Spatial big data analytics of influenza epidemic in Vellore, India

Lopez, Daphne; Gunasekaran, M.; Murugan, B. S.; Kaur, Harpreet; Abbas, Kaja

doi:10.1109/bigdata.2014.7004422

Cited by 79 publications

(29 citation statements)

References 18 publications

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“…A field study indicated that an increased risk of influenza in horses was associated with Wv of >30 km/h (Firestone et al, 2012). In India, Wv was positively correlated to an increase risk for influenza (H1N1) (linear regression coefficient: 1.02, p < 0.05) (Lopez et al, 2014). This may due to the effects of high wind speed on the longer travel of air-borne aerosols, which contributed to the transmission of influenza virus (Ssematimba et al, 2012).…”

Section: N=100%mentioning

confidence: 99%

The complex associations of climate variability with seasonal influenza A and B virus transmission in subtropical Shanghai, China

Zhang

et al. 2020

Science of The Total Environment

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h i g h l i g h t sHigh temperature only associated with influenza A occurrence. High diurnal temperature range (DTR) causes more influenza A cases. Low DTR causes more influenza B cases. High DTR and low temperature were the key drivers for influenza A and B separately. g r a p h i c a l a b s t r a c t X-axis: The value of climate variable; Y-axis: 2-weeks lagged cumulative relative risk (RR), indicating the number of times more likely to have influenza compared to reference value (Ref); Solid line: RR value; Grey shadow: 95% confidence interval (95% CI). a b s t r a c tMost previous studies focused on the association between climate variables and seasonal influenza activity in tropical or temperate zones, little is known about the associations in different influenza types in subtropical China. The study aimed to explore the associations of multiple climate variables with influenza A (Flu-A) and B virus (Flu-B) transmissions in Shanghai, China. Weekly influenza virus and climate data (mean temperature (MeanT), diurnal temperature range (DTR), relative humidity (RH) and wind velocity (Wv)) were collected between June 2012 and December 2018. Generalized linear models (GLMs), distributed lag non-linear models (DLNMs) and regression tree models were developed to assess such associations. MeanT exerted the peaking risk of Flu-A at 1.4°C (2-weeks' cumulative relative risk (RR): 14.88, 95% confidence interval (CI): 8.67-23.31) and 25.8°C (RR: 12.21, 95%CI: 6.64-19.83), Flu-B had the peak at 1. 4°C (RR: 26.44,). The highest RR of Flu-A was 23.05 (95%CI: 5.12-88.45) at DTR of 15.8°C, that of Flu-B was 38.25 (95%CI: 15.82-87.61) at 3.2°C. RH of 51.5% had the highest RR of Flu-A (9.98, 95%CI: 4.03-26.28) and Flu-B (4.63, 95%CI: 1.95-11.27). Wv of 3.5 m/s exerted the peaking RR of Flu-A (7.48, 95%CI: 2.73-30.04) and . DTR 12°C and MeanT <22°C were the key drivers for Flu-A and Flu-B, separately. The study found j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l oc a t e / s c i t o t e n v complex non-linear relationships between climate variability and different influenza types in Shanghai. We suggest the careful use of meteorological variables in influenza prediction in subtropical regions, considering such complex associations, which may facilitate government and health authorities to better minimize the impacts of seasonal influenza.

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Section: N=100%mentioning

confidence: 99%

The complex associations of climate variability with seasonal influenza A and B virus transmission in subtropical Shanghai, China

Zhang

et al. 2020

Science of The Total Environment

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“…The communicable diseases that were chosen for this experiment were Influenza Virus, RS-Virus, Rhinovirus and Pneumonia [1].…”

Section: Resultsmentioning

confidence: 99%

“…With this form of vibrancy, people are placing an increased importance on health and the threat of diseases [1]. The health officials are doing everything they can to obtain timely information on epidemic outbreaks.…”

Section: Introductionmentioning

confidence: 99%

Spread Patterns of Epidemics: Survey

Satish¹,

Smitha²

2017

IJCA

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The main purpose of this paper is to bring out a distinct strategy to promote and predict the dispersion behaviors of epidemic diseases before they actually happen. With innumerable cases of epidemic outbreaks being recorded in various parts of the world, techniques such as those discussed in this paper, if widely used, under supervision can help to circumvent such occurrences even before they actually happen with accurate predictions. This would also ensure a better coping mechanism is provided to study the spread of such infections and adequate control mechanisms are put in place to prevent loss of human life. Through the course of the paper, the hope is to develop a well-defined prediction methodology that can predict the likeliness of an individual being affected by a particular epidemic through assessing of early symptoms and also predicting future instances in their infancy.

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“…Nowadays, health care systems are rapidly adopting clinical data, which will rapidly enlarge the size of the health records that are accessible, electronically [3,4]. A recent study expounds, six use cases of big data to decrease the cost of patients, triage and readmissions [5].…”

Section: Introductionmentioning

confidence: 99%

Big Data Knowledge System in Healthcare

Manogaran

Thota

Lopez

et al. 2017

Studies in Big Data

Self Cite

106

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The health care systems are rapidly adopting large amounts of data, driven by record keeping, compliance and regulatory requirements, and patient care. The advances in healthcare system will rapidly enlarge the size of the health records that are accessible electronically. Concurrently, fast progress has been made in clinical analytics. For example, new techniques for analyzing large size of data and gleaning new business insights from that analysis is part of what is known as big data. Big data also hold the promise of supporting a wide range of medical and healthcare functions, including among others disease surveillance, clinical decision support and population health management. Hence, effective big data based knowledge management system is needed for monitoring of patients and identify the clinical decisions to the doctor. The chapter proposes a big data based knowledge management system to develop the clinical decisions. The proposed knowledge system is developed based on variety of databases such as Electronic Health Record (EHR), Medical Imaging Data, Unstructured Clinical Notes and Genetic Data. The proposed methodology asynchronously communicates with different data sources and produces many alternative decisions to the doctor.

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Spatial big data analytics of influenza epidemic in Vellore, India

Cited by 79 publications

References 18 publications

The complex associations of climate variability with seasonal influenza A and B virus transmission in subtropical Shanghai, China

The complex associations of climate variability with seasonal influenza A and B virus transmission in subtropical Shanghai, China

Spread Patterns of Epidemics: Survey

Big Data Knowledge System in Healthcare

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