Estimating the number of COVID-19-related infections, deaths and hospitalizations in Iran under different physical distancing and isolation scenarios: A compartmental mathematical modeling
Bibliographic record
Abstract
Abstract 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 9 th to 9,400 on March 1 st . 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. Funding We received no funding for this work. Research in context Evidence before this study Iran has been heavily impacted by the COVID-19 outbreak, and the virus has now spread to all of its provinces. Iran has been implementing different levels of partial physical distancing and isolation policies in the past few months. We searched PubMed and preprint archives for articles published up to April 15, 2020 that included information about control measures against COVID-19 in Iran using the following terms: (“coronavirus” OR “2019-nCoV” OR “COVID-19”) AND “Iran” AND (“intervention” OR “prevention” OR “physical distancing” OR “social distancing”). We found no studies that had quantified the impact of policies in Iran. Added value of this study Given the scarcity of evidence on the magnitude of the outbreak and the burden of COVID-19 in Iran, we used multiple sources of data to estimate the number of COVID-19 infections, hospitalizations, and deaths under different physical distancing and isolation scenarios until mid-June. We showed that implementing no control measures could lead to over five million infections in Iran; ∼19% of whom would be hospitalized, and ∼2% would die. However, under our most optimistic scenario, these estimates could be reduced by ∼90%. Implications of all the available evidence With no effective vaccination or treatment, advocating and enforcing physical distancing and isolation along with public education on prevention measures could significantly reduce the burden of COVID-19 in Iran. Nonetheless, even under the most optimistic scenario, the burden of COVID-19 would be substantial and well beyond the current capacity of the healthcare system in Iran.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".