Sensitivity Analysis of COVID-19 in Mediterranean Island

The aim of this study is to examine the progress of the worldwide pandemic Covid-19.As authors,we have decided to analyze the situation of COVID-19 onMediterranean islandwith accurate data.For this purpose,amathematical model is constructed and proposed by dividing the whole population into sensible and suitable compartments.The study captures the dates February 01 till May 15,2021.For the control of the spread of disease,vaccination and infection rates are compared and calculated.During calculations and comparison,MatLab software is used.All of the data that are used are taken from the Ministry of Health.The effect of parameters is examined with sensitivity analysis.Furthermore,with this analysis,values of parameters are obtained.Afterwards,by using the constructed model,the effect of vaccine on infected individuals is analyzed separately.As a result,it is concluded that the studied part of the island is late for the control of the disease via vaccine.This can be explained by two main reasons;vaccinating the people that are not inmobilitymost of the time(aged people and people with chronic diseases)and getting the vaccine late.Hence,the results showed that this rate and distribution of vaccines would not be enough to control the pandemic on the island.

A Binomial Model Approach: Comparing the R0 Values of SARS-CoV-2 rRT-PCR Data from Laboratories across Northern Cyprus

Northern Cyprus has implemented relatively strict measures in the battle against the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The measures were introduced at the beginning of the COVID-19pandemic, in order to prevent the spread of the disease. One of these measures was the use of two separate realtime reverse transcription polymerase chain reaction (rRT-PCR) tests for SARS-CoV-2 referred to as the doublescreening procedure, which was adopted following the re-opening of the sea, air and land borders for passengersafter the first lockdown. The rRT-PCR double screening procedure involved reporting a negative rRT-PCR testwhich was carried in 72 to 120 h before departure whilst presenting no known symptoms of the COVID-19and performing a second rRT-PCR test at the point of arrival. This study compares the results of SARS-CoV-2rRT-PCR tests performed on incoming flight passengers from the 1st July to 9th of September 2020 to NorthernCyprus. The rRT-PCR test results collected by the Near East University (NEU) DESAM COVID-19 laboratory werecompared with the rRT-PCR test results collected by the Ministry of Health and/or private COVID-19 laboratoriesin Northern Cyprus. This comparative study was conducted using binomial distribution. In addition, by applyingthe Susceptible-Exposed-Infected-Removed (SEIR) model to Northern Cyprus, overall basic reproduction number(R0) value of the COVID-19 was analysed for the same time period to act as a threshold for this comparison.In both the statistical and SEIR mathematical model, R0 was calculated. It was assumed that, the more similarthe R0 results of NEU DESAM COVID-19 laboratory and other laboratories were with the overall R0 value ofNorthern Cyprus, the more reliable the results would be. We calculated that the median R0 values of the NEUDESAM COVID-19 laboratory and other laboratories performing the SARS-CoV-2 rRT-PCR on air passengersduring the studied period to be 0.96 and 1.29, respectively, compared to Northern Cyprus median R0 value whichwas 0.99. The rRT-PCR screening results from the NEU DESAM COVID-19 laboratory were closely aligned withthe screening results of Northern Cyprus whereas the screening results reported by other laboratories were not in afit with the regional pattern. This study also aimed to point out the importance of the rRT-PCR screening procedure since asymptomatic positive SARS-CoV-2 cases entery to Northern Cyprus was inhibited and this prevented thespread of the disease within the population.

Reducing the Range of Cancer Risk on BI-RADS 4 Subcategories via Mathematical Modelling

Breast Imaging Reporting and Data System,also known as BI-RADS is a universal system used by radiologists and doctors.It constructs a comprehensive language for the diagnosis of breast cancer.BI-RADS 4 category has a wide range of cancer risk since it is divided into 3 categories.Mathematicalmodels play an important role in the diagnosis and treatment of cancer.In this study,data of 42 BI-RADS 4 patients taken fromthe Center for Breast Health,Near East University Hospital is utilized.Regarding the analysis,a mathematical model is constructed by dividing the population into 4 compartments.Sensitivity analysis is applied to the parameters with the desired outcome of a reduced range of cancer risk.Numerical simulations of the parameters are demonstrated.The results of the model have revealed that an increase in the lactation rate and earlymenopause have a negative correlation with the chance of being diagnosed with BI-RADS 4 whereas a positive correlation increase in age,the palpable mass,and family history is distinctive.Furthermore,the negative effects of smoking and late menopause on BI-RADS 4C diagnosis are vehemently outlined.Consequently,the model showed that the percentages of parameters play an important role in the diagnosis of BI-RADS 4 subcategories.All things considered,with the assistance of the most effective parameters,the range of cancer risks in BI-RADS 4 subcategories will decrease.

Near Future Perspective of ESBL-Producing Klebsiella pneumoniae Strains Using Mathematical Modeling

While antibiotic resistance is becoming increasingly serious today,there is almost no doubt that more challenging times await us in the future.Resistant microorganisms have increased in the past decades leading to limited treatment options,along with higher morbidity and mortality.Klebsiella pneumoniae is one of the significant microorganisms causing major public health problems by acquiring resistance to antibiotics and acting as an opportunistic pathogen of healthcare-associated infections.The production of extended spectrumbeta-lactamases(ESBL)is one of the resistance mechanisms of K.pneumoniae against antibiotics.In this study,the future clinical situation of ESBL-producing K.pneumoniae was investigated in order to reflect the future scenarios to understand the severity of the issue and to determine critical points to prevent the spread of the ESBL-producing strain.For evaluation purposes,SIS-type mathematical modeling was used with retrospective medical data from the period from 2016 to 2019.Stability of the disease-free equilibrium and basic reproduction ratios were calculated.Numerical simulation of the SISmodel was conducted to describe the dynamics of non-ESBL and ESBL-producing K.pneumoniae.In order to determine the most impactful parameter on the basic reproduction ratio,sensitivity analysis was performed.A study on mathematical modeling using data on ESBL-producing K.pneumoniae strains has not previously been performed in any health institution in Northern Cyprus,and the efficiency of the parameters determining the spread of the relevant strains has not been investigated.Through this study,the spread of ESBL-producing K.pneumoniae in a hospital environment was evaluated using a different approach.According to the study,in approximately seventy months,ESBL-producing K.pneumoniae strains will exceed non-ESBL K.pneumoniae strains.As a result,the analyses showed that hospital admissions and people infected with non-ESBL or ESBL-producing K.pneumoniae have the highest rate of spreading the infections.In addition,it was observed that the use of antibiotics plays a major role in the spread of ESBL-producing K.pneumoniae compared to other risk factors such as in-hospital transmissions.As amatter of course,recoveries fromKlebsiella infections were seen to be the most effective way of limiting the spread.It can be concluded from the results that although the use of antibiotics is one of themost effective factors in the development of the increasing ESBL-producing K.pneumoniae,regulation of antibiotic use policy could be a remedial step together with effective infection control measures.Such steps may hopefully lead to decreased morbidity and mortality rates as well as improved medical costs.