Log-logistic Model Research Articles

Syringe immersion test as in vitro bioassay against Rhipicephalus microplus: Macrocyclic lactones dose-response relationship.

For the diagnosis of tick sensitivity against different acaricides, there are in vitro and in vivo methods. The main in vivo method, the stable test, is considered a defining methodology. In Uruguay, the Rhipicephalus microplus (R. microplus) strain Mozo is used as the standard susceptible strain by the regulatory authorities. In vitro techniques applied both on adult and larvae stages are validated by FAO and can serve as an orientation diagnosis of the resistance profile developed in field conditions. An alternative was proposed as a modification of the larval immersion test (LIT), where syringes were used seeking to reduce the work necessary to perform the original technique, resulting in the syringe immersion test (SIT). The aim of this study was to expand the SIT for the characterization of sensitivity to Macrocyclic Lactones (MLs) in R. microplus and provide information on field strain sensitivity of R. microplus larvae. Log-logistic dose-response model for Ivermectin (IVM), Doramectin (DRM), and Moxidectin (MOX) were performed using concentrations ranging from 0.01 to 20.0 ppm (n = 6, 3 replicates per level on each drug). Larvae sensitivity results were determined after 24 hours of incubation at 27°C/90% RH, counting live/dead larvae. The final model will be decided as the best fit according to the model selection AIC criteria for each drug. Pharmacodynamic parameters [lower limit, slope, and effective dose at different levels (ED20, ED50, ED80, and ED95)] and its 95% confidence interval were considered for drug comparison. Dose-response models were fitted for IVM, DRM, and MOX. MOX had the lowest ED50 of the three drugs, implying that MOX is of higher potency (two folds) when compared to IVM and DRM on R. microplus larvae using SIT. DRM had a different slope compared to IVM and MOX (p < 0.05), while IVM and MOX showed a similar slope (p > 0.05). This study allowed us to standardize the technique for larvae immersion for each ML, granting a new tool for in vitro test as a screening technique for tick sensitivity.

Mixed-Effects Parametric Proportional Hazard Model with Generalized Log-Logistic Baseline Distribution

Clustered survival data are widely observed in a variety of setting. Most survival models incorporate clustering and grouping of data accounting for between-cluster variability that creates correlation in order to prevent underestimate of the standard errors of the parameter estimators but do not include random effects. In this study, we developed a mixed-effect parametric proportional hazard (MEPPH) model with a generalized log-logistic distribution baseline. The parameters of the model were estimated by the application of the maximum likelihood estimation technique with an iterative optimization procedure (quasi-Newton Raphson). The developed MEPPH model’s performance was evaluated using Monte Carlo simulation. The Leukemia dataset with right-censored data was used to demonstrate the model’s applicability. The results revealed that all covariates, except age in PH models, were significant in all considered distributions. Age and Townsend score were significant when the GLL distribution was used in MEPPH, while sex, age and Townsend score were significant in MEPPH model when other distributions were used. Based on information criteria values, the Generalized Log-Logistic Mixed-Effects Parametric Proportional Hazard model (GLL-MEPPH) outperformed other models.

Neutrosophic Structure of the Log-logistic Model with Applications to Medical Data

In practical scenarios, it is common to encounter fuzzy data that contains numerous imprecise observations. The uncertainty associated with this type of data often leads to the use of interval statistical measures and the proposal of neutrosophic versions of probability distributions to better handle such data. This study introduces a new generalized design of the log-logistic distribution within a neutrosophic framework, building upon encouraging applications of this distribution in fields such as economics, engineering, survival analysis, and lifetime modeling. The proposed neutrosophic log-logistic distribution (NLLD) is analyzed in terms of statistical properties, including moments, shape coefficients, and various survival characteristics. To evaluate the performance of the predicted neutrosophic parameters, an estimation procedure is conducted. Finally, the practical application of the proposed model is demonstrated using a sample dataset consisting of 128 bladder cancer patients.

Distribution of Computerized Datasets To Fit Weibull, Log-Logistics and Gompertz Survival Models

The lack of detailed and analytic study on the risk factors of Breast cancer in Nigerian presents us with the challenge of not knowing the risk factors distinct to the Nigerian settings. Effectively handling the breast cancer treatment in co-infected patients is delicate. Some challenges to the treatment of breast cancer infection include drug-to-drug interactions between procedures required to treat the cancer cells, thus the hormonal drugs burdens patient’s inability to comply with dosage intake thereby making women with breast cancer prone to developing other forms of cancer. The aim of the research was to analyzed distributed dataset to fit Weibull, Log-logistics and Gompertz survival models. The research methodology involved the collection of data cases on oncological study analyzed using descriptive statistics and parametric survival models. The result shows that the Cramer-von Misses (W), the Anderson Darling (A) and the Kolmogorov Smirnov (D) statistics, Log-logistic distribution has greater p-value than other distributions, hence it is the best fit for survival times of a group of patients given chemotherapy treatment. The research concluded that Gompertz model was the best fitted model to survival times of a group of patients given chemotherapy treatment data, while log-logistic distribution was the best fit distribution for the data. It also concluded that Weibull and log-logistic models perform better than Gompertz model in fatigue fracture data, while log-logistic distribution was the best fit for the data and Gompertz model and the distribution was the best fit to nicotine measurements.

A Versatile Family of Generalized Log-logistic Distributions: Unimodality, Bimodality, Regression, and Applications

In real-world applications, it is not uncommon to encounter situations in which a set of data exhibits asymmetry and bimodality. Because of this, this paper proposes a new versatile family of generalized log-logistic distributions using the method of T-R{Y} framework. The resulting flexible classes of this family includes both unimodal and bimodal distributions which can be expected to model a wide variety of data with different levels of skewness. The distributional and structural properties of the classes are discussed. The method of maximum likelihood is used for estimating the distributions parameters and a simulation study is conducted to examine its performance. The usefulness and goodness-of-fit of some members of these classes are illustrated by means of six real data sets. The strength of these members is shown consistently by giving better fits than some of the competitors with the same number of parameters. In addition, a new generalized log-logistic lifetime regression model is introduced and applied to fit a right-censored data with covariates. The flexibility provided by this model could be very helpful in describing and explaining different types of lifetime data.

The Determination of the Most Appropriate Probability Distribution Models for the Meteorological Variables

Every component of the hydrological cycle is essential for controlling water supplies and assessing the potential catastrophic events like floods and droughts. The variables of hydrological system are unexpected and unique to each place. In this paper, the most crucial variables including precipitation, temperature, relative humidity, and evaporation are examined for Ankara province. For meteorological parameters, the Lognormal, Log-logistic, Gamma, Weibull, Normal, and Gumbel models are used to find the best suitable distributions. Kolmogorov-Smirnov, Cramers-von Mises, Akaike's Information Criterion, Bayesian Information Criterion, Anderson-Darling, and Maximum Loglikelihood methods are utilized to test these models. Results shows that there is a distinct distribution model for each parameter. In particular, it has been determined that the Gumbel distribution is a better model for annual total precipitation, whereas the Normal distribution is a better model for annual minimum temperature. At stations 17130 and 17664, the gamma distribution is observed to be the best fit distribution at annual total precipitation, but station 17128 is found to be the most appropriate Log-logistic and normal distribution. Stations 17128, 17130, and 17664 for annual maximum temperature series are fitted with the Normal, Log-logistic, and Lognormal, respectively. Gamma is found to be the best fit when analyzing annual mean temperature for stations 17128 and 17130, whereas Lognormal is selected for station 17664. It is expected that these results will contribute to the planning of water resources projects in the region.

Ayçiçeğinde Yabancı Ot Kontrolünde Kritik Periyot ve Uygulamaların Yabancı Ot Türleri ve Çeşitliliğine Etkileri

This study was carried out under field conditions in Adana, Turkey in 2018 and 2019 to determine the critical period for weed control in sunflower depending on the daily growth temperature (GDD) and weed species. A log-logistic model with four parameters was used to determine the relationship between relative crop yield and both increased weed intervention time and length of weed-free periods. In addition, dominant weeds and weed densities were determined in the experimental area. Data obtained from different periods of weed intervention were compared with data obtained from seasonal weed-free plots. During the sunflower growing season, 37.4% – 41.04% yield loss was determined in sunflower due to weed competition. For 5% acceptable yield loss in the first year, the critical period in weed control was determined as 243-1181 GDD; this is 24-86 days after the crop emergence (DAE) . It was found between 269 and 1409 GDD (16-72 DAE) in the second year. In sunflower, it was determined that the removal of weeds from the plot in the weed-free period started within 2-3 weeks from the emergence and continued for 10-12 weeks. These findings may help sunflower growers to plan and implement cost-effective and appropriate weed control programs.

Modeling shipping accidents economic loss and the compensation in Nigeria

The arbitrary reservation of between 25 and 45 % of premium revenue as funds for the compensation of insured unexpired marine risks by underwriters in Nigeria, in line with local insurance regulations, has created a problem of financial insolvency, making it impossible for underwriters to provide timely and adequate compensation for insured shipping risks. The aim of the study is to overcome this challenge by formulating models based on the coefficient of elasticity of the relationships between shipping accident economic loss, the value of seaborne trade, and compensation funds maintained for insured shipping risks. 21 years of time series data from 1999 and 2019 were obtained from secondary sources. Log-log constant elasticity model was used to analyze the data. The results indicate that, for each 1 % increase in shipping accident economic loss, compensation funds maintained for shipping accident economic risks increase by 0.364 %. The policy implications are discussed and models developed for the reservation of funds for sustainable compensation of insured shipping risks are developed.

Salinity-dependent aquatic life criteria of inorganic mercury in coastal water and its ecological risk assessment

Mercury (Hg) is one of the most toxic pollutants to aquatic organisms. The influence of salinity on Hg toxicity, an important factor restricting the development of global marine aquatic life criteria (ALC), is unclear. Therefore, mercury toxicity data were corrected based on salinity using the aggregate slope method, and the ALC values were derived. Short-term aquatic life criteria (SALC) and long-term aquatic life criteria (LALC) were derived using the species sensitivity distribution method based on Log-logistic, Log-normal, Burr III, Gumbel, and Weibull models. The hazard quotient (HQ) and joint probability curve (JPC) methods were used to evaluate the ecological risk of Hg in the coastal waters of China. The results showed that the SALC and LALC of Hg in the coastal waters of China were 2.21 and 0.54 μg/L. The toxicity data and salinity were positively correlated for Chordate and Arthropoda and negatively correlated for Mollusca. The SALC values increased by approximately 75%, with salinities ranging from 10 to 20 ppt. A slight peak in the SALC at mid-salinities was also observed. The ecological risk assessment of Hg in China's coastal waters showed that attention should be paid to Hg pollution in the Bohai Sea and East China Sea, especially the ecological risk of Hg to crustacean organisms. This study could promote the development of water quality criteria for coastal waters and provide a technical reference for mercury management in the coastal waters of China.

Development of EVA and LLDPE polymer-based carvone and spearmint essential oil release systems for citrus postharvest diseases applications

Carvone and spearmint essential oil vapours are suitable candidates for the control of citrus postharvest pathogens due to their strong antifungal activity. However, the high volatility of essential oils remains a hurdle that needs to be overcome before it can be applied as an alternative fungicide. This work investigates the antifungal activity of 20 and 30 wt-% spearmint essential oil or carvone, incorporated into ethylene–vinyl acetate (EVA) and linear low-density polyethylene (LLDPE) strands postharvest pathogens affecting kumquats. Following the melt-extrusion process, an average yield of 16–29 % and 15–28 % of the total carvone and spearmint essential oil, respectively, was determined in the polymer strands. Morphological studies using scanning electron microscopy (SEM) revealed the presence of microporous structures in the internal structure of the strands. The membrane-like skin that covers the polymer strand was also confirmed by SEM. Variations in the release rates were observed between carvone and spearmint essential oils, which was attributed to the difference in the vapour pressure of the neat essential oils. Furthermore, in this study, the results demonstrated that the diffusion exponent ‘n’ of the Korsmeyer–Peppas, Weibull and Log-logistic models exceeded 1.0. In most cases, the Super Case II mechanism was observed. In the case of the Mapossa model, as k2 was ˃> 1, it implies that the outer skin-like membrane covering the polymer strand fully controlled the carvone release. The application of the essential oil-loaded polymer strands resulted in a significant reduction in deterioration, caused by citrus postharvest pathogens, of kumquats kept at room temperature for 21 days. This study indicates that the incorporation of spearmint essential oil or carvone into LLDPE or EVA polymer strands promoted the preservation of kumquat over a 21-day period.

Distribution and Carbon Sequestration Potential of Cola Laurifolia Mast.: A Dominant Native Riparian Species Along Permanent Rivers in Sub-Saharan Africa

Species-specific models for estimating aboveground biomass (AGB) are the accurate means of quantifying species’ carbon pools. Cola laurifolia Mast., a dominant and multi-purpose riparian species along the Mouhoun River in Burkina Faso have a regressive population. Few scientific studies exist concerning this riparian species population and carbon stock capacity. This study aims to allow this gap by formulating a species-specific allometric model for assessing with direct method for Cola laurifolia leave, branches, stem and whole AGB. Parameters used to perform models are tree diameter at breast height (DBH), basal diameter at 20 cm (D20), height (H), and mean crown diameter (CD) using data from 30 trees. Population structure shows a low regeneration potential at all of the studied river zones (i.e. upstream, intermediate and downstream zones). The carbon stock was found to be 54.14 kg C tree-1 and 9.24 Mg C. ha-1. The density of C. laurifolia was higher in downstream zone, and consequently the carbon stock was higher in these areas. The log-log linear model is the best-fitted form incorporated DBH and H as predictors. This form is best fitted for the three tree components (i.e. leaves, branches, stem) and the AGB. The AGB model is more accurate with high coefficient of determination and low RSE (R²=0.92; RSE=0.28) contrasted with leaves models. The global model has the best goodness of fit because of a low relative error (-0.213 %) compared to the use of three component models. The accuracy of our species-specific model confirms the need to develop such models for greater accuracy in AGB estimations.

Estimating the Value of Statistical Life in a Road Safety Context Based on the Contingent Valuation Method

A cost-benefit analysis in a road safety context fundamentally analyzes the advantage of higher safety or lower risk. It can help determine if increasing spending on road safety programs is cost-effective. This study estimates the value of statistical life (VSL)—the amount of money that might be justified to save one person’s life. The VSL is calculated using the willingness to pay (WTP) data obtained through a contingent valuation survey. Three discrete choice models are developed: log-logistic, log-normal, and Weibull. The log-logistic model outperforms the log-normal and Weibull models, comparing Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC) values. We consider the log-logistic model’s mean and median WTP values to estimate the VSL value in the Ethiopian road transport safety context. The VSL estimate in the Ethiopian road transport safety context is 53.52 million ETB (USD 1.07 million). The respondents’ median WTP is ETB 714.44 (USD 14.23). Although the study is in Ethiopia, the findings can be applied to other low- and middle-income countries (LMICs) for the same purpose with modifications. The research findings will aid in a better understanding of the economic efficiencies of increased spending on road safety initiatives. Future research could compare current trends in road safety investment to the amount that should be spent based on the economic justifications from this study.

Glyphosate Efficacy in Chloris virgata Sw. in Response to Temperature and Tank Mixing

Glyphosate alone or a tank mixture of glyphosate and 2,4-D is commonly used for broad-spectrum weed control under fallow conditions in Australia. Air temperature or mixing glyphosate with 2,4-D, may influence the efficacy of glyphosate on feather fingergrass (Chloris virgata Sw.), a problematic summer-season weed of Australia. Dose-response studies were conducted with four populations of feather fingergrass under temperature-controlled glasshouse conditions (35/25 °C and 25/15 °C at 12 h/12 h) to assess the level of glyphosate resistance in relation to temperature regimes. Four parameter log-logistic models were used to develop dose-response curves. Based on plant mortality percentage, LD50 (lethal dose for 50% mortality) values of glyphosate at 25/15 °C for populations Ch, SGM2, SGW2, and CP2 were 137, 60, 650, and 1067 g ae ha-1, respectively. However, at 35/25 °C, the corresponding LD50 values were 209, 557, 2108, and 2554 g ae ha-1, respectively. A similar response was observed for the parameter GR50 (dose for 50% growth reduction) values of glyphosate. These results indicate that populations SGW2 and CP2 are highly glyphosate-resistant and in the summer season, it may be very difficult to control these populations due to poor glyphosate efficacy. These results further suggest that the efficacy of glyphosate for feather fingergrass control can be improved if applied during cooler temperatures (25/15 °C) or the spring season compared with warmer temperatures (35/25 °C) or the summer season. In another study, 2,4-D antagonized glyphosate remarkably in the CP2 (glyphosate-resistant) population but only marginally in the Ch (glyphosate-susceptible) population. Thus, it is not advisable to mix 2,4-D with glyphosate for the control of glyphosate-resistant feather fingergrass populations. The results further suggest that the use of this mixture is useful if the feather fingergrass is not glyphosate-resistant; however, the use of the mixture is to be avoided if the population is glyphosate-resistant in order to not exacerbate the potential resistance problem.

Price elasticity log-log model for cost optimization in D2D underlay mobile edge computing system

Price elasticity log-log model for cost optimization in D2D underlay mobile edge computing system

Minimal Residual Disease Negativity after Blinatumomab Is Predictive of Survival in B-Cell Acute Lymphoblastic Leukemia: Data from a Real-Life Study

Introduction The novel pediatric-like and TKI-based induction regimens induce up to 90 % of complete hematologic response in, respectively, Ph'-negative and Ph'-positive B-Cell Acute Lymphoblastic Leukemia (B-ALL). Minimal residual disease (MRD) strongly correlates with relapse and clearance of MRD is the goal of treatment. Blinatumomab is a bispecific T-cell engager that redirects T-Cells to kill CD19+ cells. Data from clinical trials show that the MRD-positive patients responding to Blinatumomab achieve a better survival than the non-responsive. This retrospective study from real-life demonstrates a superior outcome for MRD-responsive patients to blinatumomab over non-responsive patients. Patients and methods Data from adults treated with blinatumomab in six centers were analyzed to identify factors correlated to survival. Each cycle of blinatumomab was administered in 4 weeks at 15 mg/m2/day by continuous infusion followed by a 2-week interval. Intrathecal prophylaxis with ARA-C, Methotrexate and Dexamethasone was performed between cycles. Twenty-nine (87 %) patients received levetiracetam for seizure-prophylaxis. MRD was measured after each cycle by RT-qPCR with a sensitivity of 10-4 for clonal IgH and for BCR-ABL. As MRD response was considered clonal IgH or BCR-ABL < 10-4. Relapse-free survival (RFS) and overall survival (OS) were estimated by the Kaplan-Meier analysis and compared by the log-rank across the sub-groups using 95 % confidence intervals. The significant variables were compared in multivariate analysis by the log-logistic regression model. Toxicity was graded according to CTCAEs v.4 scale. Results From January 2016 to February 2021 34 B-ALL patients received Blinatumomab. The median age was 37 (18-72). Male to female ratio was 14/20. B-ALL was Ph'+ in 9 and Ph'- in 25 patients, respectively. Indications to blinatumomab were, respectively, relapsed/refractory B-ALL in 19 and MRD-positive in 15 patients. SNC disease was negative before blinatumomab in all patients. Blinatumomab was administered, respectively, as 2nd line in 26 and as > 2nd line of therapy in 8 patients. Overall, 24 patients received hematopoietic transplantation (HCT): 10 before and 14 after blinatumomab, respectively. The cycles of blinatumomab were 1, 2, 3 and 4, respectively, in 10, 13, 5, and 5 patients. The response to blinatumomab was, respectively, complete remission without MRD in 17 (50 %), complete remission with MRD in 8 (23 %), refractory in 4 (12 %) and extramedullary progression in 2 (6 %) patients. Three patients (9 %) had to suspend treatment in 1st cycle because of grade III-IV neuro-toxicity. The response was obtained after 1 and 2 cycles in 18 (72 %) and 7 patients (28 %), respectively. Among the MRD-responsive, MRD-negativity was obtained after 1 and 2 cycles in 16 (93 %) and 1 (7 %) patient, respectively. At a median follow-up of 16 months (1-47) 15 patients (44 %) had relapsed, 15 (44 %) were alive and in complete remission and 4 (12 %) died of transplant-related mortality. The median RFS was 12.5 months (1-39). The median OS was 15.5 months (1-46). The median RFS was 21 vs 4.4 months among MRD-responsive and non-responsive patients, respectively (p = .0004). The median OS was 27 vs 8.7 months among MRD-responsive and non-responsive patients, respectively (p = .008). Apart from MRD-response, the other variables significant for RFS were: blinatumomab as ≤ 2nd line (p = .0008), blinatumomab administered pre-HCT (p = .002) and MRD as an indication to treatment (p = .001). Apart from MRD-response, the other variable significant for OS was MRD as an indication (p = .01). By multivariate analysis only the MRD-response resulted significant for RFS (p = .0009) and OS (p = .02). MRD-response resulted significantly for RFS and OS also by censoring at HCT (p < .0001 and .005, respectively) [Figure 1]. Clinically significant toxicity was: grade II cytokine-release syndrome in 2 patients, grade III and IV neuro-toxicity in 2 and 1 patient, respectively. Overall, the percentage of patients experiencing grade III-IV neuro-toxicity, which occurred in 1st cycle, was 9 %. Conclusion This retrospective study in adult B-ALL Ph'- and Ph'+ patients, confirms the ability of blinatumomab to reach the MRD-negativity in a substantial proportion of patients. MRD-responsive gain a significant survival advantage over non-responsive patients. This advantage was observed both for transplanted and non-transplanted patients. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Comparison between Quantile Regression Technique and Generalised Additive Model for Regional Flood Frequency Analysis: A Case Study for Victoria, Australia

For design flood estimation in ungauged catchments, Regional Flood Frequency Analysis (RFFA) is commonly used. Most of the RFFA methods are primarily based on linear modelling approaches, which do not account for the inherent nonlinearity of rainfall-runoff processes. Using data from 114 catchments in Victoria, Australia, this study employs the Generalised Additive Model (GAM) in RFFA and compares the results with linear method known as Quantile Regression Technique (QRT). The GAM model performance is found to be better for smaller return periods (i.e., 2, 5 and 10 years) with a median relative error ranging 16–41%. For higher return periods (i.e., 20, 50 and 100 years), log-log linear regression model (QRT) outperforms the GAM model with a median relative error ranging 31–59%.

Designing the pricing mechanism of residents’ self-selection sales electricity based on household size

The current tiered electricity pricing (TEP) system for residents in China reflects neither the cost of electricity supply nor the real demand of residents of different household sizes; therefore, we design a classified self-selection sales electricity pricing (CSSSEP) mechanism for different household sizes in this paper. First, based on the log-log model and single threshold model, residential electricity demand models of different household sizes are constructed. Then, the combination of factors other than electricity price is regarded as the residential electricity consumption type, and on this basis, a residential electricity price information screening model is proposed. Finally, the model is empirically compared and analysed. The results indicate that (1) the optimal power supply and electricity charges increase with changes in the type of residents. In addition, (2) the CSSSEP mechanism is better than the self-selection sales electricity pricing (SSSEP) mechanism and the TEP system; i.e., the CSSSEP mechanism can not only save energy and increase the income of power suppliers but also reduce residents' electricity consumption. Furthermore, (3) the average electricity price under the TEP system is relatively low, and the power regulation department should greatly increase it to better realize the reasonable allocation of resources. Finally, (4) the price cap has a positive impact on total social welfare, total profit and average surplus, but the increased total social welfare is mainly manifested in the profit of the power supplier.

How are vineyards management strategies and climate-related conditions affecting economic performance? A case study of Chilean wine grape growers

In wine grape production, growers decide between alternative management strategies of the vineyard that have direct consequences on competitiveness. The aim of this study is to evaluate the impact on economic performance of four management strategies: training system, reserve quality production, irrigation method, and mechanization of labors. The data used in the study comes from face-to-face interviews to 336 wine grape growers of Central Chile, which was complemented with climatic variables retrieved from Geographic Information Systems. A log-log regression model of total value product (TVP) for the main variety grown in the vineyard was estimated, using production factors, vineyards’ attributes, management strategies and climate-related conditions as explanatory variables. An interesting contribution of this study is the identification of TVP functions for land, fertilizers, fungicides, other agrochemicals, labor, and age of vines. Our results show that the training system has the most impact on TVP, where tendone-trained vineyards demonstrated 63% higher TVP than those vertically trained when holding all other variables constant. Reserve quality production also has a positive effect on TVP, increasing it by 25% compared to vineyards producing varietal quality grapes. In contrast, the use of pressurized irrigation systems and mechanization in harvesting do not present a significant effect on TVP. The findings of this paper represent an advance in the understanding of the economic performance factors associated with wine grape growing and could serve to guide on-farm decisions and sectoral policies in pursuing the competitive development of wine grape growers.

Global-scale modeling of early factors and country-specific trajectories of COVID-19 incidence: a cross-sectional study of the first 6 months of the pandemic

BackgroundStudies examining factors responsible for COVID-19 incidence have been mostly focused at the national or sub-national level. A global-level characterization of contributing factors and temporal trajectories of disease incidence is currently lacking. Here we conducted a global-scale analysis of COVID-19 infections to identify key factors associated with early disease incidence. Additionally, we compared longitudinal trends of COVID-19 incidence at a per-country level, and classified countries based on COVID-19 incidence trajectories and effects of lockdown responses.MethodsThis is an observational cross-sectional study covering COVID-19 incidence over the first 6 months of the pandemic (Jan 1, 2020 to June 30, 2020). A retrospective analysis was performed using publicly available data for total confirmed COVID-19 cases by country, and using recent data on demographic, meteorological, economic and health-related indicators per country. Data was analyzed in a regression modeling framework. Longitudinal trends were assessed via linear and non-linear model fitting. Competing models of disease trajectories were ranked by the Akaike’s Information Criterion (AIC). A novel approach involving hierarchical clustering was developed to classify countries based on the effects of lockdown measures on new COVID-19 caseloads surrounding the lockdown period.ResultsUnivariate analysis identified 11 variables (employments in the agriculture, service and industrial sectors, percent population residing in urban areas, population age, number of visitors, and temperatures in the months of Jan-Apr) as independently associated with COVID-19 infections at a global level (variable p < 1E-05). Multivariable analysis identified a 5-variable model (percent urban population, percent employed in agriculture, population density, percent population aged 15–64 years, and temperature in March) as optimal for explaining global variations in COVID-19 (model adjusted R-squared = 0.68, model p < 2.20E-16). COVID-19 case trajectories for most countries were best captured by a log-logistic model, as determined by AIC estimates. Six predominant country clusters were identified when characterizing the effects of lockdown intervals on variations in COVID-19 new cases per country.ConclusionsGlobally, economic and meteorological factors are important determinants of early COVID-19 incidence. Analysis of longitudinal trends and lockdown effects on COVID-19 highlights important nuances in country-specific responses to infections. These results provide valuable insights into disease incidence at a per-country level, possibly allowing for more informed decision making by individual governments in future disease outbreaks.

Caesarean delivery in Uganda: Do non-clinical factors explain the trend?

The aim of this paper was to assess the association between non-clinical factors and Caesarean delivery in Uganda. Self-reported data from the individual recode file were extracted from the 2016 Uganda Demographic and Health Survey (UDHS), with a sub sample of 9929 women aged 15-49 with a recent birth in the last 5 years preceding the survey. Chi-square tests and multivariate comlementary log-log regression models were used to examine the relationship between non-clinical factors and Caesarean section delivery. About one in ten (7%) of the women aged 15-49 had Caesarean deliveries. Non-clinical factors which were significantly associated with Caesarean section delivery include advanced maternal age, having the first birth compared to subsequent births, having 1-3 children compared to 4 or more children, higher level of women's education relative to no education, being in the middle, richer, and richest wealth quintile compared to the poorest quintile. In conclusion, evidence suggests that the trend in Caesarean delivery can be attributed partially to non-clinical factors including advanced maternal age, birth order, parity, women's education level, and wealth quintile. Thus, efforts to address the trend in Caesarean section delivery, need to take account of non-clinical factors.