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Compartmental models that describe infectious disease transmission across subpopulations are central for assessing the impact of non-pharmaceutical interventions, behavioral changes and seasonal effects on the spread of respiratory infections. We present a Bayesian workflow for such models, including four features: (1) an adjustment for incomplete case ascertainment, (2) an adequate sampling distribution of laboratory-confirmed cases, (3) a flexible, time-varying transmission rate, and (4) a stratification by age group. Within the workflow, we benchmarked the performance of various implementations of two of these features (2 and 3). For the second feature, we used SARS-CoV-2 data from the canton of Geneva (Switzerland) and found that a quasi-Poisson distribution is the most suitable sampling distribution for describing the overdispersion in the observed laboratory-confirmed cases. For the third feature, we implemented three methods: Brownian motion, B-splines, and approximate Gaussian processes (aGP). We compared their performance in terms of the number of effective samples per second, and the error and sharpness in estimating the time-varying transmission rate over a selection of ordinary differential equation solvers and tuning parameters, using simulated seroprevalence and laboratory-confirmed case data. Even though all methods could recover the time-varying dynamics in the transmission rate accurately, we found that B-splines perform up to four and ten times faster than Brownian motion and aGPs, respectively. We validated the B-spline model with simulated age-stratified data. We applied this model to 2020 laboratory-confirmed SARS-CoV-2 cases and two seroprevalence studies from the canton of Geneva. This resulted in detailed estimates of the transmission rate over time and the case ascertainment. Our results illustrate the potential of the presented workflow including stratified transmission to estimate age-specific epidemiological parameters. The workflow is freely available in the R package HETTMO, and can be easily adapted and applied to other infectious diseases.

Abstract Background Vaccination is an effective strategy to reduce morbidity and mortality from coronavirus disease 2019 (COVID-19). However, the uptake of COVID-19 vaccination has varied across and within countries. Switzerland has had lower levels of COVID-19 vaccination uptake in the general population than many other high-income countries. Understanding the socio-demographic factors associated with vaccination uptake can help to inform future vaccination strategies to increase uptake. Methods We conducted a longitudinal online survey in the Swiss population, consisting of six survey waves from June to September 2021. Participants provided information on socio-demographic characteristics, history of testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), social contacts, willingness to be vaccinated, and vaccination status. We used a multivariable Poisson regression model to estimate the adjusted rate ratio (aRR) and 95% confidence intervals (CI) of COVID-19 vaccine uptake. Results We recorded 6,758 observations from 1,884 adults. For the regression analysis, we included 3,513 observations from 1,883 participants. By September 2021, 600 (75%) of 806 study participants had received at least one vaccine dose. Participants who were older, male, and students, had a higher educational level, household income, and number of social contacts, and lived in a household with a medically vulnerable person were more likely to have received at least one vaccine dose. Female participants, those who lived in rural areas and smaller households, and people who perceived COVID-19 measures as being too strict were less likely to be vaccinated. We found no significant association between previous SARS-CoV-2 infections and vaccination uptake. Conclusions Our results suggest that socio-demographic factors as well as individual behaviours and attitudes played an important role in COVID-19 vaccination uptake in Switzerland. Therefore, appropriate communication with the public is needed to ensure that public health interventions are accepted and implemented by the population. Tailored COVID-19 vaccination strategies in Switzerland that aim to improve uptake should target specific subgroups such as women, people from rural areas or people with lower socio-demographic status.

COVID-19-releated public health measures may have indirectly impacted mortality rates by causing or averting deaths. Here, the authors use data from Switzerland until April 2022 and estimate that, after accounting for deaths directly related to COVID-19, mortality was lower than expected, indicating some evidence of an overall positive impact of control measures.

BACKGROUND Neutralizing antibodies are considered a key correlate of protection by current SARS-CoV-2 vaccines. The manner in which human infections respond to therapeutic SARS-CoV-2 antibodies, including convalescent plasma therapy, remains to be fully elucidated.METHODS We conducted a proof-of-principle study of convalescent plasma therapy based on a phase I trial in 30 hospitalized COVID-19 patients with a median interval between onset of symptoms and first transfusion of 9 days (IQR, 7–11.8 days). Comprehensive longitudinal monitoring of the virological, serological, and disease status of recipients allowed deciphering of parameters on which plasma therapy efficacy depends.RESULTS In this trial, convalescent plasma therapy was safe as evidenced by the absence of transfusion-related adverse events and low mortality (3.3%). Treatment with highly neutralizing plasma was significantly associated with faster virus clearance, as demonstrated by Kaplan-Meier analysis (P = 0.034) and confirmed in a parametric survival model including viral load and comorbidity (adjusted hazard ratio, 3.0; 95% CI, 1.1–8.1; P = 0.026). The onset of endogenous neutralization affected viral clearance, but even after adjustment for their pretransfusion endogenous neutralization status, recipients benefitted from plasma therapy with high neutralizing antibodies (hazard ratio, 3.5; 95% CI, 1.1–11; P = 0.034).CONCLUSION Our data demonstrate a clear impact of exogenous antibody therapy on the rapid clearance of viremia before and after onset of the endogenous neutralizing response, and point beyond antibody-based interventions to critical laboratory parameters for improved evaluation of current and future SARS-CoV-2 therapies.TRIAL REGISTRATION ClinicalTrials.gov NCT04869072.FUNDING This study was funded via an Innovation Pool project by the University Hospital Zurich; the Swiss Red Cross Glückskette Corona Funding; Pandemiefonds of the UZH Foundation; and the Clinical Research Priority Program “Comprehensive Genomic Pathogen Detection” of the University of Zurich.

BackgroundDebate about the level of asymptomatic Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection continues. The amount of evidence is increasing and study designs have changed over time. We updated a living systematic review to address 3 questions: (1) Among people who become infected with SARS-CoV-2, what proportion does not experience symptoms at all during their infection? (2) What is the infectiousness of asymptomatic and presymptomatic, compared with symptomatic, SARS-CoV-2 infection? (3) What proportion of SARS-CoV-2 transmission in a population is accounted for by people who are asymptomatic or presymptomatic?Methods and findingsThe protocol was first published on 1 April 2020 and last updated on 18 June 2021. We searched PubMed, Embase, bioRxiv, and medRxiv, aggregated in a database of SARS-CoV-2 literature, most recently on 6 July 2021. Studies of people with PCR-diagnosed SARS-CoV-2, which documented symptom status at the beginning and end of follow-up, or mathematical modelling studies were included. Studies restricted to people already diagnosed, of single individuals or families, or without sufficient follow-up were excluded. One reviewer extracted data and a second verified the extraction, with disagreement resolved by discussion or a third reviewer. Risk of bias in empirical studies was assessed with a bespoke checklist and modelling studies with a published checklist. All data syntheses were done using random effects models. Review question (1): We included 130 studies. Heterogeneity was high so we did not estimate a mean proportion of asymptomatic infections overall (interquartile range (IQR) 14% to 50%, prediction interval 2% to 90%), or in 84 studies based on screening of defined populations (IQR 20% to 65%, prediction interval 4% to 94%). In 46 studies based on contact or outbreak investigations, the summary proportion asymptomatic was 19% (95% confidence interval (CI) 15% to 25%, prediction interval 2% to 70%). (2) The secondary attack rate in contacts of people with asymptomatic infection compared with symptomatic infection was 0.32 (95% CI 0.16 to 0.64, prediction interval 0.11 to 0.95, 8 studies). (3) In 13 modelling studies fit to data, the proportion of all SARS-CoV-2 transmission from presymptomatic individuals was higher than from asymptomatic individuals. Limitations of the evidence include high heterogeneity and high risks of selection and information bias in studies that were not designed to measure persistently asymptomatic infection, and limited information about variants of concern or in people who have been vaccinated.ConclusionsBased on studies published up to July 2021, most SARS-CoV-2 infections were not persistently asymptomatic, and asymptomatic infections were less infectious than symptomatic infections. Summary estimates from meta-analysis may be misleading when variability between studies is extreme and prediction intervals should be presented. Future studies should determine the asymptomatic proportion of SARS-CoV-2 infections caused by variants of concern and in people with immunity following vaccination or previous infection. Without prospective longitudinal studies with methods that minimise selection and measurement biases, further updates with the study types included in this living systematic review are unlikely to be able to provide a reliable summary estimate of the proportion of asymptomatic infections caused by SARS-CoV-2.Review protocolOpen Science Framework (https://osf.io/9ewys/).

BACKGROUND: In Switzerland, SARS-CoV-2 vaccination campaigns started in early 2021. Vaccine coverage reached 65% of the population in December 2021, mostly with mRNA vaccines from Moderna and Pfizer-BioNtech. Simultaneously, the proportion of vaccinated among COVID-19-related hospitalisations and deaths rose, creating some confusion in the general population. We aimed to assess vaccine effectiveness against severe forms of SARS-CoV-2 infection using routine surveillance data on the vaccination status of COVID-19-related hospitalisations and deaths, and data on vaccine coverage in Switzerland. METHODS: We considered all routine surveillance data on COVID-19-related hospitalisations and deaths received at the Swiss Federal Office of Public Health from 1 July to 1 December 2021. We estimated the relative risk of COVID-19-related hospitalisation or death for not fully vaccinated compared with fully vaccinated individuals, adjusted for the dynamics of vaccine coverage over time, by age and location. We stratified the analysis by age group and by calendar month. We assessed variations in the relative risk of hospitalisation associated with the time since vaccination. RESULTS: We included a total of 5948 COVID-19-related hospitalisations of which 1245 (21%) were fully vaccinated patients, and a total of 739 deaths of which 259 (35%) were fully vaccinated. We found that the relative risk of COVID-19 related hospitalisation was 12.5 (95% confidence interval [CI] 11.7–13.4) times higher for not fully vaccinated than for fully vaccinated individuals. This translates into a vaccine effectiveness against hospitalisation of 92.0% (95% CI 91.4–92.5%). Vaccine effectiveness against death was estimated to be 90.3% (95% CI 88.6–91.8%). Effectiveness appeared to be comparatively lower in age groups over 70 and during the months of October and November 2021. We also found evidence of a decrease in vaccine effectiveness against hospitalisation for individuals vaccinated for 25 weeks or more, but this decrease appeared only in age groups below 70. CONCLUSIONS: The observed proportions of vaccinated among COVD-19-related hospitalisations and deaths in Switzerland were compatible with a high effectiveness of mRNA vaccines from Moderna and Pfizer-BioNtech against hospitalisation and death in all age groups. Effectiveness appears comparatively lower in older age groups, suggesting the importance of booster vaccinations. We found inconclusive evidence that vaccine effectiveness wanes over time. Repeated analyses will be able to better assess waning and the effect of boosters.

A key parameter in epidemiological modeling which characterizes the spread of an infectious disease is the generation time, or more generally the distribution of infectiousness as a function of time since infection. There is increasing evidence supporting a prolonged viral shedding window for COVID-19, but the transmissibility in this phase is unclear. Based on this, we develop a generalized Susceptible-Exposed-Infected-Resistant (SEIR) model including an additional compartment of chronically infected individuals who can stay infectious for a longer duration than the reported generation time, but with infectivity reduced to varying degrees. Using the incidence and fatality data from different countries, we first show that such an assumption also yields a plausible model in explaining the data observed prior to the easing of the lockdown measures (relaxation). We then test the predictive power of this model for different durations and levels of prolonged infectiousness using the incidence data after the introduction of relaxation in Switzerland, and compare it with a model without the chronically infected population to represent the models conventionally used. We show that in case of a gradual easing on the lockdown measures, the predictions of the model including the chronically infected population vary considerably from those obtained under a model in which prolonged infectiousness is not taken into account. Although the existence of a chronically infected population still remains largely hypothetical, we believe that our results provide tentative evidence to consider a chronically infected population as an alternative modeling approach to better interpret the transmission dynamics of COVID-19.

Beta-lactam- and in particular carbapenem-resistant Enterobacteriaceae represent a major public health threat. Despite strong variation of resistance across geographical settings, there is limited understanding of the underlying drivers. To assess these drivers, we developed a transmission model of cephalosporin- and carbapenem-resistant Klebsiella pneumoniae. The model is parameterized using antibiotic consumption and demographic data from eleven European countries and fitted to the resistance rates for Klebsiella pneumoniae for these settings. The impact of potential drivers of resistance is then assessed in counterfactual analyses. Based on reported consumption data, the model could simultaneously fit the prevalence of extended-spectrum beta-lactamase-producing and carbapenem-resistant Klebsiella pneumoniae (ESBL and CRK) across eleven European countries over eleven years. The fit could explain the large between-country variability of resistance in terms of consumption patterns and fitted differences in hospital transmission rates. Based on this fit, a counterfactual analysis found that reducing nosocomial transmission and antibiotic consumption in the hospital had the strongest impact on ESBL and CRK prevalence. Antibiotic consumption in the community also affected ESBL prevalence but its relative impact was weaker than inpatient consumption. Finally, we used the model to estimate a moderate fitness cost of CRK and ESBL at the population level. This work highlights the disproportionate role of antibiotic consumption in the hospital and of nosocomial transmission for resistance in gram-negative bacteria at a European level. This indicates that infection control and antibiotic stewardship measures should play a major role in limiting resistance even at the national or regional level.

BackgroundRising resistance of HIV-1 to non-nucleoside reverse transcriptase inhibitors (NNRTIs) threatens the success of the global scale-up of antiretroviral therapy (ART). The switch to WHO-recommended dolutegravir (DTG)-based regimens could reduce this threat due to DTG's high genetic barrier to resistance. We used mathematical modeling to predict the impact of the scale-up of DTG-based ART on NNRTI pretreatment drug resistance (PDR) in South Africa, 2020 to 2040.Methods and findingsWe adapted the Modeling Antiretroviral drug Resistance In South Africa (MARISA) model, an epidemiological model of the transmission of NNRTI resistance in South Africa. We modeled the introduction of DTG in 2020 under 2 scenarios: DTG as first-line regimen for ART initiators, or DTG for all patients, including patients on suppressive NNRTI-based ART. Given the safety concerns related to DTG during pregnancy, we assessed the impact of prescribing DTG to all men and in addition to (1) women beyond reproductive age; (2) women beyond reproductive age or using contraception; and (3) all women. The model projections show that, compared to the continuation of NNRTI-based ART, introducing DTG would lead to a reduction in NNRTI PDR in all scenarios if ART initiators are started on a DTG-based regimen, and those on NNRTI-based regimens are rapidly switched to DTG. NNRTI PDR would continue to increase if DTG-based ART was restricted to men. When given to all men and women, DTG-based ART could reduce the level of NNRTI PDR from 52.4% (without DTG) to 10.4% (with universal DTG) in 2040. If only men and women beyond reproductive age or on contraception are started on or switched to DTG-based ART, NNRTI PDR would reach 25.9% in 2040. Limitations include substantial uncertainty due to the long-term predictions and the current scarcity of knowledge about DTG efficacy in South Africa.ConclusionsOur model shows the potential benefit of scaling up DTG-based regimens for halting the rise of NNRTI resistance. Starting or switching all men and women to DTG would lead to a sustained decline in resistance levels, whereas using DTG-based ART in all men, or in men and women beyond childbearing age, would only slow down the increase in levels of NNRTI PDR.

BackgroundAs of 16 May 2020, more than 4.5 million cases and more than 300,000 deaths from disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported. Reliable estimates of mortality from SARS-CoV-2 infection are essential for understanding clinical prognosis, planning healthcare capacity, and epidemic forecasting. The case-fatality ratio (CFR), calculated from total numbers of reported cases and reported deaths, is the most commonly reported metric, but it can be a misleading measure of overall mortality. The objectives of this study were to (1) simulate the transmission dynamics of SARS-CoV-2 using publicly available surveillance data and (2) infer estimates of SARS-CoV-2 mortality adjusted for biases and examine the CFR, the symptomatic case-fatality ratio (sCFR), and the infection-fatality ratio (IFR) in different geographic locations.Method and findingsWe developed an age-stratified susceptible-exposed-infected-removed (SEIR) compartmental model describing the dynamics of transmission and mortality during the SARS-CoV-2 epidemic. Our model accounts for two biases: preferential ascertainment of severe cases and right-censoring of mortality. We fitted the transmission model to surveillance data from Hubei Province, China, and applied the same model to six regions in Europe: Austria, Bavaria (Germany), Baden-Württemberg (Germany), Lombardy (Italy), Spain, and Switzerland. In Hubei, the baseline estimates were as follows: CFR 2.4% (95% credible interval [CrI] 2.1%-2.8%), sCFR 3.7% (3.2%-4.2%), and IFR 2.9% (2.4%-3.5%). Estimated measures of mortality changed over time. Across the six locations in Europe, estimates of CFR varied widely. Estimates of sCFR and IFR, adjusted for bias, were more similar to each other but still showed some degree of heterogeneity. Estimates of IFR ranged from 0.5% (95% CrI 0.4%-0.6%) in Switzerland to 1.4% (1.1%-1.6%) in Lombardy, Italy. In all locations, mortality increased with age. Among individuals 80 years or older, estimates of the IFR suggest that the proportion of all those infected with SARS-CoV-2 who will die ranges from 20% (95% CrI 16%-26%) in Switzerland to 34% (95% CrI 28%-40%) in Spain. A limitation of the model is that count data by date of onset are required, and these are not available in all countries.ConclusionsWe propose a comprehensive solution to the estimation of SARS-Cov-2 mortality from surveillance data during outbreaks. The CFR is not a good predictor of overall mortality from SARS-CoV-2 and should not be used for evaluation of policy or comparison across settings. Geographic differences in IFR suggest that a single IFR should not be applied to all settings to estimate the total size of the SARS-CoV-2 epidemic in different countries. The sCFR and IFR, adjusted for right-censoring and preferential ascertainment of severe cases, are measures that can be used to improve and monitor clinical and public health strategies to reduce the deaths from SARS-CoV-2 infection.

The prevalence of pretreatment resistance to non-nucleoside reverse transcriptase inhibitors (NNRTIs) is >10% in many low-income countries. As a consequence, several sub-Saharan African countries have implemented, or are considering the introduction of, non-NNRTI-based first-line antiretroviral therapy (ART) for treatment-naïve and treatment-experienced patients. This is occurring at a time when ART programmes are expanding, in response to the World Health Organization guidelines, which recommend ART initiation regardless of CD4 cell count. Both those developments raise important questions regarding their potential impact on HIV drug resistance and the impact of HIV drug resistance on clinical outcomes. Those issues are particularly relevant to sub-Saharan Africa, where standardised ART regimens are used and where viral load monitoring and resistance testing are often not done routinely. It is therefore essential to forecast the impact of the implementation of universal ART, and the introduction of drugs such as dolutegravir to first-line regimens, on HIV drug resistance in order to inform future policies and to help ensure sustainable positive long-term outcomes. We discuss important public health considerations regarding HIV drug resistance, and describe how mathematical modelling, combined with real-world data from the four African Regions of the International epidemiology Databases to Evaluate AIDS consortium, could provide an early warning system for HIV drug resistance in sub-Saharan Africa.

The scale-up of antiretroviral therapy (ART) in South Africa substantially reduced AIDS-related deaths and new HIV infections. However, its success is threatened by the emergence of resistance to non-nucleoside reverse-transcriptase inhibitors (NNRTI). The MARISA (Modelling Antiretroviral drug Resistance In South Africa) model presented here aims at investigating the time trends and factors driving NNRTI resistance in South Africa. MARISA is a compartmental model that includes the key aspects of the local HIV epidemic: continuum of care, disease progression, and gender. The dynamics of NNRTI resistance emergence and transmission are then added to this framework. Model parameters are informed using data from HIV cohorts participating in the International epidemiology Databases to Evaluate AIDS (IeDEA) and literature estimates, or fitted to UNAIDS estimates. Using this novel approach of triangulating clinical and resistance data from various sources, MARISA reproduces the time trends of HIV in South Africa in 2005-2016, with a decrease in new infections, undiagnosed individuals, and AIDS-related deaths. MARISA captures the dynamics of the spread of NNRTI resistance: high levels of acquired drug resistance (ADR, in 83% of first-line treatment failures in 2016), and increasing transmitted drug resistance (TDR, in 8.1% of ART initiators in 2016). Simulation of counter-factual scenarios reflecting alternative public health policies shows that increasing treatment coverage would have resulted in fewer new infections and deaths, at the cost of higher TDR (11.6% in 2016 for doubling the treatment rate). Conversely, improving switching to second-line treatment would have led to lower TDR (6.5% in 2016 for doubling the switching rate) and fewer new infections and deaths. Implementing drug resistance testing would have had little impact. The rapid ART scale-up and inadequate switching to second-line treatment were the key drivers of the spread of NNRTI resistance in South Africa. However, even though some interventions could have substantially reduced the level of NNRTI resistance, no policy including NNRTI-based first line regimens could have prevented this spread. Thus, by combining epidemiological data on HIV in South Africa with biological data on resistance evolution, our modelling approach identified key factors driving NNRTI resistance, highlighting the need of alternative first-line regimens.