ArticleClinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases2020
A prospective prediction tool for understanding Crimean-Congo haemorrhagic fever dynamics in Turkey.
Article in Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled 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.
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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review on Crimean-Congo Hemorrhagic Fever Enzootic Cycle and Factors Favoring Virus Transmission: Special Focus on France, an Apparently Free-Disease Area in Europe.Frontiers in veterinary science · 2022Pooled it
- Co-Endemic Zoonoses, Divergent Dynamics: A Comparative Spatiotemporal Analysis of Crimean-Congo Hemorrhagic Fever and Brucellosis with Implications for Integrated Surveillance.Tropical medicine and infectious disease · 2026Article
- Looking Beyond the Lens of Crimean-Congo Hemorrhagic Fever in Africa.Emerging infectious diseases · 2024Review
- Vascular dysfunction in hemorrhagic viral fevers: opportunities for organotypic modeling.Biofabrication · 2024Review
- Replicon particle vaccination induces non-neutralizing anti-nucleoprotein antibody-mediated control of Crimean-Congo hemorrhagic fever virus.NPJ vaccines · 2024Article
- Quantifying the influence of climate, host and change in land-use patterns on occurrence of Crimean Congo Hemorrhagic Fever (CCHF) and development of spatial risk map for India.One health (Amsterdam, Netherlands) · 2023Article
- Article
- Determinants of Crimean-Congo haemorrhagic fever virus exposure dynamics in Mediterranean environments.Transboundary and emerging diseases · 2022Article
- Artificial Intelligence Models for Zoonotic Pathogens: A Survey.Microorganisms · 2022Review
- Mapping the viruses belonging to the order Bunyavirales in China.Infectious diseases of poverty · 2022Article
- Article
- Development of a protective inactivated vaccine against Crimean-Congo hemorrhagic fever infection.Heliyon · 2021Article
- Scoping future outbreaks: a scoping review on the outbreak prediction of the WHO Blueprint list of priority diseases.BMJ global health · 2021Article
- Determination of Seroprevalence and Risk Factors of Crimean-Congo Haemorrhagic Fever (CCHF) in the Endemic Region in Turkey: A Population-Based Cross-Sectional Study.Journal of tropical medicine · 2021Article
- The effect of climate variables on the incidence of Crimean Congo Hemorrhagic Fever (CCHF) in Zahedan, Iran.BMC public health · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWe aimed to develop a prospective prediction tool on Crimean-Congo haemorrhagic fever (CCHF) to identify geographic regions at risk. The tool could support public health decision-makers in implementation of an effective control strategy in a timely manner.
methodsWe used monthly surveillance data between 2004 and 2015 to predict case counts between 2016 and 2017 prospectively. The Turkish nationwide surveillance data set collected by the Ministry of Health contained 10 411 confirmed CCHF cases. We collected potential explanatory covariates about climate, land use, and animal and human populations at risk to capture spatiotemporal transmission dynamics. We developed a structured Gaussian process algorithm and prospectively tested this tool predicting the future year's cases given past years' cases.
resultsWe predicted the annual cases in 2016 and 2017 as 438 and 341, whereas the observed cases were 432 and 343, respectively. Pearson's correlation coefficient and normalized root mean squared error values for 2016 and 2017 predictions were (0.83; 0.58) and (0.87; 0.52), respectively. The most important covariates were found to be the number of settlements with fewer than 25 000 inhabitants, latitude, longitude and potential evapotranspiration (evaporation and transpiration).
conclusionsMain driving factors of CCHF dynamics were human population at risk in rural areas, geographical dependency and climate effect on ticks. Our model was able to prospectively predict the numbers of CCHF cases. Our proof-of-concept study also provided insight for understanding possible mechanisms of infectious diseases and found important directions for practice and policy to combat against emerging infectious diseases.
Indexed as
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.