Enhancing global preparedness during an ongoing pandemic from partial and noisy data

AO_SCPLOWBSTRACTC_SCPLOWAs the coronavirus disease 2019 (COVID-19) spread globally, emerging variants such as B.1.1.529 quickly became dominant worldwide. Sustained community transmission favors the proliferation of mutated sub-lineages with pandemic potential, due to cross-national mobility flows, which are responsible for consecutive cases surge worldwide. We show that, in the early stages of an emerging variant, integrating data from national genomic surveillance and global human mobility with large-scale epidemic modeling allows to quantify its pandemic potential, providing quantifiable indicators for pro-active policy interventions. We validate our framework on worldwide spreading variants and gain insights about the pandemic potential of BA.5, BA.2.75 and other sub- and lineages. We combine the different sources of information in a simple estimate of the pandemic delay and show that only in combination, the pandemic potentials of the lineages are correctly assessed relative to each other. Country-level epidemic intelligence is not enough to contrast the pandemic of respiratory pathogens such as SARS-CoV-2 and a scalable integrated approach, i.e. pandemic intelligence, is required to enhance global preparedness.

Modeling the impact of the Omicron infection wave in Germany

BACKGROUNDIn November 2021, the first case of SARS-CoV-2 "variant of concern" (VOC) B.1.1.529 ("Omicron") was reported in Germany, alongside global reports of reduced vaccine efficacy against infections with this variant. The potential threat posed by the rapid spread of this variant in Germany remained, at the time, elusive. METHODSWe developed a variant-dependent population-averaged susceptible-exposed-infected-recovered (SEIR) infectious disease model. The model was calibrated on the observed fixation dynamics of the Omicron variant in December 2021, and allowed us to estimate potential courses of upcoming infection waves in Germany, focusing on the corresponding burden on intensive care units (ICUs) and the efficacy of contact reduction strategies. RESULTSA maximum median incidence of approximately 300 000 (50% PI in 1000: [181,454], 95% PI in 1000: [55,804]) reported cases per day was expected with the median peak occurring in the mid of February 2022, reaching a cumulative Omicron case count of 16.5 million (50% PI in mio: [11.4, 21.3], 95% PI in mio: [4.1, 27.9]) until Apr 1, 2022. These figures were in line with the actual Omicron waves that were subsequently observed in Germany with respective peaks occurring in mid February (peak: 191k daily new cases) and mid March (peak: 230k daily new cases), cumulatively infecting 14.8 million individuals during the study period. The model peak incidence was observed to be highly sensitive to variations in the assumed generation time and decreased with shorter generation time. Low contact reductions were expected to lead to containment. Early, strict, and short contact reductions could have led to a strong "rebound" effect with high incidences after the end of the respective non-pharmaceutical interventions. Higher vaccine uptake would have led to a lower outbreak size. To ensure that ICU occupancy remained below maximum capacity, a relative risk of requiring ICU care of 10%-20% was necessary (after infection with Omicron vs. infection with Delta). CONCLUSIONSWe expected a large cumulative number of infections with the VOC Omicron in Germany with ICU occupancy likely remaining below capacity nevertheless, even without additional non-pharmaceutical interventions. Our estimates were in line with the retrospectively observed waves. 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.

Estimating the distribution of COVID-19-susceptible, -recovered, and -vaccinated individuals in Germany up to April 2022

After having affected the population for two years, the COVID-19 pandemic has reached a phase where a considerable number of people in Germany have been either infected with a SARS-CoV-2 variant, vaccinated, or both. Yet the full extent to which the population has been in contact with either virus or vaccine remains elusive, particularly on a regional level, because (a) infection counts suffer from under-reporting, and (b) the overlap between the vaccinated and recovered subpopulations is unknown. Since previous infection, vaccination, or especially a combination of both reduce the risk of severe disease, a high share of individuals with SARS-CoV-2 immunity lowers the probability of severe outbreaks that could potentially overburden the public health system once again, given that emerging variants do not escape this reduction in susceptibility. Here, we estimate the share of immunologically naive individuals by age group for each of the 16 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 7.0% of individuals in the German population have neither been in contact with vaccine nor any variant as of March 31, 2022 (quartile range [3.6%- 9.8%]). For the adult population at higher risk of severe disease, this figure is reduced to 3.5% [1.3%-5.5%] for ages 18-59 and 4.3% [2.7%-5.8%] for ages 60 and above. However, estimates vary between German states mostly due to heterogeneous vaccine uptake. Excluding Omicron infections from the analysis, 16.1% [14.0%-17.8%] of the population in Germany, across all ages, are estimated to be immunologically naive, highlighting the large impact the Omicron wave had until the beginning of spring in 2022.

Germany's current COVID-19 crisis is mainly driven by the unvaccinated

Vaccines are the most powerful pharmaceutical tool to combat the COVID-19 pandemic. While the majority (about 65%) of the German population were fully vaccinated, 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. At the time, it (i) remains elusive how significant this contribution is and (ii) whether targeted non-pharmaceutical interventions (NPIs) may stop the amplification of the ongoing crisis. Here, we estimate that about 67%-76% of all new infections are caused by unvaccinated individuals, implying that only 24%-33% are caused by the vaccinated. Furthermore, we estimate 38%-51% of new infections to be caused by unvaccinated individuals infecting other unvaccinated individuals. In total, unvaccinated individuals are expected to be involved in 8-9 of 10 new infections. We further show that decreasing the transmissibility of the unvaccinated by, e. g. targeted NPIs, causes a steeper decrease in the effective reproduction number [R] than decreasing the transmissibility of vaccinated individuals, potentially leading to temporary epidemic control. Furthermore, reducing contacts between vaccinated and unvaccinated individuals serves to decrease [R] in a similar manner as increasing vaccine uptake. Taken together, our results contribute to the public discourse regarding policy changes in pandemic response and highlight the importance of combined measures, such as vaccination campaigns and contact reduction, to achieve epidemic control and preventing an overload of public health systems.

Digital contact tracing contributes little to COVID-19 outbreak containment

Digital contact tracing applications have been introduced in many countries to aid in the containment of COVID-19 outbreaks. Initially, enthusiasm was high regarding their implementation as a non-pharmaceutical intervention (NPI). Yet, no country was able to prevent larger outbreaks without falling back to harsher NPIs, and the total effect of digital contact tracing remains elusive. Based on the results of empirical studies and modeling efforts, we show that digital contact tracing apps might have prevented cases on the order of single-digit percentages up until now, at best. We show that this poor impact can be attributed to a combination of low participation rates, a non-flexible reliance on symptom-based testing, low engagement of participants, and delays between testing and test result upload. We find that contact tracing does not change the epidemic threshold and exclusively prevents more cases during the supercritical phase of an epidemic, making it unfit as a tool to prevent outbreaks. Locally clustered contact structures may increase the interventions efficacy, but only if the number of contacts per individual is homogeneously distributed, a condition usually not found in contact networks. Our results suggest that policy makers cannot rely on digital contact tracing to contain outbreaks of COVID-19 or similar diseases.

Effective containment explains sub-exponential growth in confirmed cases of recent COVID-19 outbreak in Mainland China

The recent outbreak of COVID-19 in Mainland China is characterized by a distinctive algebraic, sub-exponential increase of confirmed cases with time during the early phase of the epidemic, contrasting an initial exponential growth expected for an unconstrained outbreak with sufficiently large reproduction rate. Although case counts vary significantly between affected provinces in Mainland China, the scaling law t is surprisingly universal, with a range of exponents = 2.1 {+/-} 0.3. The universality of this behavior indicates that, in spite of social, regional, demographical, geographical, and socio-economical heterogeneities of affected Chinese provinces, this outbreak is dominated by fundamental mechanisms that are not captured by standard epidemiological models. We show that the observed scaling law is 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 in response to mitigation policies or behavioral changes. For a wide range of parameters, the model reproduces the observed scaling law in confirmed cases and explains the observed exponents. Quantitative fits to empirical data permit the identification of peak times in the number of asymptomatic or oligo-symptomatic, unidentified infected individuals, as well as estimates of local variations in the basic reproduction number. The model implies that the observed scaling law in confirmed cases is a direct signature of effective contaiment strategies and/or systematic behavioral changes that affect a substantial fraction of the susceptible population. These insights may aid the implementation of containment strategies in potential export induced COVID-19 secondary outbreaks elsewhere or similar future outbreaks of other emergent infectious diseases.