ABSTRACT
The aim of this study is to propose a modified Susceptible-Exposed-Infectious-Removed (SEIR) model that describes the time behaviour of symptomatic, asymptomatic and hospitalized patients in an epidemic, taking into account the effect of the demographic evolution. Unlike most of the recent studies where a constant ratio of new individuals is considered, we consider a more correct assumption that the growth ratio is proportional to the total population, following a Logistic law, as is usual in population growth studies for humans and animals. An exhaustive theoretical study is carried out and the basic reproduction number R0 is computed from the model equations. It is proved that if R0<1 then the disease-free manifold is globally asymptotically stable, that is, the epidemics remits. Global and local stability of the equilibrium points is also studied. Numerical simulations are used to show the agreement between numerical results and theoretical properties. The model is fitted to experimental data corresponding to the pandemic evolution of COVID-19 in the Republic of Cuba, showing a proper behaviour of infected cases which let us think that can provide a correct estimation of asymptomatic cases. In conclusion, the model seems to be an adequate tool for the study and control of infectious diseases.
ABSTRACT
Objective: Identifying the epidemiological characteristics of COVID-19 could help to control the pandemic. The aim of this study was to characterize the epidemiological features of hospitalized COVID-19 patients in Iran. Methods: Data were collected on patients admitted to a military referral hospital in Tehran, Iran, from February 8, 2020 to July 28, 2021. Sex, age, clinical symptoms, outcome, type of comorbidities, level of blood Spo2, time of admission, and time of discharge were investigated. Sex ratio, case fatality rate (CFR), and daily trends of hospital admissions and deaths were also determined. Descriptive statistics and multiple logistic regression with 95% confidence intervals were used for data analysis. The statistical significance level was set at 0.05. STATA16.0 and Excel 2010 were used for data analysis. Results: The median hospital length of stay (LOS) was 6 days. The following symptoms were most common: cough (63.5%), fever (50%), respiratory distress (46.1%), and muscular pain (40.8%). Hypertension (29.5%), diabetes (24.7%), and cardiovascular diseases (21.8%) were the most prevalent comorbidities. The CFR was calculated at 8.30%. Respiratory symptoms increased the odds of death by 45% (OR 1.45, 95% CI 1.03-2.06). Gastrointestinal symptoms were associated with a reduction in the mortality of COVID-19 cases, but this association was not statistically significant (OR 0.94, 95% CI 0.73-1.21). Conclusions: The results of this study emphasize higher mortality rates among older age groups, male patients, and patients with underlying diseases.