Immune System Diseases

We considered a simple SEIR epidemic model for the simulation of the infectious-disease spread in the population under study, in which no births, deaths or introduction of new individuals occurred. Individuals were each assigned to one of the following disease states: Susceptible (S), Exposed (E), Infectious (I) or Recovered (R).
The model is individual-based and stochastic. Susceptible individuals may contract the disease with a given rate when in contact with an infectious individual, and enter the exposed disease state when they become infected but are not yet infectious themselves. These exposed individuals become infectious at a rate σ, with σ^-1 representing the mean latent period of the disease. Infectious individuals can transmit the disease during their infectious period, whose mean duration is equal to v^-1. After this period, they enter the recovered phase, acquiring permanent immunity to the disease.
To compare simulation results obtained from the three different networks, we needed to adequately define the rate of infection for a given infectious-susceptible pair, depending on the definition of the networks themselves. β was defined as the constant rate of infection from an infected individual to one of their susceptible contacts on the unitary time step dt of the process. Given two people, an infectious individual A and a susceptible individual B, who are in contact during the unitary time step, the probability of B becoming infected during this period was given by βdt. To obtain the same mean infection probability in the HET and HOM networks over an entire 24-hour period (day and night), the weights on such networks needed to be rescaled by W_AB/ΔT, defined as the ratio between the total sum of the duration of all contacts between A and B in a day, and the effective duration of the day (that is, the total time during which the links in the daily networks were considered active, discarding the 'nights'). Therefore, the probability of infection between A and B during the time step dt was βW_AB dt/ΔT for the HET network, and β<W> dt/ΔT for the HOM network (with being the mean weight of the links in the HET network).
We considered two different disease scenarios for the simulations of disease spread on all networks under study. In particular, the following values were assumed for the duration of the mean latency period (σ^-1), mean infectious period (v^-1) and transmission rate (β): (i) σ^-1 = 1 days, v^-1 = 2 days and β = 3.10^-4/s (very short incubation and infectious periods); and (ii) σ^-1= 2 days, v^-1 = 4 days and β = 15.10^-5/s (short incubation and infectious periods). These sets of parameter values were chosen to maintain the same value of β/v, which is the biologic factor responsible for the rate of increase of cases during the epidemic outbreak, while changing the global timescales of incubation and infectious periods, and assessing the role played by the social factors embedded in the contact patterns. Short incubation and infectious periods were used so as to minimize the consequences of the arbitrariness in the construction procedures of long datasets as described above. Each simulation started with a single randomly chosen infectious individual, with the rest of the population being in the susceptible state.

We calculated rates of VTE based on cohort analysis. For each broad age group of people with rheumatoid arthritis, and for VTE, we took "date of entry" into each cohort as the date of first admission for rheumatoid arthritis, or reference condition, and "date of exit" as the date of first record of VTE, death, or the end of the data file (31 December 1998 for ORLS1; 31 March 2008 for ORLS2 and English HES), whichever was the earliest. In the ORLS cohorts, in comparing the rheumatoid arthritis cohort with the reference cohort, we first calculated rates for VTE, stratified and then standardised by age (in five-year age groups), sex, calendar year of first recorded admission, and district of residence, to ensure that the results of group comparisons were equivalent in these respects. We used a similar approach to standardisation in the England dataset, stratifying by age (in five-year age groups), sex, calendar year of first recorded admission, region of residence, and quintile of patients' Index of Deprivation score (as a measure of socio-economic status). We used the indirect method of standardisation, using the combined rheumatoid arthritis and reference cohorts as the standard population. We applied the stratum-specific rates in the combined rheumatoid arthritis and reference cohorts to the number of people in each stratum in the rheumatoid arthritis cohort, separately, and then to those in the reference cohort. We calculated the ratio of the standardised rate of occurrence of VTE in the exposure cohorts relative to that in the reference cohort. The confidence interval for the rate ratio and χ² statistics for its significance were calculated as described elsewhere [13 ]. Calculated in this way, the rate ratio provides a measure of relative risk of VTE in the rheumatoid arthritis cohort, compared with the reference cohort. The fact that there is unmeasured migration in the populations covered by the study, and the use of an internal reference cohort for comparison with the VTE rates in the rheumatoid arthritis cohort (and others), preclude meaningful calculation of absolute risks.
We analysed the occurrence of an admission for VTE within 90 days of the admission for each immune-related disease, and at 91 days and more, to help establish whether any elevated risk of VTE was confined to the short-term after the episode of hospitalisation or was more prolonged.
In the analysis of diabetes mellitus, we used hospital admission for diabetes mellitus when aged under 30 as a proxy for type 1 diabetes, as the type of diabetes is not well recorded in routine hospital statistics. We analysed the data for males and females separately, as well as together, to ascertain whether or not there were differences between them in risk of VTE.

We carried out a prospective study at the Peking Union Medical College Hospital (PUMCH) in Beijing, China, between July 2019 and December 2019.
In accordance with the patients’ presentations and the outcomes of auxiliary tests, patients underwent normal diagnostic workups. Including criteria for allergy patients (Simon, 2019 (link)): diagnosed with allergy diseases by clinical doctors, including allergic rhinitis, asthma, urticaria, atopic dermatitis, cough, atopic conjunctivitis, eczema, or a history of severe anaphylactic reaction (Eigenmann, 2005 (link)); positive serum specific IgE, positive skin prick test or intradermal test. Excluding criteria for allergy patients (Simon, 2019 (link)): patients with serious other diseases, such as diabetes, liver disease, kidney disease, etc., (Eigenmann, 2005 (link)); Immunocompromised patients.
Including criteria for healthy participants (Simon, 2019 (link)): No symptoms of any allergic diseases, including allergic rhinitis, allergic asthma, atopic dermatitis, allergic conjunctivitis, etc., (Eigenmann, 2005 (link)) No history of allergic diseases, family history (Han et al., 2020 (link)). No other immune system diseases (Bønnelykke et al., 2015 (link)). No organic disease (Tamari and Hirota, 2014 (link)). Voluntary acceptance of disease-related questionnaires (Haider et al., 2022 (link)). No participation in any drug clinical trials within 3 months. Excluding criteria for healthy participants (Simon, 2019 (link)): history of allergic diseases or chronic medical conditions associated with allergy diseases in this study (Eigenmann, 2005 (link)); history of significant allergen exposure (Han et al., 2020 (link)); patients with serious other diseases, such as diabetes, liver disease, kidney disease, etc., (Bønnelykke et al., 2015 (link)); Immunocompromised patients.

According to the unique characteristics of the spread of infectious diseases, the precise allocation of emergency supplies during an epidemic can be summarized as follows: (1) During the early stages of an epidemic, it is necessary to quantify the extent of the emergency in epidemic areas. The epidemic locations are diverse, and each area has a unique crisis circumstance. (2) Because supplies demand are highly correlated with the number of confirmed infections, it is vital to forecast the demand in the epidemic area based on the number of infected people.
Based on the characteristics of the initial spread of infectious disease, hence the SEIHR warehouse models are created. We categorize the affected area's population into six subgroups: susceptible (S), exposed (E), infective (I) who have developed symptoms following the incubation period but have not been hospitalized or isolated, hospitalized infected (H), and recovered (R). N = S + E + I + H + R is used to calculate the total population in each epidemic area. Figure 1 shows the transfer of this model between these epidemic classes. Table 1 shows the definitions of the time-varying forecasting of the medical resources model.
The susceptible population (S) reduce as a result of exposure to exposed, infected, and asymptomatic infected people, and they will enter the incubation period of exposure (moving to class E). As shown in formula 1.
The exposed population (E) become infected (moving to class I) with evident symptoms. As shown in formula 2.
The infected population (I) enter the inpatient stage (moving to class H) according to the local medical treatment capability. As shown in formula 3.
After treatment, the hospitalized population (H) reduces and enter the recovery population (moving to class R). As shown in formula 4.
The recovery population is immune to this disease.