Evidence map›Paper›PMID 34141462›Full record

ArticlePeerJ2021

Estimation of the probability of daily fluctuations of incidence of COVID-19 according to official data.

Andrey Gerasimov, Elena Galkina, Elena Danilova, Irina Ikonnikova, Tamara Novoselova, Yuriy L Orlov, Irina Senenycheva

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.1field-weighted citation impact, top 62% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Andrey GerasimovDepartment of Medical Informatics and Statistics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.ORCID 0000-0003-4549-7172
Elena GalkinaDepartment of Medical Informatics and Statistics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
Elena DanilovaDepartment of Medical Informatics and Statistics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
Irina IkonnikovaDepartment of Medical Informatics and Statistics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
Tamara NovoselovaDepartment of Medical Informatics and Statistics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
Yuriy L OrlovInstitute of Digital Medicine, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.ORCID 0000-0003-0587-1609
Irina SenenychevaDepartment of Medical Informatics and Statistics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
Sechenov University · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

When studying the dynamics of morbidity and mortality, one should not limit ourselves to analyzing general trends. Interesting information can be obtained from the analysis of deviations in morbidity and mortality from the general dynamics. Comparison of the cases of morbidity or death for adjacent time intervals allows us to find out whether the changes in conditions were for short periods of time and whether the cases of morbidity or death were independent. The article consists of two parts: Study of the probability distribution (CDF) of the difference between two independent observations of the Poisson distribution; Application of the results to analyze the morbidity and mortality trends by day for the new coronavirus infection. For the distribution function of the module of difference between two independent observations of the Poisson distribution, an analytical expression has been obtained that allows to get an exact solution. A program has been created, whose software can be downloaded at http://1mgmu.com/nau/DeltaPoisson/DeltaPoisson.zip. An approximate solution that does not require complex calculations has also been obtained, which can be used for an average of more than 20. If real difference is greater than expected, it may be in the following cases: morbidity or mortality varies considerably during the day. That could happen, for example, if the registered number of morbidity on Saturday and Sunday is less than on weekdays due to the management model of the health system, or if the cases are not independent; for example, due to the active identification of infected people among those who have come into contact with the patient. If the difference is less than expected, it may be due to external limiting factors, such as a shortage of test systems for making a diagnosis, a limited number of pathologists to determine the cause of death, and so on. In the analysis of the actual data for COVID-19 it was found that for Poland and Russia, excluding Moscow, the difference in the number of cases and deaths is greater than expected, while for Moscow-less than expected. This may be due to the information policy-the effort to somehow reassure Moscow's population, which in the spring of 2020 had a high incidence rate of the new coronavirus infection.

Indexed as

Analysis of daily morbidityAnalysis of morbidity dynamicsCOVID-19Expected distribution of incidenceMathematical methods of morbidity analysisRandom fluctuations in mortalityRandom fluctuations of incidence

Identifiers

PMID34141462
PMCPMC8183426
OpenAlexW3169864210

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.