First Iranian COVID-19 cases were reported in Qom province on February 19, 49 days after identification of the first case in China . Since then, SARS-CoV-2 has spread in Iran's provinces rapidly.
In this study, we evaluate COVID-19 early phase epidemic characteristics in Alborz province in central part of Iran. Alborz province, also called small Iran, has the highest number of immigrants from all over the country and is the home to the most ethnically diverse populations in Iran. Karaj is the capital and most-populous city of this province. Eastern region of the city is the oldest part, which has the highest population density and largest household size, while wastern region is new urban districts which financial district of Karaj is located. Overall, socioeconomic situation of the people living in northern rigions are higher compared to citizens living in southern parts [14, 15]. It has been proven that lower socioeconomic situation is associated with higher risk of COVID-19 infection and death.
First case of COVID-19 was diagnosed on Febuary 19 in iran. So as to control the pandemic several national and local control measures have been taken in Alborz province. initially on February 23rd local government decided to close all schools and universities. Then on March 7th travel bans started followed by restrictions and limitations in offices’ worktime since March 9th. Eventually national holidays and nationwide restrictions began on March 19th and continued until april 4th.
The prediction of the maximum number of infected patients, and more importantly, the maximum number of patients who will require intensive care is challenging. These predictions are crucial in planning facilities in Alborz province and the readiness of their hospitals. To predict trends of the epidemic, we conducted a GIS and estimated R0 and doubling time.
This study revealed R0 of COVID-19 infection in three consecutive periods within the first month in Alborz, 2.40, 1.70, 1.54, respectively. This estimation approximates WHO findings of the COVID-19 epidemic in china on January 18 (1.95 95%CI, 1.4–2.5) . Liu et al. analyzed 12 publications that had been estimated R0, in the first month of the epidemic in China and demonstrated that R0 median and IQR are 2.79, and 1.16, respectively .
A recent study of 12 models of studies in Europe until April 7, 2020, reported the mean R0 value of COVID-19 about 3.28, with a median of 2.79 . Moreover in Italy reported R0 values were in the range of 2.7 to 3.10 during the epidemic phase of the disease .
Although R0 may be a biological reality, this value is usually calculable with complicated mathematical models developed using numerous sets of assumptions. The interpretation of R0 estimates derived from totally different models needs associate understanding of the models’ structures, inputs, and interactions. as a result of several researchers using R0 haven't been trained in refined mathematical techniques, R0 is well subject to misrepresentation, misinterpretation, and misapplication. The variations in R0 values is also attributed to different ways and models of R0 estimation. R0 can be misrepresented, misinterpreted, and misused in a variety of ways that distort the metric’s true meaning and value. Because of these various sources of misperception, R0 must be applied and discussed with cautiousness in research and practice [20,21,22,23]. Regarding contagiousness disease, R0 is dependent on the rate of contact of people in the community, probability of transmition in each contact and duration of infectiousness, so applying approaches to reduce social contacts plays an important role in reducing this index . The decrease in R0 value in this study upon time may be attributed to the enhancement of social distancing and self-quarantine policies. Specific conditions must be met for a valid estimation. These conditions are: a. complete detection of cases in the early days of the epidemic, b. calculation for a small timeline, and c. using an appropriate estimation method. R0 can be different according to the patterns of people’s contacts, structure of population and different subpopulations. In early stage of an epidemic, precisely estimating R0 is problematic, because the exact number of cases is not clear . Yang et al. showed that by a self-quarantine, R0 value declines from 3.77 to 3.00 . The physical distancing, quarantine, closing schools, and workplace distancing have been shown that were effective in decreasing R0 in Singapore . As the number of people who became probably immune during the study is a small percentage of Alborz population, its contribution to R0 change seems negligible.
An increased doubling time of cases was observed within a month in the early stages of the epidemic in Alborz province.
In the early phase in China, the Doubling time was about two days , while, Chinazzi et al. showed that the travel ban in china increased the doubling time of the COVID-19 epidemic about 5 days . Also, increasing of Doubling time by social distancing has been observed that suggests slowing down of epidemics of COVID-19 from January 20 to February 9, 2020, in China .
We can conclude from these findings that travel restrictions and social distancing increase the doubling time, causing an enhancement in healthcare systems' response to COVID-19 patients' requirements.
From March 10 to April 20, the mortality rate increased Alborz province from 8.33 to 12.9% that was higher than this index in the world and Iran in this period. This index has been ultimately affected by the number of sampled cases and considering that in the country and Alborz mostly, cases of hospitalized patients that are more severe are being tested, and the differences observed with the statistics of the whole world can be justified. However, in the case of a similar method and cases of testing, this index can show the quality of care, change in the behavior of the pathogen, or individual differences in response to the disease . A study suggested that the exact mortality rate of COVID-19 has not been yet determined. Incomplete data and differences in testing standards could affect this rate .
One of the most critical issues in public and environmental health in the COVID-19 epidemic is planning, monitoring, and evaluation of health programs. Geographical information systems (GIS) allows rapid response and provide information about the epidemic dynamics to control the outbreak [32, 33]. GIS technology was used to assess the entire process of the SARS-CoV-2 outbreak. Our findings demonstrate north to west and west to the east path of the epidemic, concluding that the incidence of COVID-19 is higher in dense areas of Karaj city, especially in eastern Karaj, which most of the population in the city are situated. While it took about two and a half years for MERS and four months for SARS to infect 1000 people, the novel SARS-CoV-2 infected about two-million people worldwide in 4 months. While SARS-CoV-2 spreads rapidly, information has to move even faster. This is where map-based dashboards become crucial. In this study, we introduce GIS as an essential and useful tool in tracking and fighting against the SARS-CoV-2 outbreak [34, 35].
We predict that before May 30, 146, new COVID-19 patients will be referred to our hospitals daily. We suggest that the health care system in Alborz province should provide requirements for hospitalization of 728 new COVID-19, 109 of those need intensive care. This prediction is highly dependent on the authorities and population compliance with social-distancing policies [36, 37]. Prior studies showed that social distancing policies could avert cases by 20% and hospitalizations and deaths by 90% .
There are two things we must consider these estimations. First, in theory, we can estimate how much percent of the population needs to be immune to create herd immunity based on R0. In the real world, estimations are more complicated. Populations usually don’t mix randomly and covid19 infected people may have imperfect immunity.
Second, the trend of changes in R0 and doubling times, and also the estimation of patients load in the future, is related to the type of prevention measures and how much people commit to them during periods used to calculate the indicators. So, change in measures and people's behaviors may alter assumptions and predictions. It seems that with better access to experimentation and an increase in the number of cases examined in terms of coronavirus, the numbers are becoming more realistic now because not all patients are tested, calculations are affected by error and bias.