Evidence map›Paper›PMID 35712272›Full record

SynthesisFrontiers in public health2022

A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions.

Surbhi Bhatia, Dhruvisha Bansal, Seema Patil, Sharnil Pandya, Qazi Mudassar Ilyas, Sajida Imran

Open access · goldAbstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
12.2field-weighted citation impact, top 1% of its field
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

37 citing papers in PubMed, 66 citations in OpenAlex.

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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

6 authors at 2 institutions in 2 countries.

Surbhi BhatiaDepartment of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia.
Dhruvisha BansalSymbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, India.
Seema PatilSymbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, India.
Sharnil PandyaSymbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, India.
Qazi Mudassar IlyasDepartment of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia.
Sajida ImranDepartment of Computer Engineering, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia.
King Faisal University · SASymbiosis International University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Climate change is unexpected weather patterns that can create an alarming situation. Due to climate change, various sectors are affected, and one of the sectors is healthcare. As a result of climate change, the geographic range of several vector-borne human infectious diseases will expand. Currently, dengue is taking its toll, and climate change is one of the key reasons contributing to the intensification of dengue disease transmission. The most important climatic factors linked to dengue transmission are temperature, rainfall, and relative humidity. The present study carries out a systematic literature review on the surveillance system to predict dengue outbreaks based on Machine Learning modeling techniques. The systematic literature review discusses the methodology and objectives, the number of studies carried out in different regions and periods, the association between climatic factors and the increase in positive dengue cases. This study also includes a detailed investigation of meteorological data, the dengue positive patient data, and the pre-processing techniques used for data cleaning. Furthermore, correlation techniques in several studies to determine the relationship between dengue incidence and meteorological parameters and machine learning models for predictive analysis are discussed. In the future direction for creating a dengue surveillance system, several research challenges and limitations of current work are discussed.

Indexed as

Climate ChangeDengueHumansIncidenceRetrospective StudiesWeatherclimatic factorsdenguemachine learningpredictive modelssurveillance system

Identifiers

PMID35712272
PMCPMC9197220
OpenAlexW4281712262

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.