ABSTRACT
Background: Coronavirus is one of the major pathogens of the human respiratory system and a major threat to the human health. Objectives: This modeling study aimed to project the epidemics trend of coronavirus disease 2019 (COVID-19) in Qom, Iran Methods: This study projected the COVID-19 outbreak in Qom using a modified susceptible-exposed-infectious-recovered (SEIR) compartmental model by the end of December 2020. The model was calibrated based on COVID-19 epidemic trend in Qom from 1 January to 11 July. The number of infected, hospitalized, and death cases were projected by 31 December. A Monte Carlo uncertainty analysis was applied to obtain 95% uncertainty interval (UI) around the estimates. Results: According to the results, the reduced contact rate and increased isolation rate were effective in reducing the size of the epidemic in all scenarios. By reducing the contact rate from eight to six, the number of new cases on the peak day, as well as the total number of cases admitted to the hospital by the end of the period (31 December), decreased. For example, in Scenario A, compared to Scenario E, with a decrease in contact rate from eight to six, the number of new cases on peak days decreased from 15,700 to 1,100. The largest decrease in the number of new cases on peak days was related to Scenario F with 270 cases. Also, the total number of cases decreased from 948,000 to 222,000 between the scenarios, and the largest decrease in this regard was related to Scenario F, with 188,000 cases. Conclusions: The parameters of contact rate and isolation rate can reduce the number of infected cases and prevent the outbreak, or at least delay the onset of the peak. This can help health policymakers and community leaders to upgrade their health care
ABSTRACT
Spreading COVID-19 pandemic has been considered as a global issue. Many international efforts including mathematical approaches have been recently discussed to control this disease more effectively. In this study, we have developed our previous SIUWR model and some transmission parameters are added. Accordingly, the basic reproduction number and elasticity coefficients are calculated at the equilibrium points. Then, some key critical model parameters are identified based on local sensitivities. In addition, the validation of the suggested model is checked by comparing some collected real data in Iraq and France from January 1st, 2021 to December 25th, 2021. Interestingly, there are good agreements between the model results and the real confirmed data using computational simulations in MATLAB. Results provide some biological interpretations and they can be used to control this pandemic more effectively. The model results will be used for both countries in minimizing the impact of this virus on their communities.
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Background: Public support plays a crucial in managing public health crises. Communicating with the public during a pandemic has a major role in gaining public support. Public information-seeking behaviors are the core element of epidemic communication.. Objectives: The current study aimed to investigate the Iranian information-seeking behaviors during the first three weeks of the COVID-19 pandemic in Iran. Methods: In this cross-sectional study, the use of web search queries to monitor the COVID-19 pandemic in Iran from December 2019 to March 1 2020, is investigated. The Iranian search queries for COVID-19 were evaluated from December 30, 2019, to March 1, 2020. Google trend reports were used to retrieve data on the number of search queries. Queries were categorized into “epidemic news”, “necessary protective equipment”, “prevention strategies”, and “treatment”. To analyze the data, segmented regression was applied. Also, the daily percent change (DPI) was estimated. Results: The frequency of Google searches for COVID19-related queries first increased during the period of 18-23 February 2020 (DPC: 34.0;P value < 0.001), and then declined to March 1, 2020 (DPS:-3.9;P value < 0.001). The most prevalent query was “Epidemic news” (54%), followed by “necessary equipment” (33%). The frequency of these two queries increased from 18-23 February. "Prevention strategies" was the most common search category on March 1. Conclusions: When an epidemic begins to spread, people try to get the “latest news” and “what they need to protect themselves”. © 2020, Author(s).
ABSTRACT
Context: Hepatic manifestations of Coronavirus Disease 2019 (COVID-19) are common among people living with Hepatitis B Virus (HBV) and Hepatitis C Virus (HCV). Objectives: This systematic review aimed to summarize the evidence on COVID-19 patients living with HBV or HCV co-infections. Data Sources: We searched multiple electronic databases and preprint servers from December 1, 2019, to August 9, 2020. Study Selection: Studies were included if they reported quantitative empirical data on COVID-19 patients living with HBV or HCV co-infections. Data Extraction: Descriptive analyses were reported, and data were synthesized narratively. The quality assessment was completed using the Joanna Briggs Institute critical appraisal tools. Results: Out of the 941 uniquely identified records, 27 studies were included. Of the eligible studies, 232 COVID-19 patients were living with HBV and 22 were living with HCV. Most patients were male, and the mean age was 49.8 and 62.8 years in patients living with HBV and HCV, respectively. Among the reported cases of SARS-CoV-2-HBV co-infection, the proportions of death were 4.7% and 15% in cross-sectional and case series/report studies, respectively. The death proportion was 8.3% among the reported cases of SARS-CoV2-HCV co-infection. Among COVID-19 patients, 34.1% and 76.2% reported at least one comorbidity besides HBV and HCV infections, mainly hypertension and type 2 diabetes mellitus. The most common COVID-19-related symptoms in both HBV and HCV groups were fever, cough, dyspnea, fatigue, and gastrointestinal symptoms. Conclusions: While understanding the pathogenesis of SARS-CoV-2 requires further investigations, the careful assessment of hepatic manifestations and chronic infections, such as HBV and HCV upon the admission of COVID-19 patients could help reduce multimorbidity among HBV or HCV patients and lead to more favorable health outcomes among them.