Role of genetic heterogeneity in determining the epidemiological severity of H1N1 influenza

Genetic differences contribute to variations in the immune response mounted by different individuals to a pathogen. Such differential response can influence the spread of infectious disease, indicating why such diseases impact some populations more than others. Here, we study the impact of population-level genetic heterogeneity on the epidemic spread of different strains of H1N1 influenza. For a population with known HLA class-I allele frequency and for a given H1N1 viral strain, we classify individuals into sub-populations according to their level of susceptibility to infection. Our core hypothesis is that the susceptibility of a given individual to a disease such as H1N1 influenza is inversely proportional to the number of high affinity viral epitopes the individual can present. This number can be extracted from the HLA genetic profile of the individual. We use ethnicity-specific HLA class-I allele frequency data, together with genome sequences of various H1N1 viral strains, to obtain susceptibility sub-populations for 61 ethnicities and 81 viral strains isolated in 2009, as well as 85 strains isolated in other years. We incorporate these data into a multi-compartment SIR model to analyse the epidemic dynamics for these (ethnicity, viral strain) epidemic pairs. Our results show that HLA allele profiles which lead to a large spread in individual susceptibility values can act as a protective barrier against the spread of influenza. We predict that populations skewed such that a small number of highly susceptible individuals coexist with a large number of less susceptible ones, should exhibit smaller outbreaks than populations with the same average susceptibility but distributed more uniformly across individuals. Our model tracks some well-known qualitative trends of influenza spread worldwide, suggesting that HLA genetic diversity plays a crucial role in determining the spreading potential of different influenza viral strains across populations

Demographic and Clinical Presentation of Hospitalised Patients with SARS-CoV-2 During the First Omicron Wave

Introduction: The objectives of this retrospective study were to describe clinical presentations and mortality outcomes of hospitalised patients with the COVID-19 Omicron variant within two acute district general hospitals, and to evaluate demographic factors associated with these presentations and mortality. Methods: Data was obtained over a month in 2021–2022 from multi-ethnic patients who were hospitalised and detected to have severe acute respiratory syndrome coronavirus 2 Omicron infection. Details included socio-demographic characteristics, vaccination, and mortality. Patients were subdivided into three groups: Group 1 were admitted with true COVID-19 pneumonitis, Group 2 had incidental COVID-19 on admission screening, and Group 3 were negative on admission but developed COVID-19 over 7 days post-admission. Results: Of 553 patients, only 24.1% (133/553) were in Group 1, 58.2% (322/553) in Group 2, and 17.7% (98/553) in Group 3. Patients in Group 1 and Group 3 were significantly older than those in Group 2 (p less than 0.001). Thirty percent of patients from Black, Asian, and minority ethnic backgrounds had COVID-19 pneumonitis compared with 19% of those with White ethnicity (p=0.002). Twenty percent of patients were admitted within nonmedical specialties, i.e., surgical specialties, paediatrics, and obstetrics. Of 36 requiring critical care, 21 were in Group 1. Of those patients, 20/21 (95%) were unvaccinated and seven of the 21 who died were all unvaccinated (100%). Common COVID-19 presentations included delirium, falls, seizures, chronic obstructive pulmonary disease, and antenatal problems. Overall, 13.7% (76/553) patients died and 4.7% (26/553) were directly attributable to COVID-19. Conclusions: This large, multi-ethnic study has described clinical presentations and mortality of hospitalised patients with Omicron. It has determined socio-demographic factors associated with these presentations, including ethnicity and vaccination rates. The study provides useful information for future COVID-19 studies examining outcomes and presentations of Omicron and future COVID-19 variants

Fitting the SIR Epidemiological Model to Influenza Data

This project sought to provide thorough instructions to fitting the SIR epidemiological model to influenza data and defend its use in this context. Directions for coding the SIR model in the R programming language are detailed. This includes estimating parameter values, such as infection and recovery rate, and how to double check these values. This project also included analysis of problems that can arise when fitting this model. This includes accounting for vaccination rate and issues with the nature of this type of data. Either these problems were explored, and solutions were provided, or suggestions were provided for future research