scholarly journals Analysis of spatial-temporal distribution of notifiable respiratory infectious diseases in Shandong Province, China during 2005–2014

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaomei Li ◽  
Dongzhen Chen ◽  
Yan Zhang ◽  
Xiaojia Xue ◽  
Shengyang Zhang ◽  
...  
Keyword(s):  
High Risk ◽  
Infectious Diseases ◽  
Scarlet Fever ◽  
Influenza A ◽  
Temporal Distribution ◽  
Meningococcal Meningitis ◽  
Shandong Province ◽  
Influenza A H1n1 ◽  
Respiratory Infectious Diseases ◽  
Risk Maps

Abstract Background Little comprehensive information on overall epidemic trend of notifiable respiratory infectious diseases is available in Shandong Province, China. This study aimed to determine the spatiotemporal distribution and epidemic characteristics of notifiable respiratory infectious diseases. Methods Time series was firstly performed to describe the temporal distribution feature of notifiable respiratory infectious diseases during 2005–2014 in Shandong Province. GIS Natural Breaks (Jenks) was applied to divide the average annual incidence of notifiable respiratory infectious diseases into five grades. Spatial empirical Bayesian smoothed risk maps and excess risk maps were further used to investigate spatial patterns of notifiable respiratory infectious diseases. Global and local Moran’s I statistics were used to measure the spatial autocorrelation. Spatial-temporal scanning was used to detect spatiotemporal clusters and identify high-risk locations. Results A total of 537,506 cases of notifiable respiratory infectious diseases were reported in Shandong Province during 2005–2014. The morbidity of notifiable respiratory infectious diseases had obvious seasonality with high morbidity in winter and spring. Local Moran’s I analysis showed that there were 5, 23, 24, 4, 20, 8, 14, 10 and 7 high-risk counties determined for influenza A (H1N1), measles, tuberculosis, meningococcal meningitis, pertussis, scarlet fever, influenza, mumps and rubella, respectively. The spatial-temporal clustering analysis determined that the most likely cluster of influenza A (H1N1), measles, tuberculosis, meningococcal meningitis, pertussis, scarlet fever, influenza, mumps and rubella included 74, 66, 58, 56, 22, 64, 2, 75 and 56 counties, and the time frame was November 2009, March 2008, January 2007, February 2005, July 2007, December 2011, November 2009, June 2012 and May 2005, respectively. Conclusions There were obvious spatiotemporal clusters of notifiable respiratory infectious diseases in Shandong during 2005–2014. More attention should be paid to the epidemiological and spatiotemporal characteristics of notifiable respiratory infectious diseases to establish new strategies for its control.

scholarly journals Analysis of Spatial-Temporal Distribution of Respiratory Infectious Diseases in Shandong Province, China During 2005-2014

2020 ◽  
Author(s):  
Xiaomei Li ◽  
Dongzhen Chen ◽  
Yan Zhang ◽  
Xiaojia Xue ◽  
Xuewen Li ◽  
...  
Keyword(s):  
High Risk ◽  
Infectious Diseases ◽  
Scarlet Fever ◽  
Influenza A ◽  
Temporal Distribution ◽  
Meningococcal Meningitis ◽  
Shandong Province ◽  
Influenza A H1n1 ◽  
Respiratory Infectious Diseases ◽  
Risk Maps

Abstract Background: Little comprehensive information on overall epidemic trend of respiratory infectious diseases is available in Shandong Province, China. This study aimed to determine the spatiotemporal distribution and epidemic characteristics of respiratory infectious diseases.Methods: Time series was firstly performed to describe the temporal distribution feature of respiratory infectious diseases during 2005-2014 in Shandong Province. GIS Natural Breaks (Jenks) was applied to divide the average annual incidence of respiratory infectious diseases into five grades. Spatial empirical Bayesian smoothed risk maps and excess risk maps were further used to investigate spatial patterns of respiratory infectious diseases. Global and local Moran’s I statistics were used to measure the spatial autocorrelation. Spatial-temporal scanning was used to detect spatiotemporal clusters and identify high-risk locations. Results: A total of 537,506 cases of respiratory infectious diseases were reported in Shandong province during 2005-2014. The morbidity of respiratory infectious diseases had obvious seasonality with high morbidity in winter and spring. Local Moran’s I analysis showed that there were 5, 23, 24, 4, 20, 8, 14, 10 and 7 high-risk counties determined for influenza A (H1N1), measles, tuberculosis, meningococcal meningitis, pertussis, scarlet fever, influenza, mumps and rubella, respectively. The spatial-temporal clustering analysis determined that the most likely cluster of influenza A (H1N1), measles, tuberculosis, meningococcal meningitis, pertussis, scarlet fever, influenza, mumps and rubella included 74, 66, 58, 56, 22, 64, 2, 75 and 56 counties, and the time frame was November 2009, March 2008, January 2007, February 2005, July 2007, December 2011, November 2009, June 2012 and May 2005, respectively.Conclusions: There were obvious spatiotemporal clusters of respiratory infectious diseases in Shandong during 2005–2014. More attention should be paid to the epidemiological and spatiotemporal characteristics of respiratory infectious diseases to establish new strategies for its control.

scholarly journals Ocular manifestations of emerging viral diseases

Eye ◽  
2021 ◽  
Author(s):  
Ashwin Venkatesh ◽  
Ravi Patel ◽  
Simran Goyal ◽  
Timothy Rajaratnam ◽  
Anant Sharma ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Influenza A ◽  
Emerging Infectious Diseases ◽  
Global Scale ◽  
Viral Diseases ◽  
Valley Fever ◽  
Ocular Manifestations ◽  
Influenza A H1n1 ◽  
Emerging Viral Diseases

AbstractEmerging infectious diseases (EIDs) are an increasing threat to public health on a global scale. In recent times, the most prominent outbreaks have constituted RNA viruses, spreading via droplets (COVID-19 and Influenza A H1N1), directly between humans (Ebola and Marburg), via arthropod vectors (Dengue, Zika, West Nile, Chikungunya, Crimean Congo) and zoonotically (Lassa fever, Nipah, Rift Valley fever, Hantaviruses). However, specific approved antiviral therapies and vaccine availability are scarce, and public health measures remain critical. Patients can present with a spectrum of ocular manifestations. Emerging infectious diseases should therefore be considered in the differential diagnosis of ocular inflammatory conditions in patients inhabiting or returning from endemic territories, and more general vigilance is advisable in the context of a global pandemic. Eye specialists are in a position to facilitate swift diagnosis, improve clinical outcomes, and contribute to wider public health efforts during outbreaks. This article reviews those emerging viral diseases associated with reports of ocular manifestations and summarizes details pertinent to practicing eye specialists.

scholarly journals Spread of Infectious Disease Modeling and Analysis of Different Factors on Spread of Infectious Disease Based on Cellular Automata

2019 ◽  
Vol 16 (23) ◽  
pp. 4683 ◽  
Author(s):  
Sheng Bin ◽  
Gengxin Sun ◽  
Chih-Cheng Chen
Keyword(s):  
Infectious Disease ◽  
Cellular Automata ◽  
Infectious Diseases ◽  
Pandemic Influenza ◽  
Influenza A ◽  
Disease Modeling ◽  
Real Life ◽  
Automata Theory ◽  
Future Research ◽  
Influenza A H1n1

Infectious diseases are an important cause of human death. The study of the pathogenesis, spread regularity, and development trend of infectious diseases not only provides a theoretical basis for future research on infectious diseases, but also has practical guiding significance for the prevention and control of their spread. In this paper, a controlled differential equation and an objective function of infectious diseases were established by mathematical modeling. Based on cellular automata theory and a compartmental model, the SLIRDS (Susceptible-Latent-Infected-Recovered-Dead-Susceptible) model was constructed, a model which can better reflect the actual infectious process of infectious diseases. Considering the spread of disease in different populations, the model combines population density, sex ratio, and age structure to set the evolution rules of the model. Finally, on the basis of the SLIRDS model, the complex spread process of pandemic influenza A (H1N1) was simulated. The simulation results are similar to the macroscopic characteristics of pandemic influenza A (H1N1) in real life, thus the accuracy and rationality of the SLIRDS model are confirmed.

scholarly journals Administration of the Adjuvanted pH1N1 Vaccine in Egg-allergic Children at High Risk for Influenza A/H1N1 Disease

2011 ◽  
Vol 102 (3) ◽  
pp. 196-199 ◽  
Author(s):  
Jane E. SchulerCHEO ◽  
W. James King ◽  
Natalie L. Dayneka ◽  
Lynn Rastelli ◽  
Evelyn Marquis ◽  
...  
Keyword(s):  
High Risk ◽  
Influenza A ◽  
Influenza A H1n1

Transmission and control in an institutional pandemic influenza A(H1N1) 2009 outbreak

2011 ◽  
Vol 140 (6) ◽  
pp. 1102-1110 ◽  
Author(s):  
N. ARINAMINPATHY ◽  
N. RAPHAELY ◽  
L. SALDANA ◽  
C. HODGEKISS ◽  
J. DANDRIDGE ◽  
...  
Keyword(s):  
High Risk ◽  
Infection Control ◽  
Pandemic Influenza ◽  
Summer School ◽  
Influenza A ◽  
Control Measures ◽  
Infection Control Measures ◽  
Influenza A H1n1 ◽  
Hygiene Measures ◽  
The Impact

SUMMARYA pandemic influenza A(H1N1) 2009 outbreak in a summer school affected 117/276 (42%) students. Residential social contact was associated with risk of infection, and there was no evidence for transmission associated with the classroom setting. Although the summer school had new admissions each week, which provided susceptible students the outbreak was controlled using routine infection control measures (isolation of cases, basic hygiene measures and avoidance of particularly high-risk social events) and prompt treatment of cases. This was in the absence of chemoprophylaxis or vaccination and without altering the basic educational activities of the school. Modelling of the outbreak allowed estimation of the impact of interventions on transmission. These models and follow-up surveillance supported the effectiveness of routine infection control measures to stop the spread of influenza even in this high-risk setting for transmission.

scholarly journals Active surveillance of acute paediatric hospitalisations demonstrates the impact of vaccination programmes and informs vaccine policy in Canada and Australia

2020 ◽  
Vol 25 (25) ◽  
Author(s):  
Karina A Top ◽  
Kristine Macartney ◽  
Julie A Bettinger ◽  
Ben Tan ◽  
Christopher C Blyth ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Active Surveillance ◽  
Influenza A ◽  
Monitoring Program ◽  
Emerging Infectious Diseases ◽  
Emerging Infections ◽  
Public Health Response ◽  
Influenza A H1n1 ◽  
The Impact

Sentinel surveillance of acute hospitalisations in response to infectious disease emergencies such as the 2009 influenza A(H1N1)pdm09 pandemic is well described, but recognition of its potential to supplement routine public health surveillance and provide scalability for emergency responses has been limited. We summarise the achievements of two national paediatric hospital surveillance networks relevant to vaccine programmes and emerging infectious diseases in Canada (Canadian Immunization Monitoring Program Active; IMPACT from 1991) and Australia (Paediatric Active Enhanced Disease Surveillance; PAEDS from 2007) and discuss opportunities and challenges in applying their model to other contexts. Both networks were established to enhance capacity to measure vaccine preventable disease burden, vaccine programme impact, and safety, with their scope occasionally being increased with emerging infectious diseases’ surveillance. Their active surveillance has increased data accuracy and utility for syndromic conditions (e.g. encephalitis), pathogen-specific diseases (e.g. pertussis, rotavirus, influenza), and adverse events following immunisation (e.g. febrile seizure), enabled correlation of biological specimens with clinical context and supported responses to emerging infections (e.g. pandemic influenza, parechovirus, COVID-19). The demonstrated long-term value of continuous, rather than incident-related, operation of these networks in strengthening routine surveillance, bridging research gaps, and providing scalable public health response, supports their applicability to other countries.

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