Evidence map›Paper›PMID 33594343›Full record

ArticleHeliyon2021

Identification of risk factors contributing to COVID-19 incidence rates in Bangladesh: A GIS-based spatial modeling approach.

Md Hamidur Rahman, Niaz Mahmud Zafri, Fajle Rabbi Ashik, Md Waliullah, Asif Khan

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Article in Heliyon, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 3 pooled it
–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

23 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.International journal of environmental research and public health · 2022
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  19. COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms.International journal of environmental research and public health · 2021
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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

5 authors.

Md Hamidur RahmanDepartment of Urban and Regional Planning, Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, Bangladesh.
Niaz Mahmud ZafriDepartment of Urban and Regional Planning, Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, Bangladesh.
Fajle Rabbi AshikDepartment of Urban and Regional Planning, Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, Bangladesh.
Md WaliullahDepartment of Urban and Regional Planning, Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, Bangladesh.
Asif KhanDepartment of Urban and Regional Planning, Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCOVID-19 pandemic outbreak is an unprecedented shock throughout the world, which has generated a massive social, human, and economic crisis. Identification of risk factors is crucial to prevent the COVID-19 spread by taking appropriate countermeasures effectively. Therefore, this study aimed to identify the potential risk factors contributing to the COVID-19 incidence rates at the district-level in Bangladesh.

methodSpatial regression methods were applied in this study to fulfill the aim. Data related to 28 demographic, economic, built environment, health, and facilities related factors were collected from secondary sources and analyzed to explain the spatial variability of this disease incidence. Three global (ordinary least squares (OLS), spatial lag model (SLM), and spatial error model (SEM)) and one local (geographically weighted regression (GWR)) regression models were developed in this study.

resultsThe results of the models identified four factors: percentage of the urban population, monthly consumption, number of health workers, and distance from the capital city, as significant risk factors affecting the COVID-19 incidence rates in Bangladesh. Among the four developed models, the GWR model performed the best in explaining the variation of COVID-19 incidence rates across Bangladesh, with an

conclusionFindings and discussions from this research offer a better insight into the COVID-19 situation, which helped discuss policy implications to negotiate the future epidemic crisis. The primary policy response would be to decentralize the urban population and economic activities from and around the capital city, Dhaka, to create self-sufficient regions throughout the country, especially in the north-western region.

Indexed as

Built environmentDemographyGeographically weighted regression (GWR)PandemicSpatial regression model (SRM)Spatial variation

Identifiers

PMID33594343
PMCPMC7874928

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LicenceCC BY-NC-ND
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Registered trials

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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.