In agriculture, crop yield and quality are critical for global food supply and human survival. Challenges such as plant leaf diseases necessitate a fast, automatic, economical, and accurate method. This paper utilizes deep learning, transfer learning, and specific feature learning modules (CBAM, Inception-ResNet) for their outstanding performance in image processing and classification. The ResNet model, pretrained on ImageNet, serves as the cornerstone, with introduced feature learning modules in our IRCResNet model. Experimental results show our model achieves an average prediction accuracy of 96.8574% on public datasets, thoroughly validating our approach and significantly enhancing plant leaf disease identification.

Foodborne diseases are a global health problem. Every year, millions of people die worldwide from these diseases. It has been determined that the high prevalence of these diseases is related to unfavorable socioeconomic conditions of the population. In this study, the relationship between foodborne diseases and socioeconomic conditions of the population was determined using principal component analysis as a multivariate statistical analysis technique. In this study, the socioeconomic variables of each Ecuador province and the prevalence of foodborne diseases (hepatitis A, salmonella, shigellosis and typhoid fever) during the years 2018 and 2019 were considered. The results show the relationship between foodborne diseases and the socioeconomic conditions of the population, as well as identifying regions more vulnerable to present high levels of prevalence of foodborne diseases, thus facilitating the implementation of social investment programs to reduce the prevalence of these diseases.

Background: Various studies have demonstrated the usefulness of Google search data for public health-monitoring systems. The aim of this study is to be estimated interest of public in infectious diseases in infectious diseases in South Korea, the five other countries. Methods: We conducted cross-country comparisons for queries related to the H1N1 virus and Middle East respiratory syndrome coronavirus (MERS-CoV). We analyzed queries related to the novel coronavirus disease (COVID-19) from 20 January to 13 April 2020, and performed time-descriptive and correlation analyses on trend patterns. Results: Trends in H1N1, MERS-CoV, and COVID-19 queries in South Korea matched those in the five other countries and worldwide. The relative search volume (RSV) for the MERS-CoV virus increased as the cumulative number of confirmed cases in South Korea increased and decreased significantly as the number of confirmed cases decreased. The volume of COVID-19 queries dramatically increased as South Korea’s confirmed COVID-19 cases grew significantly at the community level. However, RSV remained stable over time. Conclusions: Google Trends provides real-time data based on search patterns related to infectious diseases, allowing for continuous monitoring of public reactions, disease spread, and changes in perceptions or concerns. We can use this information to adjust their strategies of the prevention of epidemics or provide timely updates to the public.

Leukemia is a major public health problem in China, but epidemiological studies on leukemia in China are still insufficient. This study aims to analyze leukemia's disease burden and risk factors in China from 2010 to 2021 and provide a basis for leukemia prevention and treatment. Using data from the Global Burden of Disease (GBD) database, trends in the burden of leukemia in China from 2010 to 2021 were analyzed. Additionally, epidemiological differences by gender and age groups were explored. In 2021, there were 531,000 leukemia patients in China, with 106,000 new cases and 59,000 deaths. Compared to 2010, the mortality rate and disability-adjusted life years (DALYs) per 100,000 population in 2021 decreased by 5% and 18%, respectively, while the incidence and prevalence rates increased by 12% and 29%, respectively. Gender and age stratification indicated that males had higher rates across all indicators than females, and elderly individuals faced higher leukemia mortality and DALYs. The most significant decrease in DALYs was observed in children and adolescents under 20. The highest burden of leukemia for males was found in the 85–90 age group, while for females, it was in the 70–74 age group. Major risk factors for leukemia included smoking, high BMI, and exposure to carcinogens, benzene, and formaldehyde. The overall burden of leukemia in China showed a decreasing trend, with significant gender and age differences. More measures are needed to reduce leukemia mortality, particularly focusing on the prevention and treatment of leukemia in males and the elderly.