Eugenio Valdano

Bio: Eugenio Valdano is an academic researcher from University of Paris. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 13, co-authored 39 publications receiving 1560 citations. Previous affiliations of Eugenio Valdano include Rovira i Virgili University & Semel Institute for Neuroscience and Human Behavior.

Analytical Computation of the Epidemic Threshold on Temporal Networks

Abstract: The time variation of contacts in a networked system may fundamentally alter the properties of spreading processes and affect the condition for large-scale propagation, as encoded in the epidemic threshold. Despite the great interest in the problem for the physics, applied mathematics, computer science, and epidemiology communities, a full theoretical understanding is still missing and currently limited to the cases where the timescale separation holds between spreading and network dynamics or to specific temporal network models. We consider a Markov chain description of the susceptible-infectious-susceptible process on an arbitrary temporal network. By adopting a multilayer perspective, we develop a general analytical derivation of the epidemic threshold in terms of the spectral radius of a matrix that encodes both network structure and disease dynamics. The accuracy of the approach is confirmed on a set of temporal models and empirical networks and against numerical results. In addition, we explore how the threshold changes when varying the overall time of observation of the temporal network, so as to provide insights on the optimal time window for data collection of empirical temporal networked systems. Our framework is of both fundamental and practical interest, as it offers novel understanding of the interplay between temporal networks and spreading dynamics.

Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study.

Abstract: Summary Background On March 17, 2020, French authorities implemented a nationwide lockdown to respond to the COVID-19 epidemic and curb the surge of patients requiring critical care. Assessing the effect of lockdown on individual displacements is essential to quantify achievable mobility reductions and identify the factors driving the changes in social dynamics that affected viral diffusion. We aimed to use mobile phone data to study how mobility in France changed before and during lockdown, breaking down our findings by trip distance, user age and residency, and time of day, and analysing regional data and spatial heterogeneities. Methods For this population-based study, we used temporally resolved travel flows among 1436 administrative areas of mainland France reconstructed from mobile phone trajectories. Data were stratified by age class (younger than 18 years, 18–64 years, and 65 years or older). We distinguished between residents and non-residents and used population data and regional socioeconomic indicators from the French National Statistical Institute. We measured mobility changes before and during lockdown at both local and country scales using a case-crossover framework. We analysed all trips combined and trips longer than 100 km (termed long trips), and separated trips by daytime or night-time, weekdays or weekends, and rush hours. Findings Lockdown caused a 65% reduction in the countrywide number of displacements (from about 57 million to about 20 million trips per day) and was particularly effective in reducing work-related short-range mobility, especially during rush hour, and long trips. Geographical heterogeneities showed anomalous increases in long-range movements even before lockdown announcement that were tightly localised in space. During lockdown, mobility drops were unevenly distributed across regions (eg, Ile-de-France, the region of Paris, went from 585 000 to 117 000 outgoing trips per day). They were strongly associated with active populations, workers employed in sectors highly affected by lockdown, and number of hospitalisations per region, and moderately associated with the socioeconomic level of the regions. Major cities largely shrank their pattern of connectivity, reducing it mainly to short-range commuting (95% of traffic leaving Paris was contained in a 201 km radius before lockdown, which was reduced to 29 km during lockdown). Interpretation Lockdown was effective in reducing population mobility across scales. Caution should be taken in the timing of policy announcements and implementation, because anomalous mobility followed policy announcements, which might act as seeding events. Conversely, risk aversion might be beneficial in further decreasing mobility in highly affected regions. We also identified socioeconomic and demographic constraints to the efficacy of restrictions. The unveiled links between geography, demography, and timing of the response to mobility restrictions might help to design interventions that minimise invasiveness while contributing to the current epidemic response. Funding Agence Nationale de la Recherche, EU, REACTing.

Underdetection of cases of COVID-19 in France threatens epidemic control.

Abstract: As countries in Europe gradually relaxed lockdown restrictions after the first wave, test–trace–isolate strategies became critical to maintain the incidence of coronavirus disease 2019 (COVID-19) at low levels1,2. Reviewing their shortcomings can provide elements to consider in light of the second wave that is currently underway in Europe. Here we estimate the rate of detection of symptomatic cases of COVID-19 in France after lockdown through the use of virological3 and participatory syndromic4 surveillance data coupled with mathematical transmission models calibrated to regional hospitalizations2. Our findings indicate that around 90,000 symptomatic infections, corresponding to 9 out 10 cases, were not ascertained by the surveillance system in the first 7 weeks after lockdown from 11 May to 28 June 2020, although the test positivity rate did not exceed the 5% recommendation of the World Health Organization (WHO)5. The median detection rate increased from 7% (95% confidence interval, 6–8%) to 38% (35–44%) over time, with large regional variations, owing to a strengthening of the system as well as a decrease in epidemic activity. According to participatory surveillance data, only 31% of individuals with COVID-19-like symptoms consulted a doctor in the study period. This suggests that large numbers of symptomatic cases of COVID-19 did not seek medical advice despite recommendations, as confirmed by serological studies6,7. Encouraging awareness and same-day healthcare-seeking behaviour of suspected cases of COVID-19 is critical to improve detection. However, the capacity of the system remained insufficient even at the low epidemic activity achieved after lockdown, and was predicted to deteriorate rapidly with increasing incidence of COVID-19 cases. Substantially more aggressive, targeted and efficient testing with easier access is required to act as a tool to control the COVID-19 pandemic. The testing strategy will be critical to enable partial lifting of the current restrictive measures in Europe and to avoid a third wave. Analyses of virological and surveillance data in France show that a substantial proportion of symptomatic cases of COVID-19 have remained undetected and that easily accessible and efficient testing is required to control the pandemic.

Novel coronavirus (2019-nCoV) early-stage importation risk to Europe, January 2020

Abstract: As at 27 January 2020, 42 novel coronavirus (2019-nCoV) cases were confirmed outside China. We estimate the risk of case importation to Europe from affected areas in China via air travel. We consider travel restrictions in place, three reported cases in France, one in Germany. Estimated risk in Europe remains high. The United Kingdom, Germany and France are at highest risk. Importation from Beijing and Shanghai would lead to higher and widespread risk for Europe.

Early dynamics of transmission and control of COVID-19: a mathematical modelling study.

Abstract: Summary Background An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to 95 333 confirmed cases as of March 5, 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced. Methods We combined a stochastic transmission model with data on cases of coronavirus disease 2019 (COVID-19) in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January, 2020, and February, 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas. To estimate the early dynamics of transmission in Wuhan, we fitted a stochastic transmission dynamic model to multiple publicly available datasets on cases in Wuhan and internationally exported cases from Wuhan. The four datasets we fitted to were: daily number of new internationally exported cases (or lack thereof), by date of onset, as of Jan 26, 2020; daily number of new cases in Wuhan with no market exposure, by date of onset, between Dec 1, 2019, and Jan 1, 2020; daily number of new cases in China, by date of onset, between Dec 29, 2019, and Jan 23, 2020; and proportion of infected passengers on evacuation flights between Jan 29, 2020, and Feb 4, 2020. We used an additional two datasets for comparison with model outputs: daily number of new exported cases from Wuhan (or lack thereof) in countries with high connectivity to Wuhan (ie, top 20 most at-risk countries), by date of confirmation, as of Feb 10, 2020; and data on new confirmed cases reported in Wuhan between Jan 16, 2020, and Feb 11, 2020. Findings We estimated that the median daily reproduction number (Rt) in Wuhan declined from 2·35 (95% CI 1·15–4·77) 1 week before travel restrictions were introduced on Jan 23, 2020, to 1·05 (0·41–2·39) 1 week after. Based on our estimates of Rt, assuming SARS-like variation, we calculated that in locations with similar transmission potential to Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population. Interpretation Our results show that COVID-19 transmission probably declined in Wuhan during late January, 2020, coinciding with the introduction of travel control measures. As more cases arrive in international locations with similar transmission potential to Wuhan before these control measures, it is likely many chains of transmission will fail to establish initially, but might lead to new outbreaks eventually. Funding Wellcome Trust, Health Data Research UK, Bill & Melinda Gates Foundation, and National Institute for Health Research.

Profiling Early Humoral Response to Diagnose Novel Coronavirus Disease (COVID-19)

Abstract: Background The emergence of coronavirus disease 2019 (COVID-19) is a major healthcare threat. The current method of detection involves a quantitative polymerase chain reaction (qPCR)-based technique, which identifies the viral nucleic acids when present in sufficient quantity. False-negative results can be achieved and failure to quarantine the infected patient would be a major setback in containing the viral transmission. We aim to describe the time kinetics of various antibodies produced against the 2019 novel coronavirus (SARS-CoV-2) and evaluate the potential of antibody testing to diagnose COVID-19. Methods The host humoral response against SARS-CoV-2, including IgA, IgM, and IgG response, was examined by using an ELISA-based assay on the recombinant viral nucleocapsid protein. 208 plasma samples were collected from 82 confirmed and 58 probable cases (qPCR negative but with typical manifestation). The diagnostic value of IgM was evaluated in this cohort. Results The median duration of IgM and IgA antibody detection was 5 (IQR, 3-6) days, while IgG was detected 14 (IQR, 10-18) days after symptom onset, with a positive rate of 85.4%, 92.7%, and 77.9%, respectively. In confirmed and probable cases, the positive rates of IgM antibodies were 75.6% and 93.1%, respectively. The detection efficiency by IgM ELISA is higher than that of qPCR after 5.5 days of symptom onset. The positive detection rate is significantly increased (98.6%) when combining IgM ELISA assay with PCR for each patient compared with a single qPCR test (51.9%). Conclusions The humoral response to SARS-CoV-2 can aid in the diagnosis of COVID-19, including subclinical cases.

Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures.

Abstract: The spread of coronavirus disease 2019 (COVID-19) in Italy prompted drastic measures for transmission containment. We examine the effects of these interventions, based on modeling of the unfolding epidemic. We test modeling options of the spatially explicit type, suggested by the wave of infections spreading from the initial foci to the rest of Italy. We estimate parameters of a metacommunity Susceptible-Exposed-Infected-Recovered (SEIR)-like transmission model that includes a network of 107 provinces connected by mobility at high resolution, and the critical contribution of presymptomatic and asymptomatic transmission. We estimate a generalized reproduction number ([Formula: see text] = 3.60 [3.49 to 3.84]), the spectral radius of a suitable next-generation matrix that measures the potential spread in the absence of containment interventions. The model includes the implementation of progressive restrictions after the first case confirmed in Italy (February 21, 2020) and runs until March 25, 2020. We account for uncertainty in epidemiological reporting, and time dependence of human mobility matrices and awareness-dependent exposure probabilities. We draw scenarios of different containment measures and their impact. Results suggest that the sequence of restrictions posed to mobility and human-to-human interactions have reduced transmission by 45% (42 to 49%). Averted hospitalizations are measured by running scenarios obtained by selectively relaxing the imposed restrictions and total about 200,000 individuals (as of March 25, 2020). Although a number of assumptions need to be reexamined, like age structure in social mixing patterns and in the distribution of mobility, hospitalization, and fatality, we conclude that verifiable evidence exists to support the planning of emergency measures.