The impact of social distancing, contact tracing, and case isolation interventions to suppress the COVID-19 epidemic: A modeling study

Mathematical Contact Tracing Models for the COVID-19 Pandemic: A Systematic Review of the Literature

Background: Contact tracing (CT) is a primary means of controlling infectious diseases, such as coronavirus disease 2019 (COVID-19), especially in the early months of the pandemic. Objectives: This work is a systematic review of mathematical models used during the COVID-19 pandemic that explicitly parameterise CT as a potential mitigator of the effects of the pandemic. Methods: This review is registered in PROSPERO. A comprehensive literature search was conducted using the PubMed, EMBASE, Cochrane Library, CINAHL, and Scopus databases. Two reviewers independently selected the title/abstract, full text, data extraction, and risk of bias. Disagreements were resolved through discussion. The characteristics of the studies and mathematical models were collected from each study. Results: A total of 53 articles out of 2101 were included. The modelling of the COVID-19 pandemic was the main objective of 23 studies, while the remaining articles evaluated the forecast transmission of COVID-19. Most studies used compartmental models to simulate COVID-19 transmission (26, 49.1%), while others used agent-based (16, 34%), branching processes (5, 9.4%), or other mathematical models (6). Most studies applying compartmental models consider CT in a separate compartment. Quarantine and basic reproduction numbers were also considered in the models. The quality assessment scores ranged from 13 to 26 of 28. Conclusions: Despite the significant heterogeneity in the models and the assumptions on the relevant model parameters, this systematic review provides a comprehensive overview of the models proposed to evaluate the COVID-19 pandemic, including non-pharmaceutical public health interventions such as CT. Prospero Registration: CRD42022359060.

Public Acceptance of Measures to Control Infectious Diseases Under Different Scenarios of Severity and Transmissibility

OBJECTIVES

Public health measures to control future epidemic threats of contagious disease, such as new variants of COVID-19, may be usefully informed by evidence about how acceptable they are likely to be, and the circumstances that condition this acceptance. This study considers how the acceptability of nonpharmaceutical interventions (NPIs) might depend on scenarios about the severity and transmissibility of the disease.

METHODS

A telephone survey was conducted among a representative cross-sectional sample of the Spanish adult population. Each respondent was randomly assigned to 1 of 4 possible hypothetical scenarios about the severity and transmissibility of the disease. Participants' responses about the acceptability of 11 NPI under this scenario were analyzed using multivariate regression and latent class cluster analysis.

RESULTS

A high risk of severe disease increases the acceptability of mask wearing, social distancing outdoors, lockdown, and isolation of infected cases, close contacts, and the vulnerable. A scenario in which the disease is highly transmissible would increase the acceptability of NPI that restrict movement and isolation. Most respondents would broadly accept most NPI in situations when either the severity or transmissibility was high.

CONCLUSIONS

This study showed that people are more willing to accept NPIs such as mask wearing, social distancing outdoors, lockdown, and isolation in severe disease scenarios. A highly transmissible disease scenario increases the acceptability of NPIs that isolate. A majority would broadly accept NPIs to counter public health emergencies, whereas 3% to 9% of the population would always be strongly against.

Effectiveness of a COVID-19 contact tracing app in a simulation model with indirect and informal contact tracing

During the COVID-19 pandemic, contact tracing was used to identify individuals who had been in contact with a confirmed case so that these contacted individuals could be tested and quarantined to prevent further spread of the SARS-CoV-2 virus. Many countries developed mobile apps to find these contacted individuals faster. We evaluate the epidemiological effectiveness of the Dutch app CoronaMelder, where we measure effectiveness as the reduction of the reproduction number R. To this end, we use a simulation model of SARS-CoV-2 spread and contact tracing, informed by data collected during the study period (December 2020 - March 2021) in the Netherlands. We show that the tracing app caused a clear but small reduction of the reproduction number, and the magnitude of the effect was found to be robust in sensitivity analyses. The app could have been more effective if more people had used it, and if notification of contacts could have been done directly by the user and thus reducing the time intervals between symptom onset and reporting of contacts. The model has two innovative aspects: i) it accounts for the clustered nature of social networks and ii) cases can alert their contacts informally without involvement of health authorities or the tracing app.

Age-related model for estimating the symptomatic and asymptomatic transmissibility of COVID-19 patients

Estimation of age-dependent transmissibility of COVID-19 patients is critical for effective policymaking. Although the transmissibility of symptomatic cases has been extensively studied, asymptomatic infection is understudied due to limited data. Using a dataset with reliably distinguished symptomatic and asymptomatic statuses of COVID-19 cases, we propose an ordinary differential equation model that considers age-dependent transmissibility in transmission dynamics. Under a Bayesian framework, multi-source information is synthesized in our model for identifying transmissibility. A shrinkage prior among age groups is also adopted to improve the estimation behavior of transmissibility from age-structured data. The added values of accounting for age-dependent transmissibility are further evaluated through simulation studies. In real-data analysis, we compare our approach with two basic models using the deviance information criterion (DIC) and its extension. We find that the proposed model is more flexible for our epidemic data. Our results also suggest that the transmissibility of asymptomatic infections is significantly lower (on average, 76.45% with a credible interval (27.38%, 88.65%)) than that of symptomatic cases. In both symptomatic and asymptomatic patients, the transmissibility mainly increases with age. Patients older than 30 years are more likely to develop symptoms with higher transmissibility. We also find that the transmission burden of asymptomatic cases is lower than that of symptomatic patients.

Social Mixing and Network Characteristics of COVID-19 Patients Before and After Widespread Interventions: A Population-based Study

SARS-CoV-2 rapidly spreads among humans via social networks, with social mixing and network characteristics potentially facilitating transmission. However, limited data on topological structural features has hindered in-depth studies. Existing research is based on snapshot analyses, preventing temporal investigations of network changes. Comparing network characteristics over time offers additional insights into transmission dynamics. We examined confirmed COVID-19 patients from an eastern Chinese province, analyzing social mixing and network characteristics using transmission network topology before and after widespread interventions. Between the two time periods, the percentage of singleton networks increased from 38.9 to 62.8 ; the average shortest path length decreased from 1.53 to 1.14 ; the average betweenness reduced from 0.65 to 0.11 ; the average cluster size dropped from 4.05 to 2.72 ; and the out-degree had a slight but nonsignificant decline from 0.75 to 0.63 Results show that nonpharmaceutical interventions effectively disrupted transmission networks, preventing further disease spread. Additionally, we found that the networks’ dynamic structure provided more information than solely examining infection curves after applying descriptive and agent-based modeling approaches. In summary, we investigated social mixing and network characteristics of COVID-19 patients during different pandemic stages, revealing transmission network heterogeneities.

Aggressive behavior, boredom, and protective factors among college students during closed-off management of the COVID-19 pandemic in China

Background

Chinese colleges have implemented strict closed-off management in response to the outbreak of a new variant of the new coronavirus, Omicron. But such management measures may lead to more aggressive behavior. The study aimed to determine the associations between boredom and aggressive behavior with aggression and to examine the impact of boredom on aggression through the moderating role of cognitive flexibility.

Methods

The Multidimensional State Boredom Scale, the Reactive-Proactive Aggression Questionnaire, and the Cognitive Flexibility Inventory were applied to a sample of 719 college students who were in a closed-off management environment.

Results

For individuals with high cognitive flexibility, the relationship between state boredom and proactive aggression was not significant. The relationship between state boredom and proactive aggression was significantly positively correlated for individuals with low cognitive flexibility, especially low substitutability. Cognitive flexibility has no significant moderating effect on the relationship between state boredom and reactive aggression.

Conclusion

The findings highlighted the importance of boredom as a potential risk factor for aggression, while cognitive flexibility appears as a potential protective factor.

Suit the Remedy to the Case-The Effectiveness of COVID-19 Nonpharmaceutical Prevention and Control Policies Based on Individual Going-Out Behavior

Nonpharmaceutical policies for epidemic prevention and control have been extensively used since the outbreak of COVID-19. Policies ultimately work by limiting individual behavior. The aim of this paper is to evaluate the effectiveness of policies by combining macro nonpharmaceutical policies with micro-individual going-out behavior. For different going out scenarios triggered by individual physiological safety needs, friendship needs, and family needs, this paper categorizes policies with significant differences in intensity, parameterizes the key contents of the policies, and simulates and analyzes the effectiveness of the policies in different going-out scenarios with simulation methods. The empirical results show that enhancing policy intensity can effectively improve policy effectiveness. Among different types of policies, restricting the times of going out is more effective. Further, the effect of controlling going out based on physiological safety needs is better than other needs. We also evaluate the policy effectiveness of 26 global countries or regions. The results show that the policy effectiveness varies among 26 countries or regions. The quantifiable reference provided by this study facilitates decision makers to establish policy and practices for epidemic prevention and control.

Transmission roles of symptomatic and asymptomatic COVID-19 cases: a modelling study

Coronavirus disease 2019 (COVID-19) asymptomatic cases are hard to identify, impeding transmissibility estimation. The value of COVID-19 transmissibility is worth further elucidation for key assumptions in further modelling studies. Through a population-based surveillance network, we collected data on 1342 confirmed cases with a 90-days follow-up for all asymptomatic cases. An age-stratified compartmental model containing contact information was built to estimate the transmissibility of symptomatic and asymptomatic COVID-19 cases. The difference in transmissibility of a symptomatic and asymptomatic case depended on age and was most distinct for the middle-age groups. The asymptomatic cases had a 66.7% lower transmissibility rate than symptomatic cases, and 74.1% (95% CI 65.9-80.7) of all asymptomatic cases were missed in detection. The average proportion of asymptomatic cases was 28.2% (95% CI 23.0-34.6). Simulation demonstrated that the burden of asymptomatic transmission increased as the epidemic continued and could potentially dominate total transmission. The transmissibility of asymptomatic COVID-19 cases is high and asymptomatic COVID-19 cases play a significant role in outbreaks.

The impact of multi-level interventions on the second-wave SARS-CoV-2 transmission in China

Background

A re-emergence of COVID-19 occurred in the northeast of China in early 2021. Different levels of non-pharmaceutical interventions, from mass testing to city-level lockdown, were implemented to contain the transmission of SARS-CoV-2. Our study is aimed to evaluate the impact of multi-level control measures on the second-wave SARS-CoV-2 transmission in the most affected cities in China.

Methods

Five cities with over 100 reported COVID-19 cases within one month from Dec 2020 to Feb 2021 were included in our analysis. We fitted the exponential growth model to estimate basic reproduction number (R₀), and used a Bayesian approach to assess the dynamics of the time-varying reproduction number (R_t). We fitted linear regression lines on R_t estimates for comparing the decline rates of R_t across cities, and the slopes were tested by analysis of covariance. The effect of non-pharmaceutical interventions (NPIs) was quantified by relative R_t reduction and statistically compared by analysis of variance.

Results

A total of 2,609 COVID-19 cases were analyzed in this study. We estimated that R₀ all exceeded 1, with the highest value of 3.63 (1.36, 8.53) in Haerbin and the lowest value of 2.45 (1.44, 3.98) in Shijiazhuang. Downward trends of R_t were found in all cities, and the starting time of R_t < 1 was around the 12th day of the first local COVID-19 cases. Statistical tests on regression slopes of R_t and effect of NPIs both showed no significant difference across five cities (P = 0.126 and 0.157).

Conclusion

Timely implemented NPIs could control the transmission of SARS-CoV-2 with low-intensity measures for places where population immunity has not been established.

An optimal control policy in fighting COVID-19 and infectious diseases

When an outbreak starts spreading, policymakers have to make decisions that affect the health of their citizens and the economy. Some might induce harsh measures, such as a lockdown. Following a long, harsh lockdown, the recession forces policymakers to rethink reopening. To provide an effective strategy, here we propose a control strategy model. Our model assesses the trade-off between social performance and limited medical resources by determining individuals' propensities. The proposed strategy also helps decision-makers to find optimal lockdown and exit strategies for each region. Moreover, the financial loss is minimized. We use the public sentiment information during the pandemic to determine the percentage of individuals with high-risk behavior and the percentage of individuals with low-risk behavior. Hence, we propose an online platform using fear-sentiment information to estimate the personal protective equipment (PPE) burn rate overtime for the entire population. In addition, a study of a COVID-19 dataset for Los Angeles County is performed to validate our model and its results. The total social cost reduces by 18% compared with a control strategy where susceptible individuals are assumed to be homogeneous. We also reduce the total social costs by 26% and 22% compared to other strategies that consider the health-care cost or the social performance cost, respectively.

Synchronized spread of COVID-19 in the cities of Bahia, Brazil

The COVID-19 pandemic, caused by the highly transmissible SARS-CoV-2 virus, has overloaded health systems in many contexts Conant and Wolfe (2008). Brazil has experienced more than 345,000 deaths, as of April/2021 Conant and Wolfe (2008), with dire consequences for the country’s public and private health systems. This paper aims to estimate the synchronization graph between the cities’ contagion waves from public COVID-19 data records. For this purpose, the Motif–Synchronization method Magwire et al. (2011) was applied to publicly available COVID-19 data records to determine the sequential relationship of occurrence of the waves among Bahia’s cities. We find synchronization between waves of infection between cities, suggesting diffusion of the disease in Bahia and a potential role for inter-city transportation Saba et al. (2018), Saba et al. (2014), Araújo et al. (2018) in the dynamics of this phenomenon McKee and Stuckler (2020), Chinazzi et al. (2020), Tizzoni et al. (2014). Our main contribution lies in the use of the Motif–Synchronization method applied to COVID-19 data records, with the results revealing a pattern of disease spread that extends beyond city boundaries.

Geographical network model for COVID-19 spread among dynamic epidemic regions

Pandemic due to SARS-CoV-2 (COVID-19) has affected to world in several aspects: high number of confirmed cases, high number of deaths, low economic growth, among others. Understanding of spatio-temporal dynamics of the virus is helpful and necessary for decision making, for instance to decide where, whether and how, non-pharmaceutical intervention policies are to be applied. This point has not been properly addressed in literature since typical strategies do not consider marked differences on the epidemic spread across country or large territory. Those strategies assume similarities and apply similar interventions instead. This work is focused on posing a methodology where spatio-temporal epidemic dynamics is captured by means of dividing a territory in time-varying epidemic regions, according to geographical closeness and infection level. In addition, a novel Lagrangian-SEIR-based model is posed for describing the dynamic within and between those regions. The capabilities of this methodology for identifying local outbreaks and reproducing the epidemic curve are discussed for the case of COVID-19 epidemic in Jalisco state (Mexico). The contagions from July 31, 2020 to March 31, 2021 are analyzed, with monthly adjustments, and the estimates obtained at the level of the epidemic regions present satisfactory results since Relative Root Mean Squared Error RRMSE is below 15% in most of regions, and at the level of the whole state outstanding with RRMSE below 5%.

Age and Cultural Differences in Recognitions of Emotions from Masked Faces among Koreans and Americans

This study investigates age and cultural differences in the negative effects of senders’ wearing masks on receivers’ readabilities of senders’ facially expressed emotions in interpersonal interactions. An online experiment was thus conducted with Koreans and Americans aged over 20 years. Based on sampling quotas by nationality, age group and gender, Korean (n = 240) and American (n = 273) participants were recruited from panel members of a Korean research company and Amazon’s Mechanical Turk via email and the website, respectively. The participants played receiver roles to infer senders’ facially expressed emotions presented in photos in the experiment. They judged emotions facially expressed by the senders without masks and with masks are shown in photos. The results revealed that the senders’ wearing masks reduced the readabilities of the senders’ facially expressed anger among participants aged 30–49 years more than among participants aged 20–29 years. The senders’ wearing masks decreased the readabilities of the senders’ facially expressed fear for participants in their 50’s more than for participants in their 20’s. When the senders wore masks, the readabilities of the senders’ facially expressed happiness dropped among participants aged over 60 years more than among participants aged 20–49 years. When senders wore masks, American participants’ readabilities of disgust, fear, sadness and happiness expressed in the senders’ faces declined more than Korean participants’ readabilities of those emotions. The implications and limitations of these findings are discussed.