Narrow Results

In this work, we formulate a two-strain model of dengue infection, incorporating the possibility of co-infection through integer and Caputo-type fractional derivatives. We present the foundational theory and results related to fractional operators for the analysis of the proposed dengue model. We establish the positivity and boundedness of the solution to validate the model. It is demonstrated that the solution to the proposed model exists and is unique. The basic reproduction number, denoted by ${{ℛ}}_c$, is determined, and the local asymptotic stability of the dengue-free equilibrium point is established when ${{ℛ}}_c<1$. We also prove the existence of an endemic equilibrium and establish the global stability of solutions to the fractional model in the Ulam–Hyers sense. Additionally, we construct a numerical scheme and prove its stability under certain conditions. Several numerical simulations are performed to validate our analytical results, and we examine the impact of various input factors on the solution pathways of the system through these simulations.

Thailand is currently grappling with a severe dengue fever outbreak, with a rising threat to public health as the rainy season and El Niño draw near. This year has witnessed a troubling surge in dengue cases, prompting the Ministry of Public Health (MoPH) to issue warnings that the numbers may hit a three-year peak. Dengue outbreaks in Thailand have historically followed a cyclical pattern, excluding COVID-19 years. This research employs data analysis and predictive modeling to forecast the forthcoming dengue case numbers in Thailand, facilitating better public health preparedness. It also incorporates data visualization for enhanced data exploration. Various forecasting models, including Exponential Smoothing, Polynomial Fitting and Random Forest, are deployed to predict dengue cases within the constraints of our data. This study offers valuable insights into the potential trajectory of dengue cases in Thailand, aiding proactive measures to combat the outbreak.

Dengue fever is an important epidemic disease with a high prevalence in tropical and subtropical countries. We aimed to investigate the effects of a treatment integrating traditional Chinese (TCM) and Western medicines on dengue inpatients with warning signs (i.e., group B) according to the World Health Organization dengue classification in this retrospective cohort study of medical records. Inpatients who were treated with conventional Western therapies in the absence or presence of TCM were assigned to the control and treatment groups, respectively. Data were compared using an analysis of variance, general linear analysis, and chi-square test. The most common clinical symptoms and signs of dengue fever were fever and muscle ache. The treatment group patients were significantly more likely to present general weakness and poor appetite than the control group patients. Patients in the treatment group were more likely to experience stomachache than those in the control group. Moreover, comparisons of the changes in hemoglobin and alanine aminotransferase levels over time revealed significant differences between the patient groups. Zhu Ye Shi Gao Tang, Gui Pi Tang, Paeonia suffruticosa, and Clerodendrum cyrtophyllum were the most commonly administered TCM formula and single herbs in this study. Patients in the treatment group experienced a resolution of symptoms, signs, and laboratory data and were discharged smoothly, without deterioration to death or critical care. Our findings suggest that the integration of TCM and Western medicine may yield an appropriate treatment for dengue fever.

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Managing Dengue.

We provide the complete classification of all global analytic first integrals of the simplified multistrain/two-stream model for tuberculosis and dengue fever that can be written as

In this paper, we solved a model of a well-known infectious disease called dengue fever via fractional natural decomposition and modified Predictor–Corrector (PC) methods. A study of the dengue epidemic in the Cape Verde Islands off the coast of West Africa in 2009 has been resumed here for a better understanding of the results. The results are obtained using Liouville–Caputo and new generalized Caputo-type fractional derivatives. The numerical simulations are presented for various orders of given derivatives. Existence and uniqueness analysis of the given problem are also performed in the new generalized Caputo sense. The explored results are verified using figures. The main target of this paper is to explore the different dynamics of the given dengue fever model via two types of fractional numerical algorithms.

In this paper, we study a system of differential equations that models the population dynamics of SEIR vector transmission of dengue fever. The model studied breeding value based on the number of reported cases of dengue fever in Selangor because the state had the highest case in Malaysia. The model explains that maximum level of human infection rate of dengue fever achieved in a very short period. It is also revealed that there existed suitability result between theoretical and empirical calculation using the model. The result of SEIR model will hopefully provide an insight into the spread of dengue fever in Selangor Malaysia and basic form for modeling this area.

Recently, combination of spatial clustering methods was proposed, based on a similar idea of combining classifiers. However, this combination is able produce a binary map on which is not possible to know how many methods contributed for the final result. The goal of this paper is provide a new soft map for combination of spatial clustering methods when voting is used as combiner. A case study based on 2011 dengue fever epidemiological data from municipalities in the state of Paraíba, Brazil and combining five spatial clustering methods, using voting rule is presented. The soft map was provided considering the number of votes received for each municipality. This map can help a health manager about decision making with respect to each sub-region and its epidemiological priority.