Evidence map›Paper›PMID 36945958›Full record

ArticleTurkish journal of medical sciences2023

COVID-19 modeling based on real geographic and population data.

Emir Baysazan, Ahmet Nihat Berker, Hasan Mandal, Hakan Kaygusuz

Abstract read
In one paragraph

Article in Turkish journal of medical sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

4 authors.

Emir BaysazanTEBIP High Performers Program, Council of Higher Education, İstanbul University, İstanbul, Turkey.
Ahmet Nihat BerkerFaculty of Engineering and Natural Sciences, Kadir Has University, İstanbul, Turkey; TÜBİTAK Research Institute for Fundamental Sciences, Kocaeli, Turkey; Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Hasan MandalThe Scientific and Technological Research Council of Türkiye (TÜBİTAK), Ankara, Turkey.
Hakan KaygusuzDepartment of Basic Sciences, Faculty of Engineering and Architecture, Altınbaş University, İstanbul, Turkey; SUNUM Nanotechnology Research Center, Sabancı University, İstanbul, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background: Intercity travel is one of the most important parameters for combating a pandemic. The ongoing COVID-19 pandemic has resulted in different computational studies involving intercity connections. In this study, the effects of intercity connections during an epidemic such as COVID-19 are evaluated using a new network model.

methodsThis model considers the actual geographic neighborhood and population density data. This new model is applied to actual Turkish data by means of provincial connections and populations. A Monte Carlo algorithm with a hybrid lattice model is applied to a lattice with 8802 data points.

resultsAround Monte Carlo step 70, the number of active cases in Türkiye reaches up to 8.0% of the total population, which is followed by a second wave at around Monte Carlo step 100. The number of active cases vanishes around Monte Carlo step 160. Starting with İstanbul, the epidemic quickly expands between steps 60 and 100. Simulation results fit the actual mortality data in Türkiye. DISCUSSION: This model is quantitatively very efficient in modeling real-world COVID-19 epidemic data based on populations and geographical intercity connections, by means of estimating the number of deaths, disease spread, and epidemic termination.

Indexed as

COVID-19AlgorithmsComputer SimulationHumansPandemicsSARS-CoV-2COVID-19epidemicgeographical modelMonte Carlo simulationsusceptible-infected-quarantine-recovered model

Identifiers

PMID36945958
PMCPMC10387910

What OpenQuestion holds

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LicenceCC BY
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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.