@article{Dai2024,
   abstract = {Operating in harsh environments, drilling pumps are highly susceptible to failure and challenging to diagnose. To enhance the fault diagnosis accuracy of the drilling pump fluid end and ensure the safety and stability of drilling operations, this paper proposes a fault diagnosis method based on time-frequency analysis and convolutional neural networks. Firstly, continuous wavelet transform (CWT) is used to convert the collected vibration signals into time-frequency diagrams, providing a comprehensive database for fault diagnosis. Next, a SqueezeNet-based fault diagnosis model is developed to identify faults. To validate the effectiveness of the proposed method, fault signals from the fluid end were collected, and fault diagnosis experiments were conducted. The experimental results demonstrated that the proposed method achieved an accuracy of 97.77% in diagnosing nine types of faults at the fluid end, effectively enabling precise fault diagnosis, which is higher than the accuracy of a 1D convolutional neural network by 14.55%. This study offers valuable insights into the fault diagnosis of drilling pumps and other complex equipment.},
   author = {Maolin Dai and Zhiqiang Huang},
   doi = {10.3390/PR12091929},
   issn = {2227-9717},
   issue = {9},
   journal = {Processes 2024, Vol. 12, Page 1929},
   keywords = {SqueezeNet,continuous wavelet transform,drilling pump fluid end,fault diagnosis},
   month = {9},
   pages = {1929},
   publisher = {Multidisciplinary Digital Publishing Institute},
   title = {Research on Fault Diagnosis of Drilling Pump Fluid End Based on Time-Frequency Analysis and Convolutional Neural Network},
   volume = {12},
   url = {https://www.mdpi.com/2227-9717/12/9/1929/htm https://www.mdpi.com/2227-9717/12/9/1929},
   year = {2024},
}


@article{Turchetti2024,
   abstract = {Existing etch endpoint detection (EPD) methods, primarily based on single wavelengths, have limitations, such as low signal-to-noise ratios and the inability to consider the long-term dependencies of time series data. To address these issues, this study proposes a context of time series data using long short-term memory (LSTM), a kind of recurrent neural network (RNN). The proposed method is based on the time series data collected through optical emission spectroscopy (OES) data during the SiO2 etching process. After training the LSTM model, the proposed method demonstrated the ability to detect the etch endpoint more accurately than existing methods by considering the entire time series. The LSTM model achieved an accuracy of 97.1% in a given condition, which shows that considering the flow and context of time series data can significantly reduce the false detection rate. To improve the performance of the proposed LSTM model, we created an attention-based LSTM model and confirmed that the model accuracy is 98.2%, and the performance is improved compared to that of the existing LSTM model.},
   author = {Claudio Turchetti and Laura Falaschetti and Ye Jin Kim and Jung Ho Song and Ki Hwan Cho and Jong Hyeon Shin and Jong Sik Kim and Jung Sik Yoon and Sang Jeen Hong},
   doi = {10.3390/ELECTRONICS13173577},
   issn = {2079-9292},
   issue = {17},
   journal = {Electronics 2024, Vol. 13, Page 3577},
   keywords = {endpoint detection,machine learning,plasma etch},
   month = {9},
   pages = {3577},
   publisher = {Multidisciplinary Digital Publishing Institute},
   title = {Improved Plasma Etch Endpoint Detection Using Attention-Based Long Short-Term Memory Machine Learning},
   volume = {13},
   url = {https://www.mdpi.com/2079-9292/13/17/3577/htm https://www.mdpi.com/2079-9292/13/17/3577},
   year = {2024},
}


@article{Torcasio2024,
   abstract = {Accurate precipitation intensity forecasting is crucial for effective flood management and early warning systems. This study evaluates the performances of convolutional neural network (CNN) and long short-term memory (LSTM) models in predicting hourly precipitation intensity using data from Sainte Catherine de la Jacques Cartier station near Québec City. The models predict precipitation levels from one to six hours ahead, which are categorized into slight, moderate, heavy, and very heavy precipitation intensities. Our methodology involved gathering hourly precipitation data, defining input combinations for multistep ahead forecasting, and employing CNN and LSTM models. The performances of these models were assessed through qualitative and quantitative evaluations. The key findings reveal that the LSTM model excelled in the short-term (1HA to 2HA) and long-term (3HA to 6HA) forecasting, with higher R2 (up to 0.999) and NSE values (up to 0.999), while the CNN model was more computationally efficient, with lower AICc values (e.g., −16,041.1 for 1HA). The error analysis shows that the CNN demonstrated higher precision in the heavy and very heavy categories, with a lower relative error, whereas the LSTM performed better for the slight and moderate categories. The LSTM outperformed the CNN in minor- and high-intensity events, but the CNN exhibited a better performance for significant precipitation events with shorter lead times. Overall, both models were adequate, with the LSTM providing better accuracy for extended forecasts and the CNN offering efficiency for immediate predictions, highlighting their complementary roles in enhancing early warning systems and flood management strategies.},
   author = {Rosa Claudia Torcasio and Stefano Federico and Elenio Avolio and Isa Ebtehaj and Hossein Bonakdari},
   doi = {10.3390/ATMOS15091082},
   issn = {2073-4433},
   issue = {9},
   journal = {Atmosphere 2024, Vol. 15, Page 1082},
   keywords = {Québec,convolutional neural networks (CNNs),deep learning models,early warning systems,flood management,hourly precipitation prediction,long short,precipitation intensity forecasting,real,term memory (LSTM),time forecasting,water resource management},
   month = {9},
   pages = {1082},
   publisher = {Multidisciplinary Digital Publishing Institute},
   title = {CNN vs. LSTM: A Comparative Study of Hourly Precipitation Intensity Prediction as a Key Factor in Flood Forecasting Frameworks},
   volume = {15},
   url = {https://www.mdpi.com/2073-4433/15/9/1082/htm https://www.mdpi.com/2073-4433/15/9/1082},
   year = {2024},
}


@article{Mienye2024,
   abstract = {Recurrent neural networks (RNNs) have significantly advanced the field of machine learning (ML) by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures, such as long short-term memory (LSTM) networks, gated recurrent units (GRUs), bidirectional LSTM (BiLSTM), echo state networks (ESNs), peephole LSTM, and stacked LSTM. The study examines the application of RNNs to different domains, including natural language processing (NLP), speech recognition, time series forecasting, autonomous vehicles, and anomaly detection. Additionally, the study discusses recent innovations, such as the integration of attention mechanisms and the development of hybrid models that combine RNNs with convolutional neural networks (CNNs) and transformer architectures. This review aims to provide ML researchers and practitioners with a comprehensive overview of the current state and future directions of RNN research.},
   author = {Ibomoiye Domor Mienye and Theo G. Swart and George Obaido},
   doi = {10.3390/INFO15090517},
   issn = {2078-2489},
   issue = {9},
   journal = {Information 2024, Vol. 15, Page 517},
   keywords = {GRU,LSTM,NLP,RNN,deep learning,machine learning},
   month = {8},
   pages = {517},
   publisher = {Multidisciplinary Digital Publishing Institute},
   title = {Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications},
   volume = {15},
   url = {https://www.mdpi.com/2078-2489/15/9/517/htm https://www.mdpi.com/2078-2489/15/9/517},
   year = {2024},
}


@article{Chicco2021,
   abstract = {Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables. The difference between binary classification and regression is in the target range: in binary classification, the target can have only two values (usually encoded as 0 and 1), while in regression the target can have multiple values. Even if regression analysis has been employed in a huge number of machine learning studies, no consensus has been reached on a single, unified, standard metric to assess the results of the regression itself. Many studies employ the mean square error (MSE) and its rooted variant (RMSE), or the mean absolute error (MAE) and its percentage variant (MAPE). Although useful, these rates share a common drawback: since their values can range between zero and +infinity, a single value of them does not say much about the performance of the regression with respect to the distribution of the ground truth elements. In this study, we focus on two rates that actually generate a high score only if the majority of the elements of a ground truth group has been correctly predicted: the coefficient of determination (also known as R-squared or R2) and the symmetric mean absolute percentage error (SMAPE). After showing their mathematical properties, we report a comparison between R2 and SMAPE in several use cases and in two real medical scenarios. Our results demonstrate that the coefficient of determination (R-squared) is more informative and truthful than SMAPE, and does not have the interpretability limitations of MSE, RMSE, MAE and MAPE. We therefore suggest the usage of R-squared as standard metric to evaluate regression analyses in any scientific domain.},
   author = {Davide Chicco and Matthijs J. Warrens and Giuseppe Jurman},
   doi = {10.7717/PEERJ-CS.623/SUPP-1},
   issn = {23765992},
   journal = {PeerJ Computer Science},
   keywords = {Coefficient of determination,Mean absolute error,Mean square error,Regression,Regression analysis,Regression evaluation,Regression evaluation rates},
   month = {7},
   pages = {1-24},
   pmid = {34307865},
   publisher = {PeerJ Inc.},
   title = {The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation},
   volume = {7},
   url = {https://peerj.com/articles/cs-623},
   year = {2021},
}

@article{Sharma2022,
   abstract = {With the popularity of artificial intelligence (AI) and its integration into every field, it has yielded positive results that have increased productivity and aided us in solving complex problems. Deep learning (DL) is a subset of AI developed to mimic the human brain. It is a way for a computer to perform actions that come naturally to humans. It is a tool extensively used to organize unsupervised or unlabeled data and find patterns in them. DL has had a major impact on the field of medical science owing to its applications in medical drug discovery, medical imaging, genome synthesis, disease diagnosis, and much more. The growth of DL in this field has increased significantly owing to the processing and type of data used in the models. Focusing on the type of data, preexisting or curated, can drastically affect the success rate of a DL model. DL is used to diagnose diseases such as skin lesions, brain disease, and chronic disease. To understand the application of DL in disease diagnosis and how it has become one of the most efficient methods for disease diagnosis, this chapter will examine the different architectures of DL, such as convolutional neural networks, feed forward networks, and recurrent neural networks, and how their diagnosing methods compare with traditional disease diagnosis methods. In addition, it will explore advantages and disadvantages of DL compared with traditional methods by comparing various efficiency parameters such as accuracy, specificity, sensitivity, area under the curve, and receiver operating characteristics curve, showing how DL is more effective and produces better results. However, DL faces numerous obstacles. If these are overcome, it will lead to even better results and complete autonomous disease diagnosis.},
   author = {Deepak Kumar Sharma and Mayukh Chatterjee and Gurmehak Kaur and Suchitra Vavilala},
   doi = {10.1016/B978-0-12-824145-5.00005-8},
   isbn = {9780128241455},
   journal = {Deep Learning for Medical Applications with Unique Data},
   keywords = {Artificial intelligence,Artificial neural networks,Convolutional neural networks,Deep learning,Disease diagnosis,Pathology},
   month = {1},
   pages = {31-51},
   publisher = {Elsevier},
   title = {Deep learning applications for disease diagnosis},
   year = {2022},
}



@article{haouari_particle_2021,
	title = {A particle swarm optimization approach for predicting the number of {COVID}-19 deaths},
	volume = {11},
	copyright = {2021 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-021-96057-5},
	doi = {10.1038/s41598-021-96057-5},
	abstract = {The rapid spread of the COVID-19 pandemic has raised huge concerns about the prospect of a major health disaster that would result in a huge number of deaths. This anxiety was largely fueled by the fact that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the disease, was so far unknown, and therefore an accurate prediction of the number of deaths was particularly difficult. However, this prediction is of the utmost importance for public health authorities to make the most reliable decisions and establish the necessary precautions to protect people’s lives. In this paper, we present an approach for predicting the number of deaths from COVID-19. This approach requires modeling the number of infected cases using a generalized logistic function and using this function for inferring the number of deaths. An estimate of the parameters of the proposed model is obtained using a Particle Swarm Optimization algorithm (PSO) that requires iteratively solving a quadratic programming problem. In addition to the total number of deaths and number of infected cases, the model enables the estimation of the infection fatality rate (IFR). Furthermore, using some mild assumptions, we derive estimates of the number of active cases. The proposed approach was empirically assessed on official data provided by the State of Qatar. The results of our computational study show a good accuracy of the predicted number of deaths.},
	language = {en},
	number = {1},
	urldate = {2024-09-08},
	journal = {Scientific Reports},
	author = {Haouari, Mohamed and Mhiri, Mariem},
	month = aug,
	year = {2021},
	keywords = {Applied mathematics, Epidemiology},
	pages = {16587},
	file = {Full Text PDF:C\:\\Users\\olivi\\Zotero\\storage\\7JHJUQTG\\Haouari and Mhiri - 2021 - A particle swarm optimization approach for predict.pdf:application/pdf},
}


@article{abolmaali_comparative_2021,
	title = {A comparative study of {SIR} {Model}, {Linear} {Regression}, {Logistic} {Function} and {ARIMA} {Model} for forecasting {COVID}-19 cases},
	volume = {8},
	issn = {2327-8994},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8568588/},
	doi = {10.3934/publichealth.2021048},
	abstract = {Starting February 2020, COVID-19 was confirmed in 11,946 people worldwide, with a mortality rate of almost 2\%. A significant number of epidemic diseases consisting of human Coronavirus display patterns. In this study, with the benefit of data analytic, we develop regression models and a Susceptible-Infected-Recovered (SIR) model for the contagion to compare the performance of models to predict the number of cases. First, we implement a good understanding of data and perform Exploratory Data Analysis (EDA). Then, we derive parameters of the model from the available data corresponding to the top 4 regions based on the history of infections and the most infected people as of the end of August 2020. Then models are compared, and we recommend further research.},
	number = {4},
	urldate = {2024-06-25},
	journal = {AIMS Public Health},
	author = {Abolmaali, Saina and Shirzaei, Samira},
	month = {aug},
	year = {2021},
	pmid = {34786422},
	pmcid = {PMC8568588},
	pages = {598--613},
	file = {PubMed Central Full Text PDF:C\:\\Users\\olivi\\Zotero\\storage\\IDPVWNXX\\Abolmaali and Shirzaei - 2021 - A comparative study of SIR Model, Linear Regressio.pdf:application/pdf},
}

@Article{informatics11020040,
AUTHOR = {Vilca Huayta, Oliver Amadeo and Jimenez Chura, Adolfo Carlos and Sosa Maydana, Carlos Boris and Martínez García, Alioska Jessica},
TITLE = {Analysis of the Epidemic Curve of the Waves of COVID-19 Using Integration of Functions and Neural Networks in Peru},
JOURNAL = {Informatics},
VOLUME = {11},
YEAR = {2024},
NUMBER = {2},
ARTICLE-NUMBER = {40},
URL = {https://www.mdpi.com/2227-9709/11/2/40},
ISSN = {2227-9709},
ABSTRACT = {The coronavirus (COVID-19) pandemic continues to claim victims. According to the World Health Organization, in the 28 days leading up to 25 February 2024 alone, the number of deaths from COVID-19 was 7141. In this work, we aimed to model the waves of COVID-19 through artificial neural networks (ANNs) and the sigmoidal–Boltzmann model. The study variable was the global cumulative number of deaths according to days, based on the Peru dataset. Additionally, the variables were adapted to determine the correlation between social isolation measures and death rates, which constitutes a novel contribution. A quantitative methodology was used that implemented a non-experimental, longitudinal, and correlational design. The study was retrospective. The results show that the sigmoidal and ANN models were reasonably representative and could help to predict the spread of COVID-19 over the course of multiple waves. Furthermore, the results were precise, with a Pearson correlation coefficient greater than 0.999. The computational sigmoidal–Boltzmann model was also time-efficient. Moreover, the Spearman correlation between social isolation measures and death rates was 0.77, which is acceptable considering that the social isolation variable is qualitative. Finally, we concluded that social isolation measures had a significant effect on reducing deaths from COVID-19.},
DOI = {10.3390/informatics11020040}
}

@article{niazkar_assessment_2020,
	title = {Assessment of {Three} {Mathematical} {Prediction} {Models} for {Forecasting} the {COVID}-19 {Outbreak} in {Iran} and {Turkey}},
	volume = {2020},
	issn = {1748-670X},
	url = {https://www.hindawi.com/journals/cmmm/2020/7056285/},
	doi = {10.1155/2020/7056285},
	abstract = {COVID-19 pandemic has become a concern of every nation, and it is crucial to apply an estimation model with a favorably-high accuracy to provide an accurate perspective of the situation. In this study, three explicit mathematical prediction models were applied to forecast the COVID-19 outbreak in Iran and Turkey. These models include a recursive-based method, Boltzmann Function-based model and Beesham’s prediction model. These models were exploited to analyze the confirmed and death cases of the first 106 and 87 days of the COVID-19 outbreak in Iran and Turkey, respectively. This application indicates that the three models fail to predict the first 10 to 20 days of data, depending on the prediction model. On the other hand, the results obtained for the rest of the data demonstrate that the three prediction models achieve high values for the determination coefficient, whereas they yielded to different average absolute relative errors. Based on the comparison, the recursive-based model performs the best, while it estimated the COVID-19 outbreak in Iran better than that of in Turkey. Impacts of applying or relaxing control measurements like curfew in Turkey and reopening the low-risk businesses in Iran were investigated through the recursive-based model. Finally, the results demonstrate the merit of the recursive-based model in analyzing various scenarios, which may provide suitable information for health politicians and public health decision-makers.},
	language = {en},
	urldate = {2024-04-24},
	journal = {Computational and Mathematical Methods in Medicine},
	author = {Niazkar, Majid and Eryılmaz Türkkan, Gökçen and Niazkar, Hamid Reza and Türkkan, Yusuf Alptekin},
	month = {nov},
	year = {2020},
	note = {Publisher: Hindawi},
	pages = {e7056285},
	file = {Full Text PDF:C\:\\Users\\olivi\\Zotero\\storage\\RQ6NCHQN\\Niazkar et al. - 2020 - Assessment of Three Mathematical Prediction Models.pdf:application/pdf},
}


@article{el_aferni_covid-19_2023,
	title = {{COVID}-19 multiwaves as multiphase percolation: a general {N}-sigmoidal equation to model the spread},
	volume = {138},
	issn = {2190-5444},
	url = {https://doi.org/10.1140/epjp/s13360-023-04014-0},
	doi = {10.1140/epjp/s13360-023-04014-0},
	abstract = {The aim of the current study is to investigate the spread of the COVID-19 pandemic as a multiphase percolation process. Mathematical equations have been developed to describe the time dependence of the number of cumulative infected individuals, \$\$I{\textbackslash}left( t {\textbackslash}right)\$\$, and the velocity of the pandemic, \$\$V\_\{\{{\textbackslash}text\{p\}\}\} {\textbackslash}left( t {\textbackslash}right)\$\$, as well as to calculate epidemiological characteristics. The study focuses on the use of sigmoidal growth models to investigate multiwave COVID-19. Hill, logistic dose response and sigmoid Boltzmann models fitted successfully a pandemic wave. The sigmoid Boltzmann model and the dose response model were found to be effective in fitting the cumulative number of COVID-19 cases over time 2 waves spread (N = 2). However, for multiwave spread (N {\textgreater} 2), the dose response model was found to be more suitable due to its ability to overcome convergence issues. The spread of N successive waves has also been described as multiphase percolation with a period of pandemic relaxation between two successive waves.},
	number = {5},
	journal = {The European Physical Journal Plus},
	author = {El Aferni, Ahmed and Guettari, Moez and Hamdouni, Abdelkader},
	month = {may},
	year = {2023},
	pages = {393},
}


@article{gao_forecasting_2020,
	title = {Forecasting the cumulative number of {COVID}-19 deaths in {China}: a {Boltzmann} function-based modeling study},
	volume = {41},
	issn = {0899-823X, 1559-6834},
	shorttitle = {Forecasting the cumulative number of {COVID}-19 deaths in {China}},
	url = {https://www.cambridge.org/core/journals/infection-control-and-hospital-epidemiology/article/forecasting-the-cumulative-number-of-covid19-deaths-in-china-a-boltzmann-functionbased-modeling-study/95BEA41D8DE42B32F2E744C372E9030A\#},
	doi = {10.1017/ice.2020.101},
	language = {en},
	number = {7},
	urldate = {2024-04-24},
	journal = {Infection Control \& Hospital Epidemiology},
	author = {Gao, Yuanyuan and Zhang, Zuqin and Yao, Wei and Ying, Qi and Long, Cheng and Fu, Xinmiao},
	month = {july},
	year = {2020},
	pages = {841--843},
}

@article{namasudra_nonlinear_2023,
	title = {Nonlinear {Neural} {Network} {Based} {Forecasting} {Model} for {Predicting} {COVID}-19 {Cases}},
	volume = {55},
	issn = {1370-4621},
	doi = {10.1007/s11063-021-10495-w},
	abstract = {The recent COVID-19 outbreak has severely affected people around the world. There is a need of an efficient decision making tool to improve awareness about the spread of COVID-19 infections among the common public. An accurate and reliable neural network based tool for predicting confirmed, recovered and death cases of COVID-19 can be very helpful to the health consultants for taking appropriate actions to control the outbreak. This paper proposes a novel Nonlinear Autoregressive (NAR) Neural Network Time Series (NAR-NNTS) model for forecasting COVID-19 cases. This NAR-NNTS model is trained with Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM) and Bayesian Regularization (BR) training algorithms. The performance of the proposed model has been compared by using Root Mean Square Error (RMSE), Mean Square Error (MSE) and correlation co-efficient i.e. R-value. The results show that NAR-NNTS model trained with LM training algorithm performs better than other models for COVID-19 epidemiological data prediction.},
	language = {eng},
	number = {1},
	journal = {Neural Processing Letters},
	author = {Namasudra, Suyel and Dhamodharavadhani, S. and Rathipriya, R.},
	year = {2023},
	pmid = {33821142},
	pmcid = {PMC8012519},
	keywords = {Bayesian regularization, Forecasting, Levenberg Marquardt, Regression, Scaled conjugate gradient, Training algorithm},
	pages = {171--191},
}


@article{fu_simulating_2020,
	title = {Simulating and forecasting the cumulative confirmed cases of {SARS}-{CoV}-2 in {China} by {Boltzmann} function-based regression analyses},
	volume = {80},
	issn = {1532-2742},
	doi = {10.1016/j.jinf.2020.02.019},
	number = {5},
	journal = {The Journal of Infection},
	author = {Fu, Xinmiao and Ying, Qi and Zeng, Tieyong and Long, Tao and Wang, Yan},
	month = {may},
	year = {2020},
	pages = {578--606}
}

@article{rahaman_effect_2023,
	title = {Effect of carbons’ structure and type on {AC} electrical properties of polymer composites: predicting the percolation threshold of permittivity through different models},
	volume = {301},
	issn = {1435-1536},
	url = {https://doi.org/10.1007/s00396-023-05120-2},
	doi = {10.1007/s00396-023-05120-2},
	abstract = {The AC electrical properties of EVA- and NBR-based composites filled with different conductive fillers were investigated. Result shows several magnitudes of increment in AC electrical conductivity and dielectric permittivity after the addition of these conductive fillers, indicating that these materials can be used as supercapacitors. The magnitude of increment was varied according to polymer and filler types. Herein, we also have tested the applicability of different sigmoidal models to find out the percolation threshold value of permittivity for these binary polymer composite systems. It is observed that except sigmoidal–Boltzmann and sigmoidal–dose–response models, other sigmoidal models exhibit different values of percolation threshold when considered for any particular polymer composite system. The paper discusses the variation in results of percolation threshold with an emphasis on the advantages, disadvantages and limitations of these models. We also have applied the classical percolation theory to predict the percolation threshold of permittivity and compared with all the reported sigmoidal models. To judge the unanimous acceptability of these models, they tested vis-à-vis the permittivity results of various polymer composites reported in published literature. To comprehend, all the models except the sigmoidal–logistic-1 model were successfully applicable for predicting the percolation threshold of permittivity for polymer composites.},
	number = {8},
	journal = {Colloid and Polymer Science},
	author = {Rahaman, Mostafizur and Gupta, Prashant and Hossain, Mokarram and Periyasami, Govindasami and Das, Paramita},
	month = {aug},
	year = {2023},
	pages = {1001--1019},
}



@article{hashemireza_double_2023,
	title = {A double sigmoidal model for snow-rain phase separation},
	volume = {617},
	issn = {0022-1694},
	url = {https://www.sciencedirect.com/science/article/pii/S0022169423000951},
	doi = {https://doi.org/10.1016/j.jhydrol.2023.129153},
	abstract = {This paper presents the use of a double sigmoidal model for precipitation phase partitioning in mountains. For the model development and evaluation, daily rainfall, minimum and maximum air temperatures, and snow depth data from twelve synoptic stations in the mountainous regions of Iran are used. The model performance is compared against six air temperature-based methods using the Taylor diagram, the coefficient of determination (R2), and the Root Mean Square Error (RMSE) performance indicators. Comparing the frequency of snowfall events estimated by the proposed model with those of the existing models and observation at the selected stations showed better performance of the proposed model in precipitation phase discrimination at the studied stations. The average R2 statistic that relates the frequency of snowfall occurrences estimated by the proposed model with the frequency of snowfall events observed at the studied stations is 0.989, which is the highest compared to other available models. Its average error across the stations is 4\%, the lowest among the examined models. A split sample approach was used to ensure that the parameters of the proposed model are appropriately estimated. The result showed that the R2 and RMSE values of both calibration and validation subsets are not vastly different from each other, suggesting that the parameters of the two experiments are stable and the corresponding results are robust. The proposed model also performs better than other existing models in accurately estimating air temperature thresholds required for precipitation phase discrimination as well as the snow depth data in all studied stations.},
	journal = {Journal of Hydrology},
	author = {Hashemireza, Firoozeh and Sharafati, Ahmad and Raziei, Tayeb and Nazif, Sara},
	year = {2023},
	keywords = {Air temperature threshold, double sigmoidal-Boltzmann model, Logistic function, Logistic–logistic, Snow-rain phase},
	pages = {129153},
}



@article{tabish_covid-19_2020,
	title = {Covid-19 {Pandemic}: {Emerging} {Perspectives} and {Future} {Trends}},
	volume = {9},
	issn = {2279-9036},
	shorttitle = {Covid-19 {Pandemic}},
	url = {https://doi.org/10.4081/jphr.2020.1786},
	doi = {10.4081/jphr.2020.1786},
	abstract = {World is living on the edge. The human cost of COVID pandemic could be extraordinary. We find ourselves in a time of great economic, social, and medical uncertainty. The pandemic demands action on many fronts, from prevention to testing to treatment. We need to create simple, cheap, more accessible testing for SARS-CoV-2. A faster way has to be developed to identify antibodies that neutralize the virus. More than 100 vaccines for the SARS-CoV-2 are at various stages of development. Some six groups have already begun injecting formulations into volunteers in safety trials; others have started testing in animals. The biggest challenge is to determine which vaccine is ideal. Reason and science have to guide us. There is urgent need to critically appraise evidence in deciding how to treat patients. We need a drug or combination of drugs that work. Remdesivir has generated hope. It may prove to be a magic bullet. Countries like Taiwan, Vietnam, Singapore, Hong Kong, South Korea, New Zealand have done exceptionally well to contain the spread of COVID-19. It is widely believed that during the pandemic treatment suffers. Patients with diseases like cancer, diabetes, renal failure, CAD and pregnant women need special attention. As the pandemic pushes up levels of hunger among the global poor, governments must prevent devastating nutrition and health consequences for children missing out on school meals amid school closures. Nations will have endemic SARS-CoV-2 infection for the foreseeable future. A structured and well-coordinated approach is critical for tackling this global crisis.},
	language = {en},
	number = {1},
	urldate = {2024-02-07},
	journal = {Journal of Public Health Research},
	author = {Tabish, Syed Amin},
	month = {june},
	year = {2020},
	note = {Publisher: SAGE Publications},
	pages = {jphr.2020.1786},
	file = {SAGE PDF Full Text:C\:\\Users\\olivi\\Zotero\\storage\\6LT6EURG\\Tabish - 2020 - Covid-19 Pandemic Emerging Perspectives and Futur.pdf:application/pdf},
}



@article{bhagat_novel_2022,
	title = {Novel corona virus ({COVID}-19) pandemic: current status and possible strategies for detection and treatment of the disease},
	volume = {20},
	issn = {1478-7210},
	shorttitle = {Novel corona virus ({COVID}-19) pandemic},
	url = {https://doi.org/10.1080/14787210.2021.1835469},
	doi = {10.1080/14787210.2021.1835469},
	number = {10},
	urldate = {2024-02-07},
	journal = {Expert Review of Anti-infective Therapy},
	author = {Bhagat, Stuti and Yadav, Nisha and Shah, Juhi and Dave, Harsh and Swaraj, Shachee and Tripathi, Shashank and Singh, Sanjay},
	month = {oct},
	year = {2022},
	pmid = {33043740},
	keywords = {Corona virus, hydroxychloroquine, pandemic, remdesivir, vaccine},
	pages = {1275--1298},
}



@article{ogunleye_response_2020,
	title = {Response to the {Novel} {Corona} {Virus} ({COVID}-19) {Pandemic} {Across} {Africa}: {Successes}, {Challenges}, and {Implications} for the {Future}},
	volume = {11},
	issn = {1663-9812},
	shorttitle = {Response to the {Novel} {Corona} {Virus} ({COVID}-19) {Pandemic} {Across} {Africa}},
	Xurl = {https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2020.01205},
	urldate = {2024-02-07},
	journal = {Frontiers in Pharmacology},
	author = {Ogunleye, Olayinka O. and Basu, Debashis and Mueller, Debjani and Sneddon, Jacqueline and Seaton, R. Andrew and Yinka-Ogunleye, Adesola F. and Wamboga, Joshua and Miljković, Nenad and Mwita, Julius C. and Rwegerera, Godfrey Mutashambara and Massele, Amos and Patrick, Okwen and Niba, Loveline Lum and Nsaikila, Melaine and Rashed, Wafaa M. and Hussein, Mohamed Ali and Hegazy, Rehab and Amu, Adefolarin A. and Boahen-Boaten, Baffour Boaten and Matsebula, Zinhle and Gwebu, Prudence and Chirigo, Bongani and Mkhabela, Nongabisa and Dlamini, Tenelisiwe and Sithole, Siphiwe and Malaza, Sandile and Dlamini, Sikhumbuzo and Afriyie, Daniel and Asare, George Awuku and Amponsah, Seth Kwabena and Sefah, Israel and Oluka, Margaret and Guantai, Anastasia N. and Opanga, Sylvia A. and Sarele, Tebello Violet and Mafisa, Refeletse Keabetsoe and Chikowe, Ibrahim and Khuluza, Felix and Kibuule, Dan and Kalemeera, Francis and Mubita, Mwangana and Fadare, Joseph and Sibomana, Laurien and Ramokgopa, Gwendoline Malegwale and Whyte, Carmen and Maimela, Tshegofatso and Hugo, Johannes and Meyer, Johanna C. and Schellack, Natalie and Rampamba, Enos M. and Visser, Adel and Alfadl, Abubakr and Malik, Elfatih M. and Malande, Oliver Ombeva and Kalungia, Aubrey C. and Mwila, Chiluba and Zaranyika, Trust and Chaibva, Blessmore Vimbai and Olaru, Ioana D. and Masuka, Nyasha and Wale, Janney and Hwenda, Lenias and Kamoga, Regina and Hill, Ruaraidh and Barbui, Corrado and Bochenek, Tomasz and Kurdi, Amanj and Campbell, Stephen and Martin, Antony P. and Phuong, Thuy Nguyen Thi and Thanh, Binh Nguyen and Godman, Brian},
	year = {2020},
}


@article{alsalem_multi-criteria_2022,
	title = {Multi-criteria decision-making for coronavirus disease 2019 applications: a theoretical analysis review},
	volume = {55},
	issn = {1573-7462},
	url = {https://doi.org/10.1007/s10462-021-10124-x},
	doi = {10.1007/s10462-021-10124-x},
	abstract = {The influence of the ongoing COVID-19 pandemic that is being felt in all spheres of our lives and has a remarkable effect on global health care delivery occurs amongst the ongoing global health crisis of patients and the required services. From the time of the first detection of infection amongst the public, researchers investigated various applications in the fight against the COVID-19 outbreak and outlined the crucial roles of different research areas in this unprecedented battle. In the context of existing studies in the literature surrounding COVID-19, related to medical treatment decisions, the dimensions of context addressed in previous multidisciplinary studies reveal the lack of appropriate decision mechanisms during the COVID-19 outbreak. Multiple criteria decision making (MCDM) has been applied widely in our daily lives in various ways with numerous successful stories to help analyse complex decisions and provide an accurate decision process. The rise of MCDM in combating COVID-19 from a theoretical perspective view needs further investigation to meet the important characteristic points that match integrating MCDM and COVID-19. To this end, a comprehensive review and an analysis of these multidisciplinary fields, carried out by different MCDM theories concerning COVID19 in complex case studies, are provided. Research directions on exploring the potentials of MCDM and enhancing its capabilities and power through two directions (i.e. development and evaluation) in COVID-19 are thoroughly discussed. In addition, Bibliometrics has been analysed, visualization and interpretation based on the evaluation and development category using R-tool involves; annual scientific production, country scientific production, Wordcloud, factor analysis in bibliographic, and country collaboration map. Furthermore, 8 characteristic points that go through the analysis based on new tables of information are highlighted and discussed to cover several important facts and percentages associated with standardising the evaluation criteria, MCDM theory in ranking alternatives and weighting criteria, operators used with the MCDM methods, normalisation types for the data used, MCDM theory contexts, selected experts ways, validation scheme for effective MCDM theory and the challenges of MCDM theory used in COVID-19 studies. Accordingly, a recommended MCDM theory solution is presented through three distinct phases as a future direction in COVID19 studies. Key phases of this methodology include the Fuzzy Delphi method for unifying criteria and establishing importance level, Fuzzy weighted Zero Inconsistency for weighting to mitigate the shortcomings of the previous weighting techniques and the MCDM approach by the name Fuzzy Decision by Opinion Score method for prioritising alternatives and providing a unique ranking solution. This study will provide MCDM researchers and the wider community an overview of the current status of MCDM evaluation and development methods and motivate researchers in harnessing MCDM potentials in tackling an accurate decision for different fields against COVID-19.},
	number = {6},
	journal = {Artificial Intelligence Review},
	author = {Alsalem, M. A. and Alamoodi, A. H. and Albahri, O. S. and Dawood, K. A. and Mohammed, R. T. and Alnoor, Alhamzah and Zaidan, A. A. and Albahri, A. S. and Zaidan, B. B. and Jumaah, F. M. and Al-Obaidi, Jameel R.},
	month = {aug},
	year = {2022},
	pages = {4979--5062},
}


@article{kumar_monitoring_2020,
	title = {Monitoring {Novel} {Corona} {Virus} ({COVID}-19) {Infections} in {India} by {Cluster} {Analysis}},
	volume = {7},
	issn = {2198-5812},
	url = {https://doi.org/10.1007/s40745-020-00289-7},
	doi = {10.1007/s40745-020-00289-7},
	abstract = {It is a great challenge of identification as well as formation of groups of infectious disease data set. Data mining, a process of uncovering silent characteristics of big data is one of such techniques which have nowadays become more popular for treating massive volume of infectious disease data set. In the current study, we apply cluster analysis, one of the data mining techniques to classify real groups of infectious disease “novel corona virus disease (COVID-19)” data set of different states and union territories (UTs) in India according to their high similarity to each other. The results obtained permit us to have a sense of clusters of affected Indian states and UTs. The main objective of clustering in this study is to optimize monitoring techniques in affected states and UTs in India which will be very valuable to the government, doctors, the police and others involved in understanding seriousness of the spread of novel coronavirus (COVID-19) to improve government policies, decisions, medical facilities (ventilators, testing kits, masks etc.), treatment etc. to reduce number of infected and deceased persons.},
	number = {3},
	journal = {Annals of Data Science},
	author = {Kumar, Sanjay},
	month = {sep},
	year = {2020},
	pages = {417--425},
}



@article{din_mathematical_2020,
	title = {Mathematical analysis of spread and control of the novel corona virus ({COVID}-19) in {China}},
	volume = {141},
	issn = {0960-0779},
	url = {https://www.sciencedirect.com/science/article/pii/S0960077920306822},
	doi = {https://doi.org/10.1016/j.chaos.2020.110286},
	abstract = {Number of well-known contagious diseases exist around the world that mainly include HIV, Hepatitis B, influenzas etc., among these, a recently contested coronavirus (COVID-19) is a serious class of such transmissible syndromes. Abundant scientific evidence the wild animals are believed to be the primary hosts of the virus. Majority of such cases are considered to be human-to-human transmission, while a few are due to wild animals-to-human transmission and substantial burdens on healthcare system following this spread. To understand the dynamical behavior such diseases, we fitted a susceptible-infectious-quarantined model for human cases with constant proportions. We proposed a model that provide better constraints on understanding the climaxes of such unseen disastrous spread, relevant consequences, and suggesting future imperative strategies need to be adopted. The main features of the work include the positivity, boundedness, existence and uniqueness of solution of the model. The conditions were derived under which the COVID-19 may extinct or persist in the population. Sensitivity and estimation of those important parameters have been carried out that plays key role in the transmission mechanism. To optimize the spread of such disease, we present a control problem for further analysis using two control measures. The necessary conditions have been derived using the Pontryagin’s maximum principle. Parameter values have been estimated from the real data and experimental numerical simulations are presented for comparison as well as verification of theoretical results. The obtained numerical results also present the verification, accuracy, validation, and robustness of the proposed scheme.},
	journal = {Chaos, Solitons \& Fractals},
	author = {Din, Anwarud and Li, Yongjin and Khan, Tahir and Zaman, Gul},
	year = {2020},
	keywords = {Control theory, Corona virus, Numerical simulations, Real data, Stability analysis},
	pages = {110286},
}



@article{sajed_corona_2020,
	title = {Corona {Virus} {Disease} ({COVID}-19) {Outbreak} and the {Strategy} for {Prevention}},
	volume = {2},
	copyright = {Copyright (c) 2020 Author(s)},
	issn = {2717-4654},
	url = {http://nepmed.nhrc.gov.np/index.php/EJMS/article/view/836},
	abstract = {The Corona Virus Disease (COVID-19) is the novel disease of the respiratory system, causing the ongoing pandemic with flu-like symptoms. It is caused by a virus known as SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus-2). This disease affected the entire world and various myths are circulating about its diagnosis, treatment, and prevention. The choice for diagnosis should be based on clinical and epidemiological factors and linked to an assessment of the likelihood of infection. A number of rapid diagnostic and serological tests are being used for the detection of suspected COVID-19 cases. However, the sensitivity and specificity of such newly marketed tests have not been evaluated and never up to standard yet. Therefore, it is not recommended any such immunochromatographic or serological rapid screening tests for the diagnosis of SARS-CoV-2 infections. The most reliable diagnostic test to confirm COVID-19 is PCR (Polymerase Chain Reaction) which is a molecular-based test for the detection of SARS-CoV-2. Eradication of highly contagious  SARS-CoV-2  Virus that causing the ongoing deadly pandemic  COVID-19,  demands individual attention and awareness are necessary regarding the route and mode of transmission across the bounders throughout the entire world.  Proper hand-washing, staying at the home and maintaining the social distance are proved to the most effective preventive measures and are immediate solutions to save human beings from this unseen enemy.},
	language = {en},
	number = {1},
	urldate = {2024-02-07},
	journal = {Europasian Journal of Medical Sciences},
	author = {Sajed, Ahmad Naeem and Amgain, Kapil},
	month = {july},
	year = {2020},
	note = {Number: 1},
	keywords = {COVID-19, Outbreak Prevention, Pandemic, SARS-CoV-2},
	pages = {1--3},
	file = {Full Text PDF:C\:\\Users\\olivi\\Zotero\\storage\\W7AMMI64\\Sajed y Amgain - 2020 - Corona Virus Disease (COVID-19) Outbreak and the S.pdf:application/pdf},
}


@article{hong_enhanced_2022,
	title = {Enhanced refractive index sensitivity of localized surface plasmon resonance inflection points in single hollow gold nanospheres with inner cavity},
	volume = {12},
	copyright = {2022 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-022-11197-6},
	doi = {10.1038/s41598-022-11197-6},
	abstract = {Hollow gold nanoparticles have great potential for localized surface plasmon resonance (LSPR) sensing. In this study, we investigated the refractive index (RI) sensitivities of single hollow gold nanosphere (HAuNS) with thin Au shell and inner cavity and single solid gold nanosphere (AuNS) in media with different RIs. The HAuNS exhibited a remarkable improvement in the RI sensitivity than the AuNS of similar size. The increased RI sensitivity of HAuNSs was ascribed to plasmon coupling between the inner and outer surface of the Au nanoshell. We then investigated the homogeneous LSPR scattering inflection points (IFs) to better understand the RI sensitivity of single HAuNS. The LSPR IF at the long wavelength side exhibited a better RI sensitivity compared to the wavelength shift of its counterpart LSPR maximum peak. Furthermore, the single HAuNS showed a remarkable improvement in the RI sensitivity at the LSPR IFs when compared to the AuNS of similar size. Therefore, we provided a new insight into the effect of inner cavity of HAuNS on the RI sensitivity of homogeneous LSPR IFs for use in LSPR-based biosensors.},
	language = {en},
	number = {1},
	urldate = {2024-02-07},
	journal = {Scientific Reports},
	author = {Hong, Yun A. and Ha, Ji Won},
	month = {apr},
	year = {2022},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Optical spectroscopy, Sensors},
	pages = {6983},
	file = {Full Text PDF:C\:\\Users\\olivi\\Zotero\\storage\\FD8N4AIZ\\Hong y Ha - 2022 - Enhanced refractive index sensitivity of localized.pdf:application/pdf},
}



@article{gu_inflection_2021,
	title = {Inflection point: a perspective on photonic nanojets},
	volume = {9},
	copyright = {\&\#169; 2021 Chinese Laser Press},
	issn = {2327-9125},
	shorttitle = {Inflection point},
	url = {https://opg.optica.org/prj/abstract.cfm?uri=prj-9-7-1157},
	doi = {10.1364/PRJ.419106},
	abstract = {When light propagates through the edge or middle part of a microparticle\&\#x2019;s incoming interface, there is a basic rule that light converges and diverges rapidly or slowly at the output port. These two parts are referred to as the region of rapid change (RRC) and region of slow change (RSC), respectively. Finding the boundary point between RRC and RSC is the key to reveal and expound upon this rule scientifically. Based on the correlation between light convergence\&\#x2013;divergence and the slope of emergent light, combined with the relationship between a natural logarithm and growth in physical reality and the second derivative of a function in practical significance, we determine the boundary point between RRC and RSC, namely, the inflection point. From such a perspective, a photonic nanojet (PNJ) and near-field focusing by light irradiation on RSC and RRC, as well as the position of the inflection point under different refractive index contrasts and the field distribution of light focusing, are studied with finite-element-method-based numerical simulation and ray-optics-based theoretical analysis. By illuminating light of different field intensity ratios to the regions divided by the inflection point, we demonstrate the generation of a photonic hook (PH) and the modulation of PNJ/PH in a new manner.},
	language = {EN},
	number = {7},
	urldate = {2024-02-07},
	journal = {Photonics Research},
	author = {Gu, Guoqiang and Zhang, Pengcheng and Chen, Sihui and Zhang, Yi and Yang, Hui},
	month = {july},
	year = {2021},
	note = {Publisher: Optica Publishing Group},
	pages = {1157--1171},
}


@article{heusser_baroreflex_2021,
	title = {Baroreflex {Curve} {Fitting} {Using} a {WYSIWYG} {Boltzmann} {Sigmoidal} {Equation}},
	volume = {15},
	issn = {1662-453X},
	url = {https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.697582},
	abstract = {Arterial baroreflex assessment using vasoactive substances enables investigators to collect data pairs over a wide range of blood pressures and reflex reactions. These data pairs relate intervals between heartbeats or sympathetic neural activity to blood pressure values. In an X-Y plot the data points scatter around a sigmoidal curve. After fitting the parameters of a sigmoidal function to the data, the graph’s characteristics represent a rather comprehensive quantitative reflex description. Variants of the 4-parameter Boltzmann sigmoidal equation are widely used for curve fitting. Unfortunately, their ‘slope parameters’ do not correspond to the graph’s actual slope which complicates the analysis and bears the risk of misreporting. We propose a modified Boltzmann sigmoidal function with preserved goodness of fit whose parameters are one-to-one equivalent to the sigmoidal curve’s characteristics.},
	urldate = {2024-02-07},
	journal = {Frontiers in Neuroscience},
	author = {Heusser, Karsten and Heusser, Ramona and Jordan, Jens and Urechie, Vasile and Diedrich, André and Tank, Jens},
	year = {2021},
	file = {Full Text PDF:C\:\\Users\\olivi\\Zotero\\storage\\V92KTCM6\\Heusser et al. - 2021 - Baroreflex Curve Fitting Using a WYSIWYG Boltzmann.pdf:application/pdf},
}

@article{vikova_transition_2023,
	title = {Transition {Temperature} of {Color} {Change} in {Thermochromic} {Systems} and {Its} {Description} {Using} {Sigmoidal} {Models}},
	volume = {16},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {1996-1944},
	url = {https://www.mdpi.com/1996-1944/16/23/7478},
	doi = {10.3390/ma16237478},
	abstract = {Background: Symmetric sigmoidal models with four parameters based on an idealized S/Z-shaped curve are commonly used to analyze the optical parameters of thermochromic materials. However, our experimental findings show that this approach leads to systematic errors involving the incorrect estimation of the transition temperature or the possibility of a virtual indication of the hysteresis nature of a reversible thermochromic change. For this reason, we sought to find a five-parameter model that would appropriately avoid this problem. Methods: Two commercial thermochromic pigments were used for the test and applied to a textile substrate at different concentrations. The optical properties were measured using reflectance spectrophotometry and then converted to Kubelka–Munk function values and colorimetric coordinates. The following statistics were used to assess the quality of the selected sigmoidal models: coefficient of determination, R2; adjusted coefficient of determination, AR2; root mean square error, RMSE; and Akaike Information Criterion, AIC. Results: The four-parameter models were compared with each other and with the five-parameter models using nested F-tests based on residual variance to obtain a statistical measure of superior performance. For all thermochromic color change data examined, the five-parameter models resulted in significantly better fitting. It could be shown that the five-parameter model showed significantly higher accuracy and precision in determining the transition temperature, like non-sigmoidal quantification methods. Conclusions: We concluded that the asymmetric five-parameter model is a valuable extension of the symmetric model in the investigation of thermochromic color changes, providing better parameter estimates and a new approach to investigating the mechanisms contributing to the asymmetry of the thermochromic curve.},
	language = {en},
	number = {23},
	urldate = {2024-02-07},
	journal = {Materials},
	author = {Viková, Martina and Vik, Michal},
	month = {jan},
	year = {2023},
	note = {Number: 23
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {color, sigmoidal function, temperature sensing, textiles, thermochromism},
	pages = {7478},
	file = {Full Text PDF:C\:\\Users\\olivi\\Zotero\\storage\\75WN7KQR\\Viková y Vik - 2023 - Transition Temperature of Color Change in Thermoch.pdf:application/pdf},
}

@misc{DatasetCovid19,
   author = {Johns Hopkins University},
   title = {GitHub - CSSEGISandData/COVID-19: Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE},
   url = {https://github.com/CSSEGISandData/COVID-19},

}

@article{jawad_hashim_population_2020,
	title = {Population {Risk} {Factors} for {COVID}-19 {Mortality} in 93 {Countries}},
	volume = {10},
	issn = {2210-6014},
	url = {https://doi.org/10.2991/jegh.k.200721.001},
	doi = {10.2991/jegh.k.200721.001},
	abstract = {Death rates due to COVID-19 pandemic vary considerably across regions and countries. Case Mortality Rates (CMR) per 100,000 population are more reliable than case-fatality rates per 100 test-positive cases, which are heavily dependent on the extent of viral case testing carried out in a country. We aimed to study the variations in CMR against population risk factors such as aging, underlying chronic diseases and social determinants such as poverty and overcrowding. Data on COVID-19 CMR in 93 countries was analyzed for associations with preexisting prevalence rates of eight diseases [asthma, lung cancer, Chronic Obstructive Pulmonary Disease (COPD), Alzheimer’s Disease (AD), hypertension, ischemic heart disease, depression and diabetes], and six socio-demographic factors [Gross Domestic Product (GDP) per capita, unemployment, age over 65 years, urbanization, population density, and socio-demographic index]. These data were analyzed in three steps: correlation analysis, bivariate comparison of countries, and multivariate modelling. Bivariate analysis revealed that COVID-19 CMR were higher in countries that had high prevalence of population risk factors such as AD, lung cancer, asthma and COPD. On multivariate modeling however, AD, COPD, depression and higher GDP predicted increased death rates. Comorbid illnesses such as AD and lung diseases may be more influential than aging alone.},
	number = {3},
	journal = {Journal of Epidemiology and Global Health},
	author = {Jawad Hashim, Muhammad and Alsuwaidi, Ahmed R. and Khan, Gulfaraz},
	month = {september},
	year = {2020},
	pages = {204--208},
}

@article{pearce_comparisons_2020,
	title = {Comparisons between countries are essential for the control of {COVID}-19},
	volume = {49},
	issn = {0300-5771},
	url = {https://doi.org/10.1093/ije/dyaa108},
	doi = {10.1093/ije/dyaa108},
	abstract = {On 6 May 2020, the UK Prime Minister Boris Johnson quoted statistician David Spiegelhalter, arguing that it was not appropriate to compare the COVID-19 death rate in the UK with other countries.1 Within a few hours, Spiegelhalter tweeted a request for the Prime Minister to stop quoting him and affirmed that ‘of course we should now use other countries to try and learn why our numbers are high’.2The issue was that, although it is difficult to reliably compare COVID-19 population fatality rates between countries, it is also quite clear that some countries (e.g. UK, USA, Italy, Spain, Belgium, France) have markedly higher burdens of COVID-19 mortality than others (e.g. New Zealand, Australia, Singapore, South Korea, Germany). Although one could not say definitively that the UK was the worst in the world, it was performing worse than some countries which had tried alternative control strategies, and there are things that we can learn from that contrast.},
	number = {4},
	journal = {International Journal of Epidemiology},
	author = {Pearce, Neil and Lawlor, Deborah A and Brickley, Elizabeth B},
	month = {jun},
	year = {2020},
	note = {\_eprint: https://academic.oup.com/ije/article-pdf/49/4/1059/34275473/dyaa108.pdf},
	pages = {1059--1062},
}


@article{sujathmachine2020,
journal = {Stochastic Environmental Research and Risk Assessment},
year = {2020},
	title = {A machine learning forecasting model for {COVID}-19 pandemic in India},
	volume = {34},
	issn = {1436-3259},
	url = {https://doi.org/10.1007/s00477-020-01827-8},
	doi = {10.1007/s00477-020-01827-8},
	pages = {959--972},
	number = {7},
	journaltitle = {Stochastic Environmental Research and Risk Assessment},
	shortjournal = {Stochastic Environmental Research and Risk Assessment},
	author = {Sujath, R. and Chatterjee, Jyotir Moy and Hassanien, Aboul Ella},
	date = {2020-07-01}
}

@article{fernandeslong2022,
	title = {Long short-term memory stacking model to predict the number of cases and deaths caused by {COVID}-19},
	volume = {42},
	issn = {1875-8967},
	doi = {10.3233/JIFS-212788},
	abstract = {The long short-term memory (LSTM) is a high-efficiency model for forecasting time series, for being able to deal with a large volume of data from a time series with nonlinearities. As a case study, the stacked LSTM will be used to forecast the growth of the pandemic of COVID-19, based on the increase in the number of contaminated and deaths in the State of Santa Catarina, Brazil. COVID-19 has been spreading very quickly, causing great concern in relation to the ability to care for critically ill patients. Control measures are being imposed by governments with the aim of reducing the contamination and the spreading of viruses. The forecast of the number of contaminated and deaths caused by COVID-19 can help decision making regarding the adopted restrictions, making them more or less rigid depending on the pandemic’s control capacity. The use of LSTM stacking shows an R2 of 0.9625 for confirmed cases and 0.9656 for confirmed deaths caused by COVID-19, being superior to the combinations among other evaluated models.},
	number = {6},
	journal = {Journal of Intelligent \& Fuzzy Systems},
	author = {Fernandes, Filipe and Stefenon, Stéfano Frizzo and Seman, Laio Oriel and Nied, Ademir and Ferreira, Fernanda Cristina Silva and Subtil, Maria Cristina Mazzetti and Klaar, Anne Carolina Rodrigues and Leithardt, Valderi Reis Quietinho},
	year = {2022},
	note = {Publisher: IOS Press},
	keywords = {COVID-19, Long short-term memory, spreading viruses},
	pages = {6221--6234}
}


@article{Vilca-Huayta2022,
title = {Efficient Function Integration and a Case Study with Gompertz Functions for Covid-19 Waves},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130863},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130863},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {8},
author = {Oliver Amadeo Vilca-Huayta and Ubaldo Yancachajlla Tito}
}

@article{el_aferni_mathematical_2021,
	title = {Mathematical model of {Boltzmann}’s sigmoidal equation applicable to the spreading of the coronavirus ({Covid}-19) waves},
	volume = {28},
	issn = {1614-7499},
	url = {https://doi.org/10.1007/s11356-020-11188-y},
	doi = {10.1007/s11356-020-11188-y},
	abstract = {Currently, investigations are intensively conducted on modeling, forecasting, and studying the dynamic spread of coronavirus (Covid-19) new pandemic. In the present work, the sigmoidal-Boltzmann mathematical model was applied to study the Covid-19 spread in 15 different countries. The cumulative number of infected persons I has been accurately fitted by the sigmoidal-Boltzmann equation (SBE), giving rise to different epidemiological parameters such as the pandemic peak tp, the maximum number of infected persons Imax, and the time of the epidemic stabilization t∞. The time constant relative to the sigmoid Δt (called also the slope factor) was revealed to be the determining parameter which influences all the epidemiological parameters. Empirical laws between the different parameters allowed us to propose a modified sigmoidal-Boltzmann equation describing the spread of the pandemic. The expression of the spread speed Vp was further determined as a function of the sigmoid parameters. This made it possible to assess the maximum speed of spread of the virus Vpmax and to trace the speed profile in each country. In addition, for countries undergoing a second pandemic wave, the cumulative number of infected people I has been successfully adjusted by a double sigmoidal-Boltzmann equation (DSBE) allowing the comparison between the two waves. Finally, the comparison between the maximum virus spread of two waves Vp max 1 and Vp max 2 showed that the intensity of the second wave of Covid-19 is low compared to the first for all the countries studied.},
	number = {30},
	journal = {Environmental Science and Pollution Research},
	author = {El Aferni, Ahmed and Guettari, Moez and Tajouri, Tahar},
	month = {aug},
	year = {2021},
	pages = {40400--40408},
}


@article{SHASTRI2020110227,
title = {Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study},
journal = {Chaos, Solitons and Fractals},
volume = {140},
pages = {110227},
year = {2020},
issn = {0960-0779},
doi = {https://doi.org/10.1016/j.chaos.2020.110227},
url = {https://www.sciencedirect.com/science/article/pii/S0960077920306238},
author = {Sourabh Shastri and Kuljeet Singh and Sachin Kumar and Paramjit Kour and Vibhakar Mansotra},
keywords = {Recurrent neural networks, Time series, Covid-19, LSTM, Forecasting, Deep learning},
abstract = {Covid-19}
}


@misc{WHOcovid-19,
author = {{World Health Organization}},
	title = {{COVID}-19 cases {\textbar} {WHO} {COVID}-19 dashboard},
	url = {https://data.who.int/dashboards1},
website = {https://data.who.int/dashboards2},
howpublished = {https://data.who.int/dashboards},
	abstract = {The latest data for coronavirus (COVID-19) cases from the WHO COVID-19 dashboard.},
	language = {en},
	urldate = {2024-01-25},
	journal = {datadot},
	file = {Snapshot:C\:\\Users\\olivi\\Zotero\\storage\\USX9L4AS\\dashboards.html:text/html},
lastchecked = {21 Enero 2024},
ACCESSED = {21 Enero 2024},
note = {Accesado en: 22 enero 2024},
year = {2024},
month = {Enero}
}


