Comparing the similarity and difference of three influenza surveillance systems in China

Yang, Xiaoting; Liu, Dongpeng; Wei, Kongfu; Liu, Xinfeng; Meng, Lu; Yu, Deshan; Li, Hongyu; Li, Baodi; He, Jian

doi:10.1038/s41598-018-21059-9

Cited by 22 publications

(24 citation statements)

References 19 publications

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“…It should be noted that another peak of RR in high temperature for Flu-A was observed in the study. This finding was supported by previous studies, which reported the semiannual epidemic in the summer in subtropical cities in China (Yang et al, 2018a;Yang et al, 2018b;Ye et al, 2019). The potential reasons for this semiannual epidemic required further research.…”

Section: Discussionsupporting

confidence: 87%

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: Discussionsupporting

confidence: 87%

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 PVP increases with increasing specificity and prevalence. A similar study conducted in China to evaluate influenza surveillance emphasized laboratory-confirmed surveillance and sensitivity tools for developing influenza early warning systems (18).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the COVID-19 Laboratory-Based Surveillance System in Islamabad-Pakistan 2020

Mushtaq¹,

Ikram²,

Chaudhry³

et al. 2020

Global Biosecurity

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Background: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; named 2019 novel coronavirus or 2019-nCoV) disease in China at the end of 2019 has led to pandemic. A robust surveillance system is required for true estimation of burden of the disease. This study was conducted to identify the strengths and weaknesses of laboratory-based COVID-19 surveillance system in Pakistan and to propose some doable actions for improvement.

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“…The influenza surveillance system is a major data source for monitoring and evaluating the transmission and evolution of influenza 30–32 . Two major surveillance systems, the Sentinel Influenza-Like Illness (ILI) Surveillance System and the Chinese Notifiable Infectious Diseases Reporting System (CNIDRS), have been widely used for influenza surveillance and research in China 16,30,33,34 .…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of Influenza in China, 2005–2018

Zhang

2019

Sci Rep

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Influenza is a major cause of morbidity and mortality worldwide, as well as in China. Knowledge of the spatial and temporal characteristics of influenza is important in evaluating and developing disease control programs. This study aims to describe an accurate spatiotemporal pattern of influenza at the prefecture level and explore the risk factors associated with influenza incidence risk in mainland China from 2005 to 2018. The incidence data of influenza were obtained from the Chinese Notifiable Infectious Disease Reporting System (CNIDRS). The Besag York Mollié (BYM) model was extended to include temporal and space-time interaction terms. The parameters for this extended Bayesian spatiotemporal model were estimated through integrated nested Laplace approximations (INLA) using the package R-INLA in R. A total of 702,226 influenza cases were reported in mainland China in CNIDRS from 2005–2018. The yearly reported incidence rate of influenza increased 15.6 times over the study period, from 3.51 in 2005 to 55.09 in 2008 per 100,000 populations. The temporal term in the spatiotemporal model showed that much of the increase occurred during the last 3 years of the study period. The risk factor analysis showed that the decreased number of influenza vaccines for sale, the new update of the influenza surveillance protocol, the increase in the rate of influenza A (H1N1)pdm09 among all processed specimens from influenza-like illness (ILI) patients, and the increase in the latitude and longitude of geographic location were associated with an increase in the influenza incidence risk. After the adjusting for fixed covariate effects and time random effects, the map of the spatial structured term shows that high-risk areas clustered in the central part of China and the lowest-risk areas in the east and west. Large space-time variations in influenza have been found since 2009. In conclusion, an increasing trend of influenza was observed from 2005 to 2018. The insufficient flu vaccine supplements, the newly emerging influenza A (H1N1)pdm09 and expansion of influenza surveillance efforts might be the major causes of the dramatic changes in outbreak and spatio-temporal epidemic patterns. Clusters of prefectures with high relative risks of influenza were identified in the central part of China. Future research with more risk factors at both national and local levels is necessary to explain the changing spatiotemporal patterns of influenza in China.

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Comparing the similarity and difference of three influenza surveillance systems in China

Cited by 22 publications

References 19 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

Evaluation of the COVID-19 Laboratory-Based Surveillance System in Islamabad-Pakistan 2020

Spatiotemporal Analysis of Influenza in China, 2005–2018

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