ArticleGeoHealth2020
Understanding the Epidemic Course in Order to Improve Epidemic Forecasting.
Article in GeoHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Epidemiological and clinical characteristics of cancer patients with COVID-19: A systematic review and meta-analysis of global data.Cancer letters · 2021Pooled it
- Associations between nighttime light and COVID-19 incidence and mortality in the United States.International journal of applied earth observation and geoinformation : ITC journal · 2022Article
- Spatial technologies to strengthen traditional testing for SARS-CoV-2.Trends in microbiology · 2021Article
- Cross-disciplinary approaches to assist with nucleic acid testing for SARS-CoV-2.Applied microbiology and biotechnology · 2021Review
- New Approaches to Anticipate the Risk of Reverse Zoonosis.Trends in ecology & evolution · 2021Article
- Addressing Biodisaster X Threats With Artificial Intelligence and 6G Technologies: Literature Review and Critical Insights.Journal of medical Internet research · 2021Review
- Spatiobehavioral Characteristics - Defining the Epidemiology of New Contagious Diseases at the Earliest Moment Possible.Trends in parasitology · 2021Article
- An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China.Communications biology · 2021Article
- Pitfalls in Modeling Asymptomatic COVID-19 Infection.Frontiers in public health · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The epidemic course of the severe acute respiratory syndrome (SARS) has been differently divided according to its transmission pattern and the infection and mortality status. Unfortunately, such efforts for the coronavirus disease 2019 (COVID-19) have been lacking. Does every epidemic have a unique epidemic course? Can we coordinate two arbitrary courses into an integrated course, which could better reflect a common real-world progression pattern of the epidemics? To what degree can such arbitrary divisions help predict future trends of the COVID-19 pandemic and future epidemics? Spatial lifecourse epidemiology provides a new perspective to understand the course of epidemics, especially pandemics, and a new toolkit to predict the course of future epidemics on the basis of big data. In the present data-driven era, data should be integrated to inform us how the epidemic is transmitting at the present moment, how it will transmit at the next moment, and which interventions would be most cost-effective to curb the epidemic. Both national and international legislations are needed to facilitate the integration of relevant policies of data sharing and confidentiality protection into the current pandemic preparedness guidelines.
Identifiers
What OpenQuestion holds
Registered trials
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.