Spatial spread of COVID-19 during the early pandemic phase in Italy.

Quantifying the potential spatial spread of an infectious pathogen is key to defining effective containment and control strategies. The aim of this study is to estimate the risk of SARS-CoV-2 transmission at different distances in Italy before the first regional lockdown was imposed, identifying important sources of national spreading. To do this, we leverage on a probabilistic model applied to daily symptomatic cases retrospectively ascertained in each Italian municipality with symptom onset between January 28 and March 7, 2020. Results are validated using a multi-patch dynamic transmission model reproducing the spatiotemporal distribution of identified cases. Our results show that the contribution of short-distance ( ≤ 10 k m ) transmission increased from less than 40% in the last week of January to more than 80% in the first week of March 2020. On March 7, 2020, that is the day before the first regional lockdown was imposed, more than 200 local transmission foci were contributing to the spread of SARS-CoV-2 in Italy. At the time, isolation measures imposed only on municipalities with at least ten ascertained cases would have left uncontrolled more than 75% of spillover transmission from the already affected municipalities. In early March, national-wide restrictions were required to curb short-distance transmission of SARS-CoV-2 in Italy.

Estimating the potential risk of transmission of arboviruses in the Americas and Europe: a modelling study.

BACKGROUND: Estimates of the spatiotemporal distribution of different mosquito vector species and the associated risk of transmission of arboviruses are key to design adequate policies for preventing local outbreaks and reducing the number of human infections in endemic areas. In this study, we quantified the abundance of Aedes albopictus and Aedes aegypti and the local transmission potential for three arboviral infections at an unprecedented spatiotemporal resolution in areas where no entomological surveillance is available. METHODS: We developed a computational model to quantify the daily abundance of Aedes mosquitoes, leveraging temperature and precipitation records. The model was calibrated on mosquito surveillance data collected in 115 locations in Europe and the Americas between 2007 and 2018. Model estimates were used to quantify the reproduction number of dengue virus, Zika virus, and chikungunya in Europe and the Americas, at a high spatial resolution. FINDINGS: In areas colonised by both Aedes species, A aegypti was estimated to be the main vector for the transmission of dengue virus, Zika virus, and chikungunya, being associated with a higher estimate of R0 when compared with A albopictus. Our estimates highlighted that these arboviruses were endemic in tropical and subtropical countries, with the highest risks of transmission found in central America, Venezuela, Colombia, and central-east Brazil. A non-negligible potential risk of transmission was also estimated for Florida, Texas, and Arizona (USA). The broader ecological niche of A albopictus could contribute to the emergence of chikungunya outbreaks and clusters of dengue autochthonous cases in temperate areas of the Americas, as well as in mediterranean Europe (in particular, in Italy, southern France, and Spain). INTERPRETATION: Our results provide a comprehensive overview of the transmission potential of arboviral diseases in Europe and the Americas, highlighting areas where surveillance and mosquito control capacities should be prioritised. FUNDING: EU and Ministero dell'Università e della Ricerca, Italy (Piano Nazionale di Ripresa e Resilienza Extended Partnership initiative on Emerging Infectious Diseases); EU (Horizon 2020); Ministero dell'Università e della Ricerca, Italy (Progetti di ricerca di Rilevante Interesse Nazionale programme); Brazilian National Council of Science, Technology and Innovation; Ministry of Health, Brazil; and Foundation of Research for Minas Gerais, Brazil.

Estimating SARS-CoV-2 infections and associated changes in COVID-19 severity and fatality.

Background: The difficulty in identifying SARS-CoV-2 infections has not only been the major obstacle to control the COVID-19 pandemic but also to quantify changes in the proportion of infections resulting in hospitalization, intensive care unit (ICU) admission, or death. Methods: We developed a model of SARS-CoV-2 transmission and vaccination informed by official estimates of the time-varying reproduction number to estimate infections that occurred in Italy between February 2020 and 2022. Model outcomes were compared with the Italian National surveillance data to estimate changes in the SARS-CoV-2 infection ascertainment ratio (IAR), infection hospitalization ratio (IHR), infection ICU ratio (IIR), and infection fatality ratio (IFR) in five different sub-periods associated with the dominance of the ancestral lineages and Alpha, Delta, and Omicron BA.1 variants. Results: We estimate that, over the first 2 years of pandemic, the IAR ranged between 15% and 40% (range of 95%CI: 11%-61%), with a peak value in the second half of 2020. The IHR, IIR, and IFR consistently decreased throughout the pandemic with 22-44-fold reductions between the initial phase and the Omicron period. At the end of the study period, we estimate an IHR of 0.24% (95%CI: 0.17-0.36), IIR of 0.015% (95%CI: 0.011-0.023), and IFR of 0.05% (95%CI: 0.04-0.08). Conclusions: Since 2021, changes in the dominant SARS-CoV-2 variant, vaccination rollout, and the shift of infection to younger ages have reduced SARS-CoV-2 infection ascertainment. The same factors, combined with the improvement of patient management and care, contributed to a massive reduction in the severity and fatality of COVID-19.

SARS-CoV-2 transmission patterns in educational settings during the Alpha wave in Reggio-Emilia, Italy.

Different monitoring and control policies have been implemented in schools to minimize the spread of SARS-CoV-2. Transmission in schools has been hard to quantify due to the large proportion of asymptomatic carriers in young individuals. We applied a Bayesian approach to reconstruct the transmission chains between 284 SARS-CoV-2 infections ascertained during 87 school outbreak investigations conducted between March and April 2021 in Italy. Under the policy of reactive quarantines, we found that 42.5% (95%CrI: 29.5-54.3%) of infections among school attendees were caused by school contacts. The mean number of secondary cases infected at school by a positive individual during in-person education was estimated to be 0.33 (95%CrI: 0.23-0.43), with marked heterogeneity across individuals. Specifically, we estimated that only 26.0% (95%CrI: 17.6-34.1%) of students and school personnel who tested positive during in-person education caused at least one secondary infection at school. Positive individuals who attended school for at least 6 days before being isolated or quarantined infected on average 0.49 (95%CrI: 0.14-0.83) secondary cases. Our findings provide quantitative insights on the contribution of school transmission to the spread of SARS-CoV-2 in young individuals. Identifying positive cases within 5 days after exposure to their infector could reduce onward transmission at school by at least 30%.

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

As the coronavirus disease 2019 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. Compared to a country-level epidemic intelligence, our scalable integrated approach, that is pandemic intelligence, permits to enhance global preparedness to contrast the pandemic of respiratory pathogens such as SARS-CoV-2.

Evaluation of Waning of SARS-CoV-2 Vaccine-Induced Immunity: A Systematic Review and Meta-analysis.

Importance: Estimates of the rate of waning of vaccine effectiveness (VE) against COVID-19 are key to assess population levels of protection and future needs for booster doses to face the resurgence of epidemic waves. Objective: To quantify the progressive waning of VE associated with the Delta and Omicron variants of SARS-CoV-2 by number of received doses. Data Sources: PubMed and Web of Science were searched from the databases' inception to October 19, 2022, as well as reference lists of eligible articles. Preprints were included. Study Selection: Selected studies for this systematic review and meta-analysis were original articles reporting estimates of VE over time against laboratory-confirmed SARS-CoV-2 infection and symptomatic disease. Data Extraction and Synthesis: Estimates of VE at different time points from vaccination were retrieved from original studies. A secondary data analysis was performed to project VE at any time from last dose administration, improving the comparability across different studies and between the 2 considered variants. Pooled estimates were obtained from random-effects meta-analysis. Main Outcomes and Measures: Outcomes were VE against laboratory-confirmed Omicron or Delta infection and symptomatic disease and half-life and waning rate associated with vaccine-induced protection. Results: A total of 799 original articles and 149 reviews published in peer-reviewed journals and 35 preprints were identified. Of these, 40 studies were included in the analysis. Pooled estimates of VE of a primary vaccination cycle against laboratory-confirmed Omicron infection and symptomatic disease were both lower than 20% at 6 months from last dose administration. Booster doses restored VE to levels comparable to those acquired soon after the administration of the primary cycle. However, 9 months after booster administration, VE against Omicron was lower than 30% against laboratory-confirmed infection and symptomatic disease. The half-life of VE against symptomatic infection was estimated to be 87 days (95% CI, 67-129 days) for Omicron compared with 316 days (95% CI, 240-470 days) for Delta. Similar waning rates of VE were found for different age segments of the population. Conclusions and Relevance: These findings suggest that the effectiveness of COVID-19 vaccines against laboratory-confirmed Omicron or Delta infection and symptomatic disease rapidly wanes over time after the primary vaccination cycle and booster dose. These results can inform the design of appropriate targets and timing for future vaccination programs.

Priority age targets for COVID-19 vaccination in Ethiopia under limited vaccine supply.

The worldwide inequitable access to vaccination claims for a re-assessment of policies that could minimize the COVID-19 burden in low-income countries. Nine months after the launch of the national vaccination program in March 2021, only 3.4% of the Ethiopian population received two doses of COVID-19 vaccine. We used a SARS-CoV-2 transmission model to estimate the level of immunity accrued before the launch of vaccination in the Southwest Shewa Zone (SWSZ) and to evaluate the impact of alternative age priority vaccination targets in a context of limited vaccine supply. The model was informed with available epidemiological evidence and detailed contact data collected across different geographical settings (urban, rural, or remote). We found that, during the first year of the pandemic, the mean proportion of critical cases occurred in SWSZ attributable to infectors under 30 years of age would range between 24.9 and 48.0%, depending on the geographical setting. During the Delta wave, the contribution of this age group in causing critical cases was estimated to increase on average to 66.7-70.6%. Our findings suggest that, when considering the vaccine product available at the time (ChAdOx1 nCoV-19; 65% efficacy against infection after 2 doses), prioritizing the elderly for vaccination remained the best strategy to minimize the disease burden caused by Delta, irrespectively of the number of available doses. Vaccination of all individuals aged ≥ 50 years would have averted 40 (95%PI: 18-60), 90 (95%PI: 61-111), and 62 (95%PI: 21-108) critical cases per 100,000 residents in urban, rural, and remote areas, respectively. Vaccination of all individuals aged ≥ 30 years would have averted an average of 86-152 critical cases per 100,000 individuals, depending on the setting considered. Despite infections among children and young adults likely caused 70% of critical cases during the Delta wave in SWSZ, most vulnerable ages should remain a key priority target for vaccination against COVID-19.

Estimating SARS-CoV-2 transmission in educational settings: A retrospective cohort study.

BACKGROUND: School closures and distance learning have been extensively adopted to counter the COVID-19 pandemic. However, the contribution of school transmission to the spread of SARS-CoV-2 remains poorly quantified. METHODS: We analyzed transmission patterns associated with 976 SARS-CoV-2 exposure events, involving 460 positive individuals, as identified in early 2021 through routine surveillance and an extensive screening conducted on students, school personnel, and their household members in a small Italian municipality. In addition to population screenings and contact-tracing operations, reactive closures of class and schools were implemented. RESULTS: From the analysis of 152 clear infection episodes and 584 exposure events identified by epidemiological investigations, we estimated that approximately 50%, 21%, and 29% of SARS-CoV-2 transmission was associated with household, school, and community contacts, respectively. We found substantial transmission heterogeneities, with 20% positive individuals causing 75% to 80% of ascertained infection episodes. A higher proportion of infected individuals causing onward transmission was found among students (46.2% vs. 25%, on average), who also caused a markedly higher number of secondary cases (mean: 1.03 vs. 0.35). By reconstructing likely transmission chains from the entire set of exposures identified during contact-tracing operations, we found that clusters originated from students or school personnel were associated with a larger average cluster size (3.32 vs. 1.15) and a larger average number of generations in the transmission chain (1.56 vs. 1.17). CONCLUSIONS: Uncontrolled SARS-CoV-2 transmission at school could disrupt the regular conduct of teaching activities, likely seeding the transmission into other settings, and increasing the burden on contact-tracing operations.

Estimation of the incubation period and generation time of SARS-CoV-2 Alpha and Delta variants from contact tracing data.

Quantitative information on epidemiological quantities such as the incubation period and generation time of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants is scarce. We analysed a dataset collected during contact tracing activities in the province of Reggio Emilia, Italy, throughout 2021. We determined the distributions of the incubation period for the Alpha and Delta variants using information on negative polymerase chain reaction tests and the date of last exposure from 282 symptomatic cases. We estimated the distributions of the intrinsic generation time using a Bayesian inference approach applied to 9724 SARS-CoV-2 cases clustered in 3545 households where at least one secondary case was recorded. We estimated a mean incubation period of 4.9 days (95% credible intervals, CrI, 4.4-5.4) for Alpha and 4.5 days (95% CrI 4.0-5.0) for Delta. The intrinsic generation time was estimated to have a mean of 7.12 days (95% CrI 6.27-8.44) for Alpha and of 6.52 days (95% CrI 5.54-8.43) for Delta. The household serial interval was 2.43 days (95% CrI 2.29-2.58) for Alpha and 2.74 days (95% CrI 2.62-2.88) for Delta, and the estimated proportion of pre-symptomatic transmission was 48-51% for both variants. These results indicate limited differences in the incubation period and intrinsic generation time of SARS-CoV-2 variants Alpha and Delta compared to ancestral lineages.

Tracking the progressive spread of the SARS-CoV-2 Omicron variant in Italy, December 2021 to January 2022.

BackgroundThe SARS-CoV-2 variant of concern Omicron was first detected in Italy in November 2021.AimTo comprehensively describe Omicron spread in Italy in the 2 subsequent months and its impact on the overall SARS-CoV-2 circulation at population level.MethodsWe analyse data from four genomic surveys conducted across the country between December 2021 and January 2022. Combining genomic sequencing results with epidemiological records collated by the National Integrated Surveillance System, the Omicron reproductive number and exponential growth rate are estimated, as well as SARS-CoV-2 transmissibility.ResultsOmicron became dominant in Italy less than 1 month after its first detection, representing on 3 January 76.9-80.2% of notified SARS-CoV-2 infections, with a doubling time of 2.7-3.3 days. As of 17 January 2022, Delta variant represented < 6% of cases. During the Omicron expansion in December 2021, the estimated mean net reproduction numbers respectively rose from 1.15 to a maximum of 1.83 for symptomatic cases and from 1.14 to 1.36 for hospitalised cases, while remaining relatively stable, between 0.93 and 1.21, for cases needing intensive care. Despite a reduction in relative proportion, Delta infections increased in absolute terms throughout December contributing to an increase in hospitalisations. A significant reproduction numbers' decline was found after mid-January, with average estimates dropping below 1 between 10 and 16 January 2022.ConclusionEstimates suggest a marked growth advantage of Omicron compared with Delta variant, but lower disease severity at population level possibly due to residual immunity against severe outcomes acquired from vaccination and prior infection.

Intrinsic generation time of the SARS-CoV-2 Omicron variant: An observational study of household transmission.

Background: Starting from the final months of 2021, the SARS-CoV-2 Omicron variant expanded globally, swiftly replacing Delta, the variant that was dominant at the time. Many uncertainties remain about the epidemiology of Omicron; here, we aim to estimate its generation time. Methods: We used a Bayesian approach to analyze 23,122 SARS-CoV-2 infected individuals clustered in 8903 households as determined from contact tracing operations in Reggio Emilia, Italy, throughout January 2022. We estimated the distribution of the intrinsic generation time (the time between the infection dates of an infector and its secondary cases in a fully susceptible population), realized household generation time, realized serial interval (time between symptom onset of an infector and its secondary cases), and contribution of pre-symptomatic transmission. Findings: We estimated a mean intrinsic generation time of 6.84 days (95% credible intervals, CrI, 5.72-8.60), and a mean realized household generation time of 3.59 days (95%CrI: 3.55-3.60). The household serial interval was 2.38 days (95%CrI 2.30-2.47) with about 51% (95%CrI 45-56%) of infections caused by symptomatic individuals being generated before symptom onset. Interpretation: These results indicate that the intrinsic generation time of the SARS-CoV-2 Omicron variant might not have shortened as compared to previous estimates on ancestral lineages, Alpha and Delta, in the same geographic setting. Like for previous lineages, pre-symptomatic transmission appears to play a key role for Omicron transmission. Estimates in this study may be useful to design quarantine, isolation and contact tracing protocols and to support surveillance (e.g., for the accurate computation of reproduction numbers). Funding: The study was partially funded by EU grant 874850 MOOD.

Individual risk perception and empirical social structures shape the dynamics of infectious disease outbreaks.

The dynamics of a spreading disease and individual behavioral changes are entangled processes that have to be addressed together in order to effectively manage an outbreak. Here, we relate individual risk perception to the adoption of a specific set of control measures, as obtained from an extensive large-scale survey performed via Facebook-involving more than 500,000 respondents from 64 countries-showing that there is a "one-to-one" relationship between perceived epidemic risk and compliance with a set of mitigation rules. We then develop a mathematical model for the spreading of a disease-sharing epidemiological features with COVID-19-that explicitly takes into account non-compliant individual behaviors and evaluates the impact of a population fraction of infectious risk-deniers on the epidemic dynamics. Our modeling study grounds on a wide set of structures, including both synthetic and more than 180 real-world contact patterns, to evaluate, in realistic scenarios, how network features typical of human interaction patterns impact the spread of a disease. In both synthetic and real contact patterns we find that epidemic spreading is hindered for decreasing population fractions of risk-denier individuals. From empirical contact patterns we demonstrate that connectivity heterogeneity and group structure significantly affect the peak of hospitalized population: higher modularity and heterogeneity of social contacts are linked to lower peaks at a fixed fraction of risk-denier individuals while, at the same time, such features increase the relative impact on hospitalizations with respect to the case where everyone correctly perceive the risks.

The advantage of the right in social media news sharing.

We analyze social media activity during one of the largest protest mobilizations in US history to examine ideological asymmetries in the posting of news content. Using an unprecedented combination of four datasets (tracking offline protests, social media activity, web browsing, and the reliability of news sources), we show that there is no evidence of unreliable sources having any prominent visibility during the protest period, but we do identify asymmetries in the ideological slant of the sources shared on social media, with a clear bias towards right-leaning domains. These results support the "amplification of the right" thesis, which points to the structural conditions (social and technological) that lead to higher visibility of content with a partisan bent towards the right. Our findings provide evidence that right-leaning sources gain more visibility on social media and reveal that ideological asymmetries manifest themselves even in the context of movements with progressive goals.

Persistence of information flow: A multiscale characterization of human brain.

Information exchange in the human brain is crucial for vital tasks and to drive diseases. Neuroimaging techniques allow for the indirect measurement of information flows among brain areas and, consequently, for reconstructing connectomes analyzed through the lens of network science. However, standard analyses usually focus on a small set of network indicators and their joint probability distribution. Here, we propose an information-theoretic approach for the analysis of synthetic brain networks (based on generative models) and empirical brain networks, and to assess connectome's information capacity at different stages of dementia. Remarkably, our framework accounts for the whole network state, overcoming limitations due to limited sets of descriptors, and is used to probe human connectomes at different scales. We find that the spectral entropy of empirical data lies between two generative models, indicating an interpolation between modular and geometry-driven structural features. In fact, we show that the mesoscale is suitable for characterizing the differences between brain networks and their generative models. Finally, from the analysis of connectomes obtained from healthy and unhealthy subjects, we demonstrate that significant differences between healthy individuals and the ones affected by Alzheimer's disease arise at the microscale (max. posterior probability smaller than 1%) and at the mesoscale (max. posterior probability smaller than 10%).

Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms.

Online social networks are the perfect test bed to better understand large-scale human behavior in interacting contexts. Although they are broadly used and studied, little is known about how their terms of service and posting rules affect the way users interact and information spreads. Acknowledging the relation between network connectivity and functionality, we compare the robustness of two different online social platforms, Twitter and Gab, with respect to banning, or dismantling, strategies based on the recursive censor of users characterized by social prominence (degree) or intensity of inflammatory content (sentiment). We find that the moderated (Twitter) vs. unmoderated (Gab) character of the network is not a discriminating factor for intervention effectiveness. We find, however, that more complex strategies based upon the combination of topological and content features may be effective for network dismantling. Our results provide useful indications to design better strategies for countervailing the production and dissemination of anti-social content in online social platforms.

State-dependent representation of stimulus-evoked activity in high-density recordings of neural cultures.

Neuronal responses to external stimuli vary from trial to trial partly because they depend on continuous spontaneous variations of the state of neural circuits, reflected in variations of ongoing activity prior to stimulus presentation. Understanding how post-stimulus responses relate to the pre-stimulus spontaneous activity is thus important to understand how state dependence affects information processing and neural coding, and how state variations can be discounted to better decode single-trial neural responses. Here we exploited high-resolution CMOS electrode arrays to record simultaneously from thousands of electrodes in in-vitro cultures stimulated at specific sites. We used information-theoretic analyses to study how ongoing activity affects the information that neuronal responses carry about the location of the stimuli. We found that responses exhibited state dependence on the time between the last spontaneous burst and the stimulus presentation and that the dependence could be described with a linear model. Importantly, we found that a small number of selected neurons carry most of the stimulus information and contribute to the state-dependent information gain. This suggests that a major value of large-scale recording is that it individuates the small subset of neurons that carry most information and that benefit the most from knowledge of its state dependence.

Alpha- and beta-band oscillations subserve different processes in reactive control of limb movements.

The capacity to rapidly suppress a behavioral act in response to sudden instruction to stop is a key cognitive function. This function, called reactive control, is tested in experimental settings using the stop signal task, which requires subjects to generate a movement in response to a go signal or suppress it when a stop signal appears. The ability to inhibit this movement fluctuates over time: sometimes, subjects can stop their response, and at other times, they can not. To determine the neural basis of this fluctuation, we recorded local field potentials (LFPs) in the alpha (6-12 Hz) and beta (13-35 Hz) bands from the dorsal premotor cortex of two nonhuman primates that were performing the task. The ability to countermand a movement after a stop signal was predicted by the activity of both bands, each purportedly representing a distinct neural process. The beta band represents the level of movement preparation; higher beta power corresponds to a lower level of movement preparation, whereas the alpha band supports a proper phasic, reactive inhibitory response: movements are inhibited when alpha band power increases immediately after a stop signal. Our findings support the function of LFP bands in generating the signatures of various neural computations that are multiplexed in the brain.