Rapid review of COVID-19 epidemic estimation studies for Iran.
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ABSTRACT: Background
To inform researchers about the methodology and results of epidemic estimation studies performed for COVID-19 epidemic in Iran, we aimed to perform a rapid review.Methods
We searched for and included published articles, preprint manuscripts and reports that estimated numbers of cumulative or daily deaths or cases of COVID-19 in Iran. We found 131 studies and included 29 of them.Results
The included studies provided outputs for a total of 84 study-model/scenario combinations. Sixteen studies used 3-4 compartmental disease models. At the end of month two of the epidemic (2020-04-19), the lowest (and highest) values of predictions were 1,777 (388,951) for cumulative deaths, 20,588 (2,310,161) for cumulative cases, and at the end of month four (2020-06-20), were 3,590 (1,819,392) for cumulative deaths, and 144,305 (4,266,964) for cumulative cases. Highest estimates of cumulative deaths (and cases) for latest date available in 2020 were 418,834 on 2020-12-19 (and 41,475,792 on 2020-12-31). Model estimates predict an ominous course of epidemic progress in Iran. Increase in percent population using masks from the current situation to 95% might prevent 26,790 additional deaths (95% confidence interval 19,925-35,208) by the end of year 2020.Conclusions
Meticulousness and degree of details reported for disease modeling and statistical methods used in the included studies varied widely. Greater heterogeneity was observed regarding the results of predicted outcomes. Consideration of minimum and preferred reporting items in epidemic estimation studies might better inform future revisions of the available models and new models to be developed. Not accounting for under-reporting drives the models' results misleading.
SUBMITTER: Pourmalek F
PROVIDER: S-EPMC7848865 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This paper studies the SEIRD epidemic model for COVID-19. First, I show that the model is poorly identified from the observed number of deaths and confirmed cases. There are many sets of parameters that are observationally equivalent in the short run but lead to markedly different long run forecasts. Second, I show that the basic reproduction number R0 can be identified from the data, conditional on epidemiologic parameters, and propose several nonlinear SUR approaches to estimate R0 . I examine the performance of these methods using Monte Carlo studies and demonstrate that they yield fairly accurate estimates of R0 . Next, I apply these methods to estimate R0 for the US, California, and Japan, and document heterogeneity in the value of R0 across regions. My estimation approach accounts for possible underreporting of the number of cases. I demonstrate that if one fails to take underreporting into account and estimates R0 from the reported cases data, the resulting estimate of R0 may be biased downward and the resulting forecasts may exaggerate the long run number of deaths. Finally, I discuss how auxiliary information from random tests can be used to calibrate the initial parameters of the model and narrow down the range of possible forecasts of the future number of deaths.
| S-EPMC7392128 | biostudies-literature
Project description:ObjectiveTo characterize the dynamics of the coronavirus disease 2019 (COVID-19) epidemic, for modeling purposes.MethodsData from Colombian official case information were collated for a period of 5 months. Dynamical parameters of the disease spread were then estimated from the data. Probability distribution models were identified, representing the time from symptom onset to hospitalization, to intensive care unit (ICU) admission, and to death. Kaplan-Meier estimates were also computed for the probability of eventually requiring hospitalization, needing ICU attention, and dying from the disease (the case fatality ratio).ResultsProbability distributions of the times and probabilities were computed for the population and for groups based on age and sex. The results showed that for the times that characterize the course of the disease for a given patient (time to hospitalization, ICU admission, or death), the variation from one age group to another was very small (around 10% of the fixed effect intercept) and the effect of sex was even smaller (around 1%). The course of the disease appeared to be very similar for all patients. On the other hand, the probability that a patient would advance from one stage of the disease to another (to hospitalization, ICU admission, or death) was heavily influenced by sex and age. The relative risk of death for male individuals was 1.7 times that of female individuals (based on 22 924 deaths).ConclusionsThe times from one stage of the disease to another were almost independent of the major patient variables (sex, age). This was in stark contrast to the probabilities of progressing from one stage to another, which showed a strong dependence on age and sex. Data also showed that the length of hospital and ICU stays were almost independent of sex and age. The only factor that affected this length was the eventual outcome of the disease (survival or death); the time was significantly longer for surviving patients.
| S-EPMC7846888 | biostudies-literature
Project description:The practicality of administrative measures for covid-19 prevention is crucially based on quantitative information on impacts of various covid-19 transmission influencing elements, including social distancing, contact tracing, medical facilities, vaccine inoculation, etc. A scientific approach of obtaining such quantitative information is based on epidemic models of SIR family. The fundamental SIR model consists of S-susceptible, I-infected, and R-recovered from infected compartmental populations. To obtain the desired quantitative information, these compartmental populations are estimated for varying metaphoric parametric values of various transmission influencing elements, as mentioned above. This paper introduces a new model, named SEIRRPV model, which, in addition to the S and I populations, consists of the E-exposed, Re -recovered from exposed, R-recovered from infected, P-passed away, and V-vaccinated populations. Availing of this additional information, the proposed SEIRRPV model helps in further strengthening the practicality of the administrative measures. The proposed SEIRRPV model is nonlinear and stochastic, requiring a nonlinear estimator to obtain the compartmental populations. This paper uses cubature Kalman filter (CKF) for the nonlinear estimation, which is known for providing an appreciably good accuracy at a fairly small computational demand. The proposed SEIRRPV model, for the first time, stochastically considers the exposed, infected, and vaccinated populations in a single model. The paper also analyzes the non-negativity, epidemic equilibrium, uniqueness, boundary condition, reproduction rate, sensitivity, and local and global stability in disease-free and endemic conditions for the proposed SEIRRPV model. Finally, the performance of the proposed SEIRRPV model is validated for real-data of covid-19 outbreak.
| S-EPMC9968492 | biostudies-literature
Project description:BackgroundPatients with COVID-19 may feel under pressure to participate in research during the pandemic. Safeguards to protect research participants include ethical guidelines [e.g. Declaration of Helsinki and good clinical practice (GCP)], legislation to protect participants' privacy, research ethics committees (RECs) and informed consent. The International Committee of Medical Journal Editors (ICMJE) advises researchers to document compliance with these safeguards. Adherence to publication guidelines has been suboptimal in other specialty fields. The aim of this rapid review was to determine whether COVID-19 human research publications report compliance with these ethical safeguards.MethodsA rapid systematic literature review was conducted in MEDLINE using the search term 'COVID-19'. The search was performed in April 2020 with no start date and repeated to include articles published in November 2020. Filters were 'Full free text available' and 'English Language'. Two reviewers assessed article title, abstracts and full texts. Non-COVID-19 articles and non-clinical studies were excluded. Independent reviewers conducted a second assessment of a random 20% of articles. The outcomes included reporting of compliance with the Declaration of Helsinki and GCP, REC approval, informed consent and participant privacy.ResultsThe searches yielded 1275 and 1942 articles of which 247 and 717 were deemed eligible, from the April search and November respectively. The majority of journals had editorial policies which purported to comply with ICMJE ethical standards. Reporting of compliance with ethical guidelines was low across all study types but was higher in the November search for case series and observational studies. Reporting of informed consent for case studies and observational studies was higher in the November search, but similar for case series. Overall, participant confidentiality was maintained but some case studies included a combination of details which would have enabled participant identification. Reporting of REC approval was higher in the November search for observational studies.ConclusionsWhile the majority of journal's editorial policies purported to support the ethical safeguards, many COVID-19 clinical research publications identified in this rapid review lacked documentation of these important safeguards for research participants. In order to promote public trust, ethical declarations should be included consistently.
| S-EPMC8237766 | biostudies-literature
Project description:BackgroundMortality statistics are fundamental to understand the magnitude of the COVID-19 pandemic. Due to limitation of real-time data availability, researchers had used mathematical models to estimate excess mortality globally during COVID-19 pandemic. As they demonstrated variations in scope, assumptions, estimations, and magnitude of the pandemic, and hence raised a controversy all over the world. This paper aims to review the mathematical models and their estimates of mortality due to COVID-19 in the Indian context.MethodsThe PRISMA and SWiM guidelines were followed to the best possible extent. A two-step search strategy was used to identify studies that estimated excess deaths from January 2020 to December 2021 on Medline, Google Scholar, MedRxiv and BioRxiv available until 0100 h, 16 May 2022 (IST). We selected 13 studies based on a predefined criteria and extracted data on a standardised, pre-piloted form by two investigators, independently. Any discordance was resolved through consensus with a senior investigator. Estimated excess mortality was analysed using statistical software and depicted using appropriate graphs.ResultsSignificant variations in scope, population, data sources, time period, and modelling strategies existed across studies along with a high risk of bias. Most of the models were based on Poisson regression. Predicted excess mortality by various models ranged from 1.1 to 9.5 million.ConclusionThe review presents a summary of all the estimates of excess deaths and is important to understand the different strategies used for estimation, and it highlights the importance of data availability, assumptions, and estimates.
| S-EPMC10105293 | biostudies-literature
Project description:IntroductionMany countries with an early outbreak of SARS-CoV-2 struggled to gauge the size and start date of the epidemic mainly due to limited testing capacities and a large proportion of undetected asymptomatic and mild infections. Iran was among the first countries with a major outbreak outside China.MethodsWe constructed a globally representative sample of 802 genomes, including 46 samples from patients inside or with a travel history to Iran. We then performed a phylogenetic analysis to identify clades related to samples from Iran and estimated the start of the epidemic and early doubling times in cases. We leveraged air travel data from 36 exported cases of COVID-19 to estimate the point-prevalence and the basic reproductive number across the country. We also analysed the province-level all-cause mortality data during winter and spring 2020 to estimate under-reporting of COVID-19-related deaths. Finally, we use this information in an SEIR model to reconstruct the early outbreak dynamics and assess the effectiveness of intervention measures in Iran.ResultsBy identifying the most basal clade that contained genomes from Iran, our phylogenetic analysis showed that the age of the root is placed on 2019-12-21 (95 % HPD: 2019-09-07 - 2020-02-14). This date coincides with our estimated epidemic start date on 2019-12-25 (95 %CI: 2019-12-11 - 2020-02-24) based air travel data from exported cases with an early doubling time of 4.0 (95 %CI: 1.4-6.7) days in cases. Our analysis of all-cause mortality showed 21.9 (95 % CI: 16.7-27.2) thousand excess deaths by the end of summer. Our model forecasted the second epidemic peak and suggested that by 2020-08-31 a total of 15.0 (95 %CI: 4.9-25.0) million individuals recovered from the disease across the country.ConclusionThese findings have profound implications for assessing the stage of the epidemic in Iran despite significant levels of under-reporting. Moreover, the results shed light on the dynamics of SARS-CoV-2 transmissions in Iran and central Asia. They also suggest that in the absence of border screening, there is a high risk of introduction from travellers from areas with active outbreaks. Finally, they show both that well-informed epidemic models are able to forecast episodes of resurgence following a relaxation of interventions, and that NPIs are key to controlling ongoing epidemics.
| S-EPMC8163697 | biostudies-literature
Project description:ObjectivesThis systematic review aims to summarize the mycological and clinical features of COVID-19-associated fungal infections (CAFIs) in Iran.MethodsPubMed, Web of Science, Scopus, Cochrane Library, SID, Magiran, IranDoc, and Google Scholar were searched for Persian and English articles published from January 1, 2020, to November 5, 2021, using a systematic search strategy. Studies on Iranian patients suffering from CAFIs were included in the review.ResultsTwenty-two studies comprising 169 patients were retrieved. Reported CAFIs included candidiasis (85, 50.30%), mucormycosis (35, 20.71%), aspergillosis (29, 17.16%), fusariosis (6, 3.55%), three cases caused by rare pathogens (Rhodotorula mucilaginosa, Diaporthe foeniculina, and Sarocladium kiliense) and 11 (6.51%) uncharacterized mold infections. The most common underlying diseases were diabetes (67/168, 39.88%), cardiovascular diseases (55/168, 32.74%), and hypertension (43/168, 25.59%). The use of antibiotics (111/124, 89.52%), corticosteroids (93/132, 70.44%), and mechanical ventilation (66, 51.16%) were the most common predisposing factors. Totally, 72 (50.35%) of 143 patients with CAFIs died (data were not available for 26 patients).ConclusionFungal infections are evident to be a complication of COVID-19 in Iran; thus, clinicians should consider them as a differential diagnosis, especially in patients with comorbidities and previous antibiotic or corticosteroid use.
| S-EPMC9273100 | biostudies-literature
Project description:Highlights • A predicting model for the long-term epidemic trend of COVID-19 by using LSTM with rolling update mechanism is proposed.• The 150-days ahead epidemic trend of COVID-19 in Russia, Peru and Iran are estimated by our proposed model.• The results provide that the epidemic of Peru will end in early December.• The number of daily cases in Russia and Iran is expected to fall below 2000 and 1000 by mid-November and early December.• By introducing diffusion index, the effectiveness of preventive measures taken by the government are analyzed. The COVID-19 outbreak in late December 2019 is still spreading rapidly in many countries and regions around the world. It is thus urgent to predict the development and spread of the epidemic. In this paper, we have developed a forecasting model of COVID-19 by using a deep learning method with rolling update mechanism based on the epidemical data provided by Johns Hopkins University. First, as traditional epidemical models use the accumulative confirmed cases for training, it can only predict a rising trend of the epidemic and cannot predict when the epidemic will decline or end, an improved model is built based on long short-term memory (LSTM) with daily confirmed cases training set. Second, considering the existing forecasting model based on LSTM can only predict the epidemic trend within the next 30 days accurately, the rolling update mechanism is embedded with LSTM for long-term projections. Third, by introducing Diffusion Index (DI), the effectiveness of preventive measures like social isolation and lockdown on the spread of COVID-19 is analyzed in our novel research. The trends of the epidemic in 150 days ahead are modeled for Russia, Peru and Iran, three countries on different continents. Under our estimation, the current epidemic in Peru is predicted to continue until November 2020. The number of positive cases per day in Iran is expected to fall below 1000 by mid-November, with a gradual downward trend expected after several smaller peaks from July to September, while there will still be more than 2000 increase by early December in Russia. Moreover, our study highlights the importance of preventive measures which have been taken by the government, which shows that the strict controlment can significantly reduce the spread of COVID-19.
| S-EPMC7437443 | biostudies-literature
Project description:BackgroundVarious mathematical models were published to predict the epidemiological consequences of the COVID-19 pandemic. This systematic review has studied the initial epidemiological models.MethodsArticles published from January to June 2020 were extracted from databases using search strings and those peer-reviewed with full text in English were included in the study. They were analysed as to whether they made definite predictions in terms of time and numbers, or contained only mathematical assumptions and open-ended predictions. Factors such as early vs. late prediction models, long-term vs. curve-fitting models and comparisons based on modelling techniques were analysed in detail.ResultsAmong 56,922 hits in 05 databases, screening yielded 434 abstracts, of which 72 articles were included. Predictive models comprised over 70% (51/72) of the articles, with susceptible, exposed, infectious and recovered (SEIR) being the commonest type (mean duration of prediction being 3 months). Common predictions were regarding cumulative cases (44/72, 61.1%), time to reach total numbers (41/72, 56.9%), peak numbers (22/72, 30.5%), time to peak (24/72, 33.3%), hospital utilisation (7/72, 9.7%) and effect of lockdown and NPIs (50/72, 69.4%). The commonest countries for which models were predicted were China followed by USA, South Korea, Japan and India. Models were published by various professionals including Engineers (12.5%), Mathematicians (9.7%), Epidemiologists (11.1%) and Physicians (9.7%) with a third (32.9%) being the result of collaborative efforts between two or more professions.ConclusionThere was a wide diversity in the type of models, duration of prediction and the variable that they predicted, with SEIR model being the commonest type.
| S-EPMC8313025 | biostudies-literature
Project description:Understanding the state of the COVID-19 pandemic relies on infection and mortality data. Yet official data may underestimate the actual cases due to limited symptoms and testing capacity. We offer a simulation-based approach which combines various sources of data to estimate the magnitude of outbreak. Early in the epidemic we applied the method to Iran's case, an epicenter of the pandemic in winter 2020. Estimates using data up to March 20th, 2020, point to 916,000 (90% UI: 508?K, 1.5 M) cumulative cases and 15,485 (90% UI: 8.4 K, 25.8 K) total deaths, numbers an order of magnitude higher than official statistics. Our projections suggest that absent strong sustaining of contact reductions the epidemic may resurface. We also use data and studies from the succeeding months to reflect on the quality of original estimates. Our proposed approach can be used for similar cases elsewhere to provide a more accurate, early, estimate of outbreak state. © 2020 System Dynamics Society.
| S-EPMC7361282 | biostudies-literature