scholarly journals Temporal-Microclimatic Factors Affect the Phenology of Lipoptena fortisetosa in Central European Forests

Animals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 2012 ◽  
Author(s):  
Remigiusz Gałęcki ◽  
Jerzy Jaroszewski ◽  
Xuenan Xuan ◽  
Tadeusz Bakuła
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Generalized Additive Model ◽  
Mixed Forest ◽  
Additive Model ◽  
Field Surveys ◽  
Host Seeking ◽  
Sampling Session ◽  
Microclimatic Conditions ◽  
Highly Correlated

The objective of this study was to determine the correlations between the abundance of Lipoptena fortisetosa on new potential hosts and selected temporal-microclimatic conditions in a forest at the beginning of the host-seeking period. Louse flies were collected between 6 May and 15 July of 2019 and 2020 in a natural mixed forest in Poland. Keds were collected by three investigators walking along the same forest route during each sampling session. The number of captured keds and the date (time), temperature (°C), relative humidity (%), air pressure (hPa) and wind speed (km/h) were recorded. A total of five measurements were performed during each sampling session. The influence of temporal-microclimatic conditions on the number of collected ectoparasites was evaluated with the use of a Generalized Additive Model (GAM). A total of 1995 individuals were obtained during field surveys. The results of the GAM revealed a correlation between the number of host seeking L. fortisetosa vs. time, temperature, relative humidity, and wind speed. An increase in temperature was most highly correlated with the abundance of louse flies in the environment.

scholarly journals A layer-averaged relative humidity profile retrieval for microwave observations: design and results for the Megha-Tropiques payload

2015 ◽  
Vol 8 (3) ◽  
pp. 1055-1071 ◽  
Author(s):  
R. G. Sivira ◽  
H. Brogniez ◽  
C. Mallet ◽  
Y. Oussar
Keyword(s):  
Neural Network ◽  
Relative Humidity ◽  
Large Scale ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Training Phase ◽  
Learning Machine

Abstract. A statistical method trained and optimized to retrieve seven-layer relative humidity (RH) profiles is presented and evaluated with measurements from radiosondes. The method makes use of the microwave payload of the Megha-Tropiques platform, namely the SAPHIR sounder and the MADRAS imager. The approach, based on a generalized additive model (GAM), embeds both the physical and statistical characteristics of the inverse problem in the training phase, and no explicit thermodynamical constraint – such as a temperature profile or an integrated water vapor content – is provided to the model at the stage of retrieval. The model is built for cloud-free conditions in order to avoid the cases of scattering of the microwave radiation in the 18.7–183.31 GHz range covered by the payload. Two instrumental configurations are tested: a SAPHIR-MADRAS scheme and a SAPHIR-only scheme to deal with the stop of data acquisition of MADRAS in January 2013 for technical reasons. A comparison to learning machine algorithms (artificial neural network and support-vector machine) shows equivalent performance over a large realistic set, promising low errors (biases < 2.2%RH) and scatters (correlations > 0.8) throughout the troposphere (150–900 hPa). A comparison to radiosonde measurements performed during the international field experiment CINDY/DYNAMO/AMIE (winter 2011–2012) confirms these results for the mid-tropospheric layers (correlations between 0.6 and 0.92), with an expected degradation of the quality of the estimates at the surface and top layers. Finally a rapid insight of the estimated large-scale RH field from Megha-Tropiques is presented and compared to ERA-Interim.

scholarly journals Estimating Confidence Intervals around Relative Humidity Profiles from Satellite Observations: Application to the SAPHIR Sounder

2016 ◽  
Vol 33 (5) ◽  
pp. 1005-1022 ◽  
Author(s):  
Hélène Brogniez ◽  
Renaud Fallourd ◽  
Cécile Mallet ◽  
Ramsès Sivira ◽  
Christophe Dufour
Keyword(s):  
Relative Humidity ◽  
Confidence Intervals ◽  
Bayesian Method ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Correlation Coefficients ◽  
Mean Square ◽  
Atmospheric Relative Humidity ◽  
Humidity Profiles

AbstractA novel scheme for the estimation of layer-averaged relative humidity (RH) profiles from spaceborne observations in the 183.31-GHz line is presented. Named atmospheric relative humidity profiles including analysis of confidence intervals (ARPIA), it provides for each vector of observations the parameters of the distribution of the RH instead of its expectation, as is usually done by the current methods. The profiles are composed of six layers distributed between 100 and 950 hPa. The approach combines the six channels of the Sondeur Atmosphérique du Profil d’Humidité Intertropical par Radiométrie (SAPHIR) instrument on board the Megha-Tropiques satellite and the generalized additive model for location, scale and shape (GAMLSS) method to infer the parametric distributions, assuming that they follow a Gaussian law. The knowledge of the conditional uncertainty is an asset in the evaluation using radiosounding profiles of RH with a dedicated Bayesian method. Taking the uncertainties into account in both the ARPIA estimates and the in situ measurements yields biases, root-mean-square, and correlation coefficients in the range of −0.56% to 9.79%, 1.58% to 13.32%, and 0.55 to 0.98, respectively, with the largest biases being obtained over the continent, in the midtropospheric layers.

scholarly journals A relative humidity profile retrieval from Megha-Tropiques observations without explicit thermodynamical constraints

2014 ◽  
Vol 7 (9) ◽  
pp. 8983-9023 ◽  
Author(s):  
R. G. Sivira ◽  
H. Brogniez ◽  
C. Mallet ◽  
Y. Oussar
Keyword(s):  
Relative Humidity ◽  
Large Scale ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Least Square ◽  
Support Vector ◽  
Training Phase ◽  
Multi Layer Perceptron ◽  
Vector Machines

Abstract. A statistical method trained and optimized to retrieve relative humidity (RH) profiles is presented and evaluated with measurements from radiosoundings. The method makes use of the microwave payload of the Megha-Tropiques plateform, namely the SAPHIR sounder and the MADRAS imager. The approach, based on a Generalized Additive Model (GAM), embeds both the physical and statistical characteritics of the inverse problem in the training phase and no explicit thermodynamical constraint, such as a temperature profile or an integrated water vapor content, is provided to the model at the stage of retrieval. The model is built for cloud-free conditions in order to avoid the cases of scattering of the microwave radiation in the 18.7–183.31 GHz range covered by the payload. Two instrumental configurations are tested: a SAPHIR-MADRAS scheme and a SAPHIR-only scheme, to deal with the stop of data acquisition of MADRAS in January 2013 for technical reasons. A comparison to retrievals based on the Multi-Layer Perceptron (MLP) technique and on the Least Square-Support Vector Machines (LS-SVM) shows equivalent performance over a large realistic set, promising low errors (bias < 2.2%) and scatters (correlation > 0.8) throughout the troposphere (150–900 hPa). A comparison to radiosounding measurements performed during the international field experiment CINDY/DYNAMO/AMIE of winter 2011–2012 confirms these results for the mid-tropospheric layers (correlation between 0.6 and 0.92), with an expected degradation of the quality of the estimates at the surface and top layers. Finally a rapid insight of the large-scale RH field from Megha-Tropiques is discussed and compared to ERA-Interim.

scholarly journals A field-based modeling study on ecological characterization of hourly host-seeking behavior and its associated climatic variables in Aedes albopictus

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Qingqing Yin ◽  
Li Li ◽  
Xiang Guo ◽  
Rangke Wu ◽  
Benyun Shi ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Aedes Albopictus ◽  
Regression Models ◽  
Negative Binomial ◽  
Control Measures ◽  
Climatic Variables ◽  
Host Seeking ◽  
Seeking Behavior ◽  
Modeling Study

Abstract Background The global spread of mosquito-borne diseases (MBD) has presented increasing challenges to public health. The transmission of MBD is mainly attributable to the biting behaviors of female mosquitoes. However, the ecological pattern of hourly host-seeking behavior in Aedes albopictus and its association with climatic variables are still not well understood, especially for a precise requirement for establishing an effective risk prediction system of MBD transmission. Methods Mosquito samples and data on mosquito hourly density and site-specific climatic variables, including temperature, relative humidity, illuminance and wind speed, were collected simultaneously in urban outdoor environments in Guangzhou during 2016–2018. Kernel regression models were used to assess the temporal patterns of hourly host-seeking behavior in mosquito populations, and negative binomial regression models in the Bayesian framework were used to investigate the associations of host-seeking behavior with climatic variables. Results Aedes albopictus was abundant, constituting 82% (5569/6790) of the total collected mosquitoes. Host-seeking behavior in Ae. albopictus varied across time and was significantly influenced by climatic variables. The predicted hourly mosquito densities showed non-linear relationships with temperature and illuminance, whereas density increased with relative humidity but generally decreased with wind speed. The range of temperature estimates for female biting was 16.4–37.1 °C, peaking at 26.5 °C (95% credible interval: 25.3–28.1). During the favorable periods, biting behavior of female Ae. albopictus was estimated to occur frequently all day long, presenting a bimodal distribution with peaks within 2–3 h around both dawn and dusk (05:00–08:00 h and 16:00–19:00 h). Moreover, a short-term association in hourly density between the females and males was found. Conclusions Our field-based modeling study reveals that hourly host-seeking behavior of Ae. albopictus exhibits a complex pattern, with hourly variation constrained significantly by climatic variables. These findings lay a foundation for improving MBD risk assessments as well as practical strategies for vector control. For instances of all-day-long frequent female biting during the favorable periods in Guangzhou, effective integrated mosquito control measures must be taken throughout the day and night.

scholarly journals Associations of Acute Exposure to Airborne Pollutants with COVID-19 Infection: Evidence from China

2021 ◽  
Author(s):  
Bingqing Lu ◽  
Na Wu ◽  
Jiakui Jiang ◽  
Xiang Li
Keyword(s):  
Relative Humidity ◽  
Environmental Factors ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Mitigation Strategies ◽  
Atmospheric Pollutants ◽  
Acute Exposure ◽  
Airborne Pollutants ◽  
Relative Risks ◽  
Pollutant Concentrations

Abstract The outbreak of COVID-19, caused by SARS-CoV-2 has spread across many countries globally. Greatly limited study concerned the effect of airborne pollutants on COVID-19 infection, while exposure to airborne pollutants may harm human health. This paper aimed to examine the associations of acute exposure to ambient atmospheric pollutants to daily newly COVID-19 confirmed cases in 41 Chinese cities. Using a generalized additive model with Poisson distribution controlling for temperature and relative humidity, we evaluated the association between pollutant concentrations and daily COVID-19 confirmation at single-city level and multi-city level. We observed a 10 μg/m3 rise in levels of PM2.5 (lag 0−14), O3 (lag 0−1), SO2 (lag 0) and NO2 (lag 0−14) were positively associated with relative risks of 1.050 (95% CI: 1.028, 1.073), 1.011 (1.007, 1.015), 1.052 (1.022, 1.083) and 1.094 (1.028, 1.164) of daily newly confirmed cases, respectively. Further adjustment for other pollutants did not change the associations materially (excepting in the model for SO2). Our results indicated that COVID-19 incidence may be susceptible to airborne pollutants such as PM2.5, O3, SO2 and NO2, and mitigation strategies of environmental factors are required to prevent spreading.

Generalized Additive Model of Air Pollution to Daily Mortality

2005 ◽  
Vol 277-279 ◽  
pp. 487-491
Author(s):  
Jae Hee Kim ◽  
Hee Eun Yang
Keyword(s):  
Cardiovascular Disease ◽  
Air Pollution ◽  
Wind Speed ◽  
Generalized Additive Model ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Additive Models ◽  
Daily Mortality ◽  
Pollution Levels ◽  
Poisson Models

The association of air pollution with daily mortality due to cardiovascular disease, respiratory disease, and old age (65 or older) in Seoul, Korea was investigated in 1999 using daily values of TSP, PM10, O3, SO2, NO2, and CO. Generalized additive Poisson models were applied to allow for the highly flexible fitting of daily trends in air pollution as well as nonlinear association with meteorological variables such as temperature, humidity, and wind speed. To estimate the effect of air pollution and weather on mortality, LOESS smoothing was used in generalized additive models. The findings suggest that air pollution levels affect significantly the daily mortality.

scholarly journals Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

2020 ◽  
Vol 17 (17) ◽  
pp. 6339
Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  
Keyword(s):  
Wind Speed ◽  
Malaria Incidence ◽  
Generalized Additive Models ◽  
Community Health Center ◽  
Additive Model ◽  
Additive Models ◽  
Functional Regression ◽  
Novel Approach ◽  
Malaria Incidence Rate ◽  
Mean Wind Speed

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.

scholarly journals Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance

Risks ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 53
Author(s):  
Yves Staudt ◽  
Joël Wagner
Keyword(s):  
Random Forests ◽  
Goodness Of Fit ◽  
Generalized Additive Model ◽  
Mean Squared Error ◽  
Additive Model ◽  
Continuous Variables ◽  
Insurance Portfolio ◽  
Normal Transformation ◽  
Car Insurance ◽  
Log Normal

For calculating non-life insurance premiums, actuaries traditionally rely on separate severity and frequency models using covariates to explain the claims loss exposure. In this paper, we focus on the claim severity. First, we build two reference models, a generalized linear model and a generalized additive model, relying on a log-normal distribution of the severity and including the most significant factors. Thereby, we relate the continuous variables to the response in a nonlinear way. In the second step, we tune two random forest models, one for the claim severity and one for the log-transformed claim severity, where the latter requires a transformation of the predicted results. We compare the prediction performance of the different models using the relative error, the root mean squared error and the goodness-of-lift statistics in combination with goodness-of-fit statistics. In our application, we rely on a dataset of a Swiss collision insurance portfolio covering the loss exposure of the period from 2011 to 2015, and including observations from 81 309 settled claims with a total amount of CHF 184 mio. In the analysis, we use the data from 2011 to 2014 for training and from 2015 for testing. Our results indicate that the use of a log-normal transformation of the severity is not leading to performance gains with random forests. However, random forests with a log-normal transformation are the favorite choice for explaining right-skewed claims. Finally, when considering all indicators, we conclude that the generalized additive model has the best overall performance.

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