The importance of the population age-structure: insights from Covid-19 dynamics model structured by age, time since infection and acquired immunity (preprint)

The Covid-19 pandemic outbreak was followed by an huge amount of modeling studies in order to rapidly gain insights to implement the best public health policies. However, most of those compartmental models used a classical ordinary differential equations (ODEs) system based formalism that came with the tacit assumption the time spent in each compartment does not depend of the time already spent in it. To overcome this "memoryless" issue, a widely used workaround is to artificially increase and chain the number of compartments of an unique reality (e.g. many compartments for infected individuals). It allows for a greater heterogeneity and thus be closer to the observed situation, at the cost of rendering the whole model more difficult to apprehend and parametrize. We propose here an alternative formalism based on a partial differential equations (PDEs) system instead of ordinary differential equations, which provides naturally a memory structure for each compartment, and thus allows to keep a restrained number of compartments. We use such a model applied to the French situation, accounting for vaccinal and natural immunity. The results seem to indicate that the vaccination rate is not enough to ensure the end of the epidemic, but, above all, highlight a huge uncertainty attributable to the age-structured contact matrix.

Predicting COVID-19 incidence in French hospitals using human contact network analytics (preprint)

Coronavirus disease (COVID-19) was detected in Wuhan, China in 2019 and spread worldwide within few weeks. The COVID-19 epidemic started to gain traction in France in March 2020. Sub-national hospital admissions and deaths were then recorded daily and served as the main policy indicators. Concurrently, mobile phone positioning data have been curated to determine the frequency of users being colocalized within a given distance. Contrarily to individual tracking data, these can provide a proxy of human contact networks between subnational administrative units. Motivated by numerous studies correlating human mobility data and disease incidence, we developed predictive time series models of hospital incidence between July 2020 and April 2021. Adding human contact network analytics such as clustering coefficients, contact network strength, null links or curvature as regressors, we found that predictions can be improved substantially (more than 50%) both at the national and sub-national for up to two weeks. Our sub-national analysis also revealed the importance of spatial structure, as incidence in colocalized administrative units improved predictions. This original application of network analytics from co-localisation data to epidemic spread opens new perspectives for epidemics forecasting and public health.

Epidemiological and clinical insights from SARS-CoV-2 RT-PCR cycle amplification values (preprint)

The SARS-CoV-2 pandemic has led to an unprecedented daily use of molecular RT-PCR tests. These tests are interpreted qualitatively for diagnosis, and the relevance of the test result intensity, i.e. the number of amplification cycles (Ct), is debated because of strong potential biases. We analyze a national database of tests performed on more than 2 million individuals between January and November 2020. Although we find Ct values to vary depending on the testing laboratory or the assay used, we detect strong significant trends with patient age, number of days after symptoms onset, or the state of the epidemic (the temporal reproduction number) at the time of the test. These results suggest that Ct values can be used to improve short-term predictions for epidemic surveillance.

Rapid SARS-CoV-2 variants spread detected in France using specific RT-PCR testing (preprint)

SARS-CoV-2 variants raise major concerns regarding the control of COVID-19 epidemics. We analyse 40,000 specific RT-PCR tests performed on SARS-CoV-2-positive samples collected between Jan 26 and Feb 16, 2021. We find a high transmission advantage of variants and show that their spread in the country is more advanced than anticipated.

The UK IBD Registry COVID-19 Risk Tool; Patient Generated Data Can Improve the Hospital Record (preprint)

Background: The COVID-19 pandemic has necessitated identifying individuals at higher risk of severe outcomes who should be shielded and provided with government support. We describe the initial results and validation of a web-based tool targeted at patients in the UK with inflammatory bowel disease (IBD) to self-stratify their risk according to a risk grid developed by an expert consensus body within gastroenterology.Methods: We designed a secure web-based survey, compliant with information governance, which was targeted directly at patients in the UK with IBD and promoted via social media. Patient-entered data were directly compared to data held by their local IBD specialist teams.Findings: Between 1st April and 3rd August 2020, we received responses from 34,078 participants with IBD from 176 UK trusts or health boards. Overall, based on these data, 25·9% of participants met the consensus criteria defined for shielding and a further 46·5% were in a moderate risk category. We assessed intra-rater reliability in 1,442 participants using the tool twice or more; most items had almost perfect agreement (kappa >0·80).We validated the patient-entered data against hospital-entered data and medical records for 2,862 patient datasets from ten hospitals. Weighted kappa was 0·59 (95% confidence interval 0·56 - 0·62). After manual resolution of discrepancies, kappa was 0·89 (95% CI 0·87 - 0·91). Of 966 patients identified as requiring shielding in the final dataset for these ten centres, 51·0% had been missed by the hospital-entered data, largely because of incomplete or discrepant information on comorbidity and current disease activity.Interpretation: We have demonstrated that patient-generated data can facilitate rapid risk stratification with respect to COVID-19 and compensate for deficiencies in hospital data. We have validated these data across repeat entry and have demonstrated their reliability compared to pre-existing secondary care data. These findings have important implications for public health and chronic disease management.Funding Statement: Galapagos Biotech Ltd; Biogen GmbH; Tillotts Pharma UK Ltd; Amgen LtdDeclaration of Interests: Author Vida Cairnes has received honoraria for speaking from Falk, Pharmacosmos, Abbvie and Tillots. I have received support to attend conferences fromPharmacosmos, Ferring, Takeda, Falk and Abbvie. I am in receipt of education funding from Crohn’s & Colitis UK. All other authors have nothing to declare. Ethics Approval Statement: The IBD Registry was the Data Controller for the COVID-19 UK IBD Risk Tool. Information Governance (IG) included the DPIA, Privacy Notices, approved information/content development for patients plus Information Sharing Agreements for hospital teams in order to allow the data to be shared. The lawful basis for data collection was under the COPI Notice issued for COVID-19.10 Information was freely provided by participants.

Wearing masks and establishing COVID-19 areas reduces secondary attack risk in nursing homes (preprint)

Background: COVID-19 is spreading rapidly in nursing homes (NHs). It is urgent to evaluate the effect of infection prevention and control (IPC) measures to reduce COVID spreading. Methods: We analysed COVID-19 outbreaks in 12 NH using rRT-PCR for SARS-CoV2. We estimated secondary attack risks (SARs) and identified cofactors associated with the proportion of infected residents. Results: The SAR was below 5%, suggesting a high efficiency of IPC measures. Mask-wearing or establishment of COVID-19 zones for infected residents were associated with lower SAR. Conclusions: Wearing masks and isolating potentially infected residents appear to limit SARS-CoV-2 spread in nursing homes.

Epidemiological monitoring and control perspectives: application of a parsimonious modelling framework to the COVID-19 dynamics in France (preprint)

SARS-Cov-2 virus has spread over the world creating one of the fastest pandemics ever. The absence of immunity, asymptomatic transmission, and the relatively high level of virulence of the COVID-19 infection it causes led to a massive flow of patients in intensive care units (ICU). This unprecedented situation calls for rapid and accurate mathematical models to best inform public health policies. We develop an original parsimonious model that accounts for the effect of the age of infection on the natural history of the disease. Analysing the ongoing COVID-19 in France, we estimate the value of the key epidemiological parameters, such as the basic reproduction number (R0), and the efficiency of the national control strategy. We then use our deterministic model to explore several scenarios posterior to lock-down lifting and compare the efficiency of non pharmaceutical interventions (NPI) described in the literature.