Demographic and climatic factors associated with dengue prevalence in a hyperendemic zone in Mexico: an empirical approach

2020 ◽  
Author(s):  
Francisco Espinoza-Gomez ◽  
Oscar Alberto Newton-Sanchez ◽  
Arnulfo Hernan Nava-Zavala ◽  
Maria G Zavala-Cerna ◽  
Fabian Rojas-Larios ◽  
...  
Keyword(s):  
Linear Models ◽  
Climatic Factors ◽  
Economic Status ◽  
Real Life ◽  
Climate Variables ◽  
Mexican Pacific ◽  
Non Linear ◽  
Main Determinants ◽  
Socio Economic Factors

Abstract Background Many models for predicting dengue epidemics use incidence and short-term changes in climate variables, however, studies in real-life scenarios for correlations of seroprevalence (SP) with long-term climate variables and with integration of socio-economic factors are scarce. Our objective was to analyse the combined correlation between socio-economic and climate variables with the SP of dengue in Mexico. Methods We performed a seroepidemiological ecological study on the Mexican Pacific coast. Dengue SP was estimated by the presence of immunoglobulin G antibodies in 1278 inhabitants. We implemented multiple correlations with socio-economic, climatic and topographic characteristics using logistic regression, generalized linear models and non-linear regressions. Results Dengue SP was 58%. The age-adjusted correlation was positive with the male sex, while a negative correlation was seen with socio-economic status (SES) and scholl level (SL). The annual temperature showed a positive correlation, while the altitude was negative. It should be noted that these correlations showed a marked ‘S’ shape in the non-linear model, suggesting three clearly defined scenarios for dengue risk. Conclusion Low SES and SL showed an unexpected paradoxical protective effect. Altitude above sea level and annual temperature are the main determinants for dengue in the long term. The identification of three clearly delineated scenarios for transmission could improve the accuracy of predictive models.

Climate conditions and consequences for de-icing operations as exemplified by the situation on a motorway and airport at Gardermoen, Norway

2010 ◽  
Vol 41 (3-4) ◽  
pp. 269-281 ◽  
Author(s):  
H. K. French ◽  
H.-O. Eggestad ◽  
J. Øvstedal ◽  
P.-E. Jahren
Keyword(s):  
Human Factor ◽  
Climatic Factors ◽  
Multiple Linear Regression Model ◽  
Dew Point ◽  
Climate Change Scenarios ◽  
Climate Variables ◽  
Total Consumption ◽  
Climate Conditions ◽  
Using Data

Large amounts of de-icing chemicals are used in the northern hemisphere to maintain winter safety on roads and airports every year. At Gardermoen, potassium formate (KFo) is used on runways, sodium chloride (NaCl) on roads and propylene glycol (PG) for aeroplanes. The total use of de-icing chemicals is an important part of the risk assessment related to water contamination at Gardermoen. The objective of this paper is to examine how climatic factors affect the use of de-icing chemicals through interviews with de-icing operators and by statistical methods using data on climate variables and de-icing operations. A multiple linear regression model shows a good relationship between daily dew point temperature, precipitation, wind speed, number of departures and the use of PG. The results were less promising for the prediction of KFo. This might be explained by the human factor and insufficiency of the standard climate variables to represent the situation near the runway. An analysis of daily downscaled climate change scenarios for the Gardermoen area revealed insufficient detail for any accurate estimates of change in total consumption of de-icing chemicals. The predicted mean increase of 7.6°C during winter does, however, suggest a reduced need for de-icing chemicals in the long term (2071–2100).

scholarly journals The effect of environmental parameters on the incidence of Shigella outbreaks in Yazd province, Iran

2017 ◽  
Vol 18 (4) ◽  
pp. 1388-1395 ◽  
Author(s):  
Farzaneh Aminharati ◽  
Mohammad Mehdi Soltan Dallal ◽  
Mohammad Hassan Ehrampoush ◽  
Abbasali Dehghani-Tafti ◽  
Mehdi Yaseri ◽  
...  
Keyword(s):  
Climate Changes ◽  
Climatic Factors ◽  
Temporal Trends ◽  
Environmental Parameters ◽  
Seasonal Patterns ◽  
Economic Factors ◽  
Ecological Process ◽  
Future Studies ◽  
Socio Economic Factors

Abstract Climate changes have a profound effect on human health, especially when infectious diseases are concerned. Variable factors including temperature, precipitation, and relative humidity directly influence the magnitude and frequency of water-borne pathogen transfers. In this study, we determined the long-term temporal trends and seasonal patterns of shigellosis and evaluated the effects of demographic and climatic factors on its incidence in Yazd province, Iran, during 2012 through 2015. The incidence of shigellosis was highest among patients in the age group of 6–20 years and peaked in every summer of the years studied, especially during August. Furthermore, there was a significant association between climatic variables (such as monthly temperature, humidity, and atmospheric dust) and the incidence of shigellosis. However, contrary to expectations, rainfall did not affect incidence of the disease. The transmission of Shigella to humans is a complex ecological process. Socio-economic factors and lifestyle behaviours need to be addressed in future studies.

scholarly journals Socio-economic factors do also matter: comments on the article “can climatic factors explain the differences in COVID-19 incidence and severity across the SPANISH regions?: an ecological study”

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Pedro Muñoz Cacho ◽  
José L. Hernández ◽  
Marcos López-Hoyos ◽  
Víctor M. Martínez-Taboada
Keyword(s):  
Ecological Study ◽  
Climatic Factors ◽  
Economic Factors ◽  
Climate Variables ◽  
Ecological Studies ◽  
Individual Level ◽  
Advantages And Disadvantages ◽  
Research Designs ◽  
The Impact ◽  
Socio Economic Factors

AbstractPhosri et al., commented on our previous study about the influence of climate variables at the beginning of the SARS-CoV-2 pandemic in Spain. They showed the impact of the association of gross domestic product (GDP) with the cumulative COVID-19 incidence per 105 inhabitants in our country and the rise of several methodologic issues. Here we discussed the main advantages and disadvantages of ecological studies and we advocate to test the hypothesis created in this type of studies using individual-level research designs.

scholarly journals An Attempt to Use Non-Linear Regression Modelling Technique in Long-Term Seasonal Rainfall Forecasting for Australian Capital Territory

Geosciences ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 282 ◽  
Author(s):  
Iqbal Hossain ◽  
Rijwana Esha ◽  
Monzur Alam Imteaz
Keyword(s):  
Linear Regression ◽  
Linear Models ◽  
Southern Oscillation ◽  
Seasonal Rainfall ◽  
Modelling Technique ◽  
Australian Capital Territory ◽  
Rainfall Forecasting ◽  
Australian Capital ◽  
Non Linear

The objective of this research is the assessment of the efficiency of a non-linear regression technique in predicting long-term seasonal rainfall. The non-linear models were developed using the lagged (past) values of the climate drivers, which have a significant correlation with rainfall. More specifically, the capabilities of SEIO (South-eastern Indian Ocean) and ENSO (El Nino Southern Oscillation) were assessed in reproducing the rainfall characteristics using the non-linear regression approach. The non-linear models developed were tested using the individual data sets, which were not used during the calibration of the models. The models were assessed using the commonly used statistical parameters, such as Pearson correlations (R), root mean square error (RMSE), mean absolute error (MAE) and index of agreement (d). Three rainfall stations located in the Australian Capital Territory (ACT) were selected as a case study. The analysis suggests that the predictors which has the highest correlation with the predictands do not necessarily produce the least errors in rainfall forecasting. The non-linear regression was able to predict seasonal rainfall with correlation coefficients varying from 0.71 to 0.91. The outcomes of the analysis will help the watershed management authorities to adopt efficient modelling technique by predicting long-term seasonal rainfall.

scholarly journals Climate windows of opportunity for plant expansion during the Phanerozoic

2021 ◽  
Author(s):  
Khushboo Gurung ◽  
Katie Field ◽  
Sarah Batterman ◽  
Yves Godderis ◽  
Yannick Donnadieu ◽  
...  
Keyword(s):  
Water Availability ◽  
Plant Biomass ◽  
Climatic Factors ◽  
Climate Variables ◽  
Geological Time ◽  
Windows Of Opportunity ◽  
Profound Influence ◽  
Plant Expansion ◽  
The Impact

Abstract Plants are likely to have had a profound influence on Earth’s long-term climate through their role in drawing down CO2 and emitting O2 into the atmosphere and their interactions with soils and minerals. Local climatic factors, including water availability, light, and temperature, play a key role in plant physiology and growth and have fluctuated substantially over geological time. However, the impact of these key climate variables on global plant biomass across the Phanerozoic have not yet been established.

Non-Linear Dynamics of Cardiovascular Variability Signals

1994 ◽  
Vol 33 (01) ◽  
pp. 81-84 ◽  
Author(s):  
S. Cerutti ◽  
S. Guzzetti ◽  
R. Parola ◽  
M.G. Signorini
Keyword(s):  
Dimensional Space ◽  
Severe Heart Failure ◽  
Parametric Models ◽  
Deterministic Systems ◽  
Finite Dimensional ◽  
Return Maps ◽  
Pathological Conditions ◽  
Non Linear ◽  
Space State

Abstract:Long-term regulation of beat-to-beat variability involves several different kinds of controls. A linear approach performed by parametric models enhances the short-term regulation of the autonomic nervous system. Some non-linear long-term regulation can be assessed by the chaotic deterministic approach applied to the beat-to-beat variability of the discrete RR-interval series, extracted from the ECG. For chaotic deterministic systems, trajectories of the state vector describe a strange attractor characterized by a fractal of dimension D. Signals are supposed to be generated by a deterministic and finite dimensional but non-linear dynamic system with trajectories in a multi-dimensional space-state. We estimated the fractal dimension through the Grassberger and Procaccia algorithm and Self-Similarity approaches of the 24-h heart-rate variability (HRV) signal in different physiological and pathological conditions such as severe heart failure, or after heart transplantation. State-space representations through Return Maps are also obtained. Differences between physiological and pathological cases have been assessed and generally a decrease in the system complexity is correlated to pathological conditions.

Paradoxes of reservation wage setting in the Russian labor market. Part I

2016 ◽  
pp. 5-27
Author(s):  
R. Kapeliushnikov ◽  
A. Lukyanova
Keyword(s):  
Labor Market ◽  
Empirical Studies ◽  
Reservation Wage ◽  
Wage Setting ◽  
Reservation Wages ◽  
Monitoring Survey ◽  
Main Determinants ◽  
Subjective Estimates ◽  
Objective Indicators

Using panel data from the Russian Longitudinal Monitoring Survey for 2006-2014, the paper investigates reservation wages setting in the Russian labor market. The sample includes non-employed individuals wishing to get a job (both searchers and non-searchers). The first part of the paper provides a survey of previous empirical studies, describes data and analyzes subjective estimates of reservation wages in comparison with various objective indicators of actual wages. The analysis shows that wage aspirations of the majority of Russian non-employed individuals are overstated. However their wage expectations are rather flexible and decrease rapidly as the search continues that prevents high long-term unemployment. The second part of the paper provides an econometric analysis of main determinants of reservation wage and its impact on probability of re-employment and wages on searchers’ new jobs.

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