The COVID - 19 pandemic is currently causing authorities and public health officials more concern. The goal of the project is to convert a deterministic model for COVID-19 transmissions to a stochastic model, and then analyze the results to see how media-driven awareness campaigns have an impact on the disease's spread. The dynamic COVID-19 model was converted to a stochastic model, which was then examined. The model includes the following categories: Susceptible (S), Exposed (E), Infected class (I), Isolated class ( ), Aware class and Recovered class (R), as well as the Cumulative density of awareness programs by media denoted by . With the help of MATLAB, the converted model is then numerically solved using the Eula Maruyama approach, allowing the existence and uniqueness of the model to be examined. The implementation of awareness programs has been found to have a significant positive impact on the spread of COVID-19. As the rate of implementation of these programs rises, the population that is exposed to the virus and those who are infected with it declines, and it has been hypothesized that this will eventually cause COVID-19 to become extinct. According to the report, putting awareness campaigns into place can help stop the COVID-19 epidemic from spreading.
Background: The current malaria diagnosis methods, which rely on microscopy and Histidine Rich Protein2 (HRP2) based RDT, have drawbacks that necessitate the development of improved and complementary malaria diagnostic methods to overcome some or all the limitations. Consequently, automated detection and classification of malaria can provide patients with a faster and more accurate diagnosis. This study, therefore, used a machine learning model to predict the occurrence of malaria based on socio-demographicbehaviour, environment, and clinical features. Methods:Data from 200 Nigerian patients were used to develop predictive models using nested cross-validation and sequential backward features selection (SBFS), with 80% of the dataset randomly selected for training and optimisation and the remaining 20% for testing the models. Results: Among the three machine learningmodels examined, penalised logistic regression model had the best area under the receiver operating characteristic (ROC) curve performance for the training set (84%; 95% confidence interval (CI) =75–93%) and test set (83%; 95% CI =63–100%). The model included age, BMI, body temperature, bushes in surroundings, body weight, dizziness, fever, headache, mosquito repellant, muscle pain, sex, sore throat, stagnant water in the home, and vomiting. An increased odd of patients having malaria was associated with high body weight (adjusted odd ratio (AOR) = 4.50, 95% CI =2.27-8.01, p-value <0.0001). Even though the association between the odds of having malaria and body temperature was insignificant, patients who had body temperature had higher odds of having malaria than those who did not have body temperature (AOR = 1.40, CI =0.99-1.91, p-value = 0.068). Also, patients who had bushes in the surroundings (AOR = 2.60, 95% CI =1.30-4.66, p-value = 0.006) or experienced fever (AOR = 2.10, CI =0.88–4.24, p-value = 0.099), headache (AOR = 2.07; CI =0.95–3.95, p-value = 0.068), muscle pain (AOR =1.49; CI =0.66–3.39, p-value = 0.333) and vomiting (AOR = 2.32; CI =0.85–6.82, p-value = 0.097) were more likely to experience malaria compared to those without bushes in the surrounding or those who did not experience fever, headache, muscle pain and vomiting. In contrast, decreased odds of malaria were associated with age (AOR = 0.62, 95% CI= 0.41-0.90, p-value = 0.012) or BMI (AOR = 0.47, 95% CI= 0.26-0.80, p-value =0.006). Conclusion:Newly developed routinely collected baseline socio-demographical, environmental, and clinical features topredict malaria types and may serve as a valuable tool for clinical decision making.
Introduction: The probability of contamination is frequently elevated in scenarios where a well and pit latrine coexist, or in situations where heavy rain causes the overflow of open excreta dumps, which in turn flush into wells and surface water. Many possible negative health effects might arise from exposure to various ecological and biological agents in the environment. Therefore, there is a need to examine the risk of disease transmission in Ife North Local Government Area (LGA) of Osun state, using epidemiological, environmental, and ecological factors. Materials and Methods: Geostatistical analysis was used to examine the epidemiological risk, based on various environmental, biological, and ecological variables. The technique employed demonstrated the complexity and multiple parameters that raise the probability of an epidemic. The Shapiro-Wilk test was used to determine whether or not the data were normally distributed. Fuzzy logic, correlation, and spline surface interpolation analysis were conducted using ArcGIS 10.3 and ENVI 5.0 software. Three levels of epidemic risk were used to construct the disease surveillance and projection maps. Results: According to the final susceptibility map, 8.08 km2 of 460.12 km2 of the research area were considered to be at very low risk for an epidemic, followed by 364.98km2 of low risk and 87.06km2 of moderate risk areas, with percentages of 1.75%, 79.32%, and 18.92%, respectively. Conclusion: A very substantial correlation was observed between biological and ecological components and water-borne diseases. It is, therefore, advised that all water sources be treated before consumption, and community involvement be encouraged in environmental sanitation programs.
Background Current malaria diagnosis methods that rely on microscopy and Histidine Rich Protein2 (HRP2)-based rapid diagnostic tests (RDT) have drawbacks that necessitate the development of improved and complementary malaria diagnostic methods to overcome some or all of these limitations. Consequently, the automated detection and classification of malaria can provide patients with a faster and more accurate diagnosis. Therefore, this study used a machine-learning model to predict the occurrence of malaria based on sociodemographic behaviour, environment, and clinical features.Method Data from 200 Nigerian patients were used to develop predictive models using nested cross-validation and sequential backward feature selection (SBFS), with 80% of the dataset randomly selected for training and optimisation and the remaining 20% for testing the models.Results Among the three machine learning models examined, the penalised logistic regression model had the best area under the receiver operating characteristic (ROC) curve for the training set (84%; 95% confidence interval (CI) = 75–93%) and test set (83%; 95% CI = 63–100%). Increased odds of malaria was associated with high body weight (adjusted odds ratio (AOR) = 4.50, 95% CI = 2.27–8.01, p < 0.0001). Even though the association between the odds of having malaria and body temperature was insignificant, patients with body temperature had higher odds of having malaria than those who did not have body temperature (AOR = 1.40, CI = 0.99–1.91, p-value = 0.068). In addition, patients who had bushes in their surroundings (AOR = 2.60, 95% CI = 1.30–4.66, p-value = 0.006) or experienced fever (AOR = 2.10, CI = 0.88–4.24, p-value = 0.099), headache (AOR = 2.07; CI = 0.95–3.95, p-value = 0.068), muscle pain (AOR = 1.49; CI = 0.66–3.39, p-value = 0.333), and vomiting (AOR = 2.32; CI = 0.85–6.82, p-value = 0.097) were more likely to experience malaria. In contrast, decreased odds of malaria were associated with age (AOR = 0.62, 95% CI = 0.41–0.90, p-value = 0.012) and BMI (AOR = 0.47, 95% CI = 0.26–0.80, p = 0.006).Conclusion Newly developed routinely collected baseline sociodemographic, environmental, and clinical features to predict malaria types may serve as a valuable tool for clinical decision-making.
HIV/AIDS is a serious health problem that continues to present a significant health concern in underdeveloped nations and may be mostly brought on via unprotected sex. This study is designed and analyzed using a dynamic modeling approach to investigate the dynamic of HIV/AIDS model with vertical transmission and the impact of knowledge on its treatment. Our proposed model exhibit disease free and the endemic equilibrium. The uniqueness and the exactness of the model were investigated and the basic reproduction number using next generation matrix was obtained, Stability analysis was also carried out. The model analysis shows that the disease free equilibrium is locally asymptomatically stable (LAS) when Ro < 1. Our research suggests that treatment and awareness campaigns, when combined with other crucial control measures, may help keep the HIV/AIDS virus from spreading.
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