Reproducibility of Research During COVID‐19: Examining the Case of Population Density and the Basic Reproductive Rate from the Perspective of Spatial Analysis

Páez, Antonio

doi:10.1111/gean.12307

Cited by 17 publications

(12 citation statements)

References 117 publications

(143 reference statements)

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“…Islam et al., 2021). For example, population density has been widely investigated, with diverging results (Paez, 2021); we chose urbanization to normalize our data because high urbanization has been shown to better explain high COVID‐19 incidence than population density (González‐Val & Sanz‐Gracia, 2021). Fourth, social vulnerability and mitigation measures may have a considerable influence on incidence (Kashem et al., 2021); particularly at the beginning of the pandemic, these measures were inconsistently applied across Europe and not well documented or easily obtained.…”

Section: Discussionmentioning

confidence: 99%

Lower COVID‐19 Incidence in Low‐Continentality West‐Coast Areas of Europe

2022

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In March 2020, the first known cases of the COVID-19 pandemic, caused by the respiratory virus SARS-CoV-2, occurred in European countries (JHUM, 2020;Ritchie et al., 2020). Since that time, scientists from many fields have been trying to understand conditions that lead some areas to have rampant COVID-19 outbreaks while other areas are spared. Identifying which areas have higher versus lower overall COVID-19 spread and unraveling factors that differ between them can help health care decision makers to better understand and prepare for future waves of the pandemic. In this study, we examine how two environmental factors, Köppen-Geiger climate zones and the Continentality Index, relate to total COVID-19 incidence between March 2020 and July 2021 in municipalities and counties in 5 European countries (Norway, Sweden, Germany, Italy, and Spain) while statistically controlling for crowding.

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Section: Discussionmentioning

confidence: 99%

Lower COVID‐19 Incidence in Low‐Continentality West‐Coast Areas of Europe

2022

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“…However, there remains concerns about reproducibility of COVID research, particularly where observational data are used to generate results (Bramstedt, 2020;Peng & Hicks, 2021). The current situation of irreproducible research may be that not much has changed during COVID (e.g., Gustot, 2020;Sumner et al, 2020;Paez, 2021).…”

Section: Research Reproducibilitymentioning

confidence: 99%

Reproducibility of Health Claims in Meta-Analysis Studies of COVID Quarantine (Stay-at-Home) Orders

Young¹,

Kindzierski²

2022

IJSP

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The coronavirus pandemic (COVID) has been an extraordinary test of modern government scientific procedures that inform and shape policy. Many governments implemented COVID quarantine (stay-at-home) orders on the notion that this nonpharmaceutical intervention would delay and flatten the epidemic peak and largely benefit public health outcomes. The overall research capacity response to COVID since late 2019 has been massive. Given a lack of research transparency, only a small fraction of published research has been judged by others to be reproducible before COVID. Independent evaluation of published meta-analysis on a common research question can be used to assess the reproducibility of a claim coming from that field of research. We used a p-value plotting statistical method to independently evaluate reproducibility of specific research claims made in four meta-analysis studies related to benefits/harms of COVID quarantine orders. Research claims that these meta-analyses covered included: mortality, mental health symptoms, incidence of domestic violence, and suicidal ideation (thoughts of killing yourself). Three of the four meta-analysis studies that we evaluated (mortality, mental health symptoms, incidence of domestic violence) raise further questions about benefits/harms of this form of intervention. The fourth meta-analysis study (suicidal ideation) is judged to be unreliable. Given lack of research transparency and irreproducibility of published research, independent evaluation of meta-analysis studies using p-value plotting is offered as a way to strengthen or refute (falsify) claims made in COVID research.

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“…Studies of spatial epidemiology and health geography have contributed significantly to knowledge on how the Covid-19 pandemic has spread and the factors that influence its uneven incidence and social and territorial risk. Among other aspects, the research developed in this field has improved the knowledge of spatiotemporal patterns of geographical propagation and the transmission routes of the pandemic (Franch-Pardo et al, 2021) and has made it possible to identify local critical points (Hu et al, 2020) and calculate the number of new infections per case (R number), as well as to assess the incidence of multiple socioeconomic, demographic, environmental, and infection risk factors (Elliott;Wartenberg, 2004;Kang et al, 2020;Xu;Kraemer, 2020;Kobayashi et al, 2021;Paez, 2021).…”

Section: Spatial Epidemiology and Health Geographymentioning

confidence: 99%

Open data on Covid-19 in the Spanish autonomous communities: reutilization in spatial epidemiology studies

Oliván

Utrilla

2022

EPI

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The Covid-19 pandemic has highlighted the need for governments and health administrations at all levels to have an open data registry that facilitates decision-making in the planning and management of health resources and provides information to citizens on the evolution of the epidemic. The concept of “open data” includes the possibility of reutilization by third parties. Space and time are basic dimensions used to structure and interpret the data of the variables that refer to the health status of the people themselves. Hence, the main objective of this study is to evaluate whether the autonomous communities’ data files regarding Covid-19 are reusable to analyze the evolution of the disease in basic spatial and temporal analysis units at the regional and national levels. To this end, open data files containing the number of diagnosed cases of Covid-19 distributed in basic health or administrative spatial units and temporal units were selected from the portals of the Spanish autonomous communities. The presence of infection-related, demographic, and temporal variables, as well as the download format and metadata, were mainly evaluated. Whether the structure of the files was homogeneous and adequate for the application of spatial analysis techniques was also analyzed. The results reveal a lack of standardization in the collection of data in both spatial and temporal units and an absence of, or ambiguity in, the meaning of the variables owing to a lack of metadata. An inadequate structure was also found in the files of seven autonomous communities, which would require subsequent processing of the data to enable their reuse and the application of analysis and spatial modeling techniques, both when carrying out global analyses and when comparing patterns of evolution between different regions.

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Reproducibility of Research During COVID‐19: Examining the Case of Population Density and the Basic Reproductive Rate from the Perspective of Spatial Analysis

Cited by 17 publications

References 117 publications

Lower COVID‐19 Incidence in Low‐Continentality West‐Coast Areas of Europe

Lower COVID‐19 Incidence in Low‐Continentality West‐Coast Areas of Europe

Reproducibility of Health Claims in Meta-Analysis Studies of COVID Quarantine (Stay-at-Home) Orders

Open data on Covid-19 in the Spanish autonomous communities: reutilization in spatial epidemiology studies

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