PFC/M1 activation and excitability: a longitudinal cohort study on fatigue symptoms in healthcare workers post-COVID-19 (preprint)

Background Fatigue is one of the most common neurological symptoms reported post coronavirus disease 2019 (COVID-19) infection. In order to establish effective early intervention strategies, more emphasis should be placed on the correlation between fatigue and cortical neurophysiological changes, especially in healthcare workers, who are at a heightened risk of COVID-19 infection.Methods A prospective cohort study was conducted involving 29 COVID-19 medical workers and 24 healthy controls. The assessment included fatigue, sleep and health quality, psychological status, and physical capacity. Functional near-infrared spectroscopy (fNIRS) was employed to detect activation of brain regions. Bilateral primary motor cortex (M1) excitabilities were measured using single- and paired-pulse transcranial magnetic stimulation. Outcomes were assessed at 1, 3, and 6 months into the disease course.Results At 1-month post-COVID-19 infection, 37.9% of patients experienced severe fatigue symptoms, dropping to 10.3% at 3 months. Interestingly, the remarkable decreased activation/excitability of bilateral prefrontal lobe (PFC) and M1 were closely linked to fatigue symptoms after COVID-19. Notably, greater increase in M1 region excitability correlated with more significant fatigue improvement. Re-infected patients exhibited lower levels of brain activation and excitability compared to single-infection patients.Conclusions Both single infection and reinfection of COVID-19 lead to decreased activation and excitability of the PFC and M1. The degree of excitability improvement in the M1 region correlates with a greater recovery in fatigue. Based on these findings, targeted interventions to enhance and regulate the excitability of M1 may represent a novel strategy for COVID-19 early rehabilitation.Trial registration The Ethics Review Committee of Xijing Hospital, No. KY20232051-F-1, registered February 3, 2023. The Chinese Clinical Trial Registry, ChiCTR2300068444, registered February 20, 2023. https://www.chictr.org.cn

Profiling of linear B-cell epitopes against human coronaviruses in pooled sera sampled early in the COVID-19 pandemic (preprint)

Background: Antibodies play a key role in the immune defence against infectious pathogens. Understanding the underlying process of B cell recognition is not only of fundamental interest; it supports important applications within diagnostics and therapeutics. Whereas the nature of conformational B cell epitope recognition is inherently complicated, linear B cell epitopes offer a straightforward approach that potentially can be reduced to one of peptide recognition. Methods: Using an overlapping peptide approach representing the entire proteomes of the seven main coronaviruses known to infect humans, we analysed sera pooled from eight PCR-confirmed COVID-19 convalescents and eight pre-pandemic controls. Using a high-density peptide microarray platform, 13-mer peptides overlapping by 11 amino acids were in situ synthesised and incubated with the pooled primary serum samples, followed by development with secondary fluorochrome-labelled anti-IgG and -IgA antibodies. Interactions were detected by fluorescence detection. Strong Ig interactions encompassing consecutive peptides were considered to represent \"high-fidelity regions\" (HFRs). These were mapped to the coronavirus proteomes using a 60% homology threshold for clustering. Results: We identified 333 human coronavirus derived HFRs. Among these, 98 (29%) mapped to SARS-CoV-2, 144 (44%) mapped to one or more of the four circulating common cold coronaviruses (CCC), and 54 (16%) cross-mapped to both SARS-CoV-2 and CCCs. The remaining 37 (11%) mapped to either SARS-CoV or MERS-CoV. Notably, the COVID-19 serum was skewed towards recognising SARS-CoV-2-mapped HFRs, whereas the pre-pandemic was skewed towards recognising CCC-mapped HFRs. In terms of absolute numbers of linear B cell epitopes, the primary targets are the ORF1ab protein (60%), the spike protein (21%), and the nucleoprotein (15%) in that order; however, in terms of epitope density, the order would be reversed. Conclusion: We identified linear B cell epitopes across coronaviruses, highlighting pan-, alpha-, beta-, or SARS-CoV-2-corona-specific B cell recognition patterns. These findings could be pivotal in deciphering past and current exposures to epidemic and endemic coronavirus. Moreover, our results suggest that pre-pandemic anti-CCC antibodies may cross-react against SARS-CoV-2, which could explain the highly variable outcome of COVID-19. Finally, the methodology used here offers a rapid and comprehensive approach to high-resolution linear B-cell epitope mapping, which could be vital for future studies of emerging infectious diseases.

Scale up of COVID-19 testing services in NYC, 2020-2021: Lessons Learned to Maximize Reach, Equity and Timeliness (preprint)

During infectious disease epidemics, accurate diagnostic testing is key to rapidly identify and treat cases, and mitigate transmission.  When a novel pathogen is involved, building testing capacity and scaling testing services at the local level can present major challenges to healthcare systems, public health agencies and laboratories.  This mixed methods study examined lessons learned from the scale-up of SARS CoV-2 testing services in New York City (NYC), as a core part of NYC’s Test & Trace program. Using quantitative and geospatial analyses, the authors assessed program success at maximizing reach, equity and timeliness of SARS CoV-2 diagnostic testing services across NYC neighborhoods. Qualitative analysis of key informant interviews elucidated key decisions, facilitators and barriers involved in the scale-up of SARS-CoV-2 testing services. A major early facilitator was the ability to establish working relationships with private sector vendors and contractors to rapidly procure and manufacture necessary supplies locally.  NYC residents were, on average, less than 25 minutes away from free SARS CoV-2 diagnostic testing services by public transport, and services were successfully directed to most neighborhoods with highest transmission rates, with only one notable exception.   A key feature was to direct mobile testing vans and rapid antigen testing services to areas based on real-time neighborhood transmission data. Municipal leaders should prioritize fortifying supply chains, establish cross-sectoral partnerships to support and extend testing services, plan for continuous testing and validation of assays, ensure open communication feedback loops with CBO partners, and maintain infrastructure to support mobile services during infectious disease emergencies.

Serum banks for serological survey of SARS-CoV-2 and emerging infectious diseases (preprint)

In June 2021, Udom et al. published their article in  Transboundary and Emerging Diseases performing a serological survey revealed evidence of anti-N-IgG antibodies suggesting SARS-CoV-2 exposure in both dogs and cats during the first and second coronavirus disease 2019 (COVID-19) outbreaks in Thailand. Seroprevalence studies have proven an important tool to monitor the progression of the COVID-19 pandemic. The duration of immunity of SARS-CoV-2 is crucial for the course of the pandemic and for this reason the monitoring of antibodies against SARS-CoV-2 is important. The serum samples from different periods and regions were valuable in terms of scientific significance for serological survey of SARS-CoV-2 and emerging infectious diseases. In order to preserve the remaining serum samples and ensure the stability of anti-virus antibodies in storage serum samples, we strongly suggest that standard serum banks should be established worldwide.

Drug repositioning in response to COVID-19 and other challenging diseases (preprint)

There are over 90 clinical trials including drug repositioning that have been initiated to get COVID-19 treatment/management. Antibiotic resistance, drug tolerance, mutation and adverse drug effects possess a great deal of setback during therapy especially with emerging infectious diseases and this necessitates the need for research into getting new drugs or repositioning the existing ones to meet up with the treatment of both infectious and non-infectious diseases affecting humanity. Drug repositioning is a stepwise process that aid in discovering new indications and therapeutic targets of drugs and it usually takes 3-12 years on the average to be completed whereas in drug discovery, an average of 10-17 years is needed for the whole process. This is because in repositioning, research process goes directly to preclinical testing and clinical trials since both the toxicological and pharmacological profile of the drug to be repositioned is known, thus reducing time, risk, and costs. Based on 2009 statistics, 30% of all drugs sold in that year are products of repositioning while only one out of one million potential drug candidates have the possibility of entry into clinical studies with a tendency of having a significant number of failures. Hence the urgent need to discover new uses of existing drugs especially with the emergence of human and animal diseases such as Covid-19 and the high incidence of drug tolerance and resistance. Drug repositioning is therefore considered as an alternative way as it entails the discovery of new therapeutic indications for already existing drugs.

Routes of importation and spatial dynamics of SARS-CoV-2 variants during localised interventions in Chile (preprint)

South America suffered large SARS-CoV-2 epidemics between 2020 and 2022 caused by multiple variants of interest and concern, some causing substantial morbidity and mortality. However, their transmission dynamics are poorly characterised. The epidemic situation in Chile enables us to investigate differences in the distribution and spread of variants Alpha, Gamma, Lambda, Mu and Delta. Chile implemented non-pharmaceutical interventions and an integrated genomic and epidemiological surveillance system that included airport and community surveillance to track SARS-CoV-2 variants. Here we combine viral genomic data and anonymised human mobility data from mobile phones to characterise the routes of importation of different variants into Chile, the relative contributions of airport-based importations to viral diversity versus land border crossings and test the impact of the mobility network on the diffusion of viral lineages within the country. We find that Alpha, Lambda and Mu were identified in Chile via airport surveillance six, four and five weeks ahead of their detection via community surveillance, respectively. Further, some variants that originated in South America were imported into Chile via land rather than international air travel, most notably Gamma. Different variants exhibited similar trends of viral dissemination throughout the country following their importation, and we show that the mobility network predicts the time of arrival of imported lineages to different Chilean comunas. Higher stringency of local NPIs was also associated with fewer domestic viral importations. Our results show how genomic surveillance combined with high resolution mobility data can help predict the multi-scale geographic expansion of emerging infectious diseases. Significance statementGlobal preparedness for pandemic threats requires an understanding of the global variations of spatiotemporal transmission dynamics. Regional differences are important because the local context sets the conditions for the unfolding of local epidemics, which in turn affect transmission dynamics at a broader scale. Knowledge gaps from the SARS-CoV-2 pandemic remain for regions like South America, where distinct sets of viral variants emerged and spread from late 2020 onwards, and where changes in human behaviour resulted in epidemics which differed from those observed in other regions. Our interdisciplinary analysis of the SARS-CoV-2 epidemic in Chile provides insights into the spatiotemporal trends of viral diffusion in the region which shed light on the drivers that can influence future epidemic waves and pandemics.

Metaverse in Dynamic Viral-shedding Surveillance for Emerging Infectious Disease: A Digital Twin Approach to COVID-19 Epidemic (preprint)

Metaverse in effective surveillance of outbreaks of emerging infectious diseases such as COVID-19 opens a new avenue for precision and efficient contact tracing, quarantine, and isolation. We adopted a digital twin model to generate digital threads for tracing and tracking virtual data on the cycle threshold (Ct) values of the repeated RT-PCR with parameters learned from real-world (physical) data fitted with Markov machine learning algorithms. Such a digital twin method is demonstrated with COVID-19 community-acquired outbreaks of the Alpha and Omicron Variants of Concern (VOCs) in Taiwan. The personalized dynamics of Ct-defined transitions were derived from the digital threads of the two community-acquired outbreaks to guide precision contact tracing, quarantine, and isolation of both Alpha and Omicron VOCs outbreaks. Metaverse surveillance with such a Ct-guided digital twin model is supposed to be useful for timely containing the spread of emerging infectious diseases in the future.

Differentiation of COVID-19 from other emergency infectious disease presentations using whole blood transcriptomics then rapid qPCR: a case-control and observational cohort study (preprint)

Background. The overlapping clinical presentations of patients with acute respiratory disease can complicate disease diagnosis. Whilst PCR diagnostic methods to identify SARS-CoV-2 are highly sensitive, they have their shortcomings including false-positive risk and slow turnaround times. Changes in host gene expression can be used to distinguish between disease groups of interest, providing a viable alternative to infectious disease diagnosis. Methods. We interrogated the whole blood gene expression profiles of patients with COVID-19 (n=87), bacterial infections (n=88), viral infections (n=36), and not-infected controls (n=27) to identify a sparse diagnostic signature for distinguishing COVID-19 from other clinically similar infectious and non-infectious conditions. The sparse diagnostic signature underwent validation in a new cohort using reverse transcription quantitative polymerase chain reaction (RT-qPCR) and then underwent further external validation in an independent in silico RNA-seq cohort. Findings. We identified a 10-gene signature (OASL, UBP1, IL1RN, ZNF684, ENTPD7, NFKBIE, CDKN1C, CD44, OTOF, MSR1) that distinguished COVID-19 from other infectious and non-infectious diseases with an AUC of 87.1% (95% CI: 82.6%-91.7%) in the discovery cohort and 88.7% and 93.6% when evaluated in the RT-qPCR validation, and in silico cohorts respectively. Interpretation. Using well-phenotyped samples collected from patients admitted acutely with a spectrum of infectious and non-infectious syndromes, we provide a detailed catalogue of blood gene expression at the time of hospital admission. The findings result in the identification of a 10-gene host diagnostic signature to accurately distinguish COVID-19 from other infection syndromes presenting to hospital. This could be developed into a rapid point-of-care diagnostic test, providing a valuable syndromic diagnostic tool for future early pandemic use.

A human primary airway microphysiological system infected with SARS-CoV-2 distinguishes the treatment efficacy between nirmatrelvir and repurposed compounds fluvoxamine and amodiaquine (preprint)

The COVID-19 pandemic necessitated a rapid mobilization of resources toward the development of safe and efficacious vaccines and therapeutics. Finding effective treatments to stem the wave of infected individuals needing hospitalization and reduce the risk of adverse events was paramount. For scientists and healthcare professionals addressing this challenge, the need to rapidly identify medical countermeasures became urgent, and many compounds in clinical use for other indications were repurposed for COVID-19 clinical trials after preliminary preclinical data demonstrated antiviral activity against SARS-CoV-2. Two repurposed compounds, fluvoxamine and amodiaquine, showed efficacy in reducing SARS-CoV-2 viral loads in preclinical experiments, but ultimately failed in clinical trials, highlighting the need for improved predictive preclinical tools that can be rapidly deployed for events such as pandemic emerging infectious diseases. The PREDICT96-ALI platform is a high-throughput, high-fidelity microphysiological system (MPS) that recapitulates primary human tracheobronchial tissue and supports highly robust and reproducible viral titers of SARS-CoV-2 variants Delta and Omicron. When amodiaquine and fluvoxamine were tested in PREDICT96-ALI, neither compound demonstrated an antiviral response, consistent with clinical outcomes and in contrast with prior reports assessing the efficacy of these compounds in other human cell-based in vitro platforms. These results highlight the unique prognostic capability of the PREDICT96-ALI proximal airway MPS to assess the potential antiviral response of lead compounds.

Strengthening Veterinary Workforce in Cambodia for Infectious Disease Detection and Response (preprint)

An efficient veterinary workforce is paramount for global health security as most emerging infectious diseases are zoonotic. Being a hotspot of disease outbreaks there is a need to strengthen the veterinary field epidemiology capacity in Cambodia. The COVID-19 pandemic has highlighted the need for a strong health security workforce in the Asia-Pacific. This study was conducted with an aim to understand veterinary epidemiology training gaps in Cambodia.A mixed method study using a concurrent triangulation design was conducted targeting the veterinary workforce. Univariable and multivariable regression and an inductive, thematic analysis was used.  Survey responses from 108 veterinarians indicated that most (70%) respondents did not have any training, while only 6.0% had been to a Field Epidemiology Training Program for Veterinarians (FETPV). Lack of formal training in epidemiology was associated with non-participation in outbreak response (P< 0.05).  The key informants suggested system level factors, limited staff, and perceived disconnect between the central and community level as likely barriers to efficient outbreak response. The need for epidemiology training of veterinarians targeting knowledge consolidation and skill development through experiential learning was emphasized. Our assessment recommends that, a multifaceted approach targeting pedagogical and structural aspects of veterinary field epidemiology in Cambodia is required.

Zoonoses and global epidemics.

PURPOSE OF REVIEW: The purpose of the review is to summarize recent advances in understanding the origins, drivers and clinical context of zoonotic disease epidemics and pandemics. In addition, we aimed to highlight the role of clinicians in identifying sentinel cases of zoonotic disease outbreaks. RECENT FINDINGS: The majority of emerging infectious disease events over recent decades, including the COVID-19 pandemic, have been caused by zoonotic viruses and bacteria. In particular, coronaviruses, haemorrhagic fever viruses, arboviruses and influenza A viruses have caused significant epidemics globally. There have been recent advances in understanding the origins and drivers of zoonotic epidemics, yet there are gaps in diagnostic capacity and clinical training about zoonoses. SUMMARY: Identifying the origins of zoonotic pathogens, understanding factors influencing disease transmission and improving the diagnostic capacity of clinicians will be crucial to early detection and prevention of further epidemics of zoonoses.

Monitoring and responding to emerging infectious diseases in a university setting: A case study using COVID-19.

Emerging infection diseases (EIDs) are an increasing threat to global public health, especially when the disease is newly emerging. Institutions of higher education (IHEs) are particularly vulnerable to EIDs because student populations frequently share high-density residences and strongly mix with local and distant populations. In fall 2020, IHEs responded to a novel EID, COVID-19. Here, we describe Quinnipiac University's response to SARS-CoV-2 and evaluate its effectiveness through empirical data and model results. Using an agent-based model to approximate disease dynamics in the student body, the University established a policy of dedensification, universal masking, surveillance testing via a targeted sampling design, and app-based symptom monitoring. After an extended period of low incidence, the infection rate grew through October, likely due to growing incidence rates in the surrounding community. A super-spreader event at the end of October caused a spike in cases in November. Student violations of the University's policies contributed to this event, but lax adherence to state health laws in the community may have also contributed. The model results further suggest that the infection rate was sensitive to the rate of imported infections and was disproportionately impacted by non-residential students, a result supported by the observed data. Collectively, this suggests that campus-community interactions play a major role in campus disease dynamics. Further model results suggest that app-based symptom monitoring may have been an important regulator of the University's incidence, likely because it quarantined infectious students without necessitating test results. Targeted sampling had no substantial advantages over simple random sampling when the model incorporated contact tracing and app-based symptom monitoring but reduced the upper boundary on 90% prediction intervals for cumulative infections when either was removed. Thus, targeted sampling designs for surveillance testing may mitigate worst-case outcomes when other interventions are less effective. The results' implications for future EIDs are discussed.

Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada.

BACKGROUND: Emerging Infectious Diseases (EID) are a significant threat to population health globally. We aimed to examine the relationship between internet search engine queries and social media data on COVID-19 and determine if they can predict COVID-19 cases in Canada. METHODS: We analyzed Google Trends (GT) and Twitter data from 1/1/2020 to 3/31/2020 in Canada and used various signal-processing techniques to remove noise from the data. Data on COVID-19 cases was obtained from the COVID-19 Canada Open Data Working Group. We conducted time-lagged cross-correlation analyses and developed the long short-term memory model for forecasting daily COVID-19 cases. RESULTS: Among symptom keywords, "cough," "runny nose," and "anosmia" were strong signals with high cross-correlation coefficients >0.8 ( rCough = 0.825, t - 9; rRunnyNose = 0.816, t - 11; rAnosmia = 0.812, t - 3 ), showing that searching for "cough," "runny nose," and "anosmia" on GT correlated with the incidence of COVID-19 and peaked 9, 11, and 3 days earlier than the incidence peak, respectively. For symptoms- and COVID-related Tweet counts, the cross-correlations of Tweet signals and daily cases were rTweetSymptoms = 0.868, t - 11 and tTweetCOVID = 0.840, t - 10, respectively. The LSTM forecasting model achieved the best performance (MSE = 124.78, R2 = 0.88, adjusted R2 = 0.87) using GT signals with cross-correlation coefficients >0.75. Combining GT and Tweet signals did not improve the model performance. CONCLUSION: Internet search engine queries and social media data can be used as early warning signals for creating a real-time surveillance system for COVID-19 forecasting, but challenges remain in modelling.

Interventions to Reduce Risk for Pathogen Spillover and Early Disease Spread to Prevent Outbreaks, Epidemics, and Pandemics.

The pathogens that cause most emerging infectious diseases in humans originate in animals, particularly wildlife, and then spill over into humans. The accelerating frequency with which humans and domestic animals encounter wildlife because of activities such as land-use change, animal husbandry, and markets and trade in live wildlife has created growing opportunities for pathogen spillover. The risk of pathogen spillover and early disease spread among domestic animals and humans, however, can be reduced by stopping the clearing and degradation of tropical and subtropical forests, improving health and economic security of communities living in emerging infectious disease hotspots, enhancing biosecurity in animal husbandry, shutting down or strictly regulating wildlife markets and trade, and expanding pathogen surveillance. We summarize expert opinions on how to implement these goals to prevent outbreaks, epidemics, and pandemics.

SARS-CoV-2 incubation period across variants of concern, individual factors, and circumstances of infection in France: a case series analysis from the ComCor study.

BACKGROUND: The incubation period of SARS-CoV-2 has been estimated for the known variants of concern. However, differences in study designs and settings make comparing variants difficult. We aimed to estimate the incubation period for each variant of concern compared with the historical strain within a unique and large study to identify individual factors and circumstances associated with its duration. METHODS: In this case series analysis, we included participants (aged ≥18 years) of the ComCor case-control study in France who had a SARS-CoV-2 diagnosis between Oct 27, 2020, and Feb 4, 2022. Eligible participants were those who had the historical strain or a variant of concern during a single encounter with a known index case who was symptomatic and for whom the incubation period could be established, those who reported doing a reverse-transcription-PCR (RT-PCR) test, and those who were symptomatic by study completion. Sociodemographic and clinical characteristics, exposure information, circumstances of infection, and COVID-19 vaccination details were obtained via an online questionnaire, and variants were established through variant typing after RT-PCR testing or by matching the time that a positive test was reported with the predominance of a specific variant. We used multivariable linear regression to identify factors associated with the duration of the incubation period (defined as the number of days from contact with the index case to symptom onset). FINDINGS: 20 413 participants were eligible for inclusion in this study. Mean incubation period varied across variants: 4·96 days (95% CI 4·90-5·02) for alpha (B.1.1.7), 5·18 days (4·93-5·43) for beta (B.1.351) and gamma (P.1), 4·43 days (4·36-4·49) for delta (B.1.617.2), and 3·61 days (3·55-3·68) for omicron (B.1.1.529) compared with 4·61 days (4·56-4·66) for the historical strain. Participants with omicron had a shorter incubation period than participants with the historical strain (-0·9 days, 95% CI -1·0 to -0·7). The incubation period increased with age (participants aged ≥70 years had an incubation period 0·4 days [0·2 to 0·6] longer than participants aged 18-29 years), in female participants (by 0·1 days, 0·0 to 0·2), and in those who wore a mask during contact with the index case (by 0·2 days, 0·1 to 0·4), and was reduced in those for whom the index case was symptomatic (-0·1 days, -0·2 to -0·1). These data were robust to sensitivity analyses correcting for an over-reporting of incubation periods of 7 days. INTERPRETATION: SARS-CoV-2 incubation period is notably reduced in omicron cases compared with all other variants of concern, in young people, after transmission from a symptomatic index case, after transmission to a maskless secondary case, and (to a lesser extent) in men. These findings can inform future COVID-19 contact-tracing strategies and modelling. FUNDING: Institut Pasteur, the French National Agency for AIDS Research-Emerging Infectious Diseases, Fondation de France, the INCEPTION project, and the Integrative Biology of Emerging Infectious Diseases project.

Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland.

Following the rapid dissemination of COVID-19 cases in Switzerland, large-scale non-pharmaceutical interventions (NPIs) were implemented by the cantons and the federal government between 28 February and 20 March 2020. Estimates of the impact of these interventions on SARS-CoV-2 transmission are critical for decision making in this and future outbreaks. We here aim to assess the impact of these NPIs on disease transmission by estimating changes in the basic reproduction number (R0) at national and cantonal levels in relation to the timing of these NPIs. We estimated the time-varying R0 nationally and in eleven cantons by fitting a stochastic transmission model explicitly simulating within-hospital dynamics. We used individual-level data from more than 1000 hospitalised patients in Switzerland and public daily reports of hospitalisations and deaths. We estimated the national R0 to be 2.8 (95% confidence interval 2.1–3.8) at the beginning of the epidemic. Starting from around 7 March, we found a strong reduction in time-varying R0 with a 86% median decrease (95% quantile range [QR] 79–90%) to a value of 0.40 (95% QR 0.3–0.58) in the period of 29 March to 5 April. At the cantonal level, R0 decreased over the course of the epidemic between 53% and 92%. Reductions in time-varying R0 were synchronous with changes in mobility patterns as estimated through smartphone activity, which started before the official implementation of NPIs. We inferred that most of the reduction of transmission is attributable to behavioural changes as opposed to natural immunity, the latter accounting for only about 4% of the total reduction in effective transmission. As Switzerland considers relaxing some of the restrictions of social mixing, current estimates of time-varying R0 well below one are promising. However, as of 24 April 2020, at least 96% (95% QR 95.7–96.4%) of the Swiss population remains susceptible to SARS-CoV-2. These results warrant a cautious relaxation of social distance practices and close monitoring of changes in both the basic and effective reproduction numbers.