scholarly journals Association Among Sentinel Surveillance, Meteorological Factors, and Infectious Disease in Gwangju, Korea

2021 ◽  
Author(s):  
You Hyun Joung ◽  
Taesu Jang ◽  
Jae Kyung Kim
Keyword(s):  
Infectious Diseases ◽  
Meteorological Factors ◽  
Climatic Factors ◽  
Meteorological Data ◽  
Sentinel Surveillance ◽  
E Coli ◽  
Average Temperature ◽  
Using Data ◽  
Strong Seasonality ◽  
Sentinel Surveillance System

Abstract Introduction: The outbreak of new infectious diseases is threatening human survival. Transmission of such diseases is determined by several factors, with climate being a very important factor. This study was conducted to assess the correlation between the occurrence of infectious diseases and climatic factors using data from the Sentinel Surveillance System and meteorological data from Gwangju, Jeollanam-do, Republic of Korea. Result The climate of Gwangju from June to September is humid, with this city having the highest average temperature, whereas that from December to February is cold and dry. Infection rates of Salmonella (Temperature: r = 0.710**; Relative humidity: r = 0.669**), E. coli (r = 0.617**; r = 0.626**), Rotavirus (r=-0.408**; r=-0.618**), Norovirus (r=-0.463**; r=-0.316**), Influenza virus (r=-0.726**; r=-0.672**), Coronavirus (r=-0.684**; r=-0.408**), and Coxsackievirus (r = 0.654**; r = 0.548**) have been shown to have a high correlation with seasonal changes, specifically in these meteorological factors. Discussion & Conclusions: Pathogens showing distinct seasonality in the occurrence of infection were observed, and there was a high correlation with the climate characteristics of Gwangju. In particular, viral diseases show strong seasonality, and further research on this matter is needed. Due to the current COVID-19 pandemic, quarantine and prevention have become important to block the spread of infectious diseases. For this purpose, studies that predicts infectivity through various types of data related to infection are important.

scholarly journals Estimating the weight of a snow cover using only meteorological factors

1993 ◽  
Vol 18 ◽  
pp. 190-192
Author(s):  
Kenji Shinojima ◽  
Hiroshi Harada
Keyword(s):  
Snow Cover ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Moving Average ◽  
Data Acquisition System ◽  
Degree Days ◽  
Forest Product Research Institute ◽  
Temperature And Precipitation ◽  
Using Data

We compute the weight of the snow cover as a function of the daily quantity of precipitation and daily melting using only data from the Automated Meteorological Data Acquisition System (AMeDAS), which is used widely in Japan. The correlation between long-term measurements and meteorological data in AMeDAS factors was computed by statistical methods from the Forestry and Forest Product Research Institute, Tokamachi Experiment Station, in Niigata Prefecture, using data for 11 winter seasons (1977–87). The daily quantity of melting is expressed with a three-day moving average of degree days. The coefficient of correlation between the daily groups of each value of the 1323 days during the 11 winter seasons was 0.986 with a standard deviation of ±590 Ν m−2. Thus, if air temperature and precipitation can be obtained for an area, the weight of the snow cover can be estimated with confidence.

scholarly journals The association of cellulitis incidence and meteorological factors in Taiwan

2019 ◽  
Vol 147 ◽  
Author(s):  
Ren-Jun Hsu ◽  
Chia-Cheng Chou ◽  
Jui-Ming Liu ◽  
See-Tong Pang ◽  
Chien-Yu Lin ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Incidence Rate ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Strongly Correlated ◽  
Total Rainfall ◽  
The Public ◽  
Increased Risk ◽  
Using Data ◽  
Central Weather Bureau

AbstractCellulitis is a common infection of the skin and soft tissue. Susceptibility to cellulitis is related to microorganism virulence, the host immunity status and environmental factors. This retrospective study from 2001 to 2013 investigated relationships between the monthly incidence rate of cellulitis and meteorological factors using data from the Taiwanese Health Insurance Dataset and the Taiwanese Central Weather Bureau. Meteorological data included temperature, hours of sunshine, relative humidity, total rainfall and total number of rainy days. In otal, 195 841 patients were diagnosed with cellulitis and the incidence rate was strongly correlated with temperature (γS = 0.84, P < 0.001), total sunshine hours (γS = 0.65, P < 0.001) and total rainfall (γS = 0.53, P < 0.001). The incidence rate of cellulitis increased by 3.47/100 000 cases for every 1° elevation in environmental temperature. Our results may assist clinicians in educating the public of the increased risk of cellulitis during warm seasons and possible predisposing environmental factors for infection.

scholarly journals Estimating the weight of a snow cover using only meteorological factors

1993 ◽  
Vol 18 ◽  
pp. 190-192
Author(s):  
Kenji Shinojima ◽  
Hiroshi Harada
Keyword(s):  
Snow Cover ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Moving Average ◽  
Data Acquisition System ◽  
Degree Days ◽  
Forest Product Research Institute ◽  
Temperature And Precipitation ◽  
Using Data

We compute the weight of the snow cover as a function of the daily quantity of precipitation and daily melting using only data from the Automated Meteorological Data Acquisition System (AMeDAS), which is used widely in Japan. The correlation between long-term measurements and meteorological data in AMeDAS factors was computed by statistical methods from the Forestry and Forest Product Research Institute, Tokamachi Experiment Station, in Niigata Prefecture, using data for 11 winter seasons (1977–87).The daily quantity of melting is expressed with a three-day moving average of degree days. The coefficient of correlation between the daily groups of each value of the 1323 days during the 11 winter seasons was 0.986 with a standard deviation of ±590 Ν m−2. Thus, if air temperature and precipitation can be obtained for an area, the weight of the snow cover can be estimated with confidence.

scholarly journals The Influence of Climate Conditions and Meteorological Factors on the Nutritional Value of Wheat (Triticum Aestivum L.) Used for Human and Animals Nutrition, in Romania

2021 ◽  
Vol 906 (1) ◽  
pp. 012019
Author(s):  
Ionela Hotea ◽  
Monica Dragomirescu ◽  
Olimpia Colibar ◽  
Emil Tirziu ◽  
Viorel Herman ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Meteorological Data ◽  
Triticum Aestivum L ◽  
Metabolizable Energy ◽  
Animal Nutrition ◽  
Annual Average ◽  
Average Temperature ◽  
Period 2 ◽  
Annual Average Temperature

Abstract Wheat (Triticum aestivum L.) is the basic cereal in human and animal nutrition. Every month, wheat is harvested somewhere in the world. In Romania, a country with a temperate-continental climate, the wheat is harvested between June and July, while the sowing is carried out between September and October. Climatic and meteorological factors during these periods can influence the nutritional quality of wheat. The aim of this study was to analyse the influence of annual average temperature and the amount of precipitate on the chemical composition and on the value of metabolizable energy of the wheat, respectively. The climatic and meteorological data used in this study come from NMA database. Were analysed the periods September 2017 - July 2018 (period 1, noted with 2018 - the year of harvesting) and September 2018 - July 2019 (period 2, noted with 2019 - the year of harvesting), respectively. For the chemical analysis, the NIR (Near InfraRed spectroscopy) method was used. The calculation of metabolizable energy was performed based on the ATWATER system, a system applicable to both human and animal nutrition. The statistical analysis of the climatic and meteorological data showed that the annual average temperature for period 1 was lower compared to the temperatures of period 2. Also, the precipitations were more abundant in period 1 compared to period 2. There were no significant statistical differences for any of the climatic and meteorological factors assayed during the analyzed periods. Following the statistical correlations between the nutrients studied by chemical analysis, for those 2 periods, significant differences were observed (p <0.001). The humidity of wheat grains harvested in 2018 was higher (average = 13.03%) compared to that of grains harvested in 2019 (average = 10.72%). The protein content was lower in 2018 (average = 10.02%) than in 2019 (average = 11.04%); and similar results were obtained for the fibre content (average 2018 = 2.17%; average 2019 = 2.96%). Also, the value of metabolizable energy was lower for wheat harvested in 2018 (average = 3517.90 kcal/kg) compared to 2019 (average = 3611.04 kcal/kg). In conclusion, the results of this study highlight the influence of temperature and precipitation on the chemical composition of wheat, thus having a direct impact on the nutritional quality of this grain for human and animal nutrition.

scholarly journals The KIzSS network, a sentinel surveillance system for infectious diseases in day care centers: study protocol

2012 ◽  
Vol 12 (1) ◽  
Author(s):  
Remko Enserink ◽  
Harold Noel ◽  
Ingrid HM Friesema ◽  
Carolien M de Jager ◽  
Anna MD Kooistra-Smid ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Study Protocol ◽  
Day Care ◽  
Surveillance System ◽  
Sentinel Surveillance ◽  
Day Care Centers ◽  
Sentinel Surveillance System

Evaluating measles surveillance: comparison of sentinel surveillance, mandatory notification, and data from health insurance claims

2010 ◽  
Vol 139 (4) ◽  
pp. 516-523 ◽  
Author(s):  
S. TANIHARA ◽  
E. OKAMOTO ◽  
T. IMATOH ◽  
Y. MOMOSE ◽  
A. KAETSU ◽  
...  
Keyword(s):  
Health Insurance ◽  
Infectious Diseases ◽  
Surveillance System ◽  
Sentinel Surveillance ◽  
Surveillance Systems ◽  
Insurance Claims ◽  
Notification System ◽  
Mandatory Notification ◽  
Health Insurance Claims ◽  
Sentinel Surveillance System

SUMMARYInadequate notification is a recognized problem of measles surveillance systems in many countries, and it should be monitored using multiple data sources. We compared data from three different surveillance sources in 2007: (1) the sentinel surveillance system mandated by the Act on Prevention of Infectious Diseases and Medical Care for Patients Suffering Infectious Diseases, (2) the mandatory notification system run by the Aichi prefectural government, and (3) health insurance claims (HICs) submitted to corporate health insurance societies. For each dataset, we examined the number of measles cases by month, within multiple age groups, and in two categories of diagnostic test groups. We found that the sentinel surveillance system underestimated the number of adult measles cases. We also found that HIC data, rather than mandatory notification data, were more likely to come from individuals who had undergone laboratory tests to confirm their measles diagnosis. Thus, HIC data may provide a supplementary and readily available measles surveillance data source.

scholarly journals Role of a sentinel surveillance system in the context of global surveillance of infectious diseases

2004 ◽  
Vol 4 (3) ◽  
pp. 171-177 ◽  
Author(s):  
Isao Arita ◽  
Miyuki Nakane ◽  
Kazunobu Kojima ◽  
Namiko Yoshihara ◽  
Takashi Nakano ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Surveillance System ◽  
Sentinel Surveillance ◽  
Sentinel Surveillance System

scholarly journals Surveillance by age-class and prefecture for emerging infectious febrile diseases with respiratory symptoms, including COVID-19

2020 ◽  
Author(s):  
Tomoaki Ueno ◽  
Junko Kurita ◽  
Tamie Sugawara ◽  
Yoshiyuki Sugishita ◽  
Yasushi Ohkusa ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Respiratory Symptoms ◽  
Sentinel Surveillance ◽  
Febrile Disease ◽  
Age Class ◽  
Proposed Model ◽  
Number Of Patients ◽  
Large Outbreak ◽  
Using Data ◽  
Do So

AbstractObjectThe COVID-19 outbreak emerged in late 2019 in China, expanding rapidly thereafter. Even in Japan, epidemiological linkage of transmission was probably lost already by February 18, 2020. From that time, it has been necessary to detect clusters using syndromic surveillance.MethodWe identified common symptoms of COVID-19 as fever and respiratory symptoms. Therefore, we constructed a model to predict the number of patients with antipyretic analgesics (AP) and multi-ingredient cold medications (MIC) controlling well-known pediatric infectious diseases including influenza or RS virus infection. To do so, we used the National Official Sentinel Surveillance for Infectious Diseases (NOSSID), even though NOSSID data are weekly data with 10 day delays, on average. The probability of a cluster with unknown febrile disease with respiratory symptoms is a product of the probabilities of aberrations in AP and MIC, which is defined as one minus the probability of the number of patients prescribed a certain type of drug in PS compared to the number predicted using a model. This analysis was conducted prospectively in 2020 using data from October 1, 2010 through 2019 by prefecture and by age-class.ResultsThe probability of unknown febrile disease with respiratory symptom cluster was estimated as less than 60% in 2020.DiscussionThe most severe limitation of the present study is that the proposed model cannot be validated. A large outbreak of an unknown febrile disease with respiratory symptoms must be experienced, at which time, practitioners will have to “wing it”. We expect that no actual cluster of unknown febrile disease with respiratory symptoms will occur, but if it should occur, we hope to detect it.

scholarly journals Identifying high risk areas of Zika virus infection by meteorological factors in Colombia

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Lung-Chang Chien ◽  
Francisco Sy ◽  
Adriana Pérez
Keyword(s):  
At Risk ◽  
High Risk ◽  
Zika Virus ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Warning System ◽  
Zikv Infection ◽  
Total Rainfall ◽  
Average Temperature ◽  
Risk Areas

Abstract Background Several Zika virus (ZIKV) outbreaks have occurred since October 2015. Because there is no effective treatment for ZIKV infection, developing an effective surveillance and warning system is currently a high priority to prevent ZIKV infection. Despite Aedes mosquitos having been known to spread ZIKV, the calculation approach is diverse, and only applied to local areas. This study used meteorological measurements to monitor ZIKV infection due to the high correlation between climate change and Aedes mosquitos and the convenience to obtain meteorological data from weather monitoring stations. Methods This study applied the Bayesian structured additive regression modeling approach to include spatial interactive terms with meteorological factors and a geospatial function in a zero-inflated Poisson model. The study area contained 32 administrative departments in Colombia from October 2015 to December 2017. Weekly ZIKV infection cases and daily meteorological measurements were collected. Mapping techniques were adopted to visualize spatial findings. A series of model selections determined the best combinations of meteorological factors in the same model. Results When multiple meteorological factors are considered in the same model, both total rainfall and average temperature can best assess the geographic disparities of ZIKV infection. Meanwhile, a 1-in. increase in rainfall is associated with an increase in the logarithm of relative risk (logRR) of ZIKV infection of at most 1.66 (95% credible interval [CI] = 1.09, 2.15) as well as a 1 °F increase in average temperature is significantly associated with at most 0.79 (95% CI = 0.12, 1.22) increase in the logRR of ZIKV. Moreover, after controlling rainfall and average temperature, an independent geospatial function in the model results in two departments with an excessive ZIKV risk which may be explained by unobserved factors other than total rainfall and average temperature. Conclusion Our study found that meteorological factors are significantly associated with ZIKV infection across departments. The study determined both total rainfall and average temperature as the best meteorological factors to identify high risk departments of ZIKV infection. These findings can help governmental agencies monitor at risk areas according to meteorological measurements, and develop preventions in those at risk areas in priority.

scholarly journals Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation

2021 ◽  
Vol 13 (17) ◽  
pp. 3502
Author(s):  
Jingshan Lu ◽  
Jan Eitel ◽  
Jyoti Jennewein ◽  
Jie Zhu ◽  
Hengbiao Zheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Least Squares ◽  
Spectral Index ◽  
Rice Plant ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Potassium Content ◽  
Estimation Accuracy ◽  
Average Temperature ◽  
Daily Average

Potassium (K) plays a significant role in the formation of crop quality and yield. Accurate estimation of plant potassium content using remote sensing (RS) techniques is therefore of great interest to better manage crop K nutrition. To improve RS of crop K, meteorological information might prove useful, as it is well established that weather conditions affect crop K uptake. We aimed to determine whether including meteorological data into RS-based models can improve K estimation accuracy in rice (Oryza sativa L.). We conducted field experiments throughout three growing seasons (2017–2019). During each year, different treatments (i.e., nitrogen, potassium levels and plant varieties) were applied and spectra were taken at different growth stages throughout the growing season. Firstly, we conducted a correlation analysis between rice plant potassium content and transformed spectra (reflectance spectra (R), first derivative spectra (FD) and reciprocal logarithm-transformed spectra (log [1/R])) to select correlation bands. Then, we performed the genetic algorithms partial least-squares and linear mixed effects model to select important bands (IBs) and important meteorological factors (IFs) from correlation bands and meteorological data (daily average temperature, humidity, etc.), respectively. Finally, we used the spectral index and machine learning methods (partial least-squares regression (PLSR) and random forest (RF)) to construct rice plant potassium content estimation models based on transformed spectra, transformed spectra + IFs and IBs, and IBs + IFs, respectively. Results showed that normalized difference spectral index (NDSI (R1210, R1105)) had a moderate estimation accuracy for rice plant potassium content (R2 = 0.51; RMSE = 0.49%) and PLSR (FD-IBs) (R2 = 0.69; RMSE = 0.37%) and RF (FD-IBs) (R2 = 0.71; RMSE = 0.40%) models based on FD could improve the prediction accuracy. Among the meteorological factors, daily average temperature contributed the most to estimating rice plant potassium content, followed by daily average humidity. The estimation accuracy of the optimal rice plant potassium content models was improved by adding meteorological factors into the three RS models, with model R2 increasing to 0.65, 0.74, and 0.76, and RMSEs decreasing to 0.42%, 0.35%, and 0.37%, respectively, suggesting that including meteorological data can improve our ability to remotely sense plant potassium content in rice.

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