ABSTRACT
The emergence of the novel coronavirus (COVID-19) generated a need to quickly and accurately assemble up-to-date information related to its spread. In this research article, we propose two methods in which Twitter is useful when modelling the spread of COVID-19: (1) machine learning algorithms trained in English, Spanish, German, Portuguese and Italian are used to identify symptomatic individuals derived from Twitter. Using the geo-location attached to each tweet, we map users to a geographic location to produce a time-series of potential symptomatic individuals. We calibrate an extended SEIRD epidemiological model with combinations of low-latency data feeds, including the symptomatic tweets, with death data and infer the parameters of the model. We then evaluate the usefulness of the data feeds when making predictions of daily deaths in 50 US States, 16 Latin American countries, 2 European countries and 7 NHS (National Health Service) regions in the UK. We show that using symptomatic tweets can result in a 6% and 17% increase in mean squared error accuracy, on average, when predicting COVID-19 deaths in US States and the rest of the world, respectively, compared to using solely death data. (2) Origin/destination (O/D) matrices, for movements between seven NHS regions, are constructed by determining when a user has tweeted twice in a 24 h period in two different locations. We show that increasing and decreasing a social connectivity parameter within an SIR model affects the rate of spread of a disease. © 2023 by the authors.
ABSTRACT
The benefits of m-RNA vaccines in immunosuppressed patients receiving anti-CD20 monoclonal antibodies such as patients with B-line haematological malignancies or multiple sclerosis (MS) are poor investigated. Several studies demonstrated that anti-CD20 therapies were associated with a reduction/absence of the humoral response but only few data are available on T-cell immunity. In our study, we evaluated the antibodies levels and the T-cellular response of 70 immunosuppressed patients receiving anti-CD20 monoclonal antibodies (45 haematological and 25 MS patients), after the administration of the third dose of BNT162b2 vaccine. We also enrolled 10 healthy individuals, as controls. Anti-CD20 therapies significantly reduced the vaccineinduced antibodies targeting the spike protein (anti-S antibodies) in most patients (both haematological and MS patients). When they were stratified based on time elapsed between therapy infusions and vaccination, the median of anti-S antibody levels showed significant differences: patients vaccinated during the treatment were seronegative;patients who began the therapy after one, two or three doses of vaccine generated increasing antibody titers (109 BAU/mL;484 BAU/mL;2532 BAU/mL, respectively);patients who started vaccination 6 months or more after the suspension of the therapy presented good antibody levels (9173 BAU/mL), slightly lower than those of controls (11914 BAU/mL). The magnitude of the T-cell response after vaccination was determined by an interferon (IFN)-gamma enzyme-linked immune absorbent spot (ELISPOT) analysis, stimulating peripheral blood mononuclear cells (PBMC) of patients and controls with overlapping peptide pools of the spike protein. The vaccination induced T cell immunity was partially preserved in patients receiving anti-CD20 monoclonal antibodies, even in those without detectable anti-S antibodies. There were important differences between haematological and MS patients: 97% of MS patients developed a good T-cell response after vaccination (with a median value of 96 spots forming units (SFU) per million of PBMC). Conversely, only 59% of haematological patients, treated in association with other cytostatic drugs, produced a protective T-cell response (with a median value of 40 SFU per million of PBMC).