Mortality Rate and Case Fatality Rate of COVID-19 Around The World: An Adjusted Estimation (preprint)

Background: The present study is designed to predict the global adjusted values for mortality rate and case fatality rate of COVID-19 around the world. Methods: This research was conducted at the ecological level using data from 100 countries which were chosen randomly. The adjusted values were predicted using beta regression considering predictive factors such as total expenditure on health per capita, expenditure on health as a percentage of GDP, life expectancy and the percentage of the population aged over 65 years, hospital beds (per 1000 population), physicians (per 1000 population), nurses (per 1000 population), prevalence of smoking, prevalence of diabetes mellitus, and number of confirmed tests in each country. In the end, applying Monte Carlo simulation, the adjusted values of mortality rate and case fatality rate for the whole world were estimated.Results: The results of this study showed that two factors including percentage of population ages 65 and above (P=0.03) and Total expenditure on health as % of GDP (P = 0.04) had a statistically significant relationship with the case fatality rate. Moreover, there was a statistically significant relationship between the mortality rate and life expectancy (P = 0.02), total expenditure on health per capita (P < 0.001), nurses (Per 1000 Population) (P=0.04), and the prevalence of Diabetes Mellitus (P=0.04). The mortality rate and case fatality rate for the whole world were estimated to be 0.000001 and 0.026, respectively.Conclusion: It seems that what can cause global concern is not the case fatality rate of the disease, but its mortality rate, which is directly related to the health status of a community. The worse the health status of a community, the greater the number of infected people likely to be there, that ultimately increases the mortality rate of the disease in the community.

Modeling of the Weekly Variation of the Reported COVID-19 Cases as A Potential Indicator of the Surveillance System Accuracy (preprint)

Periodical daily variation in the number of reported COVID-19 cases within weeks is a common observation in global and national statistics. This variation may imply that the day of week has a significant role in the number of reported cases. We compared the pattern in some countries with an acceptable surveillance system. Data of 18 European and North American countries between 6 Mar and 8 Nov 2020 were extracts. Harmonic regression models were used to quantify the peak day, the absolute intensity and the average of coefficient of variation within weeks (ACVW) classified by country. In eight countries, the within week variation was statistically significant, the maximum and minimum number reported cases were in Saturday and Monday respectively, however, this pattern varied among countries. The maximum of ACVW was observed in Belgium and France, while it was minimum in Russia. The level of intensity of infection had a positive association with the ACVW (r = 0.54, p-value = 0.021). The observed variation and its pattern may show that the coverage or the tidiness of COVID-19 surveillance system fluctuates in different days of week. In addition, we suggest that the level of this fluctuation might be used as an accuracy indicator of the surveillance system.

Impact of non-pharmaceutical interventions on the control of COVID-19 in Iran: A mathematical modeling study (preprint)

Background During the first months of the COVID-19 pandemic, Iran reported high numbers of infections and deaths in the Middle East region. In the following months, the burden of this infection decreased significantly, possibly due to the impact of a package of interventions. We modeled the dynamics of COVID-19 infection in Iran to quantify the impacts of these interventions.Methods We used a modified susceptible–exposed–infected–recovered (SEIR) model to model the COVID-19 epidemic in Iran, from 21 January to 21 September 2020, using Markov chain Monte Carlo simulation to calculate 95% uncertainty intervals (UI). We used the model to assess the effectiveness of physical distancing measures and self-isolation under different scenarios. We also estimated the control reproductive number (Rc), using our mathematical model and epidemiologic data.Results If no non-pharmaceutical interventions (NPIs) were applied, there could have been a cumulative number of 51,800,000 (95% UI: 19,100,000–77,600,000) COVID-19 infections and 266,000 (95% UI: 119,000–476,000) deaths by September 21 2020. If physical distancing interventions, such as school/border closures and self-isolation interventions, had been introduced a week earlier than they were actually launched, a 30% reduction in the number of infections and deaths could have been achieved by September 21 2020. The observed daily number of deaths showed that the Rc was one or more than one almost every day during the analysis period.Conclusions Our models suggest that the NPIs implemented in Iran between 21 January and 21 September 2020 had significant effects on the spread of the COVID-19 epidemic. Therefore, we recommend that these interventions are considered when designing future control programs, while simultaneously considering innovative approaches that can minimize harmful economic impacts on the community and the state. Our study also showed that the timely implementation of NPIs showed a profound effect on further reductions in the numbers of infections and deaths. This highlights the importance of forecasting and early detection of future waves of infection and of the need for effective preparedness and response capabilities.

Rapid review of the existing COVID-19 Case Definitions: A Bayesian Approach model to COVID-19 Case Definitions: A Research Article (preprint)

Background Since December 2019, the novel coronavirus disease (COVID-19) has rapidly spread around the world leading to a pandemic with more than 3,000,000 infected people and more than 200,000 death. Several case definitions have been released and revised by countries and organizations. However, collectivization of case definitions has not been fully investigated.Methods In this study, we rapidly reviewed existing COVID-19 case definitions, finally a dynamic case definition algorithm was provided by using Bayesian theorem models of diagnosis.Results Our results showed categorization as suspected, probable, and confirmed cases, is used in majority of case definitions. Furthermore, the criteria for suspected cases and laboratory testing priority was a point of argument. Due to pandemic situation and resource limitation, diagnostic tests were rationed and mainly administered to a selected population, thus it was shown that the fraction of positive test results does not reflect the total infection rate of the population. Case definitions for COVID-19 is changing as we learn more about the disease. RT-PCR and CT Scan of lung seem to be beneficial in COVID-19 diagnosis and combing them with epidemiological criteria helps us in better understanding of the disease.Conclusion Based on our results, in the current case definitions, only symptomatic patients categorized and tested as a susceptible case. While the majority of COVID-19 cases are asymptomatic carriers of the disease, thus making the prevention more challenging. Dynamic statistical models can provide new insights into surveillance systems.

Estimating the number of COVID-19-related infections, deaths and hospitalizations in Iran under different physical distancing and isolation scenarios: A compartmental mathematical modeling (preprint)

Background: Iran is one of the countries that has been overwhelmed with COVID-19. We aimed to estimate the total number of COVID-19 related infections, deaths, and hospitalizations in Iran under different physical distancing and isolation scenarios. Methods: We developed a Susceptible-Exposed-Infected-Removed (SEIR) model, parameterized to the COVID-19 pandemic in Iran. We used the model to quantify the magnitude of the outbreak in Iran and assess the effectiveness of isolation and physical distancing under five different scenarios (A: 0% isolation, through E: 40% isolation of all infected cases). We used Monte-Carlo simulation to calculate the 95% uncertainty intervals (UI). Findings: Under scenario A, we estimated 5,196,000 (UI 1,753,000 - 10,220,000) infections to happen till mid-June with 966,000 (UI 467,800 - 1,702,000) hospitalizations and 111,000 (UI 53,400 - 200,000) deaths. Successful implantation of scenario E would reduce the number of infections by 90% (i.e. 550,000) and change the epidemic peak from 66,000 on June 9th to 9,400 on March 1st. Scenario E also reduces the hospitalizations by 92% (i.e. 74,500), and deaths by 93% (i.e. 7,800). Interpretation: With no approved vaccination or therapy, we found physical distancing and isolation that includes public awareness and case-finding/isolation of 40% of infected people can reduce the burden of COVID-19 in Iran by 90% by mid-June.