Modeling the impact of the Omicron infection wave in Germany.

In November 2021, the first infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant of concern (VOC) B.1.1.529 ('Omicron') was reported in Germany, alongside global reports of reduced vaccine efficacy (VE) against infections with this variant. The potential threat posed by its rapid spread in Germany was, at the time, difficult to predict. We developed a variant-dependent population-averaged susceptible-exposed-infected-recovered infectious-disease model that included information about variant-specific and waning VEs based on empirical data available at the time. Compared to other approaches, our method aimed for minimal structural and computational complexity and therefore enabled us to respond to changes in the situation in a more agile manner while still being able to analyze the potential influence of (non-)pharmaceutical interventions (NPIs) on the emerging crisis. Thus, the model allowed us to estimate potential courses of upcoming infection waves in Germany, focusing on the corresponding burden on intensive care units (ICUs), the efficacy of contact reduction strategies, and the success of the booster vaccine rollout campaign. We expected a large cumulative number of infections with the VOC Omicron in Germany with ICU occupancy likely remaining below capacity, nevertheless, even without additional NPIs. The projected figures were in line with the actual Omicron waves that were subsequently observed in Germany with respective peaks occurring in mid-February and mid-March. Most surprisingly, our model showed that early, strict, and short contact reductions could have led to a strong 'rebound' effect with high incidences after the end of the respective NPIs, despite a potentially successful booster campaign. The results presented here informed legislation in Germany. The methodology developed in this study might be used to estimate the impact of future waves of COVID-19 or other infectious diseases.

Understanding the impact of digital contact tracing during the COVID-19 pandemic.

Digital contact tracing (DCT) applications have been introduced in many countries to aid the containment of COVID-19 outbreaks. Initially, enthusiasm was high regarding their implementation as a non-pharmaceutical intervention (NPI). However, no country was able to prevent larger outbreaks without falling back to harsher NPIs. Here, we discuss results of a stochastic infectious-disease model that provide insights in how the progression of an outbreak and key parameters such as detection probability, app participation and its distribution, as well as engagement of users impact DCT efficacy informed by results of empirical studies. We further show how contact heterogeneity and local contact clustering impact the intervention's efficacy. We conclude that DCT apps might have prevented cases on the order of single-digit percentages during single outbreaks for empirically plausible ranges of parameters, ignoring that a substantial part of these contacts would have been identified by manual contact tracing. This result is generally robust against changes in network topology with exceptions for homogeneous-degree, locally-clustered contact networks, on which the intervention prevents more infections. An improvement of efficacy is similarly observed when app participation is highly clustered. We find that DCT typically averts more cases during the super-critical phase of an epidemic when case counts are rising and the measured efficacy therefore depends on the time of evaluation.

Estimating the share of SARS-CoV-2-immunologically naïve individuals in Germany up to June 2022.

After the winter of 2021/2022, the coronavirus disease 2019 (COVID-19) pandemic had reached a phase where a considerable number of people in Germany have been either infected with a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, vaccinated or both, the full extent of which was difficult to estimate, however, because infection counts suffer from under-reporting, and the overlap between the vaccinated and recovered subpopulations is unknown. Yet, reliable estimates regarding population-wide susceptibility were of considerable interest: Since both previous infection and vaccination reduce the risk of severe disease, a low share of immunologically naïve individuals lowers the probability of further severe outbreaks, given that emerging variants do not escape the acquired susceptibility reduction. Here, we estimate the share of immunologically naïve individuals by age group for each of the sixteen German federal states by integrating an infectious-disease model based on weekly incidences of SARS-CoV-2 infections in the national surveillance system and vaccine uptake, as well as assumptions regarding under-ascertainment. We estimate a median share of 5.6% of individuals in the German population have neither been in contact with vaccine nor any variant up to 31 May 2022 (quartile range [2.5%-8.5%]). For the adult population at higher risk of severe disease, this figure is reduced to 3.8% [1.6%-5.9%] for ages 18-59 and 2.1% [1.0%-3.4%] for ages 60 and above. However, estimates vary between German states mostly due to heterogeneous vaccine uptake. Excluding Omicron infections from the analysis, 16.3% [14.1%-17.9%] of the population in Germany, across all ages, are estimated to be immunologically naïve, highlighting the large impact the first two Omicron waves had until the beginning of summer in 2022. The method developed here might be useful for similar estimations in other countries or future outbreaks of other infectious diseases.

Germany’s fourth COVID-19 wave was mainly driven by the unvaccinated

Background While the majority of the German population was fully vaccinated at the time (about 65%), COVID-19 incidence started growing exponentially in October 2021 with about 41% of recorded new cases aged twelve or above being symptomatic breakthrough infections, presumably also contributing to the dynamics. So far, it remained elusive how significant this contribution was and whether targeted non-pharmaceutical interventions (NPIs) may have stopped the amplification of the crisis. Methods We develop and introduce a contribution matrix approach based on the next-generation matrix of a population-structured compartmental infectious disease model to derive contributions of respective inter- and intragroup infection pathways of unvaccinated and vaccinated subpopulations to the effective reproduction number and new infections, considering empirical data of vaccine efficacies against infection and transmission. Results Here we show that about 61%–76% of all new infections were caused by unvaccinated individuals and only 24%–39% were caused by the vaccinated. Furthermore, 32%–51% of new infections were likely caused by unvaccinated infecting other unvaccinated. Decreasing the transmissibility of the unvaccinated by, e. g. targeted NPIs, causes a steeper decrease in the effective reproduction number Conclusions A minority of the German population—the unvaccinated—is assumed to have caused the majority of new infections in the fall of 2021 in Germany. Our results highlight the importance of combined measures, such as vaccination campaigns and targeted contact reductions to achieve temporary epidemic control. Plain language summary With about 65% of its citizens vaccinated at the time, Germany experienced a large wave of COVID-19 in the fall of 2021, regionally overburdening the healthcare system. We are interested in how much this crisis was driven by infections in vaccinated versus unvaccinated people. We use a mathematical model to show that transmission of the disease during this period was largely driven by the unvaccinated population, despite representing a smaller proportion of the overall population. Our results suggest that higher vaccine uptake, reduced mixing between vaccinated and unvaccinated people, and targeted contact-reduction measures would have been effective measures to control spread at the time. These findings may have implications for how we manage future waves of COVID-19 or other diseases. Maier et al. develop a mathematical model to examine the contributions of vaccinated vs. unvaccinated populations to the wave of SARS-CoV-2 infections in Germany in autumn 2021. They report that the unvaccinated population were the main drivers of transmission and that targeted non-pharmaceutical interventions would likely have mitigated this.

Impact of COVID-19 on thrombus composition and response to thrombolysis: Insights from a monocentric cohort population of COVID-19 patients with acute ischemic stroke.

BACKGROUND: Resistance to fibrinolysis, levels of procoagulant/antifibrinolytic neutrophil extracellular traps (NETs), and the severity of acute ischemic stroke (AIS) are increased by COVID-19. Whether NETs are components of AIS thrombi from COVID-19 patients and whether COVID-19 impacts the susceptibility of these thrombi to thrombolytic treatments remain unknown, however. OBJECTIVES: We aimed to characterize AIS thrombi from COVID-19 patients by immunohistology and to compare their response to thrombolysis to that of AIS thrombi from non-COVID-19 patients. PATIENTS/METHODS: For this monocentric cohort study, 14 thrombi from COVID-19 AIS patients and 16 thrombi from non-COVID-19 patients, all recovered by endovascular therapy, were analyzed by immunohistology or subjected to ex vivo thrombolysis by tissue-type plasminogen (tPA)/plasminogen. RESULTS: COVID-19 AIS thrombi were rich in neutrophils and contained NETs, but not spike protein. Thrombolysis assays revealed a mean resistance profile to tPA/plasminogen of COVID-19 AIS thrombi similar to that of non-COVID-19 AIS thrombi. The addition of DNase 1 successfully improved thrombolysis by potentiating fibrinolysis irrespective of COVID-19 status. Levels of neutrophil, NETs, and platelet markers in lysis supernatants were comparable between AIS thrombi from non-COVID-19 and COVID-19 patients. CONCLUSIONS: These results show that COVID-19 does not impact NETs content or worsen fibrinolysis resistance of AIS thrombi, a therapeutic hurdle that could be overcome by DNase 1 even in the context of SARS-CoV-2 infection.

COVID-19 lockdown induces disease-mitigating structural changes in mobility networks.

In the wake of the COVID-19 pandemic many countries implemented containment measures to reduce disease transmission. Studies using digital data sources show that the mobility of individuals was effectively reduced in multiple countries. However, it remains unclear whether these reductions caused deeper structural changes in mobility networks and how such changes may affect dynamic processes on the network. Here we use movement data of mobile phone users to show that mobility in Germany has not only been reduced considerably: Lockdown measures caused substantial and long-lasting structural changes in the mobility network. We find that long-distance travel was reduced disproportionately strongly. The trimming of long-range network connectivity leads to a more local, clustered network and a moderation of the "small-world" effect. We demonstrate that these structural changes have a considerable effect on epidemic spreading processes by "flattening" the epidemic curve and delaying the spread to geographically distant regions.

European Multicenter Study of ET-COVID-19.

BACKGROUND AND PURPOSE: Acute ischemic stroke and large vessel occlusion can be concurrent with the coronavirus disease 2019 (COVID-19) infection. Outcomes after mechanical thrombectomy (MT) for large vessel occlusion in patients with COVID-19 are substantially unknown. Our aim was to study early outcomes after MT in patients with COVID-19. METHODS: Multicenter, European, cohort study involving 34 stroke centers in France, Italy, Spain, and Belgium. Data were collected between March 1, 2020 and May 5, 2020. Consecutive laboratory-confirmed COVID-19 cases with large vessel occlusion, who were treated with MT, were included. Primary investigated outcome: 30-day mortality. SECONDARY OUTCOMES: early neurological improvement (National Institutes of Health Stroke Scale improvement ≥8 points or 24 hours National Institutes of Health Stroke Scale 0-1), successful reperfusion (modified Thrombolysis in Cerebral Infarction grade ≥2b), and symptomatic intracranial hemorrhage. RESULTS: We evaluated 93 patients with COVID-19 with large vessel occlusion who underwent MT (median age, 71 years [interquartile range, 59-79]; 63 men [67.7%]). Median pretreatment National Institutes of Health Stroke Scale and Alberta Stroke Program Early CT Score were 17 (interquartile range, 11-21) and 8 (interquartile range, 7-9), respectively. Anterior circulation acute ischemic stroke represented 93.5% of cases. The rate modified Thrombolysis in Cerebral Infarction 2b to 3 was 79.6% (74 patients [95% CI, 71.3-87.8]). Thirty-day mortality was 29% (27 patients [95% CI, 20-39.4]). Early neurological improvement was 19.5% (17 patients [95% CI, 11.8-29.5]), and symptomatic intracranial hemorrhage was 5.4% (5 patients [95% CI, 1.7-12.1]). Patients who died at 30 days exhibited significantly lower lymphocyte count, higher levels of aspartate, and LDH (lactate dehydrogenase). After adjustment for age, initial National Institutes of Health Stroke Scale, Alberta Stroke Program Early CT Score, and successful reperfusion, these biological markers remained associated with increased odds of 30-day mortality (adjusted odds ratio of 2.70 [95% CI, 1.21-5.98] per SD-log decrease in lymphocyte count, 2.66 [95% CI, 1.22-5.77] per SD-log increase in aspartate, and 4.30 [95% CI, 1.43-12.91] per SD-log increase in LDH). CONCLUSIONS: The 29% rate of 30-day mortality after MT among patients with COVID-19 is not negligible. Abnormalities of lymphocyte count, LDH and aspartate may depict a patient's profiles with poorer outcomes after MT. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT04406090.

Early Brain Imaging Shows Increased Severity of Acute Ischemic Strokes With Large Vessel Occlusion in COVID-19 Patients.

BACKGROUND AND PURPOSE: Reports are emerging regarding the association of acute ischemic strokes with large vessel occlusion and coronavirus disease 2019 (COVID-19). While a higher severity of these patients could be expected from the addition of both respiratory and neurological injury, COVID-19 patients with strokes can present with mild or none respiratory symptoms. We aimed to compare anterior circulation large vessel occlusion strokes severity between patients with and without COVID-19. METHODS: We performed a comparative cohort study between patients with COVID-19 who had anterior circulation large vessel occlusion and early brain imaging within 3 hours from onset, in our institution during the 6 first weeks of the COVID-19 outbreak and a control group admitted during the same calendar period in 2019. RESULTS: Twelve COVID-19 patients with anterior circulation large vessel occlusion and early brain imaging were included during the study period and compared with 34 control patients with anterior circulation large vessel occlusion and early brain imaging in 2019. Patients in the COVID-19 group were younger (P=0.032) and had a history of diabetes mellitus more frequently (P=0.039). Patients did not significantly differ on initial National Institutes of Health Stroke Scale nor time from onset to imaging (P=0.18 and P=0.6, respectively). Patients with COVID-19 had more severe strokes than patients without COVID-19, with a significantly lower clot burden score (median: 6.5 versus 8, P=0.016), higher rate of multivessel occlusion (50% versus 8.8%, P=0.005), lower DWI-ASPECTS (Diffusion-Weighted Imaging-Alberta Stroke Program Early CT Scores; median: 5 versus 8, P=0.006), and higher infarct core volume (median: 58 versus 6 mL, P=0.004). Successful recanalization rate was similar in both groups (P=0.767). In-hospital mortality was higher in the COVID-19 patients' group (41.7% versus 11.8%, P=0.025). CONCLUSIONS: Early brain imaging showed higher severity large vessel occlusion strokes in patients with COVID-19. Given the massive number of infected patients, concerns should be raised about the coming neurovascular impact of the pandemic worldwide.

Treatment of Acute Ischemic Stroke due to Large Vessel Occlusion With COVID-19: Experience From Paris.

BACKGROUND AND PURPOSE: Higher rates of strokes have been observed in patients with coronavirus disease 2019 (COVID-19), but data regarding the outcomes of COVID-19 patients suffering from acute ischemic stroke due to large vessel occlusion (LVO) are lacking. We report our initial experience in the treatment of acute ischemic stroke with LVO in patients with COVID-19. METHODS: All consecutive patients with COVID-19 with acute ischemic stroke due to LVO treated in our institution during the 6 first weeks of the COVID-19 outbreak were included. Baseline clinical and radiological findings, treatment, and short-term outcomes are reported. RESULTS: We identified 10 patients with confirmed COVID-19 treated for an acute ischemic stroke due to LVO. Eight were men, with a median age of 59.5 years. Seven had none or mild symptoms of COVID-19 at stroke onset. Median time from COVID-19 symptoms to stroke onset was 6 days. All patients had brain imaging within 3 hours from symptoms onset. Five patients had multi-territory LVO. Five received intravenous alteplase. All patients had mechanical thrombectomy. Nine patients achieved successful recanalization (mTICI2B-3), none experienced early neurological improvement, 4 had early cerebral reocclusion, and a total of 6 patients (60%) died in the hospital. CONCLUSIONS: Best medical care including early intravenous thrombolysis, and successful and prompt recanalization achieved with mechanical thrombectomy, resulted in poor outcomes in patients with COVID-19. Although our results require further confirmation, a different pharmacological approach (antiplatelet or other) should be investigated to take in account inflammatory and coagulation disorders associated with COVID-19.

Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China.

The recent outbreak of coronavirus disease 2019 (COVID-19) in mainland China was characterized by a distinctive subexponential increase of confirmed cases during the early phase of the epidemic, contrasting with an initial exponential growth expected for an unconstrained outbreak. We show that this effect can be explained as a direct consequence of containment policies that effectively deplete the susceptible population. To this end, we introduce a parsimonious model that captures both quarantine of symptomatic infected individuals, as well as population-wide isolation practices in response to containment policies or behavioral changes, and show that the model captures the observed growth behavior accurately. The insights provided here may aid the careful implementation of containment strategies for ongoing secondary outbreaks of COVID-19 or similar future outbreaks of other emergent infectious diseases.