Understanding epidemic data and statistics: A case study of COVID‐19

  1. Amirhoshang Hoseinpour Dehkordi
  2. Majid Alizadeh
  3. Pegah Derakhshan
  4. Peyman Babazadeh
  5. Arash Jahandideh

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Abstract

The 2019 novel‐coronavirus (COVID‐19) has affected 181 countries with approximately 1197405 confirmed cases (by 5th April). Understanding the transmission dynamics of the infection in each country which got affected on a daily basis and evaluating the effectiveness of control policies are critical for our further actions. To date, the statistics of COVID‐19 reported cases show that more than 80% of infected are mild cases of disease, around 14% of infected have severe complications, and about 5% are categorized as critical disease victims. Today's report (5th April 2020; daily updates in the prepared website) shows that the confirmed cases of COVID‐19 in the United States, Spain, Italy, and Germany are 308850, 126168, 124632, and 96092, respectively. Calculating the total case fatality rate (CFR) of Italy (4th April 2020), about 13.3% of confirmed cases have passed away. Compared with South Korea's rate of 1.8% (seven times lower than Italy) and China's 4% (69% lower than Italy), the CFR of Italy is too high. Some effective policies that yielded significant changes in the trend of cases were the lockdown policy in China, Italy, and Spain (the effect observed after some days), the shutdown of all nonessential companies in Hubei (the effect observed after 5 days), combined policy in South Korea, and reducing working hours in Iran.

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    SciScore for 10.1101/2020.03.15.20036418: (What is this?)

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    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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    Social distancing was one of the most effective policies to control past epidemic disease by limitation human to human transmission and reducing mortality and morbidity ([29], [30], [30]). However studies suggest that combination of multiple policies can boost the effectiveness. For instance, New York City Department of Health implement different policies during the influenza pandemic in 1918-19 at the same time and they have the lowest rate of mortality on the eastern seaboard of the US [31]. During COVID-19 outbreak, researchers predicted that the mass movement of people at the end of the Lunar New Year holiday, would increase the spreading of disease. Facing this concern, government of China implemented policies which was helpful in control the disease such as, extended the holiday so the holiday would long enough to shelter the incubation period of COVID-19, isolation of confirmed cases in hospitals, quarantining mild or asymptomatic persons in different hospitals, home-based quarantine of people from Hubei province (epicenter of the epidemic), and the most important one was to prevent individuals with asymptomatic infections from spreading the virus.([19], [22]) Iran is facing this concern as an important upcoming event in Iran is Nowruz which is the Iranian New Year, which recommended prompted policies from government.

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    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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  2. Version published to 10.1002/jmv.25885
  3. Version published to 10.1101/2020.03.15.20036418 on medRxiv