The north region of Brazil is characterized by significant vulnerabilities, notably surpassing national poverty indicators. These disparities exacerbated the impact of respiratory illnesses on the healthcare system during the COVID-19 pandemic, particularly in areas with limited healthcare resources, inadequate infrastructure, and barriers to healthcare access. The crisis was further influenced by multiple lineages that emerged as significant virus variants associated with increased transmissibility. Within this context, our ecological study focused on analyzing the epidemiological evolution of COVID-19 in the state of Acre. We constructed time-series trends in incidence, lethality, and mortality from March 2020 to December 2022 using the Prais–Winsten regression model. Our findings revealed that in 2020, there was an increasing trend in incidence, while mortality and lethality continued to decrease (p < 0.05). In the following year, both incidence and mortality decreased, while lethality increased at a rate of 1.02% per day. By the end of 2022, trends remained stationary across all rates. These results underscore the importance of ongoing surveillance and adaptive public health measures to bolster the resilience of healthcare systems in remote and vulnerable regions. Indeed, continuous monitoring of the most predominant SARS-CoV-2 lineages and their dynamics is imperative. Such proactive actions are essential for addressing emerging challenges and ensuring effective responses to adverse situations. Full article

HIV/AIDS has been a major threat to global public health, with India ranking third when it comes to the global burden of people living with HIV, especially women. It is imperative to assess the level of knowledge women have about transmission and prevention of this infection. This study sought to delineate the determinants of the comprehensive knowledge of HIV/AIDS among women in the reproductive age groups in India. Data from the fifth round of the National Family Health Survey conducted in India were analyzed. The sample included 95,541 women aged 15–49 years. Multilevel logistic regression was fitted with individual characteristics, household characteristics, and community characteristics to identify determinants of comprehensive knowledge on HIV/AIDS. Nearly a fourth (24.8%) of the women aged 15–49 in India who had ever heard of HIV had comprehensive knowledge of HIV/AIDS. Multilevel logistic regression showed that the likelihood of comprehensive knowledge of HIV/AIDS was higher among women aged 40–44 (AOR = 1.57) and 30–34 (AOR = 1.56). The likelihood of having comprehensive knowledge increased with the increase in the level of education. Women with secondary and higher levels of education were 1.9 times and 3.38 times more likely to have comprehensive knowledge, respectively, than those with no education. Household wealth, access to mass media, and having ever tested for HIV were also significant determinants of comprehensive knowledge of HIV/AIDS among women. The odds of having comprehensive knowledge about HIV/AIDS were higher for women with higher community wealth (AOR = 1.31), higher community education (AOR = 1.09), and higher community employment (AOR = 1.12). Factors at both the individual and community levels were shown to be indicators of comprehensive knowledge of HIV/AIDS. Policymakers and public health practitioners in India should come up with plans to close the information gaps about HIV/AIDS that exist among women and their demographic subgroups. Full article

Infectious disease epidemics are challenging for medical and public health practitioners. They require prompt treatment, but it is challenging to recognize and define epidemics in real time. Knowing the prediction of an infectious disease epidemic can evaluate and prevent the disease’s impact. Mathematical models of epidemics that work in real time are important tools for preventing disease, and data-driven deep learning enables practical algorithms for identifying parameters in mathematical models. In this paper, the SIR model was reduced to a logistic differential equation involving a constant parameter and a time-dependent function. The time-dependent function leads to constant, rational, and birational models. These models use several constant parameters from the available data to predict the time and number of people reported to be infected with the COVID-19 Omicron variant. Two out of these three models, rational and birational, provide accurate predictions for countries that practice strict mitigation measures, but fail to provide accurate predictions for countries that practice partial mitigation measures. Therefore, we introduce a time-series model based on neural networks to predict the time and number of people reported to be infected with the COVID-19 Omicron variant in a given country that practices both partial and strict mitigation measures. A logistics-informed neural network algorithm was also introduced. This algorithm takes as input the daily and cumulative number of people who are reported to be infected with the COVID-19 Omicron variant in the given country. The algorithm helps determine the analytical solution involving several constant parameters for each model from the available data. The accuracy of these models is demonstrated using error metrics on Omicron variant data for Portugal, Italy, and China. Our findings demonstrate that the constant model could not accurately predict the daily or cumulative infections of the COVID-19 Omicron variant in the observed country because of the long series of existing data of the epidemics. However, the rational and birational models accurately predicted cumulative infections in countries adopting strict mitigation measures, but they fell short in predicting the daily infections. Furthermore, both models performed poorly in countries with partial mitigation measures. Notably, the time-series model stood out for its versatility, effectively predicting both daily and cumulative infections in countries irrespective of the stringency of their mitigation measures. Full article

Theories of myopia etiology based on near work and lack of outdoor exposure have had inconsistent support and have not prevented the rising prevalence of global myopia. New scientific theories in the cause and prevention of myopia are needed. Myopia prevalence is low in native people consuming traditional diets lacking in sodium chloride, and nutritional epidemiological evidence supports the association of rising myopia prevalence with dietary sodium intake. East Asian populations have among the highest rates of myopia associated with high dietary sodium. Similar associations of sodium and rising myopia prevalence were observed in the United States in the late 20th century. The present perspective synthesizes nutritional epidemiology evidence with pathophysiological concepts and proposes that axial myopia occurs from increased fluid retention in the vitreous of the eye, induced by dietary sodium chloride intake. Salt disturbs ionic permeability of retinal membranes, increases the osmotic gradient flow of fluid into the vitreous, and stretches ocular tissue during axial elongation. Based on the present nutritional epidemiology evidence, experimental research should investigate the effect of sodium chloride as the cause of myopia, and clinical research should test a very low-salt diet in myopia correction and prevention. Full article