On forecasting the spread of the COVID-19 in Iran: The second wave

Ghanbari, Behzad

doi:10.1016/j.chaos.2020.110176

Cited by 104 publications

(71 citation statements)

References 15 publications

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“…We should beware of the second wave of COVID-19 ( Xu et al, 2020 ). One study predicted that the second wave of would be most severe than the first one ( Behzad G., 2020 ). Now, a second wave is creeping back in Europe.…”

Section: Discussionmentioning

confidence: 99%

Anxiety levels during a second local COVID-19 pandemic breakout among quarantined people: A cross sectional survey in China

Chen

Zhao

Razin

et al. 2021

Journal of Psychiatric Research

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Section: Discussionmentioning

confidence: 99%

Anxiety levels during a second local COVID-19 pandemic breakout among quarantined people: A cross sectional survey in China

Chen

Zhao

Razin

et al. 2021

Journal of Psychiatric Research

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“…Particularly, one important aspect of our tracking platform is its capability for dealing with real data resulting possibly from a second wave of Covid-19, as already pointed out by our warnings, discussed at the beginning of Section 3.4 . Moreover, with the eminence of a fast acceleration in new cases, as reported in European countries in the last months, very recent papers relying on SIR-based models have been presented in the literature (e.g., [ 52 , 53 , 54 , 55 ]). Therefore, following the aforementioned works, we make use of our SIRD + machine learning methodology for both purposes: (i) analyzing the historicity of the pandemic’s most recent past in Brazil, and (ii) supporting the state and federal government to implement immediate decisions in order to contain the advance of coronavirus in the country.…”

Section: Resultsmentioning

confidence: 99%

Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil

Amaral

Casaca

Oishi

et al. 2021

Sensors

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São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country’s fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model’s coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given.

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“…According to Karim Hemmati, the Iranian Red Crescent Society (IRCS) chief, nearly 3 million people from the 13 provinces involved in the COVID-19 pandemic, were traveling during Nowruz holidays, by 21 March [ 48 ]. Due to misconceptions about COVID-19, people were traveling in the country while Iranian authorities and the World Health Organization repeatedly urged them to stay home to prevent the spread of COVID-19 [ 49 ]. One noticeable message in this scenario is that people (citizens) are in charge of taking the necessary precautions into account—they are the ones who should actively follow the given guidelines [ 50 ].…”

Section: Country’s Overview and Resultsmentioning

confidence: 99%

Initial COVID-19 Outbreak: An Epidemiological and Socioeconomic Case Review of Iran

Blandenier

Habibi

Kousi

et al. 2020

IJERPH

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The coronavirus disease 2019 (COVID-19) pandemic has affected millions of people worldwide. It brought about the implementation of various measures and restrictions at a global level. Iran has been one of the countries with the highest rates of COVID-19 cases. This study reviews the initial outbreak of COVID-19 in Iran and examines the mitigation strategies adopted by the country. Moreover, it reports the socioeconomic challenges faced by the authorities during the efforts to contain the virus. A transdisciplinary literature review was carried out and a political measures timeline was constructed. A broad overview of the initial phase of the COVID-19 outbreak in Iran is presented, starting from the first confirmed case on 19 February 2020 until April 2020. The results of this epidemiological and socioeconomic case review of Iran suggests that the political measures undertaken by the Republic of Iran contributed to the decrease of the prevalence of COVID-19. However, due to the existing financial bottleneck, Iran has faced limited health system resources. Therefore, the response was not sufficient to restrict the spread and the efficient handling of the virus in the long-term.

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On forecasting the spread of the COVID-19 in Iran: The second wave

Cited by 104 publications

References 15 publications

Anxiety levels during a second local COVID-19 pandemic breakout among quarantined people: A cross sectional survey in China

Anxiety levels during a second local COVID-19 pandemic breakout among quarantined people: A cross sectional survey in China

Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil

Initial COVID-19 Outbreak: An Epidemiological and Socioeconomic Case Review of Iran

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