scholarly journals The effect of climate variables on the incidence of Crimean Congo Hemorrhagic Fever (CCHF) in Zahedan, Iran

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sairan Nili ◽  
Narges Khanjani ◽  
Yunes Jahani ◽  
Bahram Bakhtiari
Keyword(s):  
Goodness Of Fit ◽  
Moving Average ◽  
Seasonal Pattern ◽  
Generalized Additive Models ◽  
Hemorrhagic Fever ◽  
Additive Models ◽  
Meteorological Variables ◽  
Fatality Rate ◽  
Crimean Congo Hemorrhagic Fever ◽  
Sarima Models

Abstract Background The Crimean-Congo Hemorrhagic fever (CCHF) is endemic in Iran and has a high fatality rate. The aim of this study was to investigate the association between CCHF incidence and meteorological variables in Zahedan district, which has a high incidence of this disease. Methods Data about meteorological variables and CCHF incidence was inquired from 2010 to 2017 for Zahedan district. The analysis was performed using univariate and multivariate Seasonal Autoregressive Integrated Moving Average (SARIMA) models and Generalized Additive Models (GAM) using R software. AIC, BIC and residual tests were used to test the goodness of fit of SARIMA models, and R2 was used to select the best model in GAM/GAMM. Results During the years under study, 190 confirmed cases of CCHF were identified in Zahedan district. The fatality rate of the disease was 8.42%. The disease trend followed a seasonal pattern. The results of multivariate SARIMA showed the (0,1,1) (0,1,1)12 model with maximum monthly temperature lagged 5 months, forecasted the disease better than other models. In the GAM, monthly average temperature lagged 5 months, and the monthly minimum of relative humidity and total monthly rainfall without lag, had a nonlinear relation with the incidence of CCHF. Conclusions Meteorological variables can affect CCHF occurrence.

scholarly journals Growth overshoot and seasonal size changes in the skulls of two weasel species

2017 ◽  
Vol 4 (1) ◽  
pp. 160947 ◽  
Author(s):  
Scott LaPoint ◽  
Lara Keicher ◽  
Martin Wikelski ◽  
Karol Zub ◽  
Dina K. N. Dechmann
Keyword(s):  
Seasonal Pattern ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Energy Requirements ◽  
Ontogenetic Changes ◽  
Skull Length ◽  
Geographical Regions ◽  
Absolute Growth ◽  
Age And Sex ◽  
Dehnel’S Phenomenon

Ontogenetic changes in mammalian skulls are complex. For a very few species (i.e. some Sorex shrews), these also include seasonally driven, bidirectional size changes within individuals, presumably to reduce energy requirements during low resource availabilities. These patterns are poorly understood, but are likely most pronounced in high-metabolic species with limited means for energy conservation. We used generalized additive models to quantify the effect of location, Julian day, age and sex on the length and depth of 512 and 847 skulls of stoat ( Mustela erminea ) and weasel ( M. nivalis ) specimens collected throughout the northern hemisphere. Skull length of both species varies between sexes and geographically, with stoat skull length positively correlated with latitude. Both species demonstrate seasonal and ontogenetic patterns, including a rare, absolute growth overshoot in juvenile braincase depth. Standardized braincase depths of both species peak in their first summer, then decrease in their first winter, followed by a remarkable regrowth that peaks again during their second summer. This seasonal pattern varies in magnitude and timing between geographical regions and the sexes, matching predictions of Dehnel's phenomenon. This suggests implications for the evolution of over-wintering strategies in mammals, justifying further research on their mechanisms and value, with implications for applied osteology research.

scholarly journals Crimean-Congo hemorrhagic fever-induced liver injury: a systematic review and meta-analysis

2021 ◽  
Author(s):  
Sawai Singh Rathore ◽  
Ade Harrison Manju ◽  
Qingqing Wen ◽  
Manush Sondhi ◽  
Reshma Pydi ◽  
...  
Keyword(s):  
Liver Injury ◽  
Case Fatality Rate ◽  
Meta Analysis ◽  
Case Series ◽  
Case Fatality ◽  
Hemorrhagic Fever ◽  
Fatality Rate ◽  
High Case Fatality Rate ◽  
Crimean Congo Hemorrhagic Fever ◽  
Pooled Prevalence

Background: Crimean-Congo hemorrhagic fever (CCHF) is a fatal acute tick-borne viral infection and a substantial emerging global public health threat. This illness has a high case fatality rate of up to 40%. The liver is one of the important target organs of the CCHF virus. Objective: The aim of this meta-analysis to evaluate the correlation between CCHF  and liver injury and draw more generalized inferences about the abnormal serum markers of liver injury such as alanine aminotransferase (ALT), aspartate aminotransferase (AST) in CCHF patients. Methods: A literature search was accomplished for published eligible articles with MEDLINE/PubMed and Embase databases. All eligible observational studies and case series were included from around the world. The inclusion criteria were articles describing liver injury biomarkers AST and ALT amongst patients diagnosed with CCHF. Results: Data from 18 studies, consisting of 1238 patients with CCHF  were included in this meta-analysis. The overall pooled prevalence of at least one raised liver injury biomarker was 77.95% (95% CI, I2 = 88.50%, p < 0.0001). Similarly, pooled prevalence of elevated AST and ALT was 85.92% (95% CI, I2 = 85.27%,  p < 0.0001) and 64.30% (95% CI, I2 = 88.32%,  p < 0.0001) respectively.  Both Egger and Begg-Mazumdar’s tests detected no apparent publication bias in all three meta-analyses(p > 0.05).  Conclusion: These elevated liver injury biomarkers have been identified as significant prognostic factors. Hence, Physicians must recognize and continuously monitor these biomarkers, since these aid early stratification of prognosis and the prevention of severe outcomes in infection with such a high case fatality rate.

scholarly journals Influence of meteorology on PM<sub>10</sub> trends and variability in Switzerland from 1991 to 2008

2011 ◽  
Vol 11 (4) ◽  
pp. 1813-1835 ◽  
Author(s):  
I. Barmpadimos ◽  
C. Hueglin ◽  
J. Keller ◽  
S. Henne ◽  
A. S. H. Prévôt
Keyword(s):  
Mean Squared Error ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Weekend Effect ◽  
Meteorological Variables ◽  
Precipitation Rate ◽  
Wind Gust ◽  
Traffic Emission ◽  
Urban Street ◽  
Meteorological Observations

Abstract. Measurements of airborne particles with aerodynamic diameter of 10 μm or less (PM10) and meteorological observations are available from 13 stations distributed throughout Switzerland and representing different site types. The effect of all available meteorological variables on PM10 concentrations was estimated using Generalized Additive Models. Data from each season were treated separately. The most important variables affecting PM10 concentrations in winter, autumn and spring were wind gust, the precipitation rate of the previous day, the precipitation rate of the current day and the boundary layer depth. In summer, the most important variables were wind gust, Julian day and afternoon temperature. In addition, temperature was important in winter. A "weekend effect" was identified due to the selection of variable "day of the week" for some stations. Thursday contributes to an increase of 13% whereas Sunday contributes to a reduction of 12% of PM10 concentrations compared to Monday on average over 9 stations for the yearly data. The estimated effects of meteorological variables were removed from the measured PM10 values to obtain the PM10 variability and trends due to other factors and processes, mainly PM10 emissions and formation of secondary PM10 due to trace gas emissions. After applying this process, the PM10 variability was much lower, especially in winter where the ratio of adjusted over measured mean squared error was 0.27 on average over all considered sites. Moreover, PM10 trends in winter were more negative after the adjustment for meteorology and they ranged between −1.25 μg m−3 yr−1 and 0.07 μg m−3 yr−1. The adjusted trends for the other seasons ranged between −1.34 μg m−3 yr−1 and −0.26 μg m−3 yr−1 in spring, −1.40 μg m−3 yr−1 and −0.28 μg m−3 yr−1 in summer and −1.28 μg m−3 yr−1 and −0.11 μg m−3 yr−1 in autumn. The estimated trends of meteorologically adjusted PM10 were in general non-linear. The two urban street sites considered in the study, Bern and Lausanne, experienced the largest reduction in measured and adjusted PM10 concentrations. This indicates a verifiable effect of traffic emission reduction strategies implemented during the past two decades. The average adjusted yearly trends for rural, urban background and urban street stations were −0.37, −0.53 and −1.2 μg m−3 yr−1 respectively. The adjusted yearly trends for all stations range from −0.15 μg m−3 yr−1 to −1.2 μg m−3 yr−1 or −1.2% yr−1 to −3.3% yr−1.

scholarly journals SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA

2020 ◽  
Author(s):  
Chang Qi ◽  
Dandan Zhang ◽  
Yuchen Zhu ◽  
Lili Liu ◽  
Chunyu Li ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Predictive Ability ◽  
Information Criterion ◽  
Hemorrhagic Fever ◽  
Forecast Performance ◽  
Sarima Model ◽  
Control And Prevention ◽  
And Control

Abstract Background The early warning model of infectious diseases plays a key role in prevention and control. Our study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect. Methods Data on notified HFRS cases in Weifang city, Shandong Province were collected from the Disease Reporting Information System of the Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases. Results Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA(1, 0.11, 2)(1, 0, 1) 12 : Akaike information criterion (AIC): -631.31; SARIMA(1, 0, 2)(1, 1, 1) 12 : AIC: -227.32) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE): 0.058; SARIMA: RMSE: 0.090). Conclusions The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset.

scholarly journals Modeling tropospheric ozone and particulate matter in Tunis, Tunisia using generalized additive model

2021 ◽  
Vol 31 (2) ◽  
Author(s):  
Zouhour Hammouda ◽  
Leila Hedhili Zaier ◽  
Nadege Blond
Keyword(s):  
Particulate Matter ◽  
Tropospheric Ozone ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Meteorological Condition ◽  
Additive Models ◽  
Linear Functions ◽  
Meteorological Variables ◽  
Time Variables ◽  
The Impact

The main purpose of this paper is to analyze the sensitivity of tropospheric ozone and particulate matter concentrations to changes in local scale meteorology with the aid of meteorological variables (wind speed, wind direction, relative humidity, solar radiation and temperature) and intensity of traffic using hourly concentration of NOX, which are measured in three different locations in Tunis, (i.e. Gazela, Mannouba and Bab Aliwa). In order to quantify the impact of meteorological conditions and precursor concentrations on air pollution, a general model was developed where the logarithm of the hourly concentrations of O3 and PM10 were modeled as a sum of non-linear functions using the framework of Generalized Additive Models (GAMs). Partial effects of each predictor are presented. We obtain a good fit with R² = 85% for the response variable O3 at Bab Aliwa station. Results show the aggregate impact of meteorological variables in the models explained 29% of the variance in PM10 and 41% in O3. This indicates that local meteorological condition is an active driver of air quality in Tunis. The time variables (hour of the day, day of the week and month) also have an effect. This is especially true for the time variable “month” that contributes significantly to the description of the study area.

scholarly journals Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic

2009 ◽  
Vol 67 (1) ◽  
pp. 145-154 ◽  
Author(s):  
Matthew J. S. Windle ◽  
George A. Rose ◽  
Rodolphe Devillers ◽  
Marie-Josée Fortin
Keyword(s):  
Survey Data ◽  
Geographically Weighted Regression ◽  
Goodness Of Fit ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Weighted Regression ◽  
Snow Crab ◽  
Implicit Assumption ◽  
Northwest Atlantic ◽  
Goodness Of Fit Tests

Abstract Windle, M. J. S., Rose, G. A., Devillers, R., and Fortin, M-J. 2010. Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic. – ICES Journal of Marine Science, 67: 145–154. Analyses of fisheries data have traditionally been performed under the implicit assumption that ecological relationships do not vary within management areas (i.e. assuming spatially stationary processes). We question this assumption using a local modelling technique, geographically weighted regression (GWR), not previously used in fisheries analyses. Outputs of GWR are compared with those of global logistic regression and generalized additive models (GAMs) in predicting the distribution of northern cod off Newfoundland, Canada, based on environmental (temperature and distance from shore) and biological factors (snow crab and northern shrimp) from 2001. Results from the GWR models explained significantly more variability than the global logistic and GAM regressions, as shown by goodness-of-fit tests and a reduction in the spatial autocorrelation of model residuals. GWR results revealed spatial regions in the relationships between cod and explanatory variables and that the significance and direction of these relationships varied locally. A k-means cluster analysis based on GWR t-values was used to delineate distinct zones of species–environment relationships. The advantages and limitations of GWR are discussed in terms of potential application to fisheries ecology.

scholarly journals Sardine potential habitat and environmental forcing off western Portugal

2010 ◽  
Vol 67 (8) ◽  
pp. 1553-1564 ◽  
Author(s):  
Juan P. Zwolinski ◽  
Paulo B. Oliveira ◽  
Victor Quintino ◽  
Yorgos Stratoudakis
Keyword(s):  
Chlorophyll A ◽  
Seasonal Pattern ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Seasonal Effect ◽  
Oceanic Circulation ◽  
Environmental Forcing ◽  
Potential Habitat ◽  
The North ◽  
Spatio Temporal

Abstract Zwolinski, J. P., Oliveira, P. B., Quintino, V., and Stratoudakis, Y. 2010. Sardine potential habitat and environmental forcing off western Portugal. – ICES Journal of Marine Science, 67: 1553–1564. Relationships between sardine (Sardina pilchardus) distribution and the environment off western Portugal were explored using data from seven acoustic surveys (spring and autumn of 2000, 2001, 2005, and spring 2006). Four environmental variables (salinity, temperature, chlorophyll a, and acoustic epipelagic backscatter other than fish) were related to the acoustic presence and density of sardine. Univariate quotient analysis revealed sardine preferences for waters with high chlorophyll a content, low temperature and salinity, and low acoustic epipelagic backscatter. Generalized additive models depicted significant relationships between the environment and sardine presence but not with sardine density. Maps of sardine potential habitat (SPH) built upon the presence/absence models revealed a clear seasonal effect in the across-bathymetry and alongshelf extension of SPH off western Portugal. During autumn, SPH covered a large part of the northern Portuguese continental shelf but was almost absent from the southern region, whereas in spring SPH extended farther south but was reduced to a narrow band of shallow coastal waters in the north. This seasonal pattern agrees with the spatio-temporal variation of primary production and oceanic circulation described for the western Iberian shelf.

scholarly journals Forecast of Hemorrhagic Fever With Renal Syndrome and Meteorological Factors of Three Cities in Liaoning Province, China, 2005–2019

2021 ◽  
Vol 9 ◽  
Author(s):  
Wanwan Sun ◽  
Zhidong Liu ◽  
Qiyong Liu ◽  
Wen Li ◽  
Liang Lu
Keyword(s):  
Meteorological Factors ◽  
Generalized Additive Models ◽  
Positive Association ◽  
Hemorrhagic Fever ◽  
Additive Models ◽  
Control Measures ◽  
Liaoning Province ◽  
Interclass Correlation ◽  
Control And Prevention ◽  
Lag Times

Background: Hemorrhagic fever with renal syndrome (HFRS) is an endemic in China, accounting for 90% of HFRS cases worldwide and growing. Therefore, it is urgent to monitor and predict HFRS cases to make control measures more effective. In this study, we applied generalized additive models (GAMs) in Liaoning Province, an area with many HFRS cases. Our aim was to determine whether GAMs could be used to accurately predict HFRS cases and to explore the association between meteorological factors and the incidence of HFRS.Methods: HFRS data from Liaoning were collected from January 2005 to May 2019 and used to construct GAMs. Generalized cross-validation (GCV) and adjusted R-square (R2) values were used to evaluate the constructed models. The interclass correlation coefficient (ICC) was used as an index to assess the quality of the proposed models.Results: HFRS cases of the previous month and meteorological factors with different lag times were used to construct GAMs for three cities in Liaoning. The three models predicted the number of HFRS cases in the following month. The ICCs of the three models were 0.822, 0.832, and 0.831. Temperature and the number of cases in the previous month had a positive association with HFRS.Conclusion: GAMs applied to HFRS case data are an important tool for HFRS control in China. This study shows that meteorological factors have an effect on the occurrence of HFRS. A mathematical model based on surveillance data could also be used in forecasting. Our study will inform local CDCs and assist them in carrying out more effective measures for HFRS control and prevention through simple modeling and forecasting.

A heveny szívinfarktus gyakoriságának és az általa okozott halálozás diurnalis és szezonális jellemzői Magyarországon

2021 ◽  
Vol 162 (14) ◽  
pp. 555-560
Author(s):  
Tamás Ferenci ◽  
András Jánosi
Keyword(s):  
Myocardial Infarction ◽  
Seasonal Variation ◽  
Seasonal Pattern ◽  
Generalized Additive Models ◽  
Additive Models ◽  
St Elevation Myocardial Infarction ◽  
Incidence And Mortality ◽  
St Elevation ◽  
Public Holidays

Összefoglaló. Bevezetés: A heveny szívinfarktus gyakoriságának és halálozásának napi és szezonális ingadozása fontos epidemiológiai adat, régóta kutatás tárgya. Célkitűzés: A szívinfarktus gyakoriságának, az általa okozott halálozásnak diurnalis és szezonális vizsgálata nagy esetszámú, válogatás nélküli betegcsoport adatainak elemzésével. Módszer: A szerzők a Nemzeti Szívinfarktus Regiszterben 2014. 01. 01. és 2017. 12. 31. között regisztrált betegek adatait dolgozták fel. Az adatok többváltozós vizsgálatára általánosított additív modelleket használtak. Eredmények: Három év alatt 30 333, ST-elevációval nem járó infarktus (NSTEMI) és 23 667, ST-elevációval járó infarktus (STEMI) miatt kezelt beteg adatait rögzítettük. A betegek utánkövetésének medián értéke 563 nap volt. Szívinfarktusra utaló panasz – mindkét infarktustípus esetén – reggel 7 és 8 óra között jelentkezett a leggyakrabban, NSTEMI esetén este 20 óra körül is találtak egy második gyakorisági csúcsot. A hét napjai a gyakoriság szempontjából szignifikáns eltérést mutattak (p<0,001): hétfőn magasabb, hétvégén lényegesen alacsonyabb incidenciát találtunk. Az éven belüli mintázat mindkét nemi, életkori és infarktustípus szerinti csoportban konzisztens: tavasszal a legmagasabb, nyáron a legalacsonyabb az incidencia (p<0,001). Az incidencia munkaszüneti napokon alacsonyabb volt (p = 0,0053 STEMI-nél, p<0,001 NSTEMI-nél). A halálozás többszempontos analízise azt igazolta, hogy a hét napjai itt is eltértek, hétvégén magasabb halálozás igazolódott (p<0,001). A munkaszüneti napoknak ugyanakkor nem volt szignifikáns hatásuk a halálozásra (p = 0,4542), és az évszakok halálozási adatai sem különböztek (p = 0,0677). Következtetés: A szívinfarktus gyakrabban fordult elő hétfőn, a reggeli órákban és az évszakok esetén tavasszal. A halálozás hétvégén nagyobb volt, mint munkanapokon. Orv Hetil. 2021; 162(14): 555–560. Summary. Introduction: Daily and seasonal variation of the incidence and mortality of acute myocardial infarction has long been the subject of research. Objective: Investigation of the diurnal and seasonal pattern of the incidence and mortality of myocardial infarction by analyzing data from a large number of consecutive patients. Method: The authors processed the data of patients registered in the Hungarian Myocardial Infarction Registry between 01. 01. 2014 and 31. 12. 2017. Generalized additive models were used for the multivariate investigation of the data. Results: 30 333 patients treated for non-ST elevation myocardial infarction (NSTEMI) and 23 667 patients with ST elevation myocardial infarction (STEMI) were recorded. The median follow-up was 563 days. Patients’ complaints most commonly occurred between 7:00 and 8:00 a.m. for both types of infarction with a secondary peak at 20:00 p.m. for NSTEMI. The days of week were significantly different (p<0.001) with a higher incidence on Monday, and lower at the weekend. The seasonal pattern was consistent in every age and sex group and according to the type of infarction: incidence was the highest in spring and the lowest in summer (p<0.001). The incidence was lower on public holidays (p = 0.0053 for STEMI, p<0.001 for NSTEMI). Multivariate analysis of mortality revealed that the days of week are significantly different here as well (p<0.001) with a higher mortality at the weekends. The effect of public holidays was non-significant (p = 0.4542) as was seasonality (p = 0.0677) in mortality. Conclusion: Myocardial infarction occurs more often in the morning hours, on Monday, and – as far as seasonal variation – in spring. The mortality at the end of the week is greater than on working days. Orv Hetil. 2021; 162(14): 555–560.

scholarly journals Crimean-Congo Hemorrhagic Fever Virus (CCHFV): A Silent but Widespread Threat

2021 ◽  
Author(s):  
Paul A. Kuehnert ◽  
Christopher P. Stefan ◽  
Catherine V. Badger ◽  
Keersten M. Ricks
Keyword(s):  
One Health ◽  
Hemorrhagic Fever ◽  
Fever Virus ◽  
Virus Vector ◽  
Disease Spread ◽  
Fatality Rate ◽  
Crimean Congo Hemorrhagic Fever ◽  
Hemorrhagic Fever Virus ◽  
High Fatality Rate ◽  
Endemic Areas

Abstract Purpose of Review This review is aimed at highlighting recent research and articles on the complicated relationship between virus, vector, and host and how biosurveillance at each level informs disease spread and risk. Recent Findings While human cases of CCHFV and tick identification in non-endemic areas in 2019–2020 were reported to sites such as ProMed, there is a gap in recent published literature on these and broader CCHFV surveillance efforts from the late 2010s. Summary A review of the complex aspects of CCHFV maintenance in the environment coupled with high fatality rate and lack of vaccines and therapeutics warrants the need for a One-Health approach toward detection and increased biosurveillance programs for CCHFV.

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