transmission model calibrated with historical COVID-19 epidemiological data. We estimated that the TTI strategy alone could not control the disease spread: assuming R0 = 2.5, the infection attack rate would be reduced by 24.5%. Increased test capacity and improved contact trace efficiency only slightly improved the effectiveness of the TTI. We thus investigated the effectiveness of the TTI strategy when coupled with reactive social distancing policies. Limiting contacts on the temporal contact layer would be insufficient to control an epidemic and contacts on both layers would need to be limited simultaneously. For example, the infection attack rate would be reduced by 68.1% when the reactive distancing policy disconnects 30% and 50% of contacts on static and temporal layers, respectively. Our findings highlight that, to reduce the overall transmission, it is important to limit contacts regardless of their types in addition to identifying infected individuals through contact tracing, given the substantial proportion of asymptomatic and pre-symptomatic SARS-CoV-2 transmission.

Impacting factors and sources of perceived stress by home-quarantined residents in Shanghai during COVID-19 epidemic

BACKGROUND

Home-quarantine is one of the most common measures implemented to prevent or minimize the transmission of COVID-19 among communities. This study assessed stress levels of the home-quarantined residents in Shanghai during a massive wave of COVID-19 epidemic this year, explored the stress sources perceived by the respondents, and analyzed the association between each of the sociodemographic factors and the stress level.

METHODS

This online survey was launched during April 23 - 30, 2022, the early stage of a massive wave of COVID-19 in Shanghai, China. Participants were quarantined-residents negative for COVID-19. They were asked to list some situations that were their major concerns and perceived stressful, in addition to sociodemographic and COVID-19 related information. Moreover, they were asked to complete the Perceived Stress Scale-14 (PSS-14) for the assessment of stress level.

RESULTS

A total of 488 valid questionnaires were collected from 192 male and 296 female respondents. Overall, 207 persons (42.42%) presented high stress level (PSS-14 score ≥43). The top three concerns perceived stressful by respondents are "not allowed to go outdoors", "uncertain duration of the epidemic", and "lack of food supply". Fewer than 50% of the respondents perceived the other situations stressful. Higher proportions of young adults (≤ 29 years old), males, unemployed, singles, and those with low income (≤ 1999 yuan/month) perceived high stress compared to their counterparts, none of COVID-19 related factors is associated with the stress level, including location of residence, result of nucleic acid test, knowledge about COVID-19, whether vaccinated, and quarantine duration.

CONCLUSION

Home-quarantine applied to people negative for COVID-19 led to a lot of major concerns that may be perceived stressful, whereas the virus-related factors did not show significant impact on mental health of the respondents.

COVID-19 Test Allocation Strategy to Mitigate SARS-CoV-2 Infections across School Districts

In response to COVID-19, schools across the United States closed in early 2020; many did not fully reopen until late 2021. Although regular testing of asymptomatic students, teachers, and staff can reduce transmission risks, few school systems consistently used proactive testing to safeguard return to classrooms. Socioeconomically diverse public school districts might vary testing levels across campuses to ensure fair, effective use of limited resources. We describe a test allocation approach to reduce overall infections and disparities across school districts. Using a model of SARS-CoV-2 transmission in schools fit to data from a large metropolitan school district in Texas, we reduced incidence between the highest and lowest risk schools from a 5.6-fold difference under proportional test allocation to 1.8-fold difference under our optimized test allocation. This approach provides a roadmap to help school districts deploy proactive testing and mitigate risks of future SARS-CoV-2 variants and other pathogen threats.

Minimising school disruption under high incidence conditions due to the Omicron variant in France, Switzerland, Italy, in January 2022

BackgroundAs record cases of Omicron variant were registered in Europe in early 2022, schools remained a vulnerable setting undergoing large disruption.AimThrough mathematical modelling, we compared school protocols of reactive screening, regular screening, and reactive class closure implemented in France, in Baselland (Switzerland), and in Italy, respectively, and assessed them in terms of case prevention, testing resource demand, and schooldays lost.MethodsWe used a stochastic agent-based model of SARS-CoV-2 transmission in schools accounting for within- and across-class contacts from empirical contact data. We parameterised it to the Omicron BA.1 variant to reproduce the French Omicron wave in January 2022. We simulated the three protocols to assess their costs and effectiveness for varying peak incidence rates in the range experienced by European countries.ResultsWe estimated that at the high incidence rates registered in France during the Omicron BA.1 wave in January 2022, the reactive screening protocol applied in France required higher test resources compared with the weekly screening applied in Baselland (0.50 vs 0.45 tests per student-week), but achieved considerably lower control (8% vs 21% reduction of peak incidence). The reactive class closure implemented in Italy was predicted to be very costly, leading to > 20% student-days lost.ConclusionsAt high incidence conditions, reactive screening protocols generate a large and unplanned demand in testing resources, for marginal control of school transmissions. Comparable or lower resources could be more efficiently used through weekly screening. Our findings can help define incidence levels triggering school protocols and optimise their cost-effectiveness.

Novel estimates reveal subnational heterogeneities in disease-relevant contact patterns in the United States

Population contact patterns fundamentally determine the spread of directly transmitted airborne pathogens such as SARS-CoV-2 and influenza. Reliable quantitative estimates of contact patterns are therefore critical to modeling and reducing the spread of directly transmitted infectious diseases and to assessing the effectiveness of interventions intended to limit risky contacts. While many countries have used surveys and contact diaries to collect national-level contact data, local-level estimates of age-specific contact patterns remain rare. Yet, these local-level data are critical since disease dynamics and public health policy typically vary by geography. To overcome this challenge, we introduce a flexible model that can estimate age-specific contact patterns at the subnational level by combining national-level interpersonal contact data with other locality-specific data sources using multilevel regression with poststratification (MRP). We estimate daily contact matrices for all 50 US states and Washington DC from April 2020 to May 2021 using national contact data from the US. Our results reveal important state-level heterogeneities in levels and trends of contacts across the US over the course of the COVID-19 pandemic, with implications for the spread of respiratory diseases.

A COVID-19 model incorporating variants, vaccination, waning immunity, and population behavior

Vaccines for COVID-19 have allowed countries to combat the spread of the disease. However, new variants have resulted in significant spikes in cases and raised severe health and economic concerns. We present a COVID-19 model to predict coupled effects of vaccine multiple-dose roll-out strategies, vaccine efficacy, waning immunity, population level of caution, sense of safety, under-reporting of cases, and highly prevalent variants such as the Delta (B.1.617.2) and Omicron (B.1.1.529). The modeling framework can incorporate new variants as they emerge to give critical insights into the new cases and guide public policy decision-making concerning vaccine roll-outs and reopening strategies. The model is shown to recreate the history of COVID-19 for five countries (Germany, India, Japan, South Africa, and the United States). Parameters for crucial aspects of the pandemic, such as population behavior, new variants, vaccination, and waning immunity, can be adjusted to predict pandemic scenarios. The model was used to conduct trend analysis to simulate pandemic dynamics taking into account the societal level of caution, societal sense of safety, and the proportions of individuals vaccinated with first, second, and booster doses. We used the results of serological testing studies to estimate the actual number of cases across countries. The model allows quantification of otherwise hard to quantify aspects such as the infectious power of variants and the effectiveness of government mandates and population behavior. Some example cases are presented by investigating the competitive nature of COVID variants and the effect of different vaccine distribution strategies between immunity groups.

What is the role of aerosol transmission in SARS-Cov-2 Omicron spread in Shanghai?

The Omicron transmission has infected nearly 600,000 people in Shanghai from March 26 to May 31, 2022. Combined with different control measures taken by the government in different periods, a dynamic model was constructed to investigate the impact of medical resources, shelter hospitals and aerosol transmission generated by clustered nucleic acid testing on the spread of Omicron. The parameters of the model were estimated by least square method and MCMC method, and the accuracy of the model was verified by the cumulative number of asymptomatic infected persons and confirmed cases in Shanghai from March 26 to May 31, 2022. The result of numerical simulation demonstrated that the aerosol transmission figured prominently in the transmission of Omicron in Shanghai from March 28 to April 30. Without aerosol transmission, the number of asymptomatic subjects and symptomatic cases would be reduced to 130,000 and 11,730 by May 31, respectively. Without the expansion of shelter hospitals in the second phase, the final size of asymptomatic subjects and symptomatic cases might reach 23.2 million and 4.88 million by May 31, respectively. Our results also revealed that expanded vaccination played a vital role in controlling the spread of Omicron. However, even if the vaccination rate were 100%, the transmission of Omicron should not be completely blocked. Therefore, other control measures should be taken to curb the spread of Omicron, such as widespread antiviral therapies, enhanced testing and strict tracking quarantine measures. This perspective could be utilized as a reference for the transmission and prevention of Omicron in other large cities with a population of 10 million like Shanghai.

Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases

Computational models offer a unique setting to test strategies to mitigate the spread of infectious diseases, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high-resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency. Many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high-resolution contact data, we use detailed to coarse data representations to inform a model of SARS-CoV-2 transmission and simulate different mitigation strategies. We find that coarse data representations estimate a lower risk of superspreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs triggering the adoption of protocols, as these may depend on data representation.

Large deviations of a susceptible-infected-recovered model around the epidemic threshold

We numerically study the dynamics of the SIR disease model on small-world networks by using a large-deviation approach. This allows us to obtain the probability density function of the total fraction of infected nodes and of the maximum fraction of simultaneously infected nodes down to very small probability densities like 10^{-2500}. We analyze the structure of the disease dynamics and observed three regimes in all probability density functions, which correspond to quick mild, quick extremely severe, and sustained severe dynamical evolutions, respectively. Furthermore, the mathematical rate functions of the densities are investigated. The results indicate that the so-called large-deviation property holds for the SIR model. Finally, we measured correlations with other quantities like the duration of an outbreak or the peak position of the fraction of infections, also in the rare regions which are not accessible by standard simulation techniques.