The challenges of containing SARS-CoV-2 via test-trace-and-isolate

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.

Understanding Nash epidemics

Faced with a dangerous epidemic humans will spontaneously social distance to reduce their risk of infection at a socioeconomic cost. Compartmentalized epidemic models have been extended to include this endogenous decision making: Individuals choose their behavior to optimize a utility function, self-consistently giving rise to population behavior. Here, we study the properties of the resulting Nash equilibria, in which no member of the population can gain an advantage by unilaterally adopting different behavior. We leverage an analytic solution that yields fully time-dependent rational population behavior to obtain, 1) a simple relationship between rational social distancing behavior and the current number of infections; 2) scaling results for how the infection peak and number of total cases depend on the cost of contracting the disease; 3) characteristic infection costs that divide regimes of strong and weak behavioral response; 4) a closed form expression for the value of the utility. We discuss how these analytic results provide a deep and intuitive understanding of the disease dynamics, useful for both individuals and policymakers. In particular, the relationship between social distancing and infections represents a heuristic that could be communicated to the population to encourage, or "bootstrap," rational behavior.

Nanoparticles of natural product-derived medicines: Beyond the pandemic

This review explores the synergistic potential of natural products and nanotechnology for viral infections, highlighting key antiviral, immunomodulatory, and antioxidant properties to combat pandemics caused by highly infectious viruses. These pandemics often result in severe public health crises, particularly affecting vulnerable populations due to respiratory complications and increased mortality rates. A cytokine storm is initiated when an overload of pro-inflammatory cytokines and chemokines is released, leading to a systemic inflammatory response. Viral mutations and the limited availability of effective drugs, vaccines, and therapies contribute to the continuous transmission of the virus. The coronavirus disease-19 (COVID-19) pandemic has sparked renewed interest in natural product-derived antivirals. The efficacy of traditional medicines against pandemic viral infections is examined. Their antiviral, immunomodulatory, anti-inflammatory, and antioxidant properties are highlighted. This review discusses how nanotechnology enhances the efficacy of herbal medicines in combating viral infections.

Key factors determining the development of SARS-CoV-2 testing strategies in EU countries: a mixed-methods study

Background

The WHO and the European Center for Disease Prevention and Control (ECDC) advocated for extensive testing as a crucial pillar in managing the COVID-19 pandemic. Yet, public health emergency responses varied across European countries. In particular, there were differences in the national laboratory capacities and diagnostic testing strategies. This study was conducted during the pandemic to identify the key factors in developing national, SARS-CoV-2 testing strategies across a selection of European countries.

Methods

A mixed-methods study, comprising an interview phase and a survey phase, was performed. First, laboratory, policy-making and/or public health experts from different European countries were interviewed between 8 January 2021 and 19 March 2021, to review the development and implementation of national testing strategies.Second, a cross-sectional survey was conducted among ECDC National Focal Points (NFP) for Preparedness and Response and/or Microbiology between July and October 2022 to validate the interview findings.

Results

12 European experts were interviewed and identified the following key factors determining the development of the national SARS-COV-2 testing strategies in their countries: (1) changing testing goals over time, (2) the prevailing epidemiological situation, (3) testing capacities, (4) availability of reference laboratories, (5) supply and stockpiling of testing material, (6) availability of human resources and (7) quality management standards across laboratories. The experts interviewed stressed the important role of stockpile management, the existence of expert networks, as well as the centralisation of decision-making. Lastly, determining the actors responsible for the testing strategy and putting in place 'coordination, accountability and governance' proved to be pivotal.The survey outcome with 15 European NFPs demonstrated that the testing strategies generally changed over time to include a broader group of individuals. Furthermore, the actors 'Ministry of Health', 'Public health officials', 'National public health institutes' and 'National Expert and/or advisory groups' were selected as key players by survey respondents.

Conclusions

In general, the scope of the testing strategy in European countries included in this study expanded as the pandemic progressed. This study identified key factors discussed by European experts interviewed that contributed to the development of SARS-CoV-2 testing strategies across European countries.

To test or not to test? A new behavioral epidemiology framework for COVID-19

Evidence from clinical research suggests that in the first two waves of COVID-19, the virus spread rapidly through a large number of undocumented asymptomatic infections. These 'silent' infections camouflaged the actual incidence of the disease, leading to downward biases in the rates of transmission, disease prevalence, and fatality. These, in turn, had implications for how people and policymakers responded to changing infection prevalence. This paper posits that in the early stages of the COVID-19 pandemic, a considerable number of SARS-CoV-2 infections spread through asymptomatic infected individuals who lacked economic incentives to test and isolate adequately. The decision to undertake testing and the subsequent possibility of isolation entails a calculus of benefits and costs for an individual. Given that the perceived net benefit of such actions is correlated with the observed risk of infection, the likelihood of an asymptomatic individual choosing to undergo testing increases with the existing infection prevalence rate. This behavior, in turn, influenced disease transmission and mortality dynamics. This study presents an analytical framework that integrates prevalence-dependent testing behavior into a traditional epidemiological model. The model's predictions provide critical policy insights. It reveals that failing to account for testing and isolation behavior results in underestimation of the infection propagation and fatality rates when reported disease prevalence is low, thereby, skewing the containment strategies in the initial and late stages of a pandemic. The findings underscore the necessity of enhancing testing capacity as a crucial countermeasure for future contagions like COVID-19.

Coupled infectious disease and behavior dynamics. A review of model assumptions

To comprehend the dynamics of infectious disease transmission, it is imperative to incorporate human protective behavior into models of disease spreading. While models exist for both infectious disease and behavior dynamics independently, the integration of these aspects has yet to yield a cohesive body of literature. Such an integration is crucial for gaining insights into phenomena like the rise of infodemics, the polarization of opinions regarding vaccines, and the dissemination of conspiracy theories during a pandemic. We make a threefold contribution. First, we introduce a framework todescribemodels coupling infectious disease and behavior dynamics, delineating four distinct update functions. Reviewing existing literature, we highlight a substantial diversity in the implementation of each update function. This variation, coupled with a dearth of model comparisons, renders the literature hardly informative for researchers seeking to develop models tailored to specific populations, infectious diseases, and forms of protection. Second, we advocate an approach tocomparingmodels' assumptions about human behavior, the model aspect characterized by the strongest disagreement. Rather than representing the psychological complexity of decision-making, we show that 'influence-response functions' allow one to identify which model differences generate different disease dynamics and which do not, guiding both model development and empirical research testing model assumptions. Third, we propose recommendations for future modeling endeavors and empirical research aimed atselectingmodels of coupled infectious disease and behavior dynamics. We underscore the importance of incorporating empirical approaches from the social sciences to propel the literature forward.

Effectiveness of probabilistic contact tracing in epidemic containment: The role of superspreaders and transmission path reconstruction

The recent COVID-19 pandemic underscores the significance of early stage nonpharmacological intervention strategies. The widespread use of masks and the systematic implementation of contact tracing strategies provide a potentially equally effective and socially less impactful alternative to more conventional approaches, such as large-scale mobility restrictions. However, manual contact tracing faces strong limitations in accessing the network of contacts, and the scalability of currently implemented protocols for smartphone-based digital contact tracing becomes impractical during the rapid expansion phases of the outbreaks, due to the surge in exposure notifications and associated tests. A substantial improvement in digital contact tracing can be obtained through the integration of probabilistic techniques for risk assessment that can more effectively guide the allocation of diagnostic tests. In this study, we first quantitatively analyze the diagnostic and social costs associated with these containment measures based on contact tracing, employing three state-of-the-art models of SARS-CoV-2 spreading. Our results suggest that probabilistic techniques allow for more effective mitigation at a lower cost. Secondly, our findings reveal a remarkable efficacy of probabilistic contact-tracing techniques in performing backward and multistep tracing and capturing superspreading events.

A novel, scenario-based approach to comparing non-pharmaceutical intervention strategies across nations

Comparing COVID-19 response strategies across nations is a key step in preparing for future pandemics. Conventional comparisons, which rank individual non-pharmaceutical intervention (NPI) effects, are limited by: (i) a focus on epidemiological outcomes; (ii) NPIs typically being applied as packages of interventions; and (iii) different political, economic and social conditions among nations. Here, we develop a coupled epidemiological-behavioural-macroeconomic model that can transfer NPI effects from a reference nation to a focal nation. This approach quantifies epidemiological, behavioural and economic outcomes while accounting for both packaged NPIs and differing conditions among nations. As a first proof of concept, we take Germany as our focal nation during Spring 2020, and New Zealand and Switzerland as reference nations with contrasting NPI strategies. Our results suggest that, while New Zealand's more aggressive strategy would have yielded modest epidemiological gains in Germany, it would have resulted in substantially higher economic costs while dramatically reducing social contacts. In contrast, Switzerland's more lenient strategy would have prolonged the first wave in Germany, but would also have increased relative costs. More generally, these findings indicate that our approach can provide novel, multifaceted insights on the efficacy of pandemic response strategies, and therefore merits further exploration and development.

Inference under superspreading: Determinants of SARS-CoV-2 transmission in Germany

Superspreading, under-reporting, reporting delay, and confounding complicate statistical inference on determinants of disease transmission. A model that accounts for these factors within a Bayesian framework is estimated using German Covid-19 surveillance data. Compartments based on date of symptom onset, location, and age group allow to identify age-specific changes in transmission, adjusting for weather, reported prevalence, and testing and tracing. Several factors were associated with a reduction in transmission: public awareness rising, information on local prevalence, testing and tracing, high temperature, stay-at-home orders, and restaurant closures. However, substantial uncertainty remains for other interventions including school closures and mandatory face coverings. The challenge of disentangling the effects of different determinants is discussed and examined through a simulation study. On a broader perspective, the study illustrates the potential of surveillance data with demographic information and date of symptom onset to improve inference in the presence of under-reporting and reporting delay.

A mathematical model to assess the effectiveness of test-trace-isolate-and-quarantine under limited capacities

Diagnostic testing followed by isolation of identified cases with subsequent tracing and quarantine of close contacts-often referred to as test-trace-isolate-and-quarantine (TTIQ) strategy-is one of the cornerstone measures of infectious disease control. The COVID-19 pandemic has highlighted that an appropriate response to outbreaks of infectious diseases requires a firm understanding of the effectiveness of such containment strategies. To this end, mathematical models provide a promising tool. In this work, we present a delay differential equation model of TTIQ interventions for infectious disease control. Our model incorporates the assumption of limited TTIQ capacities, providing insights into the reduced effectiveness of testing and tracing in high prevalence scenarios. In addition, we account for potential transmission during the early phase of an infection, including presymptomatic transmission, which may be particularly adverse to a TTIQ based control. Our numerical experiments inspired by the early spread of COVID-19 in Germany demonstrate the effectiveness of TTIQ in a scenario where immunity within the population is low and pharmaceutical interventions are absent, which is representative of a typical situation during the (re-)emergence of infectious diseases for which therapeutic drugs or vaccines are not yet available. Stability and sensitivity analyses reveal both disease-dependent and disease-independent factors that impede or enhance the success of TTIQ. Studying the diminishing impact of TTIQ along simulations of an epidemic wave, we highlight consequences for intervention strategies.

SARS-CoV-2 emergency use authorization published sensitivity differences do not correlate with positivity rate in a hospital/reference laboratory setting

During the first year of the COVID-19 pandemic skyrocketing demand for testing in the United States, coupled with supply chain issues, necessitated the use of multiple SARS-CoV-2 molecular testing platforms at many health centers. At our institution these platforms consisted of 8 ordered services for sample triage, using 9 emergency use authorized (EUA) SARS-CoV-2 RNA nucleic acid amplification tests resulting in 10 possible ordered service/EAU combinations. Here we review the results of the first ∼2.9 million samples tested and note the variability in positivity rates. We conclude that differences in reported limit of detection did not translate to differences in positivity rate or show correlation to discordant results observed. This highlights the importance of balancing patient testing capacity needs with the desire to have more sensitive tests.

Predicting the use of a COVID-19 contact tracing application: A study across two points of measurements

Contact tracing mobile applications (apps) were important in combating the COVID-19 pandemic. Most previous studies predicting contact tracing app use were cross-sectional and not theory-based. This study aimed at contributing to a better understanding of app use intentions and app use by applying an extended version of the protection motivation theory across two measurement points while accounting for the development of the pandemic. A total of N = 1525 participants from Switzerland (Mage = 53.70, SD = 18.73; 47% female; n = 270 completed both assessments) reported on risk perceptions, response efficacy, self-efficacy, social norms, trust in government, trust in the healthcare system, active search of COVID-19-related information, intentions for and actual (self-reported) app use. Analyses included country-specific incidences and death toll. Increases in response-efficacy, self-efficacy, trust in government, and the active search of COVID-19-related information predicted increased app-use intentions. Increases in self-efficacy, intentions, and the active search of COVID-19-related information predicted increased self-reported app use. Risk perceptions, incidence, and death toll were unrelated to both outcomes. Across an aggravation of the pandemic situation, intentions for and app use were primarily related to response-efficacy, self-efficacy, trust in government, and the active search of COVID-19-related information.

A network meta-analysis of risk factors of infection among close contacts of COVID-19

Objective

We aimed to use network meta-analysis to compare the impact of infection risk factors of close contacts with COVID-19, identify the most influential factors and rank their subgroups. It can provide a theoretical basis for the rapid and accurate tracking and management of close contacts.

Methods

We searched nine databases from December 1, 2019 to August 2, 2023, which only took Chinese and English studies into consideration. Odd ratios (ORs) were calculated from traditional meta-estimated secondary attack rates (SARs) for different risk factors, and risk ranking of these risk factors was calculated by the surface under the cumulative ranking curve (SUCRA).

Results

25 studies with 152647 participants identified. Among all risk factors, the SUCRA of type of contact was 69.6 % and ranked first. Among six types of contact, compared with transportation contact, medical contact, social contact and other, daily contact increased risk of infection by 12.11 (OR: 12.11, 95 % confidence interval (CI): 6.51-22.55), 7.76 (OR: 7.76, 95 % CI: 4.09-14.73), 4.65 (OR: 4.65, 95 % CI: 2.66-8.51) and 8.23 OR: 8.23, 95 % CI: 4.23-16.01) times, respectively. Overall, SUCRA ranks from highest to lowest as daily contact (94.7 %), contact with pollution subjects (78.4 %), social contact (60.8 %), medical contact (31.8 %), other (27.9 %), transportation contact (6.4 %).

Conclusion

The type of contact had the greatest impact on COVID-19 close contacts infection among the risk factors we included. Daily contact carried the greatest risk of infection among six types of contact, followed by contact with pollution subjects, social contact, other, medical contact and transportation contact. The results can provide scientific basis for rapid assess the risk of infection among close contacts based on fewer risk factors and pay attention to high-risk close contacts during management, thereby reducing tracking and management costs.

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.

Association Between Rapid Antigen Detection Tests and Real-Time Reverse Transcription-Polymerase Chain Reaction Assay for SARS-CoV-2: A Systematic Review and Meta-Analyses

Objectives: We aimed to assess the association between rapid antigen detection tests and real-time reverse transcription-polymerase chain reaction assay for severe acute respiratory syndrome coronavirus 2. Methods: We searched PubMed, Cochrane Library, EMBASE, and the Web of Science from their inception to 31 May 2023. A random-effects meta-analysis was used to estimate false positives in the RADTs group, relative to those in the RT-PCR group, and subgroup analyses were conducted based on the different Ct value cut-offs (<40 or ≥40). We performed this study in accordance with the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results: Fifty-one studies were included and considered to be of moderate quality. We found a satisfactory overall false positive rate (0.01, 95% CI: 0.00-0.01) for the RADTs compared to RT-PCR. In the stratified analysis, we also found that the false positive rates of the RADTs did not increase when Ct values of RT-PCR (Ct < 40, 0.01, 95% CI: 0.00-0.01; Ct ≥ 40, 0.01, 95% CI: 0.00-0.01). Conclusion: In conclusion, the best available evidence supports an association between RADTs and RT-PCR. When Ct-values were analyzed using cut-off <40 or ≥40, this resulted in an estimated false positive rate of only 1%.

Modelling the transmission and control of COVID-19 in Yangzhou city with the implementation of Zero-COVID policy

In the fight against the COVID-19 pandemic, China has long adhered to the "Dynamic Zero COVID-19" strategy till the end of 2022. To understand the mechanism of this strategy, we used the case of the Yangzhou summer outbreak in 2021 and a multi-stage dynamical model incorporating city-wide and key area testing-trace-isolation (TTI) strategies. We defined two time-varying indexes for measuring the disease transmission risk and the public health prevention and control force, respectively, which allowed us to explore the mechanisms of TTI policies. Integrating with the historical data and literature parameter values, we first estimated the parameters and then quantified the relevant indexes over time. The findings showed that multiple rounds of rapid testing were one of the critical measures to overcome the outbreak in Yangzhou within one month. In addition, we compared the impact of the duration of the free transmission stage, tracking rate, testing interval and precise division of key areas on the epidemiological indicators, including the final sizes of infections and isolations, peak value, peak arrival time and epidemic duration and the minimum round of testing. Our results suggest that the early detection of the epidemic, an improved efficiency of tracking, and a reduced duration of each test play a positive role in restraining COVID-19; however, a considerable investment of resources was essential to achieve a significant effect quickly.

Using real-time ascertainment rate estimate from infection and hospitalization dataset for modeling the spread of infectious disease: COVID-19 case study in the Czech Republic

We present a novel approach to estimate the time-varying ascertainment rate in almost real-time, based on the surveillance of positively tested infectious and hospital admission data. We also address the age dependence of the estimate. The ascertainment rate estimation is based on the Bayes theorem. It can be easily calculated and used (i) as part of a mechanistic model of the disease spread or (ii) to estimate the unreported infections or changes in their proportion in almost real-time as one of the early-warning signals in case of undetected outbreak emergence. The paper also contains a case study of the COVID-19 epidemic in the Czech Republic. The case study demonstrates the usage of the ascertainment rate estimate in retrospective analysis, epidemic monitoring, explanations of differences between waves, usage in the national Anti-epidemic system, and monitoring of the effectiveness of non-pharmaceutical interventions on Czech nationwide surveillance datasets. The Czech data reveal that the probability of hospitalization due to SARS-CoV-2 infection for the senior population was 12 times higher than for the non-senior population in the monitored period from the beginning of March 2020 to the end of May 2021. In a mechanistic model of COVID-19 spread in the Czech Republic, the ascertainment rate enables us to explain the links between all basic compartments, including new cases, hospitalizations, and deaths.

Detection of SARS-CoV-2 Based on Nucleic Acid Amplification Tests (NAATs) and Its Integration into Nanomedicine and Microfluidic Devices as Point-of-Care Testing (POCT)

The coronavirus SARS-CoV-2 has highlighted the criticality of an accurate and rapid diagnosis in order to contain the spread of the virus. Knowledge of the viral structure and its genome is essential for diagnosis development. The virus is still quickly evolving and the global scenario could easily change. Thus, a greater range of diagnostic options is essential to face this threat to public health. In response to the global demand, there has been a rapid advancement in the understanding of current diagnostic methods. In fact, innovative approaches have emerged, leveraging the benefits of nanomedicine and microfluidic technologies. Although this development has been incredibly fast, several key areas require further investigation and optimization, such as sample collection and preparation, assay optimization and sensitivity, cost effectiveness, scalability device miniaturization, and portability and integration with smartphones. Addressing these gaps in the knowledge and these technological challenges will contribute to the development of reliable, sensitive, and user-friendly NAAT-based POCTs for the diagnosis of SARS-CoV-2 and other infectious diseases, facilitating rapid and effective patient management. This review aims to provide an overview of current SARS-CoV-2 detection methods based on nucleic acid detection tests (NAATs). Additionally, it explores promising approaches that combine nanomedicine and microfluidic devices with high sensitivity and relatively fast 'time to answer' for integration into point-of-care testing (POCT).

Optimal strategies for coordinating infection control and socio-economic activities

It becomes challenging to identify feasible control strategies for simultaneously relaxing the countermeasures and containing the Covid-19 pandemic, given China's huge population size, high susceptibility, persist vaccination waning, and relatively weak strength of health systems. We propose a novel mathematical model with waning of immunity and solve the optimal control problem, in order to provide an insight on how much detecting and social distancing are required to coordinate socio-economic activities and epidemic control. We obtain the optimal intensity of countermeasures, i.e., the dynamic nucleic acid screening and social distancing, under which the health system is functioning normally and people can engage in a certain level of socio-economic activities. We find that it is the isolation capacity or the restriction of the case fatality rate (CFR) rather than the hospital capacity that mainly determines the optimal strategies. And the solved optimal controls under quarterly CFR restrictions exhibit oscillations. It is worth noticing that, if without considering booster or very low booster rate, the optimal strategy is a "on-off" mode, alternating between lock down and opening with certain social distancing, which reflects the importance and necessity of China's static management on a certain area during Covid-19 outbreak. The findings suggest some feasible paths to smoothly transit from the Covid-19 pandemic to an endemic phase.

Application of ultrasensitive assay for SARS-CoV-2 antigen in nasopharynx in the management of COVID-19 patients with comorbidities during the peak of 2022 Shanghai epidemics in a tertiary hospital

OBJECTIVES

Various comorbidities associated with COVID-19 add up in severity of the disease and obviously prolonged the time for viral clearance. This study investigated a novel ultrasensitive MAGLUMI^® SARS-CoV-2 Ag chemiluminescent immunoassay assay (MAG-CLIA) for diagnosis and monitoring the infectivity of COVID-19 patients with comorbid conditions during the pandemic of 2022 Shanghai.

METHODS

Analytical performances of the MAG-CLIA were evaluated, including precision, limit of quantitation, linearity and specificity. Nasopharyngeal specimens from 232 hospitalized patients who were SARS-CoV-2 RT-qPCR positive and from 477 healthy donors were included. The longitudinal studies were performed by monitoring antigen concentrations alongside with RT-qPCR results in 14 COVID-19 comorbid participants for up to 22 days. The critical antigen concentration in determining virus infectivity was evaluated at the reference cycle threshold (Ct) of 35.

RESULTS

COVID-19 patients were well-identified using an optimal threshold of 0.64 ng/L antigen concentration, with sensitivity and specificity of 95.7% (95% CI: 92.2-97.9%) and 98.3% (95% CI: 96.7-99.3%), respectively, while the Wondfo LFT exhibited those of 34.9% (95% CI: 28.8-41.4%) and 100% (95% CI: 99.23-100%), respectively. The sensitivity of MAG-CLIA remained 91.46% (95% CI: 83.14-95.8%) for the samples with Ct values between 35 and 40. Close dynamic consistence was observed between MAG-CLIA and viral load time series in the longitudinal studies. The critical value of 8.82 ng/L antigen showed adequate sensitivity and specificity in evaluating the infectivity of hospitalized convalescent patients with comorbidities.

CONCLUSIONS

The MAG-CLIA SARS-CoV-2 Ag detection is an effective and alternative approach for rapid diagnosis and enables us to evaluate the infectivity of hospitalized convalescent patients with comorbidities.

Mobile SARS‑CoV‑2 screening facilities for rapid deployment and university-based diagnostic laboratory

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has created a public crisis. Many medical and public institutions and businesses went into isolation in response to the pandemic. Because SARS-CoV-2 can spread irrespective of a patient's course of disease, these institutions' continued operation or reopening based on the assessment and control of virus spread can be supported by targeted population screening. For this purpose, virus testing in the form of polymerase chain reaction (PCR) analysis and antibody detection in blood can be central. Mobile SARS-CoV-2 screening facilities with a built-in biosafety level (BSL)-2 laboratory were set up to allow the testing offer to be brought close to the subject group's workplace. University staff members, their expertise, and already available equipment were used to implement and operate the screening facilities and a certified diagnostic laboratory. This operation also included specimen collection, transport, PCR and antibody analysis, and informing subjects as well as public health departments. Screening facilities were established at different locations such as educational institutions, nursing homes, and companies providing critical supply chains for health care. Less than 4 weeks after the first imposed lockdown in Germany, a first mobile testing station was established featuring a build-in laboratory with two similar stations commencing operation until June 2020. During the 15-month project period, approximately 33,000 PCR tests and close to 7000 antibody detection tests were collected and analyzed. The presented approach describes the required procedures that enabled the screening facilities and laboratories to collect and process several hundred specimens each day under difficult conditions. This report can assist others in establishing similar setups for pandemic scenarios.

Impact of the Euro 2020 championship on the spread of COVID-19

Large-scale events like the UEFA Euro 2020 football (soccer) championship offer a unique opportunity to quantify the impact of gatherings on the spread of COVID-19, as the number and dates of matches played by participating countries resembles a randomized study. Using Bayesian modeling and the gender imbalance in COVID-19 data, we attribute 840,000 (95% CI: [0.39M, 1.26M]) COVID-19 cases across 12 countries to the championship. The impact depends non-linearly on the initial incidence, the reproduction number R, and the number of matches played. The strongest effects are seen in Scotland and England, where as much as 10,000 primary cases per million inhabitants occur from championship-related gatherings. The average match-induced increase in R was 0.46 [0.18, 0.75] on match days, but important matches caused an increase as large as +3. Altogether, our results provide quantitative insights that help judge and mitigate the impact of large-scale events on pandemic spread.

Evaluation of Four Strategies for SARS-CoV-2 Detection: Characteristics and Prospects

The pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed an enormous burden on the global public health system and has had disastrous socioeconomic consequences. Currently, single sampling tests, 20-in-1 pooling tests, nucleic acid point-of-care tests (POCTs), and rapid antigen tests are implemented in different scenarios to detect SARS-CoV-2, but a comprehensive evaluation of them is scarce and remains to be explored. In this study, 3 SARS-CoV-2 inactivated cell culture supernatants were used to evaluate the analytical performance of these strategies. Additionally, 5 recombinant SARS-CoV-2 nucleocapsid (N) proteins were also used for rapid antigen tests. For the wild-type (WT), Delta, and Omicron strains, the lowest inactivated virus concentrations to achieve 100% detection rates of single sampling tests ranged between 1.28 × 102 to 1.02 × 103, 1.28 × 102 to 4.10 × 103, and 1.28 × 102 to 2.05 × 103 copies/mL. The 20-in-1 pooling tests ranged between 1.30 × 102 to 1.04 × 103, 5.19 × 102 to 2.07 × 103, and 2.59 × 102 to 1.04 × 103 copies/mL. The nucleic acid POCTs were all 1.42 × 103 copies/mL. The rapid antigen tests ranged between 2.84 × 105 to 7.14 × 106, 8.68 × 104 to 7.14 × 106, and 1.12 × 105 to 3.57 × 106 copies/mL. For the WT, Delta AY.2, Delta AY.1/AY.3, Omicron BA.1, and Omicron BA.2 recombinant N proteins, the lowest concentrations to achieve 100% detection rates of rapid antigen tests ranged between 3.47 to 142.86, 1.74 to 142.86, 3.47 to 142.86, 3.47 to 142.86, and 5.68-142.86 ng/mL, respectively. This study provided helpful insights into the scientific deployment of tests and recommended the full-scale consideration of the testing purpose, resource availability, cost performance, result rapidity, and accuracy to facilitate a profound pathway toward the long-term surveillance of coronavirus disease 2019 (COVID-19). IMPORTANCE In the study, we reported an evaluation of 4 detection strategies implemented in different scenarios for SARS-CoV-2 detection: single sampling tests, 20-in-1 pooling tests, nucleic acid point-of-care tests, and rapid antigen tests. 3 SARS-CoV-2-inactivated SARS-CoV-2 cell culture supernatants and 5 recombinant SARS-CoV-2 nucleocapsid proteins were used for evaluation. In this analysis, we found that for the WT, Delta, and Omicron supernatants, the lowest concentrations to achieve 100% detection rates of single sampling tests ranged between 1.28 × 102 to 1.02 × 103, 1.28 × 102 to 4.10 × 103, and 1.28 × 102 to 2.05 × 103 copies/mL. The 20-in-1 pooling tests ranged between 1.30 × 102 to 1.04 × 103, 5.19 × 102 to 2.07 × 103, and 2.59 × 102 to 1.04 × 103 copies/mL. The nucleic acid POCTs were all 1.42 × 103 copies/mL. The rapid antigen tests ranged between 2.84 × 105 to 7.14 × 106, 8.68 × 104 to 7.14 × 106, and 1.12 × 105 to 3.57 × 106 copies/mL. For the WT, Delta AY.2, Delta AY.1/AY.3, Omicron BA.1, and Omicron BA.2 recombinant N proteins, the lowest concentrations to achieve 100% detection rates of rapid antigen tests ranged between 3.47 to 142.86, 1.74 to 142.86, 3.47 to 142.86, 3.47 to 142.86, and 5.68 to 142.86 ng/mL, respectively.

Knowledge about, acceptance of and willingness to use over-the-counter COVID-19 self-testing kits

Objectives

Early investments in new diagnostic technologies that allow for rapid and decentralized testing were critical in reducing SARS-CoV-2’s detrimental health and economic effects. This study evaluates public knowledge about, acceptance of and willingness to use COVID-19 self-testing kits.

Methods

An online descriptive cross-sectional questionnaire was used in this study. The final study population included all contacted national and resident adults, age 18 and over, who were willing to engage in the study. The survey was divided according to participants’ demographic information and 11 questions assessed the respondents’ understanding of and willingness to use COVID-19 self-testing kits. The statistical analysis was carried out using SPSS version 24. Multivariate linear regression models were used to identify the factors influencing respondents’ knowledge of and attitudes toward the acceptability of self-testing kits for COVID-19 and their willingness to use these kits.

Key findings

A total of 876 respondents participated in the study and completed the whole questionnaire. The average knowledge score on the acceptability of and willingness to use self-testing kits for COVID-19 was 70.2%, with a 95% confidence interval (CI) [69.1%, 71.4%]. Participants who were postgraduate, female and vaccinated against COVID-19, as well as employees and older participants, were jointly highly associated with higher levels of knowledge about, acceptance of and willingness to use self-testing kits for COVID-19. Moreover, participants who had been infected with COVID-19, were vaccinated against COVID-19 or were female, employees, older, Western or Arabic were jointly highly associated with positive attitudes about the acceptability of and willingness to use self-testing kits for COVID-19.

Conclusions

The majority of the respondents have acceptable levels of knowledge about, acceptance of and willingness to use self-testing kits for COVID-19. Nonetheless, future studies should consider the issues of pre- and post-test counselling, false negative results and the sale of unregulated testing kits. Additional information should be communicated so that people can make informed decisions and be protected from possible abuse of COVID-19 self-testing kits when they become available in pharmacies.

Assessing the mechanism of citywide test-trace-isolate Zero-COVID policy and exit strategy of COVID-19 pandemic

BACKGROUND

Countries that aimed for eliminating the cases of COVID-19 with test-trace-isolate policy are found to have lower infections, deaths, and better economic performance, compared with those that opted for other mitigation strategies. However, the continuous evolution of new strains has raised the question of whether COVID-19 eradication is still possible given the limited public health response capacity and fatigue of the epidemic. We aim to investigate the mechanism of the Zero-COVID policy on outbreak containment, and to explore the possibility of eradication of Omicron transmission using the citywide test-trace-isolate (CTTI) strategy.

METHODS

We develop a compartmental model incorporating the CTTI Zero-COVID policy to understand how it contributes to the SARS-CoV-2 elimination. We employ our model to mimic the Delta outbreak in Fujian Province, China, from September 10 to October 9, 2021, and the Omicron outbreak in Jilin Province, China for the period from March 1 to April 1, 2022. Projections and sensitivity analyses were conducted using dynamical system and Latin Hypercube Sampling/ Partial Rank Correlation Coefficient (PRCC).

RESULTS

Calibration results of the model estimate the Fujian Delta outbreak can end in 30 (95% confidence interval CI: 28-33) days, after 10 (95% CI: 9-11) rounds of citywide testing. The emerging Jilin Omicron outbreak may achieve zero COVID cases in 50 (95% CI: 41-57) days if supported with sufficient public health resources and population compliance, which shows the effectiveness of the CTTI Zero-COVID policy.

CONCLUSIONS

The CTTI policy shows the capacity for the eradication of the Delta outbreaks and also the Omicron outbreaks. Nonetheless, the implementation of radical CTTI is challenging, which requires routine monitoring for early detection, adequate testing capacity, efficient contact tracing, and high isolation compliance, which constrain its benefits in regions with limited resources. Moreover, these challenges become even more acute in the face of more contagious variants with a high proportion of asymptomatic cases. Hence, in regions where CTTI is not possible, personal protection, public health control measures, and vaccination are indispensable for mitigating and exiting the COVID-19 pandemic.

A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization

In this paper, we address the controversies of epidemic control planning by developing a novel Simulation-Deep Reinforcement Learning (SiRL) model. COVID-19 reminded constituents over the world that government decision-making could change their lives. During the COVID-19 pandemic, governments were concerned with reducing fatalities as the virus spread but at the same time also maintaining a flowing economy. In this paper, we address epidemic decision-making regarding the interventions necessary given of the epidemic based on the purpose of the decision-maker. Further, we intend to compare different vaccination strategies, such as age-based and random vaccination, to shine a light on who should get priority in the vaccination process. To address these issues, we propose a simulation-deep reinforcement learning (DRL) framework. This framework is composed of an agent-based simulation model and a governor DRL agent that can enforce interventions in the agent-based simulation environment. Computational results show that our DRL agent can learn effective strategies and suggest optimal actions given a specific epidemic situation based on a multi-objective reward structure. We compare our DRL agent's decisions to government interventions at different periods of time during the COVID-19 pandemic. Our results suggest that more could have been done to control the epidemic. In addition, if a random vaccination strategy that allows super-spreaders to get vaccinated early were used, infections would have been reduced by 32% at the expense of 4% more deaths. We also show that a behavioral change of fully quarantining 10% of the risky individuals and using a random vaccination strategy leads to a reduction of the death toll by 14% and 27% compared to the age-based vaccination strategy that was implemented and the New Jersey reported data, respectively. We have also demonstrated the flexibility of our approach to be applied to other locations by validating and applying our model to the COVID-19 case in the state of Kansas.

Contact Tracing for Disease Containment: a Network-Based Analysis

Since the outbreak of the COVID-19 pandemic in spring 2020, the concept of test, trace, and isolate (TTI) was used as a non-pharmaceutical intervention against further spreading of the disease. Hereby, recent contact partners of newly confirmed SARS-CoV-2 infected persons were identified and isolated along with the originally detected case to avoid potential secondary infections. While the policy is, given the compliance of the traced persons, generally deemed efficient, not much is known about network-specific impact factors. In this work, we aim to evaluate the effectiveness of the TTI strategy when used (1) for diseases with different infectiousness levels and (2) on different contact networks. For the prior, we vary the infection probability per contact, for the latter, we analyse different clustering coefficients. Our goal is to test the validity of two hypotheses: First, we expect the policy to be more efficient if the infectiousness of the disease is small, since the time delay for isolating persons is crucial. Second, due to the implications of the friendship paradox, we expect the policy to be more effective if the clustering coefficient of the underlying contact network is high. We make use of an agent-based network model consisting of three intertwined model parts: an epidemiological SEIR model, a quarantine model and a contact-tracing model. To test the hypotheses, the disease parameters and the clustering coefficient of the underlying contact network are varied. The simulation results show that, indeed, tracing seems to have a slightly larger containment impact for networks with higher clustering, in particular for fast-spreading diseases. Yet, the effects are small compared to the impact of the infectiousness of the disease. Therefore, we find a significant decrease of the policy effectiveness the higher the transmission probability. The latter implies that the containment impact of tracing and isolating contacts becomes more efficient, if supported by additional measures that limit the infection probability or if applied in periods with low negative seasonality effects.

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.

Deploying wearable sensors for pandemic mitigation: A counterfactual modelling study of Canada's second COVID-19 wave

Wearable sensors can continuously and passively detect potential respiratory infections before or absent symptoms. However, the population-level impact of deploying these devices during pandemics is unclear. We built a compartmental model of Canada's second COVID-19 wave and simulated wearable sensor deployment scenarios, systematically varying detection algorithm accuracy, uptake, and adherence. With current detection algorithms and 4% uptake, we observed a 16% reduction in the second wave burden of infection; however, 22% of this reduction was attributed to incorrectly quarantining uninfected device users. Improving detection specificity and offering confirmatory rapid tests each minimized unnecessary quarantines and lab-based tests. With a sufficiently low false positive rate, increasing uptake and adherence became effective strategies for scaling averted infections. We concluded that wearable sensors capable of detecting presymptomatic or asymptomatic infections have potential to help reduce the burden of infection during a pandemic; in the case of COVID-19, technology improvements or supporting measures are required to keep social and resource costs sustainable.

Flexible societies excelled in saving lives in the first phase of the COVID-19 pandemic

Background

Previous studies have shown that national cultural traits, such as collectivism–individualism and tightness–looseness, are associated with COVID-19 infection and mortality rates. However, although East Asian countries have outperformed other countries in containing COVID-19 infections and lowering mortality in the first pandemic waves, no studies to date have examined flexibility-monumentalism, a cultural trait that uniquely distinguishes East Asia from the rest of the world. Moreover, none of the previous studies have explored mechanisms underpinning the association between national culture and COVID-19 mortality.

Aims

Our study fills in these gaps by examining the association between flexibility-monumentalism and COVID-19 mortality, adjusting for important covariates and by analyzing mask wearing and fear of COVID-19 during the first weeks of the pandemic as plausible mechanisms underpinning this association.

Methods

We constructed and analyzed a dataset including 37 countries that have valid information on flexibility-monumentalism, COVID-19 deaths as of 31 October 2020 (before the start of vaccination campaigns), and relevant covariates including two other national cultural traits (individualism–collectivism and tightness–looseness) and other national characteristics (economic, political, demographic and health). Multiple linear regression with heteroscedasticity-consistent standard errors was used to assess the independent effect of flexibility-monumentalism on COVID-19 mortality. Mediation was assessed by examining the indirect effects of flexibility through mask wearing and fear of COVID-19 and determining the statistical significance through bootstrapping. Graphical and delete-one analysis was used to assess the robustness of the results.

Results

We found that flexibility was associated with a significant reduction in COVID-19 mortality as of 31 October 2020, independent of level of democracy, per capita GDP, urbanization, population density, supply of hospital beds, and median age of the population. This association with mortality is stronger and more robust than for two other prominent national cultural traits (individualism–collectivism and tightness–looseness). We also found tentative evidence that the effect of flexibility on COVID-19 mortality may be partially mediated through mask wearing in the first weeks of the pandemic.

Unravelling the dynamics of the COVID-19 pandemic with the effect of vaccination, vertical transmission and hospitalization

The coronavirus disease 2019 (COVID-19) is caused by a newly emerged virus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), transmitted through air droplets from an infected person. However, other transmission routes are reported, such as vertical transmission. Here, we propose an epidemic model that considers the combined effect of vertical transmission, vaccination and hospitalization to investigate the dynamics of the virus's dissemination. Rigorous mathematical analysis of the model reveals that two equilibria exist: the disease-free equilibrium, which is locally asymptotically stable when the basic reproduction number ( R 0 ) is less than 1 (unstable otherwise), and an endemic equilibrium, which is globally asymptotically stable when R 0 > 1 under certain conditions, implying the plausibility of the disease to spread and cause large outbreaks in a community. Moreover, we fit the model using the Saudi Arabia cases scenario, which designates the incidence cases from the in-depth surveillance data as well as displays the epidemic trends in Saudi Arabia. Through Caputo fractional-order, simulation results are provided to show dynamics behaviour on the model parameters. Together with the non-integer order variant, the proposed model is considered to explain various dynamics features of the disease. Further numerical simulations are carried out using an efficient numerical technique to offer additional insight into the model's dynamics and investigate the combined effect of vaccination, vertical transmission, and hospitalization. In addition, a sensitivity analysis is conducted on the model parameters against the R 0 and infection attack rate to pinpoint the most crucial parameters that should be emphasized in controlling the pandemic effectively. Finally, the findings suggest that adequate vaccination coupled with basic non-pharmaceutical interventions are crucial in mitigating disease incidences and deaths.

Enhancing resilience in construction against infectious diseases using stochastic multi-agent approach

To recover from the adverse impacts of COVID-19 on construction and to avoid further losses to the industry in future pandemics, the resilience of construction industry needs to be enhanced against infectious diseases. Currently, there is a gap for modelling frameworks to simulate the spread of infectious diseases in construction projects at micro-level and to test interventions' effectiveness for data-informed decision-making. Here, this gap is addressed by developing a simulation framework using stochastic agent-based modelling, which enables construction researchers and practitioners to simulate and limit the spread of infectious diseases in construction projects. This is specifically important, since the results of a building project case-study reveals that, in comparison to the general population, infectious diseases may spread faster among construction workers and fatalities can be significantly higher. The proposed framework motivates future research on micro-level modelling of infectious diseases and efforts for intervening the spread of diseases in construction projects.

Prospects of NIR fluorescent nanosensors for green detection of SARS-CoV-2

The pandemic of the novel coronavirus disease 2019 (COVID-19) is continuously causing hazards for the world. Effective detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can relieve the impact, but various toxic chemicals are also released into the environment. Fluorescence sensors offer a facile analytical strategy. During fluorescence sensing, biological samples such as tissues and body fluids have autofluorescence, giving false-positive/negative results because of the interferences. Fluorescence near-infrared (NIR) nanosensors can be designed from low-toxic materials with insignificant background signals. Although this research is still in its infancy, further developments in this field have the potential for sustainable detection of SARS-CoV-2. Herein, we summarize the reported NIR fluorescent nanosensors with the potential to detect SARS-CoV-2. The green synthesis of NIR fluorescent nanomaterials, environmentally compatible sensing strategies, and possible methods to reduce the testing frequencies are discussed. Further optimization strategies for developing NIR fluorescent nanosensors to facilitate greener diagnostics of SARS-CoV-2 for pandemic control are proposed.

Epidemic management and control through risk-dependent individual contact interventions

Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (≳ 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.

During the ongoing COVID-19 pandemic, exposure notification apps have been developed to scale up manual contact tracing. The apps use proximity data from mobile devices to automate notifying direct contacts of an infection source. The information they provide is limited because users receive only rare and binary alerts. Here we present network data assimilation (DA) as a new digital approach to epidemic management and control. Network DA uses the same data as exposure notification apps but uses it more effectively to provide frequently updated individual risk assessments to users.

Network DA is based on automated learning about individuals’ risk of exposure and infection from crowd-sourced health data and proximity data. The data are aggregated with models of disease transmission to produce statistical assessments of users’ risks. In an extensive simulation study of the COVID-19 epidemic in New York City (NYC), we show that network DA with diagnostic testing achieves epidemic control with fewer than half the deaths that occurred during NYC’s lockdown, while isolating a far smaller fraction of the population (typically only 5–10% of the population at any given time).

Implemented at scale, then, network DA has the potential to effectively control epidemics while minimizing economic and social disruption.

When might host heterogeneity drive the evolution of asymptomatic, pandemic coronaviruses?

Controlling many infectious diseases, including SARS-Coronavirus-2 (SARS-CoV-2), requires surveillance followed by isolation, contact-tracing and quarantining. These interventions often begin by identifying symptomatic individuals. However, actively removing pathogen strains causing symptomatic infections may inadvertently select for strains less likely to cause symptomatic infections. Moreover, a pathogen's fitness landscape is structured around a heterogeneous host pool; uneven surveillance efforts and distinct transmission risks across host classes can meaningfully alter selection pressures. Here, we explore this interplay between evolution caused by disease control efforts and the evolutionary consequences of host heterogeneity. Using an evolutionary epidemiology model parameterized for coronaviruses, we show that intense symptoms-driven disease control selects for asymptomatic strains, particularly when these efforts are applied unevenly across host groups. Under these conditions, increasing quarantine efforts have diverging effects. If isolation alone cannot eradicate, intensive quarantine efforts combined with uneven detections of asymptomatic infections (e.g., via neglect of some host classes) can favor the evolution of asymptomatic strains. We further show how, when intervention intensity depends on the prevalence of symptomatic infections, higher removal efforts (and isolating symptomatic cases in particular) more readily select for asymptomatic strains than when these efforts do not depend on prevalence. The selection pressures on pathogens caused by isolation and quarantining likely lie between the extremes of no intervention and thoroughly successful eradication. Thus, analyzing how different public health responses can select for asymptomatic pathogen strains is critical for identifying disease suppression efforts that can effectively manage emerging infectious diseases.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11071-022-07548-7.

Estimating the course of the COVID-19 pandemic in Germany via spline-based hierarchical modelling of death counts

We consider a retrospective modelling approach for estimating effective reproduction numbers based on death counts during the first year of the COVID-19 pandemic in Germany. The proposed Bayesian hierarchical model incorporates splines to estimate reproduction numbers flexibly over time while adjusting for varying effective infection fatality rates. The approach also provides estimates of dark figures regarding undetected infections. Results for Germany illustrate that our estimates based on death counts are often similar to classical estimates based on confirmed cases; however, considering death counts allows to disentangle effects of adapted testing policies from transmission dynamics. In particular, during the second wave of infections, classical estimates suggest a flattening infection curve following the “lockdown light” in November 2020, while our results indicate that infections continued to rise until the “second lockdown” in December 2020. This observation is associated with more stringent testing criteria introduced concurrently with the “lockdown light”, which is reflected in subsequently increasing dark figures of infections estimated by our model. In light of progressive vaccinations, shifting the focus from modelling confirmed cases to reported deaths with the possibility to incorporate effective infection fatality rates might be of increasing relevance for the future surveillance of the pandemic.

Disease spreading modeling and analysis: a survey

MOTIVATION

The control of the diffusion of diseases is a critical subject of a broad research area, which involves both clinical and political aspects. It makes wide use of computational tools, such as ordinary differential equations, stochastic simulation frameworks and graph theory, and interaction data, from molecular to social granularity levels, to model the ways diseases arise and spread. The coronavirus disease 2019 (COVID-19) is a perfect testbench example to show how these models may help avoid severe lockdown by suggesting, for instance, the best strategies of vaccine prioritization.

RESULTS

Here, we focus on and discuss some graph-based epidemiological models and show how their use may significantly improve the disease spreading control. We offer some examples related to the recent COVID-19 pandemic and discuss how to generalize them to other diseases.

Mutation induced infection waves in diseases like COVID-19

After more than 6 million deaths worldwide, the ongoing vaccination to conquer the COVID-19 disease is now competing with the emergence of increasingly contagious mutations, repeatedly supplanting earlier strains. Following the near-absence of historical examples of the long-time evolution of infectious diseases under similar circumstances, models are crucial to exemplify possible scenarios. Accordingly, in the present work we systematically generalize the popular susceptible-infected-recovered model to account for mutations leading to repeatedly occurring new strains, which we coarse grain based on tools from statistical mechanics to derive a model predicting the most likely outcomes. The model predicts that mutations can induce a super-exponential growth of infection numbers at early times, which self-amplify to giant infection waves which are caused by a positive feedback loop between infection numbers and mutations and lead to a simultaneous infection of the majority of the population. At later stages-if vaccination progresses too slowly-mutations can interrupt an ongoing decrease of infection numbers and can cause infection revivals which occur as single waves or even as whole wave trains featuring alternative periods of decreasing and increasing infection numbers. This panorama of possible mutation-induced scenarios should be tested in more detailed models to explore their concrete significance for specific infectious diseases. Further, our results might be useful for discussions regarding the importance of a release of vaccine-patents to reduce the risk of mutation-induced infection revivals but also to coordinate the release of measures following a downwards trend of infection numbers.

A spatio-temporal model based on discrete latent variables for the analysis of COVID-19 incidence

We propose a model based on discrete latent variables, which are spatially associated and time specific, for the analysis of incident cases of SARS-CoV-2 infections. We assume that for each area the sequence of latent variables across time follows a Markov chain with initial and transition probabilities that also depend on latent variables in neighboring areas. The model is estimated by a Markov chain Monte Carlo algorithm based on a data augmentation scheme, in which the latent states are drawn together with the model parameters for each area and time. As an illustration we analyze incident cases of SARS-CoV-2 collected in Italy at regional level for the period from February 24, 2020, to January 17, 2021, corresponding to 48 weeks, where we use number of swabs as an offset. Our model identifies a common trend and, for every week, assigns each region to one among five distinct risk groups.

mRNA vaccines for COVID-19 and diverse diseases

Since its outbreak in late 2019, the novel coronavirus disease 2019 (COVID-19) has spread to every continent on the planet. The global pandemic has affected human health and socioeconomic status around the world. At first, the global response to the pandemic was to isolate afflicted individuals to prevent the virus from spreading, while vaccine development was ongoing. The genome sequence was first presented in early January 2020, and the phase I clinical trial of the vaccine started in March 2020 in the United States using novel lipid-based nanoparticle (LNP), encapsulated with mRNA termed as mRNA-1273. Till now, various mRNA-based vaccines are in development, while one mRNA-based vaccine got market approval from US-FDA for the prevention of COVID-19. Previously, mRNA-based vaccines were thought to be difficult to develop, but the current development is a significant accomplishment. However, widespread production and global availability of mRNA-based vaccinations to combat the COVID-19 pandemic remains a major challenge, especially when the mutations continually occur on the virus (e.g., the recent outbreaks of Omicron variant). This review elaborately discusses the COVID-19 pandemic, the biology of SARS-CoV-2 and the progress of mRNA-based vaccines. Moreover, the review also highlighted a detailed description of mRNA delivery technologies and the application potential in controlling other life-threatening diseases. Therefore, it provides a comprehensive view and multidisciplinary insights into mRNA therapy for broader audiences.

Mass screening is a key component to fight against SARS-CoV-2 and return to normalcy

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had highly transmissible and pathogenic, which caused serious economic loss and hazard to public health. Different countries have developed strategies to deal with the COVID-19 pandemic that fit their epidemiological situations, capacities, and values. Mass screening combined with control measures rapidly reduced the transmission of the SARS-CoV-2 infection. The COVID-19 pandemic has dramatically highlighted the essential role of diagnostics capacity in the control of communicable diseases. Mass screening has been increasingly used to detect suspected COVID-19 cases and their close contacts, asymptomatic case, patients attending fever clinics, high-risk populations, employees, even all population to identify infectious individuals. Mass screening is a key component to fight against SARS-CoV-2 and return to normalcy. Here we describe the history of mass screening, define the scope of mass screening, describe its application scenarios, and discuss the impact and challenges of using this approach to control COVID-19. We conclude that through a comprehension screening program and strong testing capabilities, mass screening could help us return to normalcy more quickly.

Ultrasensitive assay for saliva-based SARS-CoV-2 antigen detection

OBJECTIVES

Widespread SARS-CoV-2 testing is invaluable for identifying asymptomatic/pre-symptomatic individuals. There remains a technological gap for highly reliable, easy, and quick SARS-CoV-2 diagnostic tests suitable for frequent mass testing. Compared to nasopharyngeal (NP) swab-based tests, saliva-based methods are attractive due to easier and safer sampling. Current saliva-based SARS-CoV-2 rapid antigen tests (RATs) are hindered by limited analytical sensitivity. Here, we report one of the first ultrasensitive, saliva-based SARS-CoV-2 antigen assays with an analytical sensitivity of <0.32 pg/mL, corresponding to four viral RNA copies/µL, which is comparable to that of PCR-based tests.

METHODS

Using the novel electrochemiluminescence (ECL)-based immunoassay, we measured the SARS-CoV-2 nucleocapsid (N) antigen concentration in 105 salivas, obtained from non-COVID-19 and COVID-19 patients. We then verified the results with a second, independent cohort of 689 patients (3.8% SARS-CoV-2 positivity rate). We also compared our method with a widely used point-of-care rapid test.

RESULTS

In the first cohort, at 100% specificity, the sensitivity was 92%. Our assay correctly identified samples with viral loads up to 35 CT cycles by saliva-based PCR. Paired NP swab-based PCR results were obtained for 86 cases. Our assay showed high concordance with saliva-based and NP swab-based PCR in samples with negative (<0.32 pg/mL) and strongly positive (>2 pg/mL) N antigen concentrations. In the second cohort, at 100% specificity, sensitivity was also 92%. Our assay is about 700-fold more sensitive than the Abbott Panbio Rapid Test.

CONCLUSIONS

We demonstrated the ultrasensitivity and specificity assay and its concordance with PCR. This novel assay is especially valuable when compliance to frequent swabbing may be problematic.

Detection of SARS-CoV-2 B.1.351 (Beta) Variant through Wastewater Surveillance before Case Detection in a Community, Oregon, USA

Genomic surveillance has emerged as a critical monitoring tool during the SARS-CoV-2 pandemic. Wastewater surveillance has the potential to identify and track SARS-CoV-2 variants in the community, including emerging variants. We demonstrate the novel use of multilocus sequence typing to identify SARS-CoV-2 variants in wastewater. Using this technique, we observed the emergence of the B.1.351 (Beta) variant in Linn County, Oregon, USA, in wastewater 12 days before this variant was identified in individual clinical specimens. During the study period, we identified 42 B.1.351 clinical specimens that clustered into 3 phylogenetic clades. Eighteen of the 19 clinical specimens and all wastewater B.1.351 specimens from Linn County clustered into clade 1. Our results provide further evidence of the reliability of wastewater surveillance to report localized SARS-CoV-2 sequence information.

Data-driven prediction of COVID-19 cases in Germany for decision making

BACKGROUND

The COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitäten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation.

METHODS

We developed a model based on ordinary differential equations for the COVID-19 spread with a time-dependent infection rate described by a spline. Furthermore, the model explicitly accounts for weekday-specific reporting and adjusts for reporting delay. The model is calibrated in a purely data-driven manner by a maximum likelihood approach. Uncertainties are evaluated using the profile likelihood method. The uncertainty about the appropriate modeling assumptions can be accounted for by including and merging results of different modelling approaches. The analysis uses data from Germany describing the COVID-19 spread from early 2020 until March 31st, 2021.

RESULTS

The model is calibrated based on incident cases on a daily basis and provides daily predictions of incident COVID-19 cases for the upcoming three weeks including uncertainty estimates for Germany and its subregions. Derived quantities such as cumulative counts and 7-day incidences with corresponding uncertainties can be computed. The estimation of the time-dependent infection rate leads to an estimated reproduction factor that is oscillating around one. Data-driven estimation of the dark figure purely from incident cases is not feasible.

CONCLUSIONS

We successfully implemented a procedure to forecast near future COVID-19 incidences for diverse subregions in Germany which are made available to various decision makers via an interactive web application. Results of the incidence modeling are also used as a predictor for forecasting the need of intensive care units.

Properties of the Omicron Variant of SARS-CoV-2 Affect Public Health Measure Effectiveness in the COVID-19 Epidemic

Nonpharmaceutical and pharmaceutical public health interventions are important to mitigate the coronavirus disease 2019 (COVID-19) epidemic. However, it is still unclear how the effectiveness of these interventions changes with the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) novel variants. This simulation study utilized data from Japan and investigated how the characteristic properties of the Omicron variant, which emerged in late 2021, influence the effectiveness of public health interventions, including vaccination, the reduction of interpersonal contact, and the early isolation of infectious people. Although the short generation time of the Omicron variant increases the effectiveness of vaccination and the reduction of interpersonal contact, it decreases the effectiveness of early isolation. The latter feature may make the containment of case clusters difficult. The increase of infected children during the Omicron-dominant epidemic diminishes the effects of previously adult-targeted interventions. These findings underscore the importance of monitoring viral evolution and consequent changes in epidemiological characteristics. An assessment and adaptation of public health measures against COVID-19 are required as SARS-CoV-2 novel variants continue to emerge.

Proposal of a population wide genome-based testing for Covid-19

Our lives (and deaths) have by now been dominated for two years by COVID-19, a pandemic that has caused hundreds of millions of disease cases, millions of deaths, trillions in economic costs, and major restrictions on our freedom. Here we suggest a novel tool for controlling the COVID-19 pandemic. The key element is a method for a population-scale PCR-based testing, applied on a systematic and repeated basis. For this we have developed a low cost, highly sensitive virus-genome-based test. Using Germany as an example, we demonstrate by using a mathematical model, how useful this strategy could have been in controlling the pandemic. We show using real-world examples how this might be implemented on a mass scale and discuss the feasibility of this approach.

Listening to bluetooth beacons for epidemic risk mitigation

The ongoing COVID-19 pandemic let to efforts to develop and deploy digital contact tracing systems to expedite contact tracing and risk notification. Unfortunately, the success of these systems has been limited, partly owing to poor interoperability with manual contact tracing, low adoption rates, and a societally sensitive trade-off between utility and privacy. In this work, we introduce a new privacy-preserving and inclusive system for epidemic risk assessment and notification that aims to address these limitations. Rather than capturing pairwise encounters between user devices as done by existing systems, our system captures encounters between user devices and beacons placed in strategic locations where infection clusters may originate. Epidemiological simulations using an agent-based model demonstrate that, by utilizing location and environmental information and interoperating with manual contact tracing, our system can increase the accuracy of contact tracing actions and may help reduce epidemic spread already at low adoption.