Assessment of climate change impact on the malaria vector Anopheles hyrcanus, West Nile disease, and incidence of melanoma in the Vojvodina Province (Serbia) using data from a regional climate model.

Motivated by the One Health paradigm, we found the expected changes in temperature and UV radiation (UVR) to be a common trigger for enhancing the risk that viruses, vectors, and diseases pose to human and animal health. We compared data from the mosquito field collections and medical studies with regional climate model projections to examine the impact of climate change on the spreading of one malaria vector, the circulation of West Nile virus (WNV), and the incidence of melanoma. We analysed data obtained from ten selected years of standardised mosquito vector sampling with 219 unique location-year combinations, and 10 years of melanoma incidence. Trends in the observed data were compared to the climatic variables obtained by the coupled regional Eta Belgrade University and Princeton Ocean Model for the period 1961-2015 using the A1B scenario, and the expected changes up to 2030 were presented. Spreading and relative abundance of Anopheles hyrcanus was positively correlated with the trend of the mean annual temperature. We anticipated a nearly twofold increase in the number of invaded sites up to 2030. The frequency of WNV detections in Culex pipiens was significantly correlated to overwintering temperature averages and seasonal relative humidity at the sampling sites. Regression model projects a twofold increase in the incidence of WNV positive Cx. pipiens for a rise of 0.5°C in overwintering TOctober-April temperatures. The projected increase of 56% in the number of days with Tmax ≥ 30°C (Hot Days-HD) and UVR doses (up to 1.2%) corresponds to an increasing trend in melanoma incidence. Simulations of the Pannonian countries climate anticipate warmer and drier conditions with possible dominance of temperature and number of HD over other ecological factors. These signal the importance of monitoring the changes to the preparedness of mitigating the risk of vector-borne diseases and melanoma.

The Choice of an Appropriate Information Dissimilarity Measure for Hierarchical Clustering of River Streamflow Time Series, Based on Calculated Lyapunov Exponent and Kolmogorov Measures.

The purpose of this paper was to choose an appropriate information dissimilarity measure for hierarchical clustering of daily streamflow discharge data, from twelve gauging stations on the Brazos River in Texas (USA), for the period 1989-2016. For that purpose, we selected and compared the average-linkage clustering hierarchical algorithm based on the compression-based dissimilarity measure (NCD), permutation distribution dissimilarity measure (PDDM), and Kolmogorov distance (KD). The algorithm was also compared with K-means clustering based on Kolmogorov complexity (KC), the highest value of Kolmogorov complexity spectrum (KCM), and the largest Lyapunov exponent (LLE). Using a dissimilarity matrix based on NCD, PDDM, and KD for daily streamflow, the agglomerative average-linkage hierarchical algorithm was applied. The key findings of this study are that: (i) The KD clustering algorithm is the most suitable among others; (ii) ANOVA analysis shows that there exist highly significant differences between mean values of four clusters, confirming that the choice of the number of clusters was suitably done; and (iii) from the clustering we found that the predictability of streamflow data of the Brazos River given by the Lyapunov time (LT), corrected for randomness by Kolmogorov time (KT) in days, lies in the interval from two to five days.

A temporal pattern in the occurrence of aneurysmal subarachnoid hemorrhage in the Province of Vojvodina, Serbia.

BACKGROUND: Numerous studies with conflicting results have tried to prove the influence of seasonal variations or different meteorological factors on the occurrence of aneurysmal subarachnoidal hemorrhage (SAH). The aim of this study was to establish a mathematical model of a series of aneurysmal rupture dates in different patients and verify a temporal pattern in the occurrence of SAH. METHODS: We analyzed a group of 563 patients with the exact aneurysm rupture dates, hospitalized at the Clinic of Neurosurgery, Clinical Center of Vojvodina, Novi Sad, Serbia, between January 1, 1998 and December 31, 2009. After the monthly distributions, we evaluated the period between two subsequent rupture dates. RESULTS: The absolute number of SAH per month varied between 0 and 10. The monthly seasonal indices show a fluctuation of the time series (with the peak in March and nadir in September), but the median values of the number of aneurysm ruptures in a particular month did not differ significantly. The time scale of the aneurysm rupture dates shows that the most frequent interval between subsequent ruptures was 1 day (in 75 cases or 13.34%). Following this period, the number of days between ruptures showed a gradually decreasing pattern that could be approximated by exponential distribution. CONCLUSIONS: The results are a clear confirmation that SAH patients do indeed present in clusters in a restricted population area. This exact clustering in our series is not particularly connected to month or season, yet strongly supports the existence of a temporal pattern in SAH occurrence.