APPROXIMATE SOLUTION OF FRACTIONAL ORDER MATHEMATICAL MODEL ON THE CO-TRANSMISSION OF ZIKA AND CHIKUNGUNYA VIRUS USING LAPLACE ADOMIAN DECOMPOSITION METHOD

Gaining insight into the transmission dynamics of the Zika and Chikungunya viruses, as well as their co-infection, is essential for implementing efficient public health interventions. This paper presents a comprehensive fractional order mathematical model consisting of thirteen non-linear compartments to accurately represent the intricate interactions between humans and infected mosquito populations, as well as the challenges associated with their identification. In order to solve this model, we utilize the Laplace Adomians Decomposition Method (LADM), which is a very effective analytical technique for solving nonlinear differential equations. By utilizing LADM, we obtained infinite series solutions for the previously given model that ultimately converged to its precise solutions. The numerical simulations of the model demonstrate the transmission patterns of Zika virus, Chikungunya virus, and their co-infections for different values of . We utilized the fmicon algorithm, a MATLAB optimization tool, to accurately fit into the model, real-life data from Espirito Santos State in Brazil, where two viruses are concurrently spreading. The simulation deduce that, reducing mosquito biting rates and promoting compliance with treated bed net usage can substantially mitigate Zika-Chikungunya co-infection dynamics

Predictors and outcomes of COVID-19 patients with hypoxemia in Lagos, Nigeria

Objectives: The coronavirus disease 2019 (COVID-19) pandemic is the current public health concern. Hypoxemia has been identified as an independent risk factor for mortality in COVID-19 patients regardless of age or sex. This study therefore aimed to assess the profile of COVID-19 patients with hypoxemia in Lagos, Nigeria and identify their associated socio-demographic and clinical risk factors, predictors, and outcomes.Materials and Methods: This was a retrospective cohort study in which data were extracted from medical records of real-time polymerase chain reaction confirmed COVID-19 positive patients admitted between April and October 2020. Data extracted included age, sex, comorbidities, disease category/classification, symptoms, lowest oxygen saturation (SPO2), and outcomes. Bivariate analysis was done to test associations between hypoxemia and other variables. Multivariate analysis was done to determine significant predictors of hypoxemia.Results: A total of 266 patients were included in the study; mean (SD) 49.80 (± 16.68) years. Hypoxemia (lowest SPO2 ≤ 90 in adults and < 92% in children) was found in 102 (38.3 %) of the cases. SPO2 of hypoxemic patients ranged from 33% to 90%, Mean ±SD of 77±13%. About half of the hypoxemic cases, 53 (52%) were ≥ 60 years and mostly male 70 (68.6%). Difficulty breathing was present in 56 (55%), while the common comorbidities were hypertension 86 (32.3%) and diabetes mellitus 47 (17.7%). Age ≥ 60, difficulty breathing, and fever were independent predictors of hypoxemia. Hypoxemia was significantly associated with death (X2-42.13; P < 0.001); odds ratio 14.5 (95% CI: 5.4–38.8).Conclusion: Hypoxemia occurred in 1 out of every 3 COVID-19 patients with poor prognosis. SPO2 monitoring and early presentation in hospital for those 60 years and above or with dyspnea may be essential for early identification and treatment of hypoxemia to reduce mortality