scholarly journals Associations between Meteorological Factors and Reported Mumps Cases from 1999 to 2020 in Japan

Epidemiologia ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 162-178
Author(s):  
Keiji Mise ◽  
Ayako Sumi ◽  
Shintaro Takatsuka ◽  
Shin-ichi Toyoda
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Time Series Data ◽  
Least Squares Method ◽  
Meteorological Factors ◽  
Vaccine Coverage ◽  
Meteorological Data ◽  
Spectral Lines ◽  
Series Data ◽  
Mean Temperature

The present study investigated associations between epidemiological mumps patterns and meteorological factors in Japan. We used mumps surveillance data and meteorological data from all 47 prefectures of Japan from 1999 to 2020. A time-series analysis incorporating spectral analysis and the least-squares method was adopted. In all power spectral densities for the 47 prefectures, spectral lines were observed at frequency positions corresponding to 1-year and 6-month cycles. Optimum least-squares fitting (LSF) curves calculated with the 1-year and 6-month cycles explained the underlying variation in the mumps data. The LSF curves reproduced bimodal and unimodal cycles that are clearly observed in northern and southern Japan, respectively. In investigating factors associated with the seasonality of mumps epidemics, we defined the contribution ratios of a 1-year cycle (Q1) and 6-month cycle (Q2) as the contributions of amplitudes of 1-year and 6-month cycles, respectively, to the entire amplitude of the time series data. Q1 and Q2 were significantly correlated with annual mean temperature. The vaccine coverage rate of a measles–mumps–rubella vaccine might not have affected the 1-year and 6-month modes of the time series data. The results of the study suggest an association between mean temperature and mumps epidemics in Japan.

The role of temperature in reported chickenpox cases from 2000 to 2011 in Japan

2015 ◽  
Vol 143 (12) ◽  
pp. 2666-2678 ◽  
Author(s):  
K. HARIGANE ◽  
A. SUMI ◽  
K. MISE ◽  
N. KOBAYASHI
Keyword(s):  
Least Squares ◽  
Maximum Entropy Method ◽  
Least Squares Method ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Entropy Method ◽  
Spectral Lines ◽  
Power Spectral ◽  
Power Spectral Densities

SUMMARYAnnual periodicities of reported chickenpox cases have been observed in several countries. Of these, Japan has reported a two-peaked, bimodal annual cycle of reported chickenpox cases. This study investigated the possible underlying association of the bimodal cycle observed in the surveillance data of reported chickenpox cases with the meteorological factors of temperature, relative humidity and rainfall. A time-series analysis consisting of the maximum entropy method spectral analysis and the least squares method was applied to the chickenpox data and meteorological data of 47 prefectures in Japan. In all of the power spectral densities for the 47 prefectures, the spectral lines were observed at the frequency positions corresponding to the 1-year and 6-month cycles. The optimum least squares fitting (LSF) curves calculated with the 1-year and 6-month cycles explained the underlying variation of the chickenpox data. The LSF curves reproduced the bimodal and unimodal cycles that were clearly observed in northern and southern Japan, respectively. The data suggest that the second peaks in the bimodal cycles in the reported chickenpox cases in Japan occurred at a temperature of approximately 8·5 °C.

scholarly journals Spaceborne Satellite for Snow Cover and Hydrological Characteristic of the Gilgit River Basin, Hindukush–Karakoram Mountains, Pakistan

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 531 ◽  
Author(s):  
Dostdar Hussain ◽  
Chung-Yen Kuo ◽  
Abdul Hameed ◽  
Kuo-Hsin Tseng ◽  
Bulbul Jan ◽  
...  
Keyword(s):  
Snow Cover ◽  
River Runoff ◽  
Time Series Data ◽  
Meteorological Data ◽  
Pearson Correlation ◽  
Indus Basin ◽  
Series Data ◽  
Northwestern Part ◽  
Temperature And Precipitation ◽  
Mean Temperature

The Indus River, which flows through China, India, and Pakistan, is mainly fed by melting snow and glaciers that are spread across the Hindukush–Karakoram–Himalaya Mountains. The downstream population of the Indus Plain heavily relies on this water resource for drinking, irrigation, and hydropower generation. Therefore, its river runoff variability must be properly monitored. Gilgit Basin, the northwestern part of the Upper Indus Basin, is selected for studying cryosphere dynamics and its implications on river runoff. In this study, 8-day snow products (MOD10A2) of moderate resolution imaging spectroradiometer, from 2001 to 2015 are selected to access the snow-covered area (SCA) in the catchment. A non-parametric Mann–Kendall test and Sen’s slope are calculated to assess whether a significant trend exists in the SCA time series data. Then, data from ground observatories for 1995–2013 are analyzed to demonstrate annual and seasonal signals in air temperature and precipitation. Results indicate that the annual and seasonal mean of SCA show a non-significant decreasing trend, but the autumn season shows a statistically significant decreasing SCA with a slope of −198.36 km2/year. The annual mean temperature and precipitation show an increasing trend with highest values of slope 0.05 °C/year and 14.98 mm/year, respectively. Furthermore, Pearson correlation coefficients are calculated for the hydro-meteorological data to demonstrate any possible relationship. The SCA is affirmed to have a highly negative correlation with mean temperature and runoff. Meanwhile, SCA has a very weak relation with precipitation data. The Pearson correlation coefficient between SCA and runoff is −0.82, which confirms that the Gilgit River runoff largely depends on the melting of snow cover rather than direct precipitation. The study indicates that the SCA slightly decreased for the study period, which depicts a possible impact of global warming on this mountainous region.

scholarly journals Analysis of Effects of Meteorological Factors on Dengue Incidence in Sri Lanka Using Time Series Data

PLoS ONE ◽  
2013 ◽  
Vol 8 (5) ◽  
pp. e63717 ◽  
Author(s):  
Kensuke Goto ◽  
Balachandran Kumarendran ◽  
Sachith Mettananda ◽  
Deepa Gunasekara ◽  
Yoshito Fujii ◽  
...  
Keyword(s):  
Time Series ◽  
Sri Lanka ◽  
Time Series Data ◽  
Meteorological Factors ◽  
Series Data

scholarly journals Deep learning classification of temporal data in ecology

2020 ◽  
Author(s):  
César Capinha ◽  
Ana Ceia-Hasse ◽  
Andrew M. Kramer ◽  
Christiaan Meijer
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Meteorological Data ◽  
Species Distribution Modelling ◽  
Series Data ◽  
Temporal Data ◽  
Climate Time Series ◽  
Biological Entities

AbstractTemporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach transforms the temporal data into static predictors of the classes. However, recent deep learning techniques can perform the classification using raw time series, eliminating subjective and resource-consuming data transformation steps, and potentially improving classification results. We present a general approach for time series classification that considers multiple deep learning algorithms and illustrate it with three case studies: i) insect species identification from wingbeat spectrograms; ii) species distribution modelling from climate time series and iii) the classification of phenological phases from continuous meteorological data. The deep learning approach delivered ecologically sensible and accurate classifications, proving its potential for wide applicability across subfields of ecology. We recommend deep learning as an alternative to techniques requiring the transformation of time series data.

scholarly journals Effect of temporal averaging of meteorological data on predictions of groundwater recharge

2018 ◽  
Vol 66 (2) ◽  
pp. 143-152 ◽  
Author(s):  
Marcia S. Batalha ◽  
Maria C. Barbosa ◽  
Boris Faybishenko ◽  
Martinus Th. van Genuchten
Keyword(s):  
Time Series ◽  
Groundwater Recharge ◽  
Time Series Data ◽  
Meteorological Data ◽  
Root Water Uptake ◽  
Series Data ◽  
Deep Penetration ◽  
Temporal Averaging ◽  
Recharge Rates ◽  
Groundwater Recharge Rate

AbstractAccurate estimates of infiltration and groundwater recharge are critical for many hydrologic, agricultural and environmental applications. Anticipated climate change in many regions of the world, especially in tropical areas, is expected to increase the frequency of high-intensity, short-duration precipitation events, which in turn will affect the groundwater recharge rate. Estimates of recharge are often obtained using monthly or even annually averaged meteorological time series data. In this study we employed the HYDRUS-1D software package to assess the sensitivity of groundwater recharge calculations to using meteorological time series of different temporal resolutions (i.e., hourly, daily, weekly, monthly and yearly averaged precipitation and potential evaporation rates). Calculations were applied to three sites in Brazil having different climatological conditions: a tropical savanna (the Cerrado), a humid subtropical area (the temperate southern part of Brazil), and a very wet tropical area (Amazonia). To simplify our current analysis, we did not consider any land use effects by ignoring root water uptake. Temporal averaging of meteorological data was found to lead to significant bias in predictions of groundwater recharge, with much greater estimated recharge rates in case of very uneven temporal rainfall distributions during the year involving distinct wet and dry seasons. For example, at the Cerrado site, using daily averaged data produced recharge rates of up to 9 times greater than using yearly averaged data. In all cases, an increase in the time of averaging of meteorological data led to lower estimates of groundwater recharge, especially at sites having coarse-textured soils. Our results show that temporal averaging limits the ability of simulations to predict deep penetration of moisture in response to precipitation, so that water remains in the upper part of the vadose zone subject to upward flow and evaporation.

scholarly journals A Novel Time Series Prediction Approach Based on a Hybridization of Least Squares Support Vector Regression and Swarm Intelligence

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Nhat-Duc Hoang ◽  
Anh-Duc Pham ◽  
Minh-Tu Cao
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Support Vector Regression ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Hybrid Approach ◽  
Series Data ◽  
Support Vector ◽  
Promising Alternative ◽  
Prediction Approach

This research aims at establishing a novel hybrid artificial intelligence (AI) approach, named as firefly-tuned least squares support vector regression for time series prediction(FLSVRTSP). The proposed model utilizes the least squares support vector regression (LS-SVR) as a supervised learning technique to generalize the mapping function between input and output of time series data. In order to optimize the LS-SVR’s tuning parameters, theFLSVRTSPincorporates the firefly algorithm (FA) as the search engine. Consequently, the newly construction model can learn from historical data and carry out prediction autonomously without any prior knowledge in parameter setting. Experimental results and comparison have demonstrated that theFLSVRTSPhas achieved a significant improvement in forecasting accuracy when predicting both artificial and real-world time series data. Hence, the proposed hybrid approach is a promising alternative for assisting decision-makers to better cope with time series prediction.

scholarly journals Simultaneous Forecasting of Meteorological Data Based on a Self-Organizing Incremental Neural Network

2018 ◽  
Vol 22 (6) ◽  
pp. 900-906 ◽  
Author(s):  
Wonjik Kim ◽  
Osamu Hasegawa ◽  
◽  
◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Atmospheric Pressure ◽  
Time Series Data ◽  
Meteorological Data ◽  
Series Data ◽  
Total Solar Radiation ◽  
Proposed Model ◽  
Self Organizing

In this study, we propose a simultaneous forecasting model for meteorological time-series data based on a self-organizing incremental neural network (SOINN). Meteorological parameters (i.e., temperature, wet bulb temperature, humidity, wind speed, atmospheric pressure, and total solar radiation on a horizontal surface) are considered as input data for the prediction of meteorological time-series information. Based on a SOINN within normalized-refined-meteorological data, proposed model succeeded forecasting temperature, humidity, wind speed and atmospheric pressure simultaneously. In addition, proposed model does not take more than 2 s in training half-year period and 15 s in testing half-year period. This paper also elucidates the SOINN and the algorithm of the learning process. The effectiveness of our model is established by comparison of our results with experimental results and with results obtained by another model. Three advantages of our model are also described. The obtained information can be effective in applications based on neural networks, and the proposed model for handling meteorological phenomena may be helpful for other studies worldwide including energy management system.

scholarly journals Estimating the SARS-CoV-2 infected population fraction and the infection to-fatality ratio: A data-driven case study based on Swedish time series data

2021 ◽  
Author(s):  
Andreas Wacker ◽  
Anna Jöud ◽  
Bo Bernhardsson ◽  
Philip Gerlee ◽  
Fredrik Gustafsson ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Least Squares Method ◽  
Finite Impulse Response ◽  
Herd Immunity ◽  
Late Summer ◽  
Epidemiological Data ◽  
Series Data ◽  
Filter Function ◽  
Study Results

Aim: To estimate the COVID-19 infection-to-fatality ratio (IFR), infection-to-case ratio (ICR), and infection-to-ICU admission ratio (IIAR) in Sweden; to suggest methods for time series reconstruction and prediction. Methods: We optimize a set of simple finite impulse response (FIR) models comprising of a scaling factor and time-delay between officially reported cases, ICU admissions and deaths time series using the least squares method. Combined with randomized PCR study results, we utilize this simple model to estimate the total number of infections in Sweden, and the corresponding IFR. Results: The model class provides a good fit between ICU admissions and deaths throughout 2020. Cases fit consistently from July 2020, by when PCR tests had become broadly available. We observe a diminished IFR in late summer as well as a strong decline during 2021, following the launch of a nation-wide vaccination program. The total number of infections during 2020 is estimated to $1.3$ million. Conclusions: A FIR model with a delta filter function describes the evolution of epidemiological data in Sweden well. The fact that we found IFR, ICR and IIAR constant over large parts of 2020 is in contrast with claims of healthcare adaptation or mutated virus variants importantly affecting these ratios. The model allows us to retrospectively estimate the COVID-19 epidemiological trajectory, and conclude that Sweden was far from herd immunity by the end of 2020.

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